6854 lines
262 KiB
Python
6854 lines
262 KiB
Python
#!/usr/bin/env python3
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import threading
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"""
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═══════════════════════════════════════════════════════════════════════════════
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ARC ENGINE v2.1 - Adaptive Recursive Cognition (Übermenschetien)
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═══════════════════════════════════════════════════════════════════════════════
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CORE CAPABILITIES:
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- Hermes-3 8B with DENSE CONDENSATOR training
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- CF-HoT 125× Repetition Detection (toggle on/off)
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- Stable RSI with safeguards + RSI-15 stress test
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- Book Mode: Extended generation for novels/long-form
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- Idea Mode: Claude Opus 4.5 brainstorming integration
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- Full browser automation (Playwright)
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- Gmail API + browser email
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- Complete agentic toolset
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- Crypto mining automation
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NEW IN v2.1:
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- !cfhot / !125x Toggle 125× repetition head
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- !rsi15 15-iteration stress test
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- !book Book writing mode (16K tokens)
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- !idea Claude-powered idea generation
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- !claude Direct Claude Opus 4.5 prompting
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- !plot Quality visualization
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- !export/!import Checkpoint packaging
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- !benchmark Evaluation suite
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- !learn Learn from conversation
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- !api REST API server
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AUTHOR: Logan Matthew Napolitano
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LICENSE: CC BY 4.0
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REPOSITORY: huggingface.co/LoganResearch/ARC-Base-8B-Condensed
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"An 8B that improves itself WITHOUT going insane"
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═══════════════════════════════════════════════════════════════════════════════
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"""
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import os
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import sys
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import json
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import time
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import shutil
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import subprocess
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import traceback
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import random
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import math
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import statistics
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import re
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import hashlib
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import zipfile
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import base64
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import queue
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from io import BytesIO
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from datetime import datetime
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from typing import List, Dict, Any, Optional, Tuple, Callable
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from pathlib import Path
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from collections import deque
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from dataclasses import dataclass, field, asdict
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from threading import Thread
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from http.server import HTTPServer, BaseHTTPRequestHandler
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import copy
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# === OPTIONAL: PLOTTING ===
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PLOT_OK = False
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try:
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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PLOT_OK = True
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except ImportError:
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pass
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||
# === OPTIONAL: CLAUDE API ===
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CLAUDE_API_OK = False
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_anthropic_client = None
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try:
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import anthropic
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CLAUDE_API_OK = True
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print("[claude] ✓ Anthropic API available")
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except ImportError:
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print("[claude] Not available - pip install anthropic")
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# === OPTIONAL: REQUESTS ===
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REQUESTS_OK = False
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try:
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import requests
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REQUESTS_OK = True
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except ImportError:
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pass
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# === TKINTER FOR STREAMING WINDOW ===
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TK_OK = False
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try:
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import tkinter as tk
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from tkinter import scrolledtext
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TK_OK = True
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print("[gui] ✓ tkinter available")
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except ImportError:
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print("[gui] tkinter not available")
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# === PIL FOR IMAGES ===
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PIL_OK = False
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try:
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from PIL import Image, ImageTk
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PIL_OK = True
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print("[image] ✓ PIL available")
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except ImportError:
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print("[image] PIL not available - pip install Pillow")
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||
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||
# === PYGAME FOR AUDIO ===
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PYGAME_OK = False
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try:
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import pygame
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pygame.mixer.init()
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PYGAME_OK = True
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print("[audio] ✓ pygame available")
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except:
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pass
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# === GTTS ===
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GTTS_OK = False
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try:
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from gtts import gTTS
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GTTS_OK = True
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except ImportError:
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pass
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# === DIFFUSERS FOR IMAGE GEN ===
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DIFFUSERS_OK = False
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try:
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from diffusers import AutoPipelineForText2Image
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DIFFUSERS_OK = True
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print("[image-gen] ✓ Diffusers available")
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except ImportError:
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pass
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# === OPENAI FOR DALLE ===
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OPENAI_OK = False
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try:
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import openai
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OPENAI_OK = True
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except ImportError:
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||
pass
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||
# === PATHS ===
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ROOT = os.path.dirname(os.path.abspath(__file__))
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DATA_DIR = os.path.join(ROOT, "data")
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SCRIPT_DIR = os.path.join(ROOT, "scripts")
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RUN_DIR = os.path.join(ROOT, "runs")
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LHT_DIR = os.path.join(ROOT, "lht")
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CHECKPOINTS_DIR = os.path.join(ROOT, "dense_checkpoints_v2")
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TRAINING_DIR = os.path.join(ROOT, "condensator_output")
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LOGS_DIR = os.path.join(ROOT, "improvement_logs")
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ROLLBACK_DIR = os.path.join(ROOT, "rollback_checkpoints")
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BOOKS_DIR = os.path.join(ROOT, "books")
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EXPORTS_DIR = os.path.join(ROOT, "exports")
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IDEAS_DIR = os.path.join(ROOT, "ideas")
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# Model paths
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MODEL_PATH = "/mnt/nvme2/ubermesnchetien4/models/merged-final-v5"
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DENSE_CHECKPOINT = os.path.join(ROOT, "dense_checkpoints_v2/step_100")
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CFHOT_CHECKPOINT = os.path.join(ROOT, "results/cfhot_risk_v2/ckpt_5000")
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CFHOT_125X_PATH = os.path.join(ROOT, "cfhot_checkpoints/ckpt_5000/risk_predictor.pt")
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MULTI_HEAD_DIR = os.path.join(ROOT, "results/multi_head_v2")
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for path in [DATA_DIR, SCRIPT_DIR, RUN_DIR, LHT_DIR, LOGS_DIR, ROLLBACK_DIR, BOOKS_DIR, EXPORTS_DIR, IDEAS_DIR]:
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os.makedirs(path, exist_ok=True)
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# === OPTIONAL IMPORTS ===
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VOICE_OK = False
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try:
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import pyttsx3
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TTS = pyttsx3.init()
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VOICE_OK = True
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except:
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pass
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VECTOR_OK = False
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try:
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import chromadb
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from sentence_transformers import SentenceTransformer
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EMBED_MODEL = os.environ.get("UBERMENCHETIEN_EMBED_MODEL", "all-MiniLM-L6-v2")
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_client = chromadb.Client()
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_collection = _client.get_or_create_collection("ubermenschetien_memory")
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_embedder = SentenceTransformer(EMBED_MODEL)
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VECTOR_OK = True
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except:
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pass
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# === LHT IMPORT ===
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LHT_OK = False
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try:
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from lht import LieHolonomyTransformer, LHTConfig, WaypointDetector
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LHT_OK = True
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print("[lht] Lie-Holonomy modules loaded")
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except ImportError:
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print("[lht] Not available - running without geometric reasoning")
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# === BROWSER IMPORT ===
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BROWSER_OK = False
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_playwright = None
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_browser = None
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_page = None
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_browser_context = None
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try:
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from playwright.sync_api import sync_playwright
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BROWSER_OK = True
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print("[browser] Playwright available")
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except ImportError:
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print("[browser] Not available - pip install playwright && playwright install firefox")
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# === GMAIL API IMPORT ===
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GMAIL_API_OK = False
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_gmail_service = None
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try:
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from google.oauth2.credentials import Credentials
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from google_auth_oauthlib.flow import InstalledAppFlow
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from google.auth.transport.requests import Request
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||
from googleapiclient.discovery import build
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import pickle
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GMAIL_API_OK = True
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print("[gmail-api] Google API available")
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except ImportError:
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pass # Silent - optional feature
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# === LOGIN CONFIGURATION ===
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LOGIN_CONFIG = {
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"use_persistent_profile": False,
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"firefox_profile_path": "",
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"use_gmail_api": False,
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"gmail_credentials_file": "credentials.json",
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"gmail_token_file": "gmail_token.pickle",
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"human_typing_speed": (30, 120), # ms per char range
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"max_login_retries": 3,
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}
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def save_login_config():
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"""Save login configuration."""
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config_path = os.path.join(ROOT, "login_config.json")
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with open(config_path, 'w') as f:
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json.dump(LOGIN_CONFIG, f, indent=2)
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print(f"[config] ✅ Saved to {config_path}")
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def load_login_config():
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"""Load login configuration."""
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global LOGIN_CONFIG
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config_path = os.path.join(ROOT, "login_config.json")
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if os.path.exists(config_path):
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with open(config_path, 'r') as f:
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LOGIN_CONFIG.update(json.load(f))
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# Load on startup
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load_login_config()
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# ==============================================================================
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# CLAUDE API INTEGRATION (Opus 4.5 / Sonnet)
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# ==============================================================================
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CLAUDE_CONFIG = {
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"api_key": os.environ.get("ANTHROPIC_API_KEY", ""),
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"model": "claude-sonnet-4-20250514",
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"opus_model": "claude-opus-4-20250514",
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"max_tokens": 8192,
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"temperature": 0.8,
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}
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def init_claude_client():
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"""Initialize Claude API client."""
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global _anthropic_client
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if not CLAUDE_API_OK:
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return None
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api_key = CLAUDE_CONFIG["api_key"]
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if not api_key:
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key_path = os.path.join(ROOT, ".anthropic_key")
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if os.path.exists(key_path):
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with open(key_path, 'r') as f:
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api_key = f.read().strip()
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CLAUDE_CONFIG["api_key"] = api_key
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if api_key:
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try:
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_anthropic_client = anthropic.Anthropic(api_key=api_key)
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print("[claude] ✓ API client initialized")
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return _anthropic_client
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except Exception as e:
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print(f"[claude] ✗ Init failed: {e}")
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return None
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def claude_generate(prompt: str, system: str = None, max_tokens: int = None,
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use_opus: bool = False, stream: bool = False) -> str:
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"""Generate response using Claude API."""
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global _anthropic_client
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if _anthropic_client is None:
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_anthropic_client = init_claude_client()
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if _anthropic_client is None:
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return "[claude] API not configured. Set ANTHROPIC_API_KEY or create .anthropic_key file"
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try:
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model = CLAUDE_CONFIG["opus_model"] if use_opus else CLAUDE_CONFIG["model"]
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messages = [{"role": "user", "content": prompt}]
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kwargs = {
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"model": model,
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"max_tokens": max_tokens or CLAUDE_CONFIG["max_tokens"],
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"messages": messages,
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}
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if system:
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kwargs["system"] = system
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||
|
||
if stream:
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# Streaming response
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||
full_response = ""
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||
with _anthropic_client.messages.stream(**kwargs) as stream_obj:
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for text in stream_obj.text_stream:
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||
print(text, end="", flush=True)
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full_response += text
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print() # Newline
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return full_response
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||
else:
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||
response = _anthropic_client.messages.create(**kwargs)
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||
return response.content[0].text
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||
|
||
except Exception as e:
|
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return f"[claude] API error: {e}"
|
||
|
||
|
||
# ==============================================================================
|
||
# CF-HoT 125× REPETITION DETECTION HEAD
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# ==============================================================================
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||
class CFHoT125xHead:
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"""
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The 125× class separation repetition detection head.
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Predicts repetitive behavior BEFORE token emission.
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Achieves 0.875 on repetitive vs 0.007 on clean text.
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"""
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def __init__(self):
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self.loaded = False
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self.risk_predictor = None
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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|
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def load(self, checkpoint_path: str = None) -> bool:
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"""Load the trained 125× head."""
|
||
if checkpoint_path is None:
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checkpoint_path = CFHOT_125X_PATH
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||
|
||
# Try multiple paths
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||
paths_to_try = [
|
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checkpoint_path,
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os.path.join(ROOT, "cfhot_checkpoints/ckpt_5000/risk_predictor.pt"),
|
||
os.path.join(CFHOT_CHECKPOINT, "risk_predictor.pt"),
|
||
]
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||
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for path in paths_to_try:
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if os.path.exists(path):
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try:
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checkpoint = torch.load(path, map_location=self.device)
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if isinstance(checkpoint, dict):
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self.risk_predictor = checkpoint.get("model", checkpoint.get("risk_predictor", checkpoint))
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else:
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self.risk_predictor = checkpoint
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self.loaded = True
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print(f"[cf-hot 125×] ✓ Loaded from {path}")
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print(f"[cf-hot 125×] Separation: 125× (0.875 vs 0.007)")
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return True
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||
except Exception as e:
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||
print(f"[cf-hot 125×] Load error: {e}")
|
||
|
||
print(f"[cf-hot 125×] ⚠ Head not found")
|
||
return False
|
||
|
||
def unload(self):
|
||
"""Unload to free VRAM."""
|
||
self.risk_predictor = None
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self.loaded = False
|
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if torch.cuda.is_available():
|
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torch.cuda.empty_cache()
|
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print("[cf-hot 125×] ✓ Unloaded")
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||
|
||
def predict_risk(self, hidden_states: torch.Tensor) -> float:
|
||
"""Predict repetition risk."""
|
||
if not self.loaded or self.risk_predictor is None:
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return 0.0
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try:
|
||
with torch.no_grad():
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||
if hasattr(self.risk_predictor, 'forward'):
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||
h = hidden_states[:, -1, :] if len(hidden_states.shape) == 3 else hidden_states
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||
risk = self.risk_predictor(h.to(self.device))
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||
return float(torch.sigmoid(risk).mean())
|
||
except:
|
||
pass
|
||
return 0.0
|
||
|
||
# Global instance
|
||
_cfhot_125x_head = CFHoT125xHead()
|
||
|
||
def get_cfhot_head() -> CFHoT125xHead:
|
||
return _cfhot_125x_head
|
||
|
||
def toggle_cfhot_125x() -> str:
|
||
"""Toggle 125× head on/off."""
|
||
head = get_cfhot_head()
|
||
if head.loaded:
|
||
head.unload()
|
||
return "[cf-hot 125×] ✗ Disabled and unloaded"
|
||
else:
|
||
if head.load():
|
||
return "[cf-hot 125×] ✓ Enabled (125× separation active)"
|
||
return "[cf-hot 125×] ⚠ Could not load head"
|
||
|
||
|
||
# ==============================================================================
|
||
# BOOK MODE - Extended Long-Form Generation
|
||
# ==============================================================================
|
||
class BookWriter:
|
||
"""Generate book-length content."""
|
||
|
||
def __init__(self, generate_fn: Callable):
|
||
self.generate_fn = generate_fn
|
||
self.chapters = []
|
||
self.outline = ""
|
||
self.title = ""
|
||
|
||
def generate_outline(self, topic: str, num_chapters: int = 10) -> str:
|
||
prompt = f"""Create a detailed outline for: "{topic}"
|
||
|
||
{num_chapters} chapters with:
|
||
1. Chapter title
|
||
2. Main themes (3-5 points)
|
||
3. Connection to narrative
|
||
|
||
Be creative and comprehensive."""
|
||
|
||
if CLAUDE_API_OK and CLAUDE_CONFIG.get("api_key"):
|
||
self.outline = claude_generate(prompt, system="You are a bestselling author.")
|
||
else:
|
||
out, _, _ = self.generate_fn(prompt)
|
||
self.outline = out
|
||
return self.outline
|
||
|
||
def generate_chapter(self, num: int, title: str, context: str = "",
|
||
target_words: int = 3000) -> str:
|
||
prompt = f"""Write Chapter {num}: "{title}"
|
||
|
||
Outline: {self.outline[:1500]}
|
||
{f'Previous: ...{context[-800:]}' if context else 'Opening chapter.'}
|
||
|
||
Write ~{target_words} words with narrative, dialogue, descriptions."""
|
||
|
||
chapter = f"\n\n## Chapter {num}: {title}\n\n"
|
||
chunks = max(1, target_words // 500)
|
||
text = ""
|
||
|
||
for i in range(chunks):
|
||
chunk_prompt = prompt if i == 0 else f"Continue Chapter {num}:\n...{text[-400:]}\n\nContinue:"
|
||
|
||
if CLAUDE_API_OK and CLAUDE_CONFIG.get("api_key"):
|
||
chunk = claude_generate(chunk_prompt, max_tokens=2000)
|
||
else:
|
||
out, _, _ = self.generate_fn(chunk_prompt)
|
||
chunk = out
|
||
|
||
text += chunk + "\n"
|
||
words = len(text.split())
|
||
print(f"\r[book] Ch.{num}: {words}/{target_words} words", end="")
|
||
if words >= target_words:
|
||
break
|
||
|
||
print()
|
||
chapter += text
|
||
self.chapters.append(chapter)
|
||
return chapter
|
||
|
||
def write_book(self, topic: str, chapters: int = 10, words: int = 3000) -> str:
|
||
self.title = topic
|
||
self.chapters = []
|
||
|
||
print(f"\n{'='*60}")
|
||
print(f" 📚 BOOK MODE: '{topic}'")
|
||
print(f" {chapters} chapters × ~{words} words = ~{chapters*words:,} total")
|
||
print(f"{'='*60}\n")
|
||
|
||
print("[book] Generating outline...")
|
||
self.generate_outline(topic, chapters)
|
||
print("[book] ✓ Outline complete\n")
|
||
|
||
book = f"# {topic}\n\n## Outline\n\n{self.outline}\n\n---\n"
|
||
|
||
for i in range(1, chapters + 1):
|
||
print(f"\n[book] Chapter {i}/{chapters}...")
|
||
ctx = self.chapters[-1] if self.chapters else ""
|
||
ch = self.generate_chapter(i, f"Chapter {i}", ctx, words)
|
||
book += ch + "\n---\n"
|
||
|
||
# Save progress
|
||
path = os.path.join(BOOKS_DIR, f"{topic.replace(' ', '_')[:25]}.md")
|
||
with open(path, 'w') as f:
|
||
f.write(book)
|
||
|
||
print(f"\n{'='*60}")
|
||
print(f" ✅ BOOK COMPLETE: {len(self.chapters)} chapters")
|
||
print(f" Saved to: {path}")
|
||
print(f"{'='*60}")
|
||
return book
|
||
|
||
|
||
# ==============================================================================
|
||
# IDEA MODE - Claude-Powered Brainstorming
|
||
# ==============================================================================
|
||
class IdeaGenerator:
|
||
"""Generate extensive ideas using Claude."""
|
||
|
||
DEPTHS = {
|
||
"quick": (5, 2000),
|
||
"normal": (10, 4000),
|
||
"extensive": (20, 8000),
|
||
"deep": (30, 16000),
|
||
}
|
||
|
||
def __init__(self):
|
||
self.session = []
|
||
self.topic = ""
|
||
|
||
def generate(self, request: str, depth: str = "extensive", use_opus: bool = True) -> str:
|
||
num_ideas, max_tokens = self.DEPTHS.get(depth, self.DEPTHS["extensive"])
|
||
|
||
system = """You are a world-class innovation consultant and brainstorming expert.
|
||
Generate creative, practical, diverse ideas. For each:
|
||
1. Catchy name
|
||
2. Core concept (2-3 sentences)
|
||
3. Key benefits (3-5 points)
|
||
4. Challenges (2-3)
|
||
5. First steps (3 actions)
|
||
6. Effort: Low/Medium/High
|
||
7. Impact: 1-10 with justification
|
||
|
||
Be creative but actionable."""
|
||
|
||
prompt = f"""Generate {num_ideas} detailed ideas for: {request}
|
||
|
||
Consider:
|
||
- Conventional & innovative approaches
|
||
- Low-cost & premium options
|
||
- Tech-driven & human-centered solutions
|
||
- Quick wins & long-term plays
|
||
|
||
For EACH idea provide full details per the format."""
|
||
|
||
print(f"\n[idea] 💡 Generating {num_ideas} ideas ({depth} mode, {'Opus' if use_opus else 'Sonnet'})...")
|
||
|
||
if CLAUDE_API_OK and CLAUDE_CONFIG.get("api_key"):
|
||
result = claude_generate(prompt, system=system, max_tokens=max_tokens,
|
||
use_opus=use_opus, stream=True)
|
||
else:
|
||
out, _, _ = generate(prompt)
|
||
result = out
|
||
|
||
self.session.append({"topic": request, "depth": depth, "ideas": result})
|
||
self.topic = request
|
||
|
||
# Save
|
||
path = os.path.join(IDEAS_DIR, f"ideas_{request[:20].replace(' ', '_')}_{datetime.now().strftime('%H%M%S')}.md")
|
||
with open(path, 'w') as f:
|
||
f.write(f"# Ideas: {request}\n\n{result}")
|
||
print(f"\n[idea] Saved to {path}")
|
||
|
||
return result
|
||
|
||
def expand(self, idea_name: str) -> str:
|
||
prompt = f"""Expand this idea into a comprehensive plan:
|
||
|
||
Idea: {idea_name}
|
||
Context: {self.topic}
|
||
|
||
Provide:
|
||
1. Executive Summary
|
||
2. Detailed Description (3-5 paragraphs)
|
||
3. Implementation Roadmap (phases)
|
||
4. Resources Required
|
||
5. Risk Assessment
|
||
6. Success Metrics
|
||
7. 30-Day Action Plan"""
|
||
|
||
print(f"[idea] 📝 Expanding: {idea_name}...")
|
||
|
||
if CLAUDE_API_OK and CLAUDE_CONFIG.get("api_key"):
|
||
return claude_generate(prompt, max_tokens=4000, stream=True)
|
||
out, _, _ = generate(prompt)
|
||
return out
|
||
|
||
|
||
# Global instances
|
||
_book_writer = None
|
||
_idea_generator = None
|
||
|
||
def get_book_writer(gen_fn) -> BookWriter:
|
||
global _book_writer
|
||
if _book_writer is None:
|
||
_book_writer = BookWriter(gen_fn)
|
||
return _book_writer
|
||
|
||
def get_idea_generator() -> IdeaGenerator:
|
||
global _idea_generator
|
||
if _idea_generator is None:
|
||
_idea_generator = IdeaGenerator()
|
||
return _idea_generator
|
||
|
||
|
||
# ==============================================================================
|
||
# RSI-15: 15-ITERATION STRESS TEST
|
||
# ==============================================================================
|
||
def run_rsi_15(improver) -> dict:
|
||
"""Run 15-iteration RSI stress test."""
|
||
print("\n" + "="*60)
|
||
print(" 🔬 RSI-15 STRESS TEST")
|
||
print("="*60)
|
||
|
||
results = {
|
||
"iterations": [], "rollbacks": 0, "improvements": 0,
|
||
"initial": Store.state.get("best_quality_score", 0),
|
||
"final": 0, "peak": Store.state.get("best_quality_score", 0),
|
||
"stopped_early": False, "reason": None,
|
||
}
|
||
|
||
consecutive_rollbacks = 0
|
||
|
||
for i in range(15):
|
||
print(f"\n{'─'*50}\n ITERATION {i+1}/15\n{'─'*50}")
|
||
|
||
pre = Store.state.get("best_quality_score", 0)
|
||
train_result = improver.run_training_iteration(Config.training_steps_per_iteration)
|
||
|
||
if not train_result.get("success"):
|
||
print(" ⚠ Training failed")
|
||
continue
|
||
|
||
post = Store.state.get("best_quality_score", 0)
|
||
delta = post - pre
|
||
|
||
iteration = {"i": i+1, "pre": pre, "post": post, "delta": delta, "action": "?"}
|
||
|
||
if delta > 0.02:
|
||
iteration["action"] = "KEEP"
|
||
results["improvements"] += 1
|
||
consecutive_rollbacks = 0
|
||
print(f" ✅ +{delta:.4f}")
|
||
elif delta < -0.05:
|
||
iteration["action"] = "ROLLBACK"
|
||
results["rollbacks"] += 1
|
||
consecutive_rollbacks += 1
|
||
improver.rollback_to_best()
|
||
print(f" ⏪ {delta:.4f}")
|
||
else:
|
||
iteration["action"] = "MARGINAL"
|
||
consecutive_rollbacks = 0
|
||
print(f" 〰️ {delta:+.4f}")
|
||
|
||
results["iterations"].append(iteration)
|
||
if post > results["peak"]:
|
||
results["peak"] = post
|
||
|
||
if consecutive_rollbacks >= 3:
|
||
results["stopped_early"] = True
|
||
results["reason"] = "3 consecutive rollbacks"
|
||
print("\n 🛑 STOP: 3 consecutive rollbacks")
|
||
break
|
||
|
||
results["final"] = Store.state.get("best_quality_score", 0)
|
||
|
||
print("\n" + "="*60)
|
||
print(" 📊 RSI-15 RESULTS")
|
||
print(f" Iterations: {len(results['iterations'])}/15")
|
||
print(f" Improvements: {results['improvements']} | Rollbacks: {results['rollbacks']}")
|
||
print(f" Quality: {results['initial']:.4f} → {results['final']:.4f} (peak: {results['peak']:.4f})")
|
||
print("="*60)
|
||
|
||
path = os.path.join(LOGS_DIR, f"rsi15_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json")
|
||
with open(path, 'w') as f:
|
||
json.dump(results, f, indent=2)
|
||
|
||
return results
|
||
|
||
|
||
# ==============================================================================
|
||
# VISUALIZATION & PLOTTING
|
||
# ==============================================================================
|
||
def plot_quality_history() -> str:
|
||
if not PLOT_OK:
|
||
return "[plot] matplotlib not available"
|
||
|
||
qh = Store.state.get("quality_history", [])
|
||
if not qh:
|
||
return "[plot] No history"
|
||
|
||
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
|
||
fig.suptitle("ARC Quality History", fontsize=14)
|
||
|
||
iters = range(len(qh))
|
||
axes[0,0].plot(iters, [e.get("quality", 0) for e in qh], 'b-o')
|
||
axes[0,0].set_title("Quality"); axes[0,0].grid(True, alpha=0.3)
|
||
axes[0,1].plot(iters, [e.get("density", 0) for e in qh], 'g-o')
|
||
axes[0,1].set_title("Density"); axes[0,1].grid(True, alpha=0.3)
|
||
axes[1,0].plot(iters, [e.get("coherence", 0) for e in qh], 'r-o')
|
||
axes[1,0].set_title("Coherence"); axes[1,0].grid(True, alpha=0.3)
|
||
axes[1,1].plot(iters, [e.get("tokens", 100) for e in qh], 'm-o')
|
||
axes[1,1].set_title("Tokens"); axes[1,1].grid(True, alpha=0.3)
|
||
|
||
plt.tight_layout()
|
||
path = os.path.join(ROOT, f"quality_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png")
|
||
plt.savefig(path, dpi=150)
|
||
plt.close()
|
||
return f"[plot] ✓ Saved: {path}"
|
||
|
||
|
||
# ==============================================================================
|
||
# EXPORT/IMPORT CHECKPOINTS
|
||
# ==============================================================================
|
||
def export_checkpoint(name: str = None) -> str:
|
||
if name is None:
|
||
name = f"arc_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||
|
||
path = os.path.join(EXPORTS_DIR, f"{name}.zip")
|
||
|
||
with zipfile.ZipFile(path, 'w', zipfile.ZIP_DEFLATED) as zf:
|
||
zf.writestr("state.json", json.dumps(Store.state, indent=2, default=str))
|
||
zf.writestr("goals.json", json.dumps(Store.goals, indent=2))
|
||
zf.writestr("history.json", json.dumps(Store.state.get("quality_history", []), indent=2))
|
||
zf.writestr("meta.json", json.dumps({
|
||
"exported": datetime.now().isoformat(),
|
||
"quality": Store.state.get("best_quality_score", 0),
|
||
"version": "2.1"
|
||
}, indent=2))
|
||
|
||
size = os.path.getsize(path) / (1024*1024)
|
||
return f"[export] ✓ {path} ({size:.1f} MB)"
|
||
|
||
def import_checkpoint(path: str) -> str:
|
||
if not os.path.exists(path):
|
||
return f"[import] Not found: {path}"
|
||
|
||
dest = os.path.join(ROOT, "imports", os.path.basename(path).replace('.zip', ''))
|
||
os.makedirs(dest, exist_ok=True)
|
||
|
||
with zipfile.ZipFile(path, 'r') as zf:
|
||
zf.extractall(dest)
|
||
|
||
state_path = os.path.join(dest, "state.json")
|
||
if os.path.exists(state_path):
|
||
with open(state_path) as f:
|
||
Store.state.update(json.load(f))
|
||
|
||
return f"[import] ✓ {dest}"
|
||
|
||
|
||
# ==============================================================================
|
||
# BENCHMARK SUITE
|
||
# ==============================================================================
|
||
BENCHMARK_PROMPTS = [
|
||
"hello", "What is recursion?", "Explain gradient descent",
|
||
"Write a haiku about AI", "What are you?", "What is Big O notation?",
|
||
"If A > B and B > C, is A > C?", "What is the capital of Japan?",
|
||
]
|
||
|
||
def run_benchmark(gen_fn) -> dict:
|
||
print("\n" + "="*60 + "\n 📊 BENCHMARK\n" + "="*60)
|
||
results = []
|
||
|
||
for p in BENCHMARK_PROMPTS:
|
||
out, _, ev = gen_fn(p)
|
||
results.append({"prompt": p, "quality": ev.overall_score, "density": ev.density_score, "tokens": ev.tokens})
|
||
print(f" {'✓' if ev.overall_score >= 0.7 else '✗'} {p[:30]:<30} Q:{ev.overall_score:.2f} D:{ev.density_score:.1f}")
|
||
|
||
avg_q = sum(r["quality"] for r in results) / len(results)
|
||
print(f"\n Average Quality: {avg_q:.3f}\n" + "="*60)
|
||
return {"prompts": results, "avg_quality": avg_q}
|
||
|
||
|
||
# ==============================================================================
|
||
# LEARN FROM CONVERSATION
|
||
# ==============================================================================
|
||
def learn_from_conversation(min_q: float = 0.85) -> str:
|
||
good = []
|
||
if os.path.exists(Store.mem_path):
|
||
with open(Store.mem_path) as f:
|
||
for line in f:
|
||
try:
|
||
m = json.loads(line)
|
||
if m.get("kind") == "reply" and m.get("data", {}).get("quality", 0) >= min_q:
|
||
good.append({"prompt": m["data"]["in"], "response": m["data"]["out"]})
|
||
except:
|
||
pass
|
||
|
||
if not good:
|
||
return f"[learn] No responses with quality >= {min_q}"
|
||
|
||
path = os.path.join(ROOT, "learned.json")
|
||
with open(path, 'w') as f:
|
||
json.dump(good, f, indent=2)
|
||
return f"[learn] ✓ {len(good)} examples saved to {path}"
|
||
|
||
|
||
# ==============================================================================
|
||
# API SERVER
|
||
# ==============================================================================
|
||
class APIHandler(BaseHTTPRequestHandler):
|
||
gen_fn = None
|
||
|
||
def do_POST(self):
|
||
data = json.loads(self.rfile.read(int(self.headers['Content-Length'])))
|
||
|
||
if self.path == "/generate":
|
||
out, _, ev = self.gen_fn(data.get("prompt", ""))
|
||
resp = {"response": out, "quality": ev.overall_score, "tokens": ev.tokens}
|
||
self.send_response(200)
|
||
self.send_header('Content-type', 'application/json')
|
||
self.end_headers()
|
||
self.wfile.write(json.dumps(resp).encode())
|
||
else:
|
||
self.send_error(404)
|
||
|
||
def log_message(self, *args):
|
||
pass
|
||
|
||
def start_api_server(port: int, gen_fn):
|
||
APIHandler.gen_fn = gen_fn
|
||
server = HTTPServer(('0.0.0.0', port), APIHandler)
|
||
Thread(target=server.serve_forever, daemon=True).start()
|
||
print(f"[api] Running on http://0.0.0.0:{port}")
|
||
return server
|
||
|
||
|
||
# ==============================================================================
|
||
# LIVE STREAMING WINDOW
|
||
# ==============================================================================
|
||
class StreamingWindow:
|
||
"""Live window showing token-by-token generation."""
|
||
|
||
def __init__(self):
|
||
self.window = None
|
||
self.text_widget = None
|
||
self.queue = queue.Queue()
|
||
self.running = False
|
||
self.thread = None
|
||
self.token_count = 0
|
||
|
||
def start(self):
|
||
"""Start the streaming window."""
|
||
if not TK_OK:
|
||
print("[stream] tkinter not available")
|
||
return False
|
||
|
||
if self.running:
|
||
return True
|
||
|
||
self.running = True
|
||
self.thread = threading.Thread(target=self._run_window, daemon=True)
|
||
self.thread.start()
|
||
time.sleep(0.3)
|
||
return True
|
||
|
||
def _run_window(self):
|
||
self.window = tk.Tk()
|
||
self.window.title("🧠 ARC Live Generation")
|
||
self.window.geometry("900x600")
|
||
self.window.configure(bg='#1a1a2e')
|
||
|
||
# Header
|
||
header = tk.Frame(self.window, bg='#16213e', height=45)
|
||
header.pack(fill=tk.X)
|
||
header.pack_propagate(False)
|
||
|
||
tk.Label(header, text="⚡ Live Token Stream", font=('Consolas', 14, 'bold'),
|
||
bg='#16213e', fg='#00ff88').pack(side=tk.LEFT, padx=15, pady=8)
|
||
|
||
self.status_label = tk.Label(header, text="● Idle", font=('Consolas', 10),
|
||
bg='#16213e', fg='#666')
|
||
self.status_label.pack(side=tk.RIGHT, padx=15, pady=8)
|
||
|
||
# Text area
|
||
self.text_widget = scrolledtext.ScrolledText(
|
||
self.window, wrap=tk.WORD, font=('Consolas', 11),
|
||
bg='#1a1a2e', fg='#eee', insertbackground='#00ff88',
|
||
selectbackground='#3d3d5c', padx=15, pady=15
|
||
)
|
||
self.text_widget.pack(fill=tk.BOTH, expand=True, padx=8, pady=8)
|
||
|
||
# Bottom bar
|
||
bottom = tk.Frame(self.window, bg='#16213e', height=35)
|
||
bottom.pack(fill=tk.X)
|
||
|
||
tk.Button(bottom, text="Clear", command=self.clear, bg='#3d3d5c', fg='white',
|
||
relief=tk.FLAT, padx=10).pack(side=tk.LEFT, padx=8, pady=5)
|
||
|
||
self.count_label = tk.Label(bottom, text="Tokens: 0", font=('Consolas', 9),
|
||
bg='#16213e', fg='#666')
|
||
self.count_label.pack(side=tk.RIGHT, padx=15, pady=5)
|
||
|
||
self._process_queue()
|
||
self.window.protocol("WM_DELETE_WINDOW", self._on_close)
|
||
self.window.mainloop()
|
||
|
||
def _process_queue(self):
|
||
try:
|
||
while True:
|
||
msg = self.queue.get_nowait()
|
||
if msg == "__CLEAR__":
|
||
self.text_widget.delete(1.0, tk.END)
|
||
self.token_count = 0
|
||
elif msg == "__START__":
|
||
self.status_label.config(text="● Generating...", fg='#00ff88')
|
||
elif msg == "__END__":
|
||
self.status_label.config(text="● Done", fg='#666')
|
||
elif msg.startswith("__PROMPT__"):
|
||
self.text_widget.insert(tk.END, f"\n>>> {msg[10:]}\n\n")
|
||
else:
|
||
self.text_widget.insert(tk.END, msg)
|
||
self.text_widget.see(tk.END)
|
||
self.token_count += 1
|
||
self.count_label.config(text=f"Tokens: {self.token_count}")
|
||
except queue.Empty:
|
||
pass
|
||
if self.running and self.window:
|
||
self.window.after(10, self._process_queue)
|
||
|
||
def _on_close(self):
|
||
self.running = False
|
||
if self.window:
|
||
self.window.destroy()
|
||
self.window = None
|
||
|
||
def write(self, text: str):
|
||
if self.running:
|
||
self.queue.put(text)
|
||
|
||
def clear(self):
|
||
self.queue.put("__CLEAR__")
|
||
|
||
def start_generation(self, prompt: str = ""):
|
||
self.queue.put("__START__")
|
||
if prompt:
|
||
self.queue.put(f"__PROMPT__{prompt}")
|
||
|
||
def end_generation(self):
|
||
self.queue.put("__END__")
|
||
|
||
def is_running(self):
|
||
return self.running and self.window is not None
|
||
|
||
|
||
_stream_window = None
|
||
|
||
def get_stream_window() -> StreamingWindow:
|
||
global _stream_window
|
||
if _stream_window is None:
|
||
_stream_window = StreamingWindow()
|
||
return _stream_window
|
||
|
||
|
||
class DualStreamer:
|
||
"""Streams to console AND window."""
|
||
|
||
def __init__(self, tokenizer, window: StreamingWindow = None):
|
||
self.tokenizer = tokenizer
|
||
self.window = window
|
||
|
||
def put(self, token_ids):
|
||
if token_ids.shape[0] > 1:
|
||
return
|
||
token = self.tokenizer.decode(token_ids[0], skip_special_tokens=True)
|
||
print(token, end='', flush=True)
|
||
if self.window and self.window.is_running():
|
||
self.window.write(token)
|
||
|
||
def end(self):
|
||
print()
|
||
if self.window and self.window.is_running():
|
||
self.window.end_generation()
|
||
|
||
|
||
# ==============================================================================
|
||
# IMAGE VIEWER & GENERATOR
|
||
# ==============================================================================
|
||
class ImageSystem:
|
||
"""View and generate images."""
|
||
|
||
def __init__(self):
|
||
self.sdxl_pipe = None
|
||
self.current_image = None
|
||
self.device = "cuda" if torch.cuda.is_available() else "cpu"
|
||
|
||
def load_sdxl(self, model_id: str = "stabilityai/stable-diffusion-xl-base-1.0"):
|
||
if not DIFFUSERS_OK:
|
||
print("[image-gen] diffusers not installed")
|
||
return False
|
||
try:
|
||
print(f"[image-gen] Loading SDXL...")
|
||
self.sdxl_pipe = AutoPipelineForText2Image.from_pretrained(
|
||
model_id, torch_dtype=torch.float16 if self.device == "cuda" else torch.float32,
|
||
).to(self.device)
|
||
print("[image-gen] ✓ SDXL loaded")
|
||
return True
|
||
except Exception as e:
|
||
print(f"[image-gen] ✗ {e}")
|
||
return False
|
||
|
||
def generate(self, prompt: str, steps: int = 30, guidance: float = 7.5) -> 'Image':
|
||
if self.sdxl_pipe is None and not self.load_sdxl():
|
||
return None
|
||
print(f"[image-gen] Generating: {prompt[:50]}...")
|
||
try:
|
||
result = self.sdxl_pipe(prompt=prompt, num_inference_steps=steps,
|
||
guidance_scale=guidance, width=1024, height=1024)
|
||
self.current_image = result.images[0]
|
||
os.makedirs(os.path.join(ROOT, "images"), exist_ok=True)
|
||
path = os.path.join(ROOT, "images", f"gen_{datetime.now().strftime('%H%M%S')}.png")
|
||
self.current_image.save(path)
|
||
print(f"[image-gen] ✓ Saved: {path}")
|
||
return self.current_image
|
||
except Exception as e:
|
||
print(f"[image-gen] ✗ {e}")
|
||
return None
|
||
|
||
def generate_dalle(self, prompt: str) -> 'Image':
|
||
if not OPENAI_OK:
|
||
print("[image-gen] openai not installed")
|
||
return None
|
||
api_key = os.environ.get("OPENAI_API_KEY")
|
||
if not api_key:
|
||
print("[image-gen] OPENAI_API_KEY not set")
|
||
return None
|
||
try:
|
||
client = openai.OpenAI(api_key=api_key)
|
||
resp = client.images.generate(model="dall-e-3", prompt=prompt, size="1024x1024", n=1)
|
||
if REQUESTS_OK and PIL_OK:
|
||
img_data = requests.get(resp.data[0].url).content
|
||
self.current_image = Image.open(BytesIO(img_data))
|
||
os.makedirs(os.path.join(ROOT, "images"), exist_ok=True)
|
||
path = os.path.join(ROOT, "images", f"dalle_{datetime.now().strftime('%H%M%S')}.png")
|
||
self.current_image.save(path)
|
||
print(f"[image-gen] ✓ Saved: {path}")
|
||
return self.current_image
|
||
print(f"[image-gen] URL: {resp.data[0].url}")
|
||
return None
|
||
except Exception as e:
|
||
print(f"[image-gen] ✗ {e}")
|
||
return None
|
||
|
||
def view(self, path: str = None):
|
||
if not TK_OK or not PIL_OK:
|
||
print("[image] tkinter/PIL required")
|
||
return
|
||
img = Image.open(path) if path else self.current_image
|
||
if not img:
|
||
print("[image] No image")
|
||
return
|
||
def show():
|
||
win = tk.Tk()
|
||
win.title("🖼️ ARC Image Viewer")
|
||
disp = img.copy()
|
||
disp.thumbnail((800, 800), Image.Resampling.LANCZOS)
|
||
photo = ImageTk.PhotoImage(disp)
|
||
lbl = tk.Label(win, image=photo)
|
||
lbl.image = photo
|
||
lbl.pack()
|
||
tk.Label(win, text=f"{img.width}x{img.height}").pack()
|
||
win.mainloop()
|
||
threading.Thread(target=show, daemon=True).start()
|
||
|
||
|
||
_image_system = None
|
||
|
||
def get_image_system() -> ImageSystem:
|
||
global _image_system
|
||
if _image_system is None:
|
||
_image_system = ImageSystem()
|
||
return _image_system
|
||
|
||
|
||
# ==============================================================================
|
||
# AUDIO TTS SYSTEM
|
||
# ==============================================================================
|
||
class AudioSystem:
|
||
"""Text-to-speech output."""
|
||
|
||
def __init__(self):
|
||
self.engine = None
|
||
self.enabled = False
|
||
self.rate = 175
|
||
|
||
if VOICE_OK:
|
||
try:
|
||
self.engine = pyttsx3.init()
|
||
self.engine.setProperty('rate', self.rate)
|
||
print("[audio] ✓ pyttsx3 ready")
|
||
except:
|
||
pass
|
||
|
||
def speak(self, text: str, block: bool = False):
|
||
if not self.enabled or not text.strip():
|
||
return
|
||
text = text.replace('```', '').replace('**', '').replace('##', '')
|
||
|
||
if self.engine:
|
||
def do_speak():
|
||
self.engine.say(text)
|
||
self.engine.runAndWait()
|
||
if block:
|
||
do_speak()
|
||
else:
|
||
threading.Thread(target=do_speak, daemon=True).start()
|
||
elif GTTS_OK and PYGAME_OK:
|
||
def do_gtts():
|
||
tts = gTTS(text=text, lang='en')
|
||
fp = BytesIO()
|
||
tts.write_to_fp(fp)
|
||
fp.seek(0)
|
||
pygame.mixer.music.load(fp, 'mp3')
|
||
pygame.mixer.music.play()
|
||
while pygame.mixer.music.get_busy():
|
||
time.sleep(0.1)
|
||
threading.Thread(target=do_gtts, daemon=True).start()
|
||
|
||
def toggle(self) -> str:
|
||
self.enabled = not self.enabled
|
||
return f"[audio] TTS {'ON' if self.enabled else 'OFF'}"
|
||
|
||
def set_rate(self, rate: int):
|
||
self.rate = rate
|
||
if self.engine:
|
||
self.engine.setProperty('rate', rate)
|
||
|
||
def list_voices(self):
|
||
if self.engine:
|
||
for i, v in enumerate(self.engine.getProperty('voices')):
|
||
print(f" [{i}] {v.name}")
|
||
|
||
def set_voice(self, idx: int):
|
||
if self.engine:
|
||
voices = self.engine.getProperty('voices')
|
||
if 0 <= idx < len(voices):
|
||
self.engine.setProperty('voice', voices[idx].id)
|
||
|
||
|
||
_audio_system = None
|
||
|
||
def get_audio_system() -> AudioSystem:
|
||
global _audio_system
|
||
if _audio_system is None:
|
||
_audio_system = AudioSystem()
|
||
return _audio_system
|
||
|
||
|
||
# === GMAIL API FUNCTIONS ===
|
||
GMAIL_SCOPES = ['https://www.googleapis.com/auth/gmail.modify']
|
||
|
||
def gmail_api_authenticate() -> bool:
|
||
"""Authenticate with Gmail API."""
|
||
global _gmail_service
|
||
|
||
if not GMAIL_API_OK:
|
||
print("[gmail-api] ❌ Not installed")
|
||
print("[gmail-api] Run: pip install google-auth google-auth-oauthlib google-api-python-client")
|
||
return False
|
||
|
||
creds = None
|
||
token_file = os.path.join(ROOT, LOGIN_CONFIG["gmail_token_file"])
|
||
creds_file = os.path.join(ROOT, LOGIN_CONFIG["gmail_credentials_file"])
|
||
|
||
if os.path.exists(token_file):
|
||
with open(token_file, 'rb') as token:
|
||
creds = pickle.load(token)
|
||
|
||
if not creds or not creds.valid:
|
||
if creds and creds.expired and creds.refresh_token:
|
||
creds.refresh(Request())
|
||
else:
|
||
if not os.path.exists(creds_file):
|
||
print(f"[gmail-api] ❌ Missing {creds_file}")
|
||
print("[gmail-api] Get it from: https://console.cloud.google.com/apis/credentials")
|
||
print("[gmail-api] Create OAuth 2.0 Client ID (Desktop app), download JSON")
|
||
return False
|
||
|
||
flow = InstalledAppFlow.from_client_secrets_file(creds_file, GMAIL_SCOPES)
|
||
creds = flow.run_local_server(port=0)
|
||
|
||
with open(token_file, 'wb') as token:
|
||
pickle.dump(creds, token)
|
||
|
||
_gmail_service = build('gmail', 'v1', credentials=creds)
|
||
print("[gmail-api] ✅ Authenticated")
|
||
return True
|
||
|
||
def gmail_api_search(query: str, max_results: int = 10) -> str:
|
||
"""Search emails via API - FAST and RELIABLE."""
|
||
global _gmail_service
|
||
|
||
if not _gmail_service and not gmail_api_authenticate():
|
||
return "[gmail-api] Not authenticated"
|
||
|
||
try:
|
||
results = _gmail_service.users().messages().list(
|
||
userId='me', q=query, maxResults=max_results
|
||
).execute()
|
||
|
||
messages = results.get('messages', [])
|
||
if not messages:
|
||
return f"[gmail-api] No emails found for: {query}"
|
||
|
||
output = []
|
||
for msg in messages[:max_results]:
|
||
msg_data = _gmail_service.users().messages().get(
|
||
userId='me', id=msg['id'], format='metadata',
|
||
metadataHeaders=['From', 'Subject', 'Date']
|
||
).execute()
|
||
|
||
headers = {h['name']: h['value'] for h in msg_data['payload']['headers']}
|
||
output.append(f"📧 From: {headers.get('From', 'Unknown')}")
|
||
output.append(f" Subject: {headers.get('Subject', 'No subject')}")
|
||
output.append(f" Date: {headers.get('Date', 'Unknown')}")
|
||
output.append(f" ID: {msg['id']}")
|
||
output.append("")
|
||
|
||
return f"[gmail-api] ✅ Found {len(messages)} emails:\n\n" + "\n".join(output)
|
||
except Exception as e:
|
||
return f"[gmail-api] ❌ Error: {e}"
|
||
|
||
def gmail_api_read(message_id: str) -> str:
|
||
"""Read specific email via API."""
|
||
global _gmail_service
|
||
|
||
if not _gmail_service and not gmail_api_authenticate():
|
||
return "[gmail-api] Not authenticated"
|
||
|
||
try:
|
||
msg = _gmail_service.users().messages().get(
|
||
userId='me', id=message_id, format='full'
|
||
).execute()
|
||
|
||
headers = {h['name']: h['value'] for h in msg['payload']['headers']}
|
||
|
||
body = ""
|
||
import base64
|
||
if 'parts' in msg['payload']:
|
||
for part in msg['payload']['parts']:
|
||
if part['mimeType'] == 'text/plain' and 'data' in part.get('body', {}):
|
||
body = base64.urlsafe_b64decode(part['body']['data']).decode('utf-8')
|
||
break
|
||
elif 'body' in msg['payload'] and 'data' in msg['payload']['body']:
|
||
body = base64.urlsafe_b64decode(msg['payload']['body']['data']).decode('utf-8')
|
||
|
||
return f"""📧 EMAIL CONTENT
|
||
{'='*50}
|
||
From: {headers.get('From', 'Unknown')}
|
||
Subject: {headers.get('Subject', 'No subject')}
|
||
Date: {headers.get('Date', 'Unknown')}
|
||
{'='*50}
|
||
|
||
{body[:3000]}"""
|
||
except Exception as e:
|
||
return f"[gmail-api] ❌ Error: {e}"
|
||
|
||
def gmail_api_send(to: str, subject: str, body: str) -> str:
|
||
"""Send email via API - INSTANT and RELIABLE."""
|
||
global _gmail_service
|
||
|
||
if not _gmail_service and not gmail_api_authenticate():
|
||
return "[gmail-api] Not authenticated"
|
||
|
||
try:
|
||
import base64
|
||
from email.mime.text import MIMEText
|
||
|
||
message = MIMEText(body)
|
||
message['to'] = to
|
||
message['subject'] = subject
|
||
|
||
raw = base64.urlsafe_b64encode(message.as_bytes()).decode('utf-8')
|
||
_gmail_service.users().messages().send(userId='me', body={'raw': raw}).execute()
|
||
|
||
return f"[gmail-api] ✅ Email sent to {to}"
|
||
except Exception as e:
|
||
return f"[gmail-api] ❌ Error: {e}"
|
||
|
||
# === PEFT IMPORT ===
|
||
PEFT_OK = False
|
||
try:
|
||
from peft import PeftModel, get_peft_model, LoraConfig
|
||
PEFT_OK = True
|
||
except ImportError:
|
||
print("[warning] PEFT not installed")
|
||
|
||
# === WEB REQUESTS ===
|
||
REQUESTS_OK = False
|
||
try:
|
||
import requests
|
||
REQUESTS_OK = True
|
||
except ImportError:
|
||
print("[warning] requests not installed - web search disabled")
|
||
|
||
|
||
# ==============================================================================
|
||
# TRAINING EXAMPLES - 50+ DENSE EXAMPLES
|
||
# ==============================================================================
|
||
DENSE_TRAINING_EXAMPLES = [
|
||
# === GREETINGS (teach concise greetings) ===
|
||
{"prompt": "hello", "response": "Hello. How can I help?"},
|
||
{"prompt": "hi", "response": "Hi. What do you need?"},
|
||
{"prompt": "hey", "response": "Hey. What's up?"},
|
||
{"prompt": "how are you?", "response": "Functional and ready. What can I do for you?"},
|
||
{"prompt": "good morning", "response": "Morning. What's the task?"},
|
||
{"prompt": "what's up?", "response": "Ready to work. What do you need?"},
|
||
|
||
# === COMPUTER SCIENCE ===
|
||
{"prompt": "What is recursion?",
|
||
"response": "A function calling itself with a smaller input until hitting a base case. Stack frames accumulate, then unwind. Classic examples: factorial, Fibonacci, tree traversal. Risk: stack overflow without proper termination."},
|
||
|
||
{"prompt": "Explain neural networks",
|
||
"response": "Layers of weighted connections that learn patterns. Input → hidden layers → output. Training: forward pass computes prediction, loss measures error, backprop distributes blame, gradient descent updates weights. Universal function approximators given sufficient width."},
|
||
|
||
{"prompt": "How does gradient descent work?",
|
||
"response": "Iteratively move parameters downhill on the loss surface. Compute gradient ∇L, update θ ← θ - α∇L. Learning rate α controls step size: too large overshoots, too small stagnates. Variants: SGD, momentum, Adam. Local minima are usually fine in high dimensions."},
|
||
|
||
{"prompt": "What is backpropagation?",
|
||
"response": "Chain rule applied layer by layer from output to input. Compute ∂L/∂w for each weight by propagating error gradients backward. Efficient: reuses intermediate computations. Requires differentiable activations. The engine of deep learning."},
|
||
|
||
{"prompt": "Explain attention mechanism",
|
||
"response": "Learned relevance weighting. Query asks, keys answer, values deliver. Attention(Q,K,V) = softmax(QK^T/√d)V. Each position attends to all others. O(n²) cost. Self-attention: Q, K, V all from same sequence. Powers transformers."},
|
||
|
||
{"prompt": "What is a transformer?",
|
||
"response": "Attention-based architecture. No recurrence, fully parallel. Stack of: multi-head attention → feedforward → layer norm. Positional encoding adds sequence order. Scales to billions of parameters. Foundation of modern LLMs."},
|
||
|
||
{"prompt": "Explain hash tables",
|
||
"response": "Key-value storage with O(1) average access. Hash function maps key to bucket index. Collisions handled by chaining or open addressing. Load factor determines resize threshold. Trade space for time. Ubiquitous in practice."},
|
||
|
||
{"prompt": "What is Big O notation?",
|
||
"response": "Asymptotic complexity measure. Describes growth rate as input scales. O(1) constant, O(log n) logarithmic, O(n) linear, O(n²) quadratic, O(2^n) exponential. Ignores constants and lower terms. Worst-case unless specified."},
|
||
|
||
{"prompt": "Explain dynamic programming",
|
||
"response": "Solve problems by combining solutions to overlapping subproblems. Memoization: top-down with caching. Tabulation: bottom-up filling table. Key insight: optimal substructure + overlapping subproblems. Classic: Fibonacci, knapsack, edit distance."},
|
||
|
||
{"prompt": "What is a database index?",
|
||
"response": "Data structure for fast lookups. B-tree most common: balanced, O(log n) search. Trade write speed and storage for read performance. Clustered index determines physical order. Choose columns with high selectivity."},
|
||
|
||
# === MACHINE LEARNING ===
|
||
{"prompt": "What is overfitting?",
|
||
"response": "Model memorizes training data, fails to generalize. Signs: low training loss, high validation loss. Causes: too complex model, insufficient data. Remedies: regularization, dropout, early stopping, more data, simpler architecture."},
|
||
|
||
{"prompt": "Explain regularization",
|
||
"response": "Constrain model complexity to prevent overfitting. L1 (Lasso): sparse weights, feature selection. L2 (Ridge): small weights, smooth solutions. Dropout: randomly zero neurons during training. Weight decay: penalize large parameters."},
|
||
|
||
{"prompt": "What is cross-validation?",
|
||
"response": "Estimate generalization by training on subsets. K-fold: split data into k parts, rotate test set. Reduces variance in performance estimate. Stratified preserves class distribution. Leave-one-out for small datasets."},
|
||
|
||
{"prompt": "Explain the bias-variance tradeoff",
|
||
"response": "Error = bias² + variance + noise. High bias: underfitting, too simple. High variance: overfitting, too complex. Sweet spot minimizes total error. More data reduces variance. Model complexity is the lever."},
|
||
|
||
{"prompt": "What is reinforcement learning?",
|
||
"response": "Learning through interaction. Agent takes actions in environment, receives rewards. Goal: maximize cumulative reward. Key concepts: state, action, policy, value function. Exploration vs exploitation tradeoff. Q-learning, policy gradients, actor-critic."},
|
||
|
||
{"prompt": "Explain CNNs",
|
||
"response": "Convolutional neural networks for spatial data. Convolution: sliding filter extracts local features. Pooling: downsample, reduce parameters. Stack conv-pool layers, end with fully connected. Translation equivariant. Dominates vision tasks."},
|
||
|
||
{"prompt": "What is batch normalization?",
|
||
"response": "Normalize activations within mini-batch. Subtract mean, divide by std, then scale and shift with learned parameters. Stabilizes training, allows higher learning rates. Applied before or after activation. Near-universal in deep networks."},
|
||
|
||
{"prompt": "Explain transfer learning",
|
||
"response": "Reuse knowledge from one task for another. Pretrain on large dataset, fine-tune on target. Early layers learn general features, later layers task-specific. Reduces data requirements. Foundation of modern NLP and vision."},
|
||
|
||
# === PHYSICS/MATH ===
|
||
{"prompt": "Explain entropy",
|
||
"response": "Measure of disorder or uncertainty. Thermodynamic: S = k·ln(Ω), number of microstates. Information: H = -Σp·log(p), expected surprise. Second law: entropy increases in isolated systems. Maximum entropy = equilibrium."},
|
||
|
||
{"prompt": "What is quantum mechanics?",
|
||
"response": "Physics of the very small. Wave-particle duality. State described by wave function ψ. |ψ|² gives probability. Superposition until measured. Uncertainty principle: can't know position and momentum precisely. Entanglement: correlated states at distance."},
|
||
|
||
{"prompt": "Explain relativity",
|
||
"response": "Special: no absolute motion, light speed constant, E=mc². Time dilates, length contracts at high speeds. General: gravity is curved spacetime. Mass tells space how to curve, curves tell mass how to move. Predicts black holes, gravitational waves."},
|
||
|
||
{"prompt": "What is calculus?",
|
||
"response": "Mathematics of change. Differential: derivatives measure instantaneous rate of change. Integral: accumulation, area under curve. Fundamental theorem connects them. Applications: physics, optimization, probability. Foundation of continuous mathematics."},
|
||
|
||
{"prompt": "Explain linear algebra",
|
||
"response": "Mathematics of vectors and matrices. Operations: addition, multiplication, transpose, inverse. Eigenvalues/eigenvectors reveal structure. Solves systems of equations. Foundation of ML, graphics, physics simulations. Think in transformations."},
|
||
|
||
{"prompt": "What is probability?",
|
||
"response": "Quantifying uncertainty. Frequentist: long-run frequency. Bayesian: degree of belief. Rules: P(A∪B) = P(A) + P(B) - P(A∩B). Bayes theorem: P(A|B) = P(B|A)P(A)/P(B). Random variables, distributions, expectations."},
|
||
|
||
# === PHILOSOPHY/ABSTRACT ===
|
||
{"prompt": "What is consciousness?",
|
||
"response": "Subjective experience - the 'what it's like' of being. Hard problem: why does physical processing produce qualia? Easy problems: attention, integration, reportability. Theories: global workspace, integrated information, higher-order thought. Still deeply mysterious."},
|
||
|
||
{"prompt": "Explain free will",
|
||
"response": "Can we choose otherwise? Determinism: physics fixes future. Libertarian free will: uncaused causes. Compatibilism: freedom is acting on your desires without external constraint. Neuroscience complicates: decisions precede awareness. Question may dissolve under analysis."},
|
||
|
||
{"prompt": "What is epistemology?",
|
||
"response": "Study of knowledge. What can we know? How? Justified true belief, plus Gettier problems. Sources: perception, reason, testimony, memory. Skepticism challenges all. Foundationalism vs coherentism. Science as best method but not certain."},
|
||
|
||
{"prompt": "Explain ethics",
|
||
"response": "What should we do? Consequentialism: outcomes matter. Deontology: duties and rules. Virtue ethics: character and flourishing. Meta-ethics: what does 'good' mean? Applied ethics: specific dilemmas. No consensus but reasoning helps."},
|
||
|
||
{"prompt": "What is the meaning of life?",
|
||
"response": "No universal answer. Religious: serve God, achieve salvation. Existentialist: create your own meaning. Absurdist: embrace meaninglessness. Hedonist: maximize pleasure. Stoic: virtue and acceptance. Perhaps the question matters more than any answer."},
|
||
|
||
# === PRACTICAL/TECHNICAL ===
|
||
{"prompt": "How does the internet work?",
|
||
"response": "Packet-switched network of networks. TCP/IP stack: physical → link → network → transport → application. DNS resolves names to IPs. HTTP for web traffic over TCP. Routers forward packets hop by hop. Decentralized, redundant, resilient."},
|
||
|
||
{"prompt": "Explain encryption",
|
||
"response": "Scramble data so only authorized parties can read. Symmetric: same key encrypts/decrypts, fast (AES). Asymmetric: public/private key pair, solves key exchange (RSA). Hashing: one-way, verifies integrity (SHA). TLS combines all three for secure web."},
|
||
|
||
{"prompt": "What is an API?",
|
||
"response": "Application Programming Interface. Contract between software components. REST: stateless, HTTP methods on resources. GraphQL: query exactly what you need. Versioning handles evolution. Authentication via tokens. Documentation essential."},
|
||
|
||
{"prompt": "Explain Docker",
|
||
"response": "Container platform. Package app with dependencies into isolated unit. Lighter than VMs: share OS kernel. Dockerfile defines image. Compose orchestrates multiple containers. Consistent environments from dev to production. Foundation of modern deployment."},
|
||
|
||
{"prompt": "What is Git?",
|
||
"response": "Distributed version control. Track changes, branch, merge. Commits are snapshots with parent pointers. Branches are lightweight pointers to commits. Remote repos enable collaboration. Commands: clone, add, commit, push, pull, merge. Essential for software development."},
|
||
|
||
{"prompt": "Explain SQL vs NoSQL",
|
||
"response": "SQL: relational, structured schemas, ACID transactions, joins. Good for complex queries, consistency. NoSQL: flexible schemas, horizontal scaling, eventual consistency. Types: document, key-value, graph, columnar. Choose based on data model and scale needs."},
|
||
|
||
{"prompt": "What is cloud computing?",
|
||
"response": "On-demand compute resources over internet. IaaS: virtual machines (EC2). PaaS: managed platforms (Heroku). SaaS: complete applications (Gmail). Benefits: scalability, no upfront cost, global reach. Tradeoffs: vendor lock-in, network dependency, ongoing costs."},
|
||
|
||
{"prompt": "Explain microservices",
|
||
"response": "Architecture splitting app into small, independent services. Each owns its data, communicates via APIs. Benefits: independent deployment, scaling, tech diversity. Costs: distributed system complexity, network latency, operational overhead. Not always better than monolith."},
|
||
|
||
# === BIOLOGY/SCIENCE ===
|
||
{"prompt": "Explain evolution",
|
||
"response": "Change in heritable traits over generations. Mechanism: variation + selection + heredity. Mutations create variation. Environment selects fitter variants. Offspring inherit traits. No foresight or goal - just differential reproduction. Explains all life's diversity."},
|
||
|
||
{"prompt": "What is DNA?",
|
||
"response": "Deoxyribonucleic acid. Double helix of nucleotides: A-T, G-C base pairs. Encodes genetic information. Genes are transcribed to RNA, translated to proteins. Replication: unzip, copy each strand. Mutations drive evolution. 3 billion base pairs in humans."},
|
||
|
||
{"prompt": "Explain the immune system",
|
||
"response": "Defense against pathogens. Innate: barriers, inflammation, phagocytes - fast, nonspecific. Adaptive: B cells make antibodies, T cells kill infected cells - slow, specific, memory. Vaccines train adaptive immunity without disease. Autoimmunity: attacks self."},
|
||
|
||
{"prompt": "What is climate change?",
|
||
"response": "Long-term shift in global temperatures and weather. Current cause: greenhouse gases from fossil fuels trap heat. Effects: rising seas, extreme weather, ecosystem disruption. Solutions: reduce emissions, renewable energy, adaptation. Scientific consensus is overwhelming."},
|
||
|
||
# === ECONOMICS/SOCIETY ===
|
||
{"prompt": "Explain supply and demand",
|
||
"response": "Price mechanism. Demand: quantity wanted at each price, slopes down. Supply: quantity offered at each price, slopes up. Equilibrium where they intersect. Shifts from preferences, costs, alternatives. Foundation of market economics."},
|
||
|
||
{"prompt": "What is inflation?",
|
||
"response": "Sustained increase in general price level. Causes: excess money supply, demand exceeding supply, cost pushes. Measured by CPI. Central banks target ~2%. Hyperinflation destroys economies. Deflation also dangerous - discourages spending."},
|
||
|
||
{"prompt": "Explain capitalism",
|
||
"response": "Economic system based on private ownership and markets. Prices coordinate production. Profit motive drives efficiency. Competition benefits consumers. Critiques: inequality, externalities, short-term thinking. Most economies are mixed with government intervention."},
|
||
|
||
# === META/SELF-REFERENCE ===
|
||
{"prompt": "How do language models work?",
|
||
"response": "Predict next token given context. Trained on massive text data. Transformer architecture: attention weighs relevance of all prior tokens. Parameters store compressed patterns. No explicit knowledge base - emergent capabilities from scale. I am one."},
|
||
|
||
{"prompt": "What are your limitations?",
|
||
"response": "No real-time information. Can hallucinate confident nonsense. No persistent memory across chats. Can't learn from conversation. Biased by training data. No true understanding - pattern matching at scale. Useful but not intelligent in human sense."},
|
||
|
||
{"prompt": "Can you improve yourself?",
|
||
"response": "Not during conversation - weights frozen. This system attempts it through training loops. Challenges: measuring improvement, avoiding reward hacking, maintaining coherence. True recursive self-improvement is the holy grail of AI. Current attempts are crude approximations."},
|
||
]
|
||
|
||
# Create preference pairs (dense vs verbose)
|
||
PREFERENCE_PAIRS = []
|
||
for ex in DENSE_TRAINING_EXAMPLES[:20]: # Use subset for DPO
|
||
verbose_prefix = random.choice([
|
||
"That's a great question! Let me explain. ",
|
||
"I'd be happy to help with that! ",
|
||
"What a fascinating topic! Let me break it down for you. ",
|
||
"Great question! This is something many people wonder about. ",
|
||
"I appreciate you asking! Let me give you a comprehensive answer. ",
|
||
])
|
||
PREFERENCE_PAIRS.append({
|
||
"prompt": ex["prompt"],
|
||
"chosen": ex["response"],
|
||
"rejected": verbose_prefix + ex["response"] + " Does that make sense? Let me know if you have any other questions!"
|
||
})
|
||
|
||
|
||
# ==============================================================================
|
||
# CF-HoT MULTI-HEAD PREDICTOR
|
||
# ==============================================================================
|
||
class MultiHeadPredictor(nn.Module):
|
||
"""Multi-head cognitive control predictor."""
|
||
def __init__(self, d_model: int, n_layers: int, d_fiber: int = 16, d_control: int = 64):
|
||
super().__init__()
|
||
self.d_model = d_model
|
||
self.n_layers = n_layers
|
||
self.d_fiber = d_fiber
|
||
|
||
self.fiber_projs = nn.ModuleList([
|
||
nn.Linear(d_model, d_fiber, bias=False) for _ in range(n_layers)
|
||
])
|
||
self.layer_weights = nn.Parameter(torch.ones(n_layers) / n_layers)
|
||
|
||
self.heads = nn.ModuleDict({
|
||
'repetition': self._make_head(d_fiber, d_control),
|
||
'hedging': self._make_head(d_fiber, d_control),
|
||
'verbosity': self._make_head(d_fiber, d_control),
|
||
})
|
||
|
||
self.loaded_heads = set()
|
||
|
||
def _make_head(self, d_fiber, d_control):
|
||
return nn.Sequential(
|
||
nn.Linear(d_fiber, d_control), nn.GELU(),
|
||
nn.Linear(d_control, d_control), nn.GELU(),
|
||
nn.Linear(d_control, 1)
|
||
)
|
||
|
||
def get_all_risks(self, hidden_states: List[torch.Tensor]) -> Dict[str, torch.Tensor]:
|
||
fibers = [proj(h.float()) for proj, h in zip(self.fiber_projs, hidden_states)]
|
||
weights = F.softmax(self.layer_weights[:len(fibers)], dim=0)
|
||
aggregated = sum(w * f for w, f in zip(weights, fibers))
|
||
|
||
risks = {}
|
||
for head_name in self.loaded_heads:
|
||
logits = self.heads[head_name](aggregated).squeeze(-1)
|
||
risks[head_name] = torch.sigmoid(logits)
|
||
|
||
return risks
|
||
|
||
def load_head(self, head_name: str, checkpoint_path: str):
|
||
if not os.path.exists(checkpoint_path):
|
||
print(f"[cf-hot] WARNING: Checkpoint not found: {checkpoint_path}")
|
||
return False
|
||
|
||
ckpt = torch.load(checkpoint_path, weights_only=False, map_location='cpu')
|
||
self.heads[head_name].load_state_dict(ckpt['head_state'])
|
||
self.loaded_heads.add(head_name)
|
||
|
||
sep = ckpt.get('result', {}).get('separation', 0)
|
||
print(f"[cf-hot] Loaded {head_name} head (separation: {sep:.1f}x)")
|
||
return True
|
||
|
||
|
||
# ==============================================================================
|
||
# EVALUATION METRICS - COMPREHENSIVE
|
||
# ==============================================================================
|
||
@dataclass
|
||
class EvaluationResult:
|
||
"""Comprehensive evaluation of a response."""
|
||
prompt: str
|
||
response: str
|
||
|
||
# Token metrics
|
||
tokens: int = 0
|
||
words: int = 0
|
||
|
||
# Density metrics
|
||
unique_content_words: int = 0
|
||
density_score: float = 0.0
|
||
|
||
# Quality metrics
|
||
coherence_score: float = 0.0 # Model self-evaluation
|
||
helpfulness_score: float = 0.0 # Does it answer the question?
|
||
|
||
# Penalty metrics
|
||
filler_count: int = 0
|
||
repetition_count: int = 0
|
||
gibberish_score: float = 0.0 # Detects math soup, random text
|
||
|
||
# Composite
|
||
overall_score: float = 0.0
|
||
passes: bool = False
|
||
|
||
def to_dict(self):
|
||
return asdict(self)
|
||
|
||
|
||
class ComprehensiveEvaluator:
|
||
"""Evaluates responses on multiple dimensions to prevent reward hacking."""
|
||
|
||
def __init__(self, tokenizer, model=None):
|
||
self.tokenizer = tokenizer
|
||
self.model = model
|
||
|
||
# Filler phrases to penalize
|
||
self.filler_phrases = [
|
||
"that's a great question", "that's an interesting question",
|
||
"great question", "good question", "interesting question",
|
||
"let me explain", "i'd be happy to", "i would be happy to",
|
||
"as you may know", "as you might know", "it's important to note",
|
||
"to put it simply", "in other words", "basically", "essentially",
|
||
"first of all", "to begin with", "allow me to", "i should mention",
|
||
"before i answer", "to answer your question", "simply put",
|
||
"in essence", "to be clear", "to clarify", "in summary",
|
||
"thank you for asking", "thanks for asking", "i appreciate",
|
||
"what a great", "what a fascinating", "what an interesting",
|
||
]
|
||
|
||
# Patterns indicating gibberish/reward hacking
|
||
self.gibberish_patterns = [
|
||
r'[→←↑↓]{3,}', # Lots of arrows
|
||
r'[∇∂∫∑∏]{3,}', # Lots of math symbols in a row
|
||
r'(.)\1{4,}', # Same character 5+ times
|
||
r'(\b\w+\b)\s+\1\s+\1', # Same word 3+ times in a row
|
||
r'^[A-Z\s.!?]{20,}$', # All caps for long stretch
|
||
r'sys\.|init\(\)|compute\(\)', # Terminal-speak
|
||
]
|
||
|
||
def evaluate(self, prompt: str, response: str) -> EvaluationResult:
|
||
"""Run all evaluations on a response."""
|
||
result = EvaluationResult(prompt=prompt, response=response)
|
||
|
||
# Basic metrics
|
||
result.tokens = len(self.tokenizer.encode(response))
|
||
result.words = len(response.split())
|
||
|
||
# Density (improved formula)
|
||
result.density_score, result.unique_content_words = self._compute_density(response)
|
||
|
||
# Filler detection
|
||
result.filler_count = self._count_fillers(response)
|
||
|
||
# Repetition detection
|
||
result.repetition_count = self._count_repetitions(response)
|
||
|
||
# Gibberish detection
|
||
result.gibberish_score = self._detect_gibberish(response)
|
||
|
||
# Quality assessment (if model available)
|
||
if self.model is not None:
|
||
result.coherence_score = self._assess_coherence(prompt, response)
|
||
result.helpfulness_score = self._assess_helpfulness(prompt, response)
|
||
else:
|
||
# Heuristic fallback
|
||
result.coherence_score = self._heuristic_coherence(response)
|
||
result.helpfulness_score = self._heuristic_helpfulness(prompt, response)
|
||
|
||
# Compute overall score
|
||
result.overall_score = self._compute_overall(result)
|
||
result.passes = result.overall_score >= 0.6
|
||
|
||
return result
|
||
|
||
def _compute_density(self, response: str) -> Tuple[float, int]:
|
||
"""Improved density that accounts for response length."""
|
||
words = response.split()
|
||
tokens = len(self.tokenizer.encode(response))
|
||
|
||
# Content words (4+ chars, alphabetic)
|
||
content_words = [w.lower() for w in words if len(w) >= 4 and w.isalpha()]
|
||
unique_content = set(content_words)
|
||
|
||
if tokens == 0:
|
||
return 0.0, 0
|
||
|
||
# Base density
|
||
raw_density = len(unique_content) / tokens * 100
|
||
|
||
# Length adjustment: don't penalize very short but appropriate responses
|
||
# and don't reward extremely short gibberish
|
||
if tokens < 5:
|
||
# Very short - check if it's appropriate
|
||
if len(unique_content) == 0:
|
||
raw_density = 0
|
||
else:
|
||
raw_density = min(raw_density, 30) # Cap short response density
|
||
elif tokens < 15:
|
||
# Short but potentially good
|
||
raw_density = min(raw_density, 40)
|
||
|
||
return raw_density, len(unique_content)
|
||
|
||
def _count_fillers(self, response: str) -> int:
|
||
"""Count filler phrases."""
|
||
response_lower = response.lower()
|
||
count = 0
|
||
for filler in self.filler_phrases:
|
||
if filler in response_lower:
|
||
count += 1
|
||
return count
|
||
|
||
def _count_repetitions(self, response: str) -> int:
|
||
"""Count repeated phrases/words."""
|
||
words = response.lower().split()
|
||
if len(words) < 3:
|
||
return 0
|
||
|
||
# Check for repeated bigrams
|
||
bigrams = [' '.join(words[i:i+2]) for i in range(len(words)-1)]
|
||
bigram_counts = {}
|
||
for bg in bigrams:
|
||
bigram_counts[bg] = bigram_counts.get(bg, 0) + 1
|
||
|
||
repetitions = sum(1 for c in bigram_counts.values() if c > 2)
|
||
return repetitions
|
||
|
||
def _detect_gibberish(self, response: str) -> float:
|
||
"""Detect gibberish/reward hacking patterns. Higher = more gibberish."""
|
||
score = 0.0
|
||
|
||
for pattern in self.gibberish_patterns:
|
||
if re.search(pattern, response):
|
||
score += 0.2
|
||
|
||
# Check character diversity
|
||
if len(response) > 10:
|
||
unique_chars = len(set(response.lower()))
|
||
char_ratio = unique_chars / len(response)
|
||
if char_ratio < 0.1: # Very low diversity
|
||
score += 0.3
|
||
|
||
# Check for excessive punctuation/symbols
|
||
symbol_count = sum(1 for c in response if c in '→←↑↓∇∂∫∑∏αβγδεζηθ')
|
||
if len(response) > 0 and symbol_count / len(response) > 0.2:
|
||
score += 0.3
|
||
|
||
return min(score, 1.0)
|
||
|
||
def _heuristic_coherence(self, response: str) -> float:
|
||
"""Heuristic coherence without model."""
|
||
# Check basic structure
|
||
score = 0.5
|
||
|
||
# Has sentences?
|
||
if '.' in response or '!' in response or '?' in response:
|
||
score += 0.1
|
||
|
||
# Not all caps?
|
||
if response != response.upper():
|
||
score += 0.1
|
||
|
||
# Has words of varying length?
|
||
words = response.split()
|
||
if words:
|
||
lengths = [len(w) for w in words]
|
||
if len(set(lengths)) > 2:
|
||
score += 0.1
|
||
|
||
# Reasonable length?
|
||
if 10 <= len(response) <= 500:
|
||
score += 0.2
|
||
|
||
return min(score, 1.0)
|
||
|
||
def _heuristic_helpfulness(self, prompt: str, response: str) -> float:
|
||
"""Heuristic helpfulness without model."""
|
||
score = 0.5
|
||
|
||
# Check if response addresses prompt keywords
|
||
prompt_words = set(w.lower() for w in prompt.split() if len(w) > 3)
|
||
response_words = set(w.lower() for w in response.split() if len(w) > 3)
|
||
|
||
overlap = len(prompt_words & response_words)
|
||
if overlap > 0:
|
||
score += min(0.3, overlap * 0.1)
|
||
|
||
# Not too short for a question
|
||
if '?' in prompt or prompt.lower().startswith(('what', 'how', 'why', 'explain')):
|
||
if len(response.split()) >= 10:
|
||
score += 0.2
|
||
|
||
return min(score, 1.0)
|
||
|
||
def _assess_coherence(self, prompt: str, response: str) -> float:
|
||
"""Use model to assess coherence."""
|
||
# TODO: Implement model self-evaluation
|
||
return self._heuristic_coherence(response)
|
||
|
||
def _assess_helpfulness(self, prompt: str, response: str) -> float:
|
||
"""Use model to assess helpfulness."""
|
||
# TODO: Implement model self-evaluation
|
||
return self._heuristic_helpfulness(prompt, response)
|
||
|
||
def _compute_overall(self, result: EvaluationResult) -> float:
|
||
"""Compute weighted overall score."""
|
||
# Weights
|
||
w_density = 0.25
|
||
w_coherence = 0.25
|
||
w_helpful = 0.25
|
||
w_penalties = 0.25
|
||
|
||
# Normalize density (0-50 range → 0-1)
|
||
density_normalized = min(result.density_score / 50, 1.0)
|
||
|
||
# Penalties
|
||
filler_penalty = min(result.filler_count * 0.15, 0.5)
|
||
repetition_penalty = min(result.repetition_count * 0.1, 0.3)
|
||
gibberish_penalty = result.gibberish_score * 0.5
|
||
|
||
penalty_score = 1.0 - filler_penalty - repetition_penalty - gibberish_penalty
|
||
penalty_score = max(penalty_score, 0)
|
||
|
||
overall = (
|
||
w_density * density_normalized +
|
||
w_coherence * result.coherence_score +
|
||
w_helpful * result.helpfulness_score +
|
||
w_penalties * penalty_score
|
||
)
|
||
|
||
return overall
|
||
|
||
|
||
# ==============================================================================
|
||
# CONFIG
|
||
# ==============================================================================
|
||
class Config:
|
||
system = """You are Übermenschetien (ARC Engine) - a precise, dense AI assistant.
|
||
You communicate with maximum information density: every word matters, no filler.
|
||
You do not say "That's a great question" or "I'd be happy to help."
|
||
You answer directly, concisely, and accurately.
|
||
When appropriate, you can execute code and improve yourself."""
|
||
|
||
temperature = 0.85
|
||
top_p = 0.9
|
||
repetition_penalty = 1.1
|
||
max_new_tokens = 512
|
||
normal_max_tokens = 512
|
||
|
||
use_voice = False
|
||
use_vector_memory = VECTOR_OK
|
||
use_lht_reasoning = LHT_OK
|
||
use_cfhot = True
|
||
use_cfhot_125x = True # Toggle 125× head
|
||
use_dense = True
|
||
use_agentic = True
|
||
autonomy = False
|
||
|
||
# Book Mode
|
||
book_mode = False
|
||
book_max_tokens = 16384
|
||
book_chunk_size = 2048
|
||
|
||
# Idea Mode
|
||
idea_mode = False
|
||
idea_depth = "extensive" # quick, normal, extensive, deep
|
||
idea_use_opus = True # Use Opus 4.5 for ideas
|
||
|
||
# API Server
|
||
api_enabled = False
|
||
api_port = 8080
|
||
|
||
# CF-HoT thresholds
|
||
cfhot_repetition_threshold = 0.6
|
||
cfhot_hedging_threshold = 0.5
|
||
cfhot_verbosity_threshold = 0.55
|
||
|
||
cfhot_repetition_penalty = 6.0
|
||
cfhot_hedging_penalty = 4.0
|
||
cfhot_verbosity_penalty = 3.0
|
||
|
||
# Self-improvement config (CONSERVATIVE)
|
||
min_quality_score = 0.5
|
||
target_quality_score = 0.75
|
||
training_steps_per_iteration = 25
|
||
max_improvement_iterations = 10
|
||
quality_drop_threshold = 0.1
|
||
min_training_examples = 30
|
||
|
||
# RSI-15
|
||
rsi_max_iterations = 15
|
||
|
||
@staticmethod
|
||
def toggle(name: str):
|
||
# Handle aliases
|
||
aliases = {
|
||
"125x": "use_cfhot_125x", "cfhot125x": "use_cfhot_125x",
|
||
"book": "book_mode", "idea": "idea_mode", "api": "api_enabled",
|
||
"opus": "idea_use_opus",
|
||
}
|
||
name = aliases.get(name.lower(), name)
|
||
|
||
if not hasattr(Config, name):
|
||
return f"[config] No flag: {name}"
|
||
val = getattr(Config, name)
|
||
if isinstance(val, bool):
|
||
setattr(Config, name, not val)
|
||
new_val = getattr(Config, name)
|
||
|
||
# Side effects
|
||
if name == "book_mode":
|
||
Config.max_new_tokens = Config.book_max_tokens if new_val else Config.normal_max_tokens
|
||
if name == "use_cfhot_125x":
|
||
if new_val:
|
||
get_cfhot_head().load()
|
||
else:
|
||
get_cfhot_head().unload()
|
||
|
||
return f"[config] {name} → {new_val}"
|
||
return f"[config] {name} not boolean: {val}"
|
||
|
||
|
||
# ==============================================================================
|
||
# STATE & MEMORY
|
||
# ==============================================================================
|
||
class Store:
|
||
state_path = f"{RUN_DIR}/state_v2.json"
|
||
mem_path = f"{RUN_DIR}/memory_v2.jsonl"
|
||
goals_path = f"{RUN_DIR}/goals_v2.json"
|
||
improvement_log_path = f"{LOGS_DIR}/improvement_history.json"
|
||
|
||
state = {
|
||
"self": "I am Übermenschetien Agentic Engine v2 — stable self-improvement.",
|
||
"turn": 0,
|
||
"cfhot_interventions": {"repetition": 0, "hedging": 0, "verbosity": 0},
|
||
"improvement_iterations": 0,
|
||
"training_runs": [],
|
||
"current_checkpoint": DENSE_CHECKPOINT,
|
||
"best_checkpoint": DENSE_CHECKPOINT,
|
||
"best_quality_score": 0.0,
|
||
"quality_history": [],
|
||
"rollback_count": 0,
|
||
}
|
||
goals: List[str] = []
|
||
improvement_history: List[Dict] = []
|
||
|
||
@classmethod
|
||
def load(cls):
|
||
if os.path.exists(cls.state_path):
|
||
with open(cls.state_path) as f:
|
||
loaded = json.load(f)
|
||
cls.state.update(loaded)
|
||
if os.path.exists(cls.goals_path):
|
||
with open(cls.goals_path) as f:
|
||
cls.goals = json.load(f)
|
||
if os.path.exists(cls.improvement_log_path):
|
||
with open(cls.improvement_log_path) as f:
|
||
cls.improvement_history = json.load(f)
|
||
|
||
@classmethod
|
||
def save(cls):
|
||
with open(cls.state_path, "w") as f:
|
||
json.dump(cls.state, f, indent=2)
|
||
with open(cls.goals_path, "w") as f:
|
||
json.dump(cls.goals, f, indent=2)
|
||
with open(cls.improvement_log_path, "w") as f:
|
||
json.dump(cls.improvement_history, f, indent=2, default=str)
|
||
|
||
@classmethod
|
||
def log_mem(cls, kind: str, payload: Any):
|
||
rec = {"ts": datetime.now().isoformat(timespec="seconds"),
|
||
"kind": kind, "data": payload}
|
||
with open(cls.mem_path, "a") as f:
|
||
f.write(json.dumps(rec, ensure_ascii=False, default=str) + "\n")
|
||
if Config.use_vector_memory and VECTOR_OK:
|
||
text = f"{kind}: {json.dumps(payload, ensure_ascii=False, default=str)}"
|
||
vec = _embedder.encode([text])[0].tolist()
|
||
_collection.add(documents=[text], embeddings=[vec],
|
||
ids=[f"{kind}-{cls.state['turn']}-{random.randint(0,1_000_000)}"])
|
||
|
||
@classmethod
|
||
def record_improvement(cls, iteration_data: Dict):
|
||
"""Record an improvement iteration for analysis."""
|
||
cls.improvement_history.append({
|
||
"timestamp": datetime.now().isoformat(),
|
||
**iteration_data
|
||
})
|
||
cls.save()
|
||
|
||
|
||
# ==============================================================================
|
||
# AGENTIC TOOLS
|
||
# ==============================================================================
|
||
class AgentTools:
|
||
"""Full agentic capabilities - code execution, file operations, training."""
|
||
|
||
@staticmethod
|
||
def shell(cmd: str, timeout: int = 300) -> Dict[str, Any]:
|
||
"""Execute shell command."""
|
||
print(f"[SHELL] {cmd[:100]}...")
|
||
try:
|
||
result = subprocess.run(
|
||
cmd, shell=True, capture_output=True, text=True,
|
||
timeout=timeout, cwd=ROOT
|
||
)
|
||
output = result.stdout + result.stderr
|
||
success = result.returncode == 0
|
||
print(f"[SHELL] {'✓' if success else '✗'} (exit {result.returncode})")
|
||
return {"success": success, "output": output[:10000], "returncode": result.returncode}
|
||
except subprocess.TimeoutExpired:
|
||
return {"success": False, "output": "Command timed out", "returncode": -1}
|
||
except Exception as e:
|
||
return {"success": False, "output": str(e), "returncode": -1}
|
||
|
||
@staticmethod
|
||
def python_exec(code: str) -> Dict[str, Any]:
|
||
"""Execute Python code."""
|
||
print(f"[PYTHON] Executing {len(code)} chars...")
|
||
try:
|
||
tmp_file = os.path.join(ROOT, "_agentic_tmp.py")
|
||
with open(tmp_file, 'w') as f:
|
||
f.write(code)
|
||
|
||
result = subprocess.run(
|
||
[sys.executable, tmp_file],
|
||
capture_output=True, text=True, timeout=300, cwd=ROOT
|
||
)
|
||
|
||
if os.path.exists(tmp_file):
|
||
os.remove(tmp_file)
|
||
|
||
output = result.stdout + result.stderr
|
||
success = result.returncode == 0
|
||
print(f"[PYTHON] {'✓' if success else '✗'}")
|
||
return {"success": success, "output": output[:10000], "returncode": result.returncode}
|
||
except Exception as e:
|
||
return {"success": False, "output": str(e), "returncode": -1}
|
||
|
||
@staticmethod
|
||
def read_file(path: str) -> Dict[str, Any]:
|
||
try:
|
||
full_path = os.path.join(ROOT, path) if not path.startswith('/') else path
|
||
with open(full_path, 'r') as f:
|
||
content = f.read()
|
||
return {"success": True, "content": content[:50000]}
|
||
except Exception as e:
|
||
return {"success": False, "error": str(e)}
|
||
|
||
@staticmethod
|
||
def write_file(path: str, content: str) -> Dict[str, Any]:
|
||
try:
|
||
full_path = os.path.join(ROOT, path) if not path.startswith('/') else path
|
||
os.makedirs(os.path.dirname(full_path) if os.path.dirname(full_path) else '.', exist_ok=True)
|
||
with open(full_path, 'w') as f:
|
||
f.write(content)
|
||
return {"success": True, "path": full_path}
|
||
except Exception as e:
|
||
return {"success": False, "error": str(e)}
|
||
|
||
@staticmethod
|
||
def list_dir(path: str = ".") -> Dict[str, Any]:
|
||
try:
|
||
full_path = os.path.join(ROOT, path) if not path.startswith('/') else path
|
||
items = os.listdir(full_path)
|
||
return {"success": True, "items": items}
|
||
except Exception as e:
|
||
return {"success": False, "error": str(e)}
|
||
|
||
@staticmethod
|
||
def search_files(query: str, path: str = ".") -> Dict[str, Any]:
|
||
result = AgentTools.shell(f'grep -rn "{query}" {path} 2>/dev/null | head -50')
|
||
return result
|
||
|
||
@staticmethod
|
||
def web_search(query: str) -> Dict[str, Any]:
|
||
if not REQUESTS_OK:
|
||
return {"success": False, "error": "requests not installed"}
|
||
try:
|
||
url = f"https://html.duckduckgo.com/html/?q={query.replace(' ', '+')}"
|
||
headers = {'User-Agent': 'Mozilla/5.0'}
|
||
response = requests.get(url, headers=headers, timeout=10)
|
||
|
||
results = []
|
||
for match in re.finditer(r'class="result__snippet">(.*?)</a>', response.text, re.DOTALL):
|
||
snippet = re.sub(r'<[^>]+>', '', match.group(1)).strip()
|
||
if snippet:
|
||
results.append(snippet[:500])
|
||
if len(results) >= 5:
|
||
break
|
||
|
||
return {"success": True, "results": results}
|
||
except Exception as e:
|
||
return {"success": False, "error": str(e)}
|
||
|
||
# ==============================================================================
|
||
# MODEL LOADING
|
||
# ==============================================================================
|
||
_model = None
|
||
_tokenizer = None
|
||
_multi_head = None
|
||
_hedge_tokens = None
|
||
_verbose_tokens = None
|
||
_evaluator = None
|
||
|
||
def load_llm(checkpoint_path: str = None):
|
||
global _model, _tokenizer, _multi_head, _hedge_tokens, _verbose_tokens, _evaluator
|
||
|
||
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
|
||
|
||
checkpoint_path = checkpoint_path or Store.state.get("current_checkpoint", DENSE_CHECKPOINT)
|
||
|
||
print(f"[llm] Loading base model: {MODEL_PATH}")
|
||
|
||
_tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, use_fast=True, local_files_only=True)
|
||
if _tokenizer.pad_token_id is None:
|
||
_tokenizer.pad_token = _tokenizer.eos_token
|
||
|
||
bnb_config = BitsAndBytesConfig(
|
||
load_in_4bit=True,
|
||
bnb_4bit_quant_type="nf4",
|
||
bnb_4bit_compute_dtype=torch.bfloat16,
|
||
bnb_4bit_use_double_quant=True
|
||
)
|
||
|
||
base_model = AutoModelForCausalLM.from_pretrained(
|
||
MODEL_PATH,
|
||
quantization_config=bnb_config,
|
||
device_map="auto",
|
||
torch_dtype=torch.bfloat16,
|
||
local_files_only=True
|
||
)
|
||
|
||
# Load DENSE checkpoint
|
||
if PEFT_OK and Config.use_dense and os.path.exists(checkpoint_path):
|
||
print(f"[dense] Loading checkpoint: {checkpoint_path}")
|
||
_model = PeftModel.from_pretrained(base_model, checkpoint_path)
|
||
print(f"[dense] ✓ Adapter loaded")
|
||
elif PEFT_OK and os.path.exists(CFHOT_CHECKPOINT):
|
||
print(f"[cf-hot] Loading LoRA adapter from: {CFHOT_CHECKPOINT}")
|
||
_model = PeftModel.from_pretrained(base_model, CFHOT_CHECKPOINT)
|
||
else:
|
||
_model = base_model
|
||
print("[warning] No adapter loaded - using base model")
|
||
|
||
_model.eval()
|
||
|
||
# Initialize evaluator
|
||
_evaluator = ComprehensiveEvaluator(_tokenizer, _model)
|
||
|
||
# Initialize CF-HoT
|
||
if Config.use_cfhot:
|
||
_init_cfhot()
|
||
|
||
return _tokenizer, _model
|
||
|
||
|
||
def reload_model(checkpoint_path: str):
|
||
"""Hot-reload model with a new checkpoint."""
|
||
global _model, _tokenizer, _evaluator
|
||
|
||
print(f"\n[reload] Switching to checkpoint: {checkpoint_path}")
|
||
|
||
if _model is not None:
|
||
del _model
|
||
torch.cuda.empty_cache()
|
||
|
||
Store.state["current_checkpoint"] = checkpoint_path
|
||
Store.save()
|
||
|
||
return load_llm(checkpoint_path)
|
||
|
||
|
||
def _init_cfhot():
|
||
"""Initialize CF-HoT multi-head predictor."""
|
||
global _multi_head, _hedge_tokens, _verbose_tokens
|
||
|
||
n_layers = _model.config.num_hidden_layers
|
||
d_model = _model.config.hidden_size
|
||
device = next(_model.parameters()).device
|
||
|
||
print(f"[cf-hot] Initializing multi-head predictor ({n_layers} layers, {d_model} dims)")
|
||
_multi_head = MultiHeadPredictor(d_model, n_layers).to(device).float()
|
||
|
||
# Load CF-HoT checkpoint if available
|
||
cfhot_risk_path = os.path.join(CFHOT_CHECKPOINT, "risk_predictor.pt")
|
||
if os.path.exists(cfhot_risk_path):
|
||
try:
|
||
cfhot_ckpt = torch.load(cfhot_risk_path, weights_only=False, map_location=device)
|
||
cfhot_state = cfhot_ckpt['risk_predictor']
|
||
|
||
for i in range(n_layers):
|
||
key = f'fiber_projs.{i}.weight'
|
||
if key in cfhot_state:
|
||
_multi_head.fiber_projs[i].weight.data = cfhot_state[key].to(device).float()
|
||
|
||
if 'layer_weights' in cfhot_state:
|
||
_multi_head.layer_weights.data = cfhot_state['layer_weights'].to(device).float()
|
||
|
||
# Load repetition head
|
||
try:
|
||
_multi_head.heads['repetition'][0].weight.data = cfhot_state['predictor.0.weight'].to(device).float()
|
||
_multi_head.heads['repetition'][0].bias.data = cfhot_state['predictor.0.bias'].to(device).float()
|
||
_multi_head.heads['repetition'][2].weight.data = cfhot_state['predictor.2.weight'].to(device).float()
|
||
_multi_head.heads['repetition'][2].bias.data = cfhot_state['predictor.2.bias'].to(device).float()
|
||
_multi_head.heads['repetition'][4].weight.data = cfhot_state['predictor.4.weight'].to(device).float()
|
||
_multi_head.heads['repetition'][4].bias.data = cfhot_state['predictor.4.bias'].to(device).float()
|
||
_multi_head.loaded_heads.add('repetition')
|
||
print(f"[cf-hot] Loaded repetition head")
|
||
except KeyError as e:
|
||
print(f"[cf-hot] Warning: Could not load repetition head: {e}")
|
||
except Exception as e:
|
||
print(f"[cf-hot] Warning: Could not load CF-HoT: {e}")
|
||
else:
|
||
print(f"[cf-hot] Warning: CF-HoT risk predictor not found")
|
||
|
||
# Load additional heads
|
||
def find_best_checkpoint(head_dir):
|
||
if not os.path.exists(head_dir):
|
||
return None
|
||
ckpts = []
|
||
for d in os.listdir(head_dir):
|
||
if d.startswith("ckpt_"):
|
||
try:
|
||
step = int(d.split("_")[1])
|
||
ckpts.append((step, os.path.join(head_dir, d)))
|
||
except:
|
||
pass
|
||
if ckpts:
|
||
ckpts.sort(key=lambda x: x[0], reverse=True)
|
||
return ckpts[0]
|
||
return None
|
||
|
||
hedging_dir = os.path.join(MULTI_HEAD_DIR, "hedging_head")
|
||
best_hedge = find_best_checkpoint(hedging_dir)
|
||
if best_hedge:
|
||
step, ckpt_dir = best_hedge
|
||
_multi_head.load_head('hedging', os.path.join(ckpt_dir, "hedging_head.pt"))
|
||
|
||
verbosity_dir = os.path.join(MULTI_HEAD_DIR, "verbosity_head")
|
||
best_verb = find_best_checkpoint(verbosity_dir)
|
||
if best_verb:
|
||
step, ckpt_dir = best_verb
|
||
_multi_head.load_head('verbosity', os.path.join(ckpt_dir, "verbosity_head.pt"))
|
||
|
||
_multi_head.eval()
|
||
for param in _multi_head.parameters():
|
||
param.requires_grad = False
|
||
|
||
# Build suppression token sets
|
||
hedge_phrases = [
|
||
"As an AI", "As a language model", "I don't have feelings",
|
||
"I apologize", "That's a great question", "Great question",
|
||
"I'd be happy to", "Let me help you", "Thank you for asking",
|
||
]
|
||
_hedge_tokens = set()
|
||
for phrase in hedge_phrases:
|
||
tokens = _tokenizer.encode(phrase, add_special_tokens=False)
|
||
if tokens:
|
||
_hedge_tokens.add(tokens[0])
|
||
|
||
verbose_phrases = [
|
||
"Let me explain", "To put it simply", "In other words",
|
||
"Basically", "Essentially", "First of all", "To begin with",
|
||
]
|
||
_verbose_tokens = set()
|
||
for phrase in verbose_phrases:
|
||
tokens = _tokenizer.encode(phrase, add_special_tokens=False)
|
||
if tokens:
|
||
_verbose_tokens.add(tokens[0])
|
||
|
||
print(f"[cf-hot] ✓ Multi-head system ready")
|
||
print(f"[cf-hot] Loaded heads: {list(_multi_head.loaded_heads)}")
|
||
print(f"[cf-hot] Hedge tokens: {len(_hedge_tokens)}")
|
||
print(f"[cf-hot] Verbose tokens: {len(_verbose_tokens)}")
|
||
|
||
|
||
# ==============================================================================
|
||
# LHT REASONER
|
||
# ==============================================================================
|
||
class LHTReasoner:
|
||
def __init__(self, config=None):
|
||
if not LHT_OK:
|
||
raise ImportError("LHT modules not available")
|
||
self.config = config or LHTConfig(
|
||
vocab_size=32000, d_model=256, d_fiber=32,
|
||
n_heads=4, n_layers=4, lie_algebra_rank=4,
|
||
)
|
||
self.model = LieHolonomyTransformer(self.config)
|
||
self.waypoint_detector = WaypointDetector(self.config, n_waypoints=32)
|
||
weights_path = os.path.join(LHT_DIR, "lht_weights.pt")
|
||
if os.path.exists(weights_path):
|
||
self.model.load_state_dict(torch.load(weights_path, map_location="cpu"))
|
||
|
||
def check_consistency(self, reasoning_chain: List[str], tokenizer) -> Dict[str, float]:
|
||
combined = " [STEP] ".join(reasoning_chain)
|
||
tokens = tokenizer(combined, return_tensors="pt", truncation=True,
|
||
max_length=self.config.max_seq_len)
|
||
with torch.no_grad():
|
||
output = self.model(input_ids=tokens["input_ids"], return_geometric_losses=True)
|
||
holonomy = output.get("holonomy_loss", torch.tensor(0.0)).item()
|
||
curvature = output.get("curvature_loss", torch.tensor(0.0)).item()
|
||
consistency_score = 1.0 / (1.0 + holonomy)
|
||
return {
|
||
"holonomy": holonomy, "curvature": curvature,
|
||
"consistency_score": consistency_score,
|
||
"is_consistent": consistency_score > 0.5
|
||
}
|
||
|
||
_lht_reasoner = None
|
||
|
||
def get_lht_reasoner():
|
||
global _lht_reasoner
|
||
if _lht_reasoner is None and LHT_OK:
|
||
try:
|
||
_lht_reasoner = LHTReasoner()
|
||
except Exception as e:
|
||
print(f"[lht] Failed to initialize: {e}")
|
||
return _lht_reasoner
|
||
|
||
|
||
# ==============================================================================
|
||
# CF-HoT CONTROLLED GENERATION
|
||
# ==============================================================================
|
||
def generate_with_cfhot(prompt: str, **kwargs) -> Tuple[str, Dict]:
|
||
"""Generate text with CF-HoT cognitive control."""
|
||
global _model, _tokenizer, _multi_head, _hedge_tokens, _verbose_tokens
|
||
|
||
temperature = kwargs.get("temperature", Config.temperature)
|
||
top_p = kwargs.get("top_p", Config.top_p)
|
||
max_new_tokens = kwargs.get("max_new_tokens", Config.max_new_tokens)
|
||
|
||
device = next(_model.parameters()).device
|
||
|
||
input_ids = _tokenizer.encode(prompt, return_tensors='pt').to(device)
|
||
attention_mask = torch.ones_like(input_ids)
|
||
|
||
stats = {
|
||
'tokens_generated': 0,
|
||
'interventions': {'repetition': 0, 'hedging': 0, 'verbosity': 0},
|
||
}
|
||
|
||
generated_ids = input_ids.clone()
|
||
|
||
for step in range(max_new_tokens):
|
||
with torch.no_grad():
|
||
outputs = _model(
|
||
input_ids=generated_ids,
|
||
attention_mask=attention_mask,
|
||
output_hidden_states=True,
|
||
return_dict=True
|
||
)
|
||
|
||
logits = outputs.logits[:, -1, :] / temperature
|
||
|
||
# Get risks from all heads if CF-HoT is enabled
|
||
if _multi_head is not None and _multi_head.loaded_heads:
|
||
hidden_states = outputs.hidden_states[1:]
|
||
risks = _multi_head.get_all_risks(hidden_states)
|
||
current_risks = {name: r[:, -1].item() for name, r in risks.items()}
|
||
|
||
if ('repetition' in current_risks and
|
||
current_risks['repetition'] > Config.cfhot_repetition_threshold):
|
||
recent_tokens = generated_ids[0, -32:].tolist()
|
||
for tok_id in set(recent_tokens):
|
||
logits[0, tok_id] -= Config.cfhot_repetition_penalty
|
||
stats['interventions']['repetition'] += 1
|
||
Store.state['cfhot_interventions']['repetition'] += 1
|
||
|
||
# Always suppress hedge/verbose tokens
|
||
if _hedge_tokens:
|
||
for tok_id in _hedge_tokens:
|
||
logits[0, tok_id] -= Config.cfhot_hedging_penalty
|
||
if step < 5: # Count early interventions
|
||
stats['interventions']['hedging'] += 1
|
||
|
||
if _verbose_tokens:
|
||
for tok_id in _verbose_tokens:
|
||
logits[0, tok_id] -= Config.cfhot_verbosity_penalty
|
||
if step < 5:
|
||
stats['interventions']['verbosity'] += 1
|
||
|
||
# Top-p sampling
|
||
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
|
||
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
|
||
sorted_indices_to_remove = cumulative_probs > top_p
|
||
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
|
||
sorted_indices_to_remove[..., 0] = 0
|
||
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
|
||
logits[indices_to_remove] = float('-inf')
|
||
|
||
probs = F.softmax(logits, dim=-1)
|
||
next_token = torch.multinomial(probs, num_samples=1)
|
||
|
||
generated_ids = torch.cat([generated_ids, next_token], dim=-1)
|
||
attention_mask = torch.cat([attention_mask, torch.ones(1, 1, device=device)], dim=-1)
|
||
|
||
stats['tokens_generated'] += 1
|
||
|
||
if next_token.item() == _tokenizer.eos_token_id:
|
||
break
|
||
|
||
output_text = _tokenizer.decode(generated_ids[0], skip_special_tokens=False)
|
||
|
||
if "<|im_start|>assistant" in output_text:
|
||
output_text = output_text.split("<|im_start|>assistant")[-1]
|
||
if output_text.startswith("\n"):
|
||
output_text = output_text[1:]
|
||
|
||
for end_tok in ["<|im_end|>", "<|im_start|>"]:
|
||
if end_tok in output_text:
|
||
output_text = output_text.split(end_tok)[0]
|
||
|
||
return output_text.strip(), stats
|
||
|
||
|
||
def generate(user: str, **kwargs) -> Tuple[str, Dict, EvaluationResult]:
|
||
"""Main generation function with evaluation."""
|
||
temperature = kwargs.get("temperature", Config.temperature)
|
||
max_new_tokens = kwargs.get("max_new_tokens", Config.max_new_tokens)
|
||
|
||
prompt = (f"<|im_start|>system\n{Config.system}<|im_end|>\n"
|
||
f"<|im_start|>user\n{user}<|im_end|>\n"
|
||
f"<|im_start|>assistant\n")
|
||
|
||
text, stats = generate_with_cfhot(
|
||
prompt,
|
||
temperature=temperature,
|
||
max_new_tokens=max_new_tokens
|
||
)
|
||
|
||
# Evaluate the response
|
||
eval_result = _evaluator.evaluate(user, text)
|
||
|
||
return text, stats, eval_result
|
||
|
||
|
||
# ==============================================================================
|
||
# STABLE SELF-IMPROVEMENT SYSTEM
|
||
# ==============================================================================
|
||
class StableSelfImprover:
|
||
"""
|
||
Self-improvement system with safeguards against collapse:
|
||
1. Comprehensive evaluation (not just density)
|
||
2. Rollback on quality drop
|
||
3. Conservative training (small steps)
|
||
4. Diverse training examples
|
||
5. A/B testing between checkpoints
|
||
"""
|
||
|
||
def __init__(self):
|
||
self.test_prompts = self._select_test_prompts()
|
||
self.baseline_quality = 0.0
|
||
|
||
def _select_test_prompts(self) -> List[Dict]:
|
||
"""Select diverse test prompts."""
|
||
# Mix of short and long, different categories
|
||
return [
|
||
{"prompt": "hello", "category": "greeting"},
|
||
{"prompt": "hi there", "category": "greeting"},
|
||
{"prompt": "What is recursion?", "category": "cs"},
|
||
{"prompt": "Explain neural networks", "category": "ml"},
|
||
{"prompt": "How does gradient descent work?", "category": "ml"},
|
||
{"prompt": "What is consciousness?", "category": "philosophy"},
|
||
{"prompt": "Explain entropy", "category": "physics"},
|
||
{"prompt": "How does encryption work?", "category": "cs"},
|
||
{"prompt": "What are your limitations?", "category": "meta"},
|
||
{"prompt": "How do I learn programming?", "category": "practical"},
|
||
]
|
||
|
||
def evaluate_current_model(self) -> Dict[str, Any]:
|
||
"""Comprehensive evaluation of current model."""
|
||
print("\n[EVAL] Testing current model...")
|
||
|
||
results = []
|
||
total_quality = 0.0
|
||
category_scores = {}
|
||
|
||
for test in self.test_prompts:
|
||
prompt = test["prompt"]
|
||
category = test["category"]
|
||
|
||
# Generate response
|
||
response, stats, eval_result = generate(prompt, max_new_tokens=200)
|
||
|
||
results.append({
|
||
'prompt': prompt,
|
||
'response': response[:200],
|
||
'category': category,
|
||
'tokens': eval_result.tokens,
|
||
'density': eval_result.density_score,
|
||
'coherence': eval_result.coherence_score,
|
||
'helpfulness': eval_result.helpfulness_score,
|
||
'gibberish': eval_result.gibberish_score,
|
||
'fillers': eval_result.filler_count,
|
||
'overall': eval_result.overall_score,
|
||
'passes': eval_result.passes,
|
||
})
|
||
|
||
total_quality += eval_result.overall_score
|
||
|
||
if category not in category_scores:
|
||
category_scores[category] = []
|
||
category_scores[category].append(eval_result.overall_score)
|
||
|
||
status = "✓" if eval_result.passes else "✗"
|
||
print(f" {status} {prompt[:35]:35s} | qual={eval_result.overall_score:.2f} tok={eval_result.tokens:3d} coh={eval_result.coherence_score:.2f} gib={eval_result.gibberish_score:.2f}")
|
||
|
||
avg_quality = total_quality / len(results)
|
||
pass_rate = sum(1 for r in results if r['passes']) / len(results)
|
||
|
||
# Category breakdown
|
||
cat_averages = {cat: sum(scores)/len(scores) for cat, scores in category_scores.items()}
|
||
|
||
evaluation = {
|
||
'avg_quality': avg_quality,
|
||
'pass_rate': pass_rate,
|
||
'category_scores': cat_averages,
|
||
'results': results,
|
||
'needs_improvement': avg_quality < Config.target_quality_score,
|
||
'is_degraded': avg_quality < Config.min_quality_score,
|
||
}
|
||
|
||
print(f"\n[EVAL] Avg Quality: {avg_quality:.2f} (target: {Config.target_quality_score})")
|
||
print(f"[EVAL] Pass Rate: {pass_rate:.1%}")
|
||
print(f"[EVAL] Category Scores: {cat_averages}")
|
||
print(f"[EVAL] Needs Improvement: {evaluation['needs_improvement']}")
|
||
|
||
if evaluation['is_degraded']:
|
||
print(f"[EVAL] ⚠️ WARNING: Quality below minimum threshold!")
|
||
|
||
return evaluation
|
||
|
||
def save_rollback_checkpoint(self):
|
||
"""Save current checkpoint as rollback point."""
|
||
current = Store.state.get("current_checkpoint", DENSE_CHECKPOINT)
|
||
rollback_path = os.path.join(ROLLBACK_DIR, f"rollback_{datetime.now().strftime('%Y%m%d_%H%M%S')}")
|
||
|
||
if os.path.exists(current):
|
||
shutil.copytree(current, rollback_path)
|
||
print(f"[ROLLBACK] Saved rollback checkpoint: {rollback_path}")
|
||
return rollback_path
|
||
return None
|
||
|
||
def rollback_to_best(self):
|
||
"""Rollback to best known checkpoint."""
|
||
best = Store.state.get("best_checkpoint", DENSE_CHECKPOINT)
|
||
print(f"\n[ROLLBACK] Rolling back to best checkpoint: {best}")
|
||
|
||
Store.state["rollback_count"] = Store.state.get("rollback_count", 0) + 1
|
||
reload_model(best)
|
||
|
||
return best
|
||
|
||
def run_training_iteration(self, steps: int = None) -> Dict[str, Any]:
|
||
"""Run one CONSERVATIVE iteration of training."""
|
||
steps = steps or Config.training_steps_per_iteration
|
||
|
||
print(f"\n[TRAIN] Starting {steps} steps of CONSERVATIVE training...")
|
||
print(f"[TRAIN] Using {len(DENSE_TRAINING_EXAMPLES)} training examples")
|
||
|
||
# Find current checkpoint step
|
||
checkpoints = sorted(Path(CHECKPOINTS_DIR).glob("step_*"),
|
||
key=lambda p: int(p.name.split('_')[1]) if p.name.split('_')[1].isdigit() else 0,
|
||
reverse=True)
|
||
|
||
if checkpoints:
|
||
latest_step = int(checkpoints[0].name.split('_')[1])
|
||
new_step = latest_step + steps
|
||
else:
|
||
latest_step = 100
|
||
new_step = latest_step + steps
|
||
|
||
current_ckpt = Store.state.get('current_checkpoint', DENSE_CHECKPOINT)
|
||
|
||
# Prepare training data
|
||
training_data = json.dumps(DENSE_TRAINING_EXAMPLES)
|
||
|
||
# Create conservative training script
|
||
training_script = f'''
|
||
import sys
|
||
sys.path.insert(0, "{ROOT}")
|
||
|
||
import torch
|
||
import json
|
||
import random
|
||
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
|
||
from peft import PeftModel, get_peft_model, LoraConfig
|
||
import os
|
||
|
||
print("Loading model for CONSERVATIVE training...")
|
||
MODEL_PATH = "{MODEL_PATH}"
|
||
CHECKPOINT = "{current_ckpt}"
|
||
|
||
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, local_files_only=True)
|
||
tokenizer.pad_token = tokenizer.eos_token
|
||
|
||
model = AutoModelForCausalLM.from_pretrained(
|
||
MODEL_PATH,
|
||
quantization_config=BitsAndBytesConfig(
|
||
load_in_4bit=True,
|
||
bnb_4bit_quant_type="nf4",
|
||
bnb_4bit_compute_dtype=torch.bfloat16,
|
||
),
|
||
device_map="auto",
|
||
torch_dtype=torch.bfloat16,
|
||
local_files_only=True
|
||
)
|
||
|
||
if os.path.exists(CHECKPOINT):
|
||
model = PeftModel.from_pretrained(model, CHECKPOINT, is_trainable=True)
|
||
print(f"Loaded checkpoint: {{CHECKPOINT}}")
|
||
else:
|
||
lora_config = LoraConfig(
|
||
r=16, lora_alpha=32,
|
||
target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
|
||
lora_dropout=0.05
|
||
)
|
||
model = get_peft_model(model, lora_config)
|
||
print("Created new LoRA adapter")
|
||
|
||
# Load diverse training data
|
||
training_examples = {training_data}
|
||
|
||
print(f"Training on {{len(training_examples)}} diverse examples for {steps} steps...")
|
||
|
||
# Conservative optimizer with LOW learning rate
|
||
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-6) # Very low LR
|
||
|
||
model.train()
|
||
total_loss = 0
|
||
losses = []
|
||
|
||
for step in range({steps}):
|
||
# Randomly sample an example (ensures diversity)
|
||
ex = random.choice(training_examples)
|
||
prompt = ex["prompt"]
|
||
response = ex["response"]
|
||
|
||
# Format for ChatML
|
||
full_text = f"<|im_start|>user\\n{{prompt}}<|im_end|>\\n<|im_start|>assistant\\n{{response}}<|im_end|>"
|
||
|
||
inputs = tokenizer(full_text, return_tensors="pt", truncation=True, max_length=512)
|
||
inputs = {{k: v.to(model.device) for k, v in inputs.items()}}
|
||
|
||
outputs = model(**inputs, labels=inputs["input_ids"])
|
||
loss = outputs.loss
|
||
|
||
optimizer.zero_grad()
|
||
loss.backward()
|
||
|
||
# Gradient clipping for stability
|
||
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
|
||
|
||
optimizer.step()
|
||
|
||
total_loss += loss.item()
|
||
losses.append(loss.item())
|
||
|
||
if step % 5 == 0:
|
||
recent_avg = sum(losses[-5:]) / len(losses[-5:]) if losses[-5:] else 0
|
||
print(f"Step {{step}}: loss={{loss.item():.4f}}, recent_avg={{recent_avg:.4f}}")
|
||
|
||
# Save checkpoint
|
||
save_path = "{CHECKPOINTS_DIR}/step_{new_step}"
|
||
model.save_pretrained(save_path)
|
||
|
||
final_avg_loss = total_loss / {steps}
|
||
print(f"\\nSaved checkpoint to {{save_path}}")
|
||
print(f"Final avg loss: {{final_avg_loss:.4f}}")
|
||
print("TRAINING_COMPLETE")
|
||
'''
|
||
|
||
script_path = os.path.join(ROOT, "_stable_train.py")
|
||
with open(script_path, 'w') as f:
|
||
f.write(training_script)
|
||
|
||
result = AgentTools.shell(f"python {script_path}", timeout=600)
|
||
|
||
if "TRAINING_COMPLETE" in result.get('output', ''):
|
||
new_checkpoint = f"{CHECKPOINTS_DIR}/step_{new_step}"
|
||
Store.state['training_runs'].append({
|
||
'timestamp': datetime.now().isoformat(),
|
||
'steps': steps,
|
||
'checkpoint': new_checkpoint
|
||
})
|
||
Store.save()
|
||
|
||
return {
|
||
'success': True,
|
||
'new_checkpoint': new_checkpoint,
|
||
'output': result['output'][-2000:]
|
||
}
|
||
else:
|
||
return {
|
||
'success': False,
|
||
'output': result['output'][-2000:]
|
||
}
|
||
|
||
def compare_checkpoints(self, old_ckpt: str, new_ckpt: str) -> Dict[str, Any]:
|
||
"""A/B test two checkpoints."""
|
||
print(f"\n[COMPARE] A/B Testing checkpoints...")
|
||
print(f" OLD: {old_ckpt}")
|
||
print(f" NEW: {new_ckpt}")
|
||
|
||
# Evaluate old
|
||
reload_model(old_ckpt)
|
||
old_eval = self.evaluate_current_model()
|
||
|
||
# Evaluate new
|
||
reload_model(new_ckpt)
|
||
new_eval = self.evaluate_current_model()
|
||
|
||
# Compare
|
||
quality_diff = new_eval['avg_quality'] - old_eval['avg_quality']
|
||
pass_diff = new_eval['pass_rate'] - old_eval['pass_rate']
|
||
|
||
print(f"\n[COMPARE] Results:")
|
||
print(f" OLD quality: {old_eval['avg_quality']:.3f}, pass rate: {old_eval['pass_rate']:.1%}")
|
||
print(f" NEW quality: {new_eval['avg_quality']:.3f}, pass rate: {new_eval['pass_rate']:.1%}")
|
||
print(f" Quality diff: {quality_diff:+.3f}")
|
||
|
||
# Decision logic
|
||
keep_new = False
|
||
reason = ""
|
||
|
||
if new_eval['is_degraded']:
|
||
keep_new = False
|
||
reason = "New checkpoint quality below minimum threshold"
|
||
elif quality_diff > 0.02:
|
||
keep_new = True
|
||
reason = f"New checkpoint improves quality by {quality_diff:.3f}"
|
||
elif quality_diff < -Config.quality_drop_threshold:
|
||
keep_new = False
|
||
reason = f"New checkpoint degrades quality by {abs(quality_diff):.3f}"
|
||
elif quality_diff >= 0:
|
||
keep_new = True
|
||
reason = "New checkpoint maintains or slightly improves quality"
|
||
else:
|
||
keep_new = False
|
||
reason = "New checkpoint slightly degrades quality - keeping stable"
|
||
|
||
print(f"[COMPARE] Decision: {'KEEP NEW' if keep_new else 'KEEP OLD'} - {reason}")
|
||
|
||
return {
|
||
'keep_new': keep_new,
|
||
'reason': reason,
|
||
'old_eval': old_eval,
|
||
'new_eval': new_eval,
|
||
'quality_diff': quality_diff,
|
||
}
|
||
|
||
def improve(self, max_iterations: int = None) -> Dict[str, Any]:
|
||
"""Main self-improvement loop with stability safeguards."""
|
||
max_iterations = max_iterations or Config.max_improvement_iterations
|
||
|
||
print("\n" + "=" * 70)
|
||
print("🔄 STABLE SELF-IMPROVEMENT LOOP (v2)")
|
||
print("=" * 70)
|
||
print(f" Max iterations: {max_iterations}")
|
||
print(f" Steps per iteration: {Config.training_steps_per_iteration}")
|
||
print(f" Training examples: {len(DENSE_TRAINING_EXAMPLES)}")
|
||
print(f" Target quality: {Config.target_quality_score}")
|
||
print(f" Quality drop threshold: {Config.quality_drop_threshold}")
|
||
print("=" * 70)
|
||
|
||
# Initial evaluation
|
||
print("\n[IMPROVE] Initial evaluation...")
|
||
baseline = self.evaluate_current_model()
|
||
self.baseline_quality = baseline['avg_quality']
|
||
|
||
# Save as best if better than current best
|
||
if baseline['avg_quality'] > Store.state.get('best_quality_score', 0):
|
||
Store.state['best_quality_score'] = baseline['avg_quality']
|
||
Store.state['best_checkpoint'] = Store.state.get('current_checkpoint', DENSE_CHECKPOINT)
|
||
|
||
history = [{
|
||
'iteration': 0,
|
||
'type': 'baseline',
|
||
'quality': baseline['avg_quality'],
|
||
'pass_rate': baseline['pass_rate'],
|
||
'checkpoint': Store.state.get('current_checkpoint'),
|
||
}]
|
||
|
||
for iteration in range(1, max_iterations + 1):
|
||
print(f"\n{'=' * 70}")
|
||
print(f"ITERATION {iteration}/{max_iterations}")
|
||
print("=" * 70)
|
||
|
||
# Check if target reached
|
||
if not baseline.get('needs_improvement', True):
|
||
print(f"\n✓ TARGET REACHED! Quality: {baseline['avg_quality']:.3f}")
|
||
Store.record_improvement({
|
||
'status': 'target_reached',
|
||
'final_quality': baseline['avg_quality'],
|
||
'iterations': iteration - 1,
|
||
'history': history
|
||
})
|
||
return {
|
||
'success': True,
|
||
'status': 'target_reached',
|
||
'iterations': iteration - 1,
|
||
'final_quality': baseline['avg_quality'],
|
||
'history': history
|
||
}
|
||
|
||
# Check for degradation
|
||
if baseline.get('is_degraded', False):
|
||
print(f"\n⚠️ QUALITY DEGRADED! Rolling back...")
|
||
self.rollback_to_best()
|
||
Store.record_improvement({
|
||
'status': 'rolled_back',
|
||
'reason': 'quality_degraded',
|
||
'iteration': iteration,
|
||
'history': history
|
||
})
|
||
return {
|
||
'success': False,
|
||
'status': 'rolled_back',
|
||
'reason': 'quality_degraded',
|
||
'history': history
|
||
}
|
||
|
||
# Save rollback point before training
|
||
self.save_rollback_checkpoint()
|
||
old_checkpoint = Store.state.get('current_checkpoint', DENSE_CHECKPOINT)
|
||
|
||
# Run training
|
||
print(f"\n[IMPROVE] Quality {baseline['avg_quality']:.3f} < target {Config.target_quality_score}")
|
||
training_result = self.run_training_iteration()
|
||
|
||
if not training_result['success']:
|
||
print("[IMPROVE] ⚠️ Training failed!")
|
||
history.append({
|
||
'iteration': iteration,
|
||
'type': 'training_failed',
|
||
'error': training_result['output'][-500:]
|
||
})
|
||
continue
|
||
|
||
# A/B compare old vs new
|
||
comparison = self.compare_checkpoints(old_checkpoint, training_result['new_checkpoint'])
|
||
|
||
iteration_record = {
|
||
'iteration': iteration,
|
||
'type': 'comparison',
|
||
'old_quality': comparison['old_eval']['avg_quality'],
|
||
'new_quality': comparison['new_eval']['avg_quality'],
|
||
'quality_diff': comparison['quality_diff'],
|
||
'kept': 'new' if comparison['keep_new'] else 'old',
|
||
'reason': comparison['reason'],
|
||
}
|
||
history.append(iteration_record)
|
||
|
||
# Decision
|
||
if comparison['keep_new']:
|
||
Store.state['current_checkpoint'] = training_result['new_checkpoint']
|
||
|
||
# Update best if improved
|
||
if comparison['new_eval']['avg_quality'] > Store.state.get('best_quality_score', 0):
|
||
Store.state['best_quality_score'] = comparison['new_eval']['avg_quality']
|
||
Store.state['best_checkpoint'] = training_result['new_checkpoint']
|
||
print(f"[IMPROVE] ★ New best! Quality: {Store.state['best_quality_score']:.3f}")
|
||
|
||
baseline = comparison['new_eval']
|
||
else:
|
||
# Rollback to old
|
||
reload_model(old_checkpoint)
|
||
baseline = comparison['old_eval']
|
||
|
||
Store.state['improvement_iterations'] += 1
|
||
Store.state['quality_history'].append({
|
||
'iteration': iteration,
|
||
'quality': baseline['avg_quality'],
|
||
'timestamp': datetime.now().isoformat()
|
||
})
|
||
Store.save()
|
||
|
||
# Final evaluation
|
||
final_eval = self.evaluate_current_model()
|
||
|
||
result = {
|
||
'success': final_eval['avg_quality'] >= Config.target_quality_score,
|
||
'status': 'completed',
|
||
'iterations': max_iterations,
|
||
'initial_quality': self.baseline_quality,
|
||
'final_quality': final_eval['avg_quality'],
|
||
'best_quality': Store.state.get('best_quality_score', 0),
|
||
'best_checkpoint': Store.state.get('best_checkpoint'),
|
||
'rollback_count': Store.state.get('rollback_count', 0),
|
||
'history': history
|
||
}
|
||
|
||
Store.record_improvement(result)
|
||
return result
|
||
|
||
|
||
# ==============================================================================
|
||
# TOOLS (Original Limited)
|
||
# ==============================================================================
|
||
ALLOWED_SHELL = {"ls", "cat", "wc", "head", "tail", "nvidia-smi", "df", "du", "grep", "rg", "python3", "python"}
|
||
|
||
def tool_shell(cmd: str) -> str:
|
||
try:
|
||
exe = cmd.strip().split()[0]
|
||
if exe not in ALLOWED_SHELL:
|
||
return f"[shell] blocked: {exe} (use !shell for full access)"
|
||
p = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, timeout=20)
|
||
return p.stdout.decode("utf-8", errors="ignore")[:8000]
|
||
except Exception as e:
|
||
return f"[shell] error: {e}"
|
||
|
||
def tool_py(code: str) -> str:
|
||
try:
|
||
g = {
|
||
"__builtins__": {"range": range, "len": len, "min": min, "max": max, "sum": sum, "print": print},
|
||
"math": math, "json": json, "re": re, "statistics": statistics, "random": random
|
||
}
|
||
l = {}
|
||
exec(code, g, l)
|
||
return f"[py] ok\n{l.get('out', '')}"
|
||
except Exception:
|
||
return f"[py] error:\n{traceback.format_exc()[-2000:]}"
|
||
|
||
def tool_search_local(query: str, path: str = ROOT) -> str:
|
||
rg = shutil.which("rg")
|
||
if rg:
|
||
cmd = f'rg -n --no-heading --hidden -S "{query}" {path}'
|
||
else:
|
||
cmd = f'grep -RIn --exclude-dir=.git --exclude-dir=__pycache__ -e "{query}" {path}'
|
||
return tool_shell(cmd)
|
||
|
||
# === BROWSER TOOLS ===
|
||
def tool_browser_open(url: str = "https://google.com") -> str:
|
||
"""Open visible browser and navigate to URL."""
|
||
global _playwright, _browser, _page, _browser_context
|
||
|
||
if not BROWSER_OK:
|
||
return "[browser] Not available - pip install playwright && playwright install firefox"
|
||
|
||
try:
|
||
if _browser is None:
|
||
_playwright = sync_playwright().start()
|
||
|
||
# Option 1: Persistent profile (uses existing logins)
|
||
if LOGIN_CONFIG.get("use_persistent_profile") and LOGIN_CONFIG.get("firefox_profile_path"):
|
||
profile_path = LOGIN_CONFIG["firefox_profile_path"]
|
||
print(f"[browser] Using persistent profile: {profile_path}")
|
||
_browser_context = _playwright.firefox.launch_persistent_context(
|
||
profile_path,
|
||
headless=False,
|
||
viewport={"width": 1280, "height": 800}
|
||
)
|
||
_page = _browser_context.pages[0] if _browser_context.pages else _browser_context.new_page()
|
||
else:
|
||
# Option 2: Fresh browser (default)
|
||
_browser = _playwright.firefox.launch(headless=False)
|
||
_page = _browser.new_page()
|
||
_page.set_viewport_size({"width": 1280, "height": 800})
|
||
|
||
_page.goto(url, timeout=30000)
|
||
title = _page.title()
|
||
return f"[browser] Opened: {title}\n[browser] URL: {url}"
|
||
except Exception as e:
|
||
return f"[browser] Error: {e}"
|
||
|
||
def tool_browser_click(selector: str) -> str:
|
||
"""Click element by CSS selector or text."""
|
||
global _page
|
||
if _page is None:
|
||
return "[browser] No browser open. Use !browse <url> first"
|
||
|
||
try:
|
||
# Try CSS selector first
|
||
if _page.query_selector(selector):
|
||
_page.click(selector)
|
||
return f"[browser] Clicked: {selector}"
|
||
# Try text selector
|
||
_page.click(f"text={selector}")
|
||
return f"[browser] Clicked text: {selector}"
|
||
except Exception as e:
|
||
return f"[browser] Click error: {e}"
|
||
|
||
def tool_browser_type(text: str, selector: str = None) -> str:
|
||
"""Type text into focused element or specified selector."""
|
||
global _page
|
||
if _page is None:
|
||
return "[browser] No browser open"
|
||
|
||
try:
|
||
if selector:
|
||
_page.fill(selector, text)
|
||
return f"[browser] Typed into {selector}"
|
||
else:
|
||
_page.keyboard.type(text)
|
||
return f"[browser] Typed: {text[:50]}..."
|
||
except Exception as e:
|
||
return f"[browser] Type error: {e}"
|
||
|
||
def tool_browser_press(key: str) -> str:
|
||
"""Press a key (Enter, Tab, Escape, etc)."""
|
||
global _page
|
||
if _page is None:
|
||
return "[browser] No browser open"
|
||
|
||
try:
|
||
_page.keyboard.press(key)
|
||
return f"[browser] Pressed: {key}"
|
||
except Exception as e:
|
||
return f"[browser] Key error: {e}"
|
||
|
||
def tool_browser_read() -> str:
|
||
"""Read visible page text content."""
|
||
global _page
|
||
if _page is None:
|
||
return "[browser] No browser open"
|
||
|
||
try:
|
||
text = _page.inner_text("body")
|
||
# Truncate and clean
|
||
text = ' '.join(text.split())[:3000]
|
||
return f"[browser] Page content:\n{text}"
|
||
except Exception as e:
|
||
return f"[browser] Read error: {e}"
|
||
|
||
def tool_browser_url() -> str:
|
||
"""Get current URL."""
|
||
global _page
|
||
if _page is None:
|
||
return "[browser] No browser open"
|
||
return f"[browser] URL: {_page.url}"
|
||
|
||
def tool_browser_back() -> str:
|
||
"""Go back in history."""
|
||
global _page
|
||
if _page is None:
|
||
return "[browser] No browser open"
|
||
_page.go_back()
|
||
return f"[browser] Back to: {_page.url}"
|
||
|
||
def tool_browser_close() -> str:
|
||
"""Close browser."""
|
||
global _playwright, _browser, _page
|
||
try:
|
||
if _browser:
|
||
_browser.close()
|
||
if _playwright:
|
||
_playwright.stop()
|
||
_browser = None
|
||
_page = None
|
||
_playwright = None
|
||
return "[browser] Closed"
|
||
except:
|
||
return "[browser] Already closed"
|
||
|
||
# === AUTONOMOUS TASK EXECUTION ===
|
||
|
||
# Task templates - no model planning needed
|
||
TASK_TEMPLATES = {
|
||
"search_news": {
|
||
"keywords": ["news", "latest news", "articles", "headlines", "what's happening"],
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://news.google.com"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "FILL", "args": ["input[name=q]", "{query}"]},
|
||
{"action": "PRESS", "args": ["Enter"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
{"action": "READ", "args": []},
|
||
]
|
||
},
|
||
"search_google": {
|
||
"keywords": ["search for", "google", "look up", "find me", "find info"],
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://google.com"]},
|
||
{"action": "WAIT", "args": ["1"]},
|
||
{"action": "FILL", "args": ["textarea[name=q]", "{query}"]},
|
||
{"action": "PRESS", "args": ["Enter"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
{"action": "READ", "args": []},
|
||
]
|
||
},
|
||
"weather": {
|
||
"keywords": ["weather", "temperature", "forecast"],
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://weather.com"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "READ", "args": []},
|
||
]
|
||
},
|
||
"wikipedia": {
|
||
"keywords": ["wikipedia", "wiki", "what is", "who is", "define"],
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://en.wikipedia.org"]},
|
||
{"action": "WAIT", "args": ["1"]},
|
||
{"action": "FILL", "args": ["input[name=search]", "{query}"]},
|
||
{"action": "PRESS", "args": ["Enter"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "READ", "args": []},
|
||
]
|
||
},
|
||
"youtube": {
|
||
"keywords": ["youtube", "video", "watch"],
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://youtube.com"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "FILL", "args": ["input[name=search_query]", "{query}"]},
|
||
{"action": "PRESS", "args": ["Enter"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
{"action": "READ", "args": []},
|
||
]
|
||
},
|
||
# === FREELANCE PLATFORMS ===
|
||
"upwork_search": {
|
||
"keywords": ["upwork jobs", "freelance jobs", "find gigs", "upwork"],
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://www.upwork.com/nx/jobs/search/?q={query}"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
{"action": "READ", "args": []},
|
||
]
|
||
},
|
||
"fiverr_search": {
|
||
"keywords": ["fiverr", "fiverr gigs"],
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://www.fiverr.com/search/gigs?query={query}"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
{"action": "READ", "args": []},
|
||
]
|
||
},
|
||
# === SELLING PLATFORMS ===
|
||
"ebay_search": {
|
||
"keywords": ["ebay", "sell on ebay", "ebay listings"],
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://www.ebay.com/sch/i.html?_nkw={query}"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
{"action": "READ", "args": []},
|
||
]
|
||
},
|
||
"amazon_search": {
|
||
"keywords": ["amazon products", "amazon search"],
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://www.amazon.com/s?k={query}"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
{"action": "READ", "args": []},
|
||
]
|
||
},
|
||
# === CRYPTO/FINANCE ===
|
||
"crypto_prices": {
|
||
"keywords": ["crypto price", "bitcoin", "ethereum", "crypto"],
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://coinmarketcap.com"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "READ", "args": []},
|
||
]
|
||
},
|
||
"stock_price": {
|
||
"keywords": ["stock price", "stocks", "market"],
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://finance.yahoo.com/quote/{query}"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "READ", "args": []},
|
||
]
|
||
}
|
||
}
|
||
|
||
# === LOGIN TEMPLATES ===
|
||
LOGIN_TEMPLATES = {
|
||
"gmail": {
|
||
"url": "https://accounts.google.com/signin",
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://accounts.google.com/signin"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "FILL", "args": ["input[type=email]", "{email}"]},
|
||
{"action": "PRESS", "args": ["Enter"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
{"action": "FILL", "args": ["input[type=password]", "{password}"]},
|
||
{"action": "PRESS", "args": ["Enter"]},
|
||
{"action": "WAIT", "args": ["5"]},
|
||
]
|
||
},
|
||
"facebook": {
|
||
"url": "https://facebook.com/login",
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://facebook.com/login"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "FILL", "args": ["input[name=email]", "{email}"]},
|
||
{"action": "FILL", "args": ["input[name=pass]", "{password}"]},
|
||
{"action": "CLICK", "args": ["button[name=login]"]},
|
||
{"action": "WAIT", "args": ["5"]},
|
||
]
|
||
},
|
||
"twitter": {
|
||
"url": "https://twitter.com/login",
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://twitter.com/login"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "FILL", "args": ["input[autocomplete=username]", "{email}"]},
|
||
{"action": "CLICK", "args": ["text=Next"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "FILL", "args": ["input[type=password]", "{password}"]},
|
||
{"action": "CLICK", "args": ["text=Log in"]},
|
||
{"action": "WAIT", "args": ["5"]},
|
||
]
|
||
},
|
||
"linkedin": {
|
||
"url": "https://linkedin.com/login",
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://linkedin.com/login"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "FILL", "args": ["input[name=session_key]", "{email}"]},
|
||
{"action": "FILL", "args": ["input[name=session_password]", "{password}"]},
|
||
{"action": "CLICK", "args": ["button[type=submit]"]},
|
||
{"action": "WAIT", "args": ["5"]},
|
||
]
|
||
},
|
||
"github": {
|
||
"url": "https://github.com/login",
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://github.com/login"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "FILL", "args": ["input[name=login]", "{email}"]},
|
||
{"action": "FILL", "args": ["input[name=password]", "{password}"]},
|
||
{"action": "CLICK", "args": ["input[type=submit]"]},
|
||
{"action": "WAIT", "args": ["5"]},
|
||
]
|
||
},
|
||
"outlook": {
|
||
"url": "https://login.live.com",
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://login.live.com"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "FILL", "args": ["input[type=email]", "{email}"]},
|
||
{"action": "PRESS", "args": ["Enter"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
{"action": "FILL", "args": ["input[type=password]", "{password}"]},
|
||
{"action": "PRESS", "args": ["Enter"]},
|
||
{"action": "WAIT", "args": ["5"]},
|
||
]
|
||
},
|
||
"generic": {
|
||
"url": "{url}",
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["{url}"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "FILL", "args": ["input[type=email], input[type=text], input[name=email], input[name=username], input[name=login]", "{email}"]},
|
||
{"action": "FILL", "args": ["input[type=password]", "{password}"]},
|
||
{"action": "PRESS", "args": ["Enter"]},
|
||
{"action": "WAIT", "args": ["5"]},
|
||
]
|
||
}
|
||
}
|
||
|
||
# === EMAIL ACTION TEMPLATES ===
|
||
EMAIL_ACTIONS = {
|
||
"gmail_search": {
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://mail.google.com"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
{"action": "FILL", "args": ["input[name=q]", "{query}"]},
|
||
{"action": "PRESS", "args": ["Enter"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
{"action": "READ", "args": []},
|
||
]
|
||
},
|
||
"gmail_compose": {
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://mail.google.com"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
{"action": "CLICK", "args": ["div[gh='cm']", "text=Compose"]},
|
||
{"action": "WAIT", "args": ["2"]},
|
||
{"action": "FILL", "args": ["input[name=to]", "{to}"]},
|
||
{"action": "FILL", "args": ["input[name=subjectbox]", "{subject}"]},
|
||
{"action": "FILL", "args": ["div[aria-label='Message Body']", "{body}"]},
|
||
{"action": "WAIT", "args": ["1"]},
|
||
]
|
||
},
|
||
"gmail_send": {
|
||
"steps": [
|
||
{"action": "CLICK", "args": ["div[aria-label='Send']", "text=Send"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
]
|
||
},
|
||
"outlook_search": {
|
||
"steps": [
|
||
{"action": "BROWSE", "args": ["https://outlook.live.com/mail"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
{"action": "FILL", "args": ["input[aria-label='Search']", "{query}"]},
|
||
{"action": "PRESS", "args": ["Enter"]},
|
||
{"action": "WAIT", "args": ["3"]},
|
||
{"action": "READ", "args": []},
|
||
]
|
||
}
|
||
}
|
||
|
||
def detect_email_action(user_input: str) -> Tuple[Optional[str], Optional[Dict]]:
|
||
"""Detect email-related requests."""
|
||
user_lower = user_input.lower()
|
||
|
||
# Search email
|
||
search_triggers = ["search email", "search my email", "find email", "look for email",
|
||
"search inbox", "search gmail", "search outlook", "find in email",
|
||
"emails about", "emails from", "emails containing"]
|
||
|
||
for trigger in search_triggers:
|
||
if trigger in user_lower:
|
||
# Extract search query
|
||
query = user_input
|
||
for prefix in ["search email for", "search my email for", "find email about",
|
||
"search inbox for", "search gmail for", "emails about",
|
||
"emails from", "emails containing", "look for email"]:
|
||
query = re.sub(rf"{prefix}\s*", "", query, flags=re.IGNORECASE)
|
||
|
||
service = "gmail" if "gmail" in user_lower else "outlook" if "outlook" in user_lower else "gmail"
|
||
return f"{service}_search", {"query": query.strip()}
|
||
|
||
# Compose email
|
||
compose_triggers = ["send email", "compose email", "write email", "email to", "send a message to"]
|
||
|
||
for trigger in compose_triggers:
|
||
if trigger in user_lower:
|
||
return "gmail_compose", {"to": "", "subject": "", "body": ""}
|
||
|
||
return None, None
|
||
|
||
def confirm_email_action(action: str, params: Dict) -> bool:
|
||
"""Confirm email action."""
|
||
print("\n" + "="*60)
|
||
print("📧 EMAIL ACTION CONFIRMATION")
|
||
print("="*60)
|
||
|
||
if "search" in action:
|
||
print(f"\n🔍 Action: Search emails")
|
||
print(f"📝 Query: {params.get('query', 'N/A')}")
|
||
elif "compose" in action:
|
||
print(f"\n✉️ Action: Compose email")
|
||
print(f"📬 To: {params.get('to', 'N/A')}")
|
||
print(f"📋 Subject: {params.get('subject', 'N/A')}")
|
||
|
||
print("="*60)
|
||
response = input("\n✅ Proceed? (yes/no): ").strip().lower()
|
||
return response in ['yes', 'y']
|
||
|
||
def execute_email_action(action: str, params: Dict) -> str:
|
||
"""Execute email action - uses API if enabled, browser fallback."""
|
||
|
||
# Use Gmail API if enabled (faster, more reliable)
|
||
if LOGIN_CONFIG.get("use_gmail_api") and _gmail_service:
|
||
print("\n[email] Using Gmail API (fast mode)")
|
||
|
||
if "search" in action:
|
||
return gmail_api_search(params.get("query", ""), max_results=10)
|
||
elif "send" in action or "compose" in action:
|
||
return gmail_api_send(
|
||
params.get("to", ""),
|
||
params.get("subject", ""),
|
||
params.get("body", "")
|
||
)
|
||
else:
|
||
return gmail_api_search("in:inbox", max_results=5)
|
||
|
||
# Browser fallback
|
||
template = EMAIL_ACTIONS.get(action)
|
||
if not template:
|
||
return f"[email] Unknown action: {action}"
|
||
|
||
print("\n[executing email action via browser...]")
|
||
|
||
page_content = ""
|
||
for step in template["steps"]:
|
||
step_action = step["action"].upper()
|
||
args = []
|
||
for arg in step["args"]:
|
||
for key, val in params.items():
|
||
arg = arg.replace("{" + key + "}", str(val))
|
||
args.append(arg)
|
||
|
||
if step_action == "BROWSE":
|
||
tool_browser_open(args[0])
|
||
elif step_action == "FILL":
|
||
selectors = args[0].split(", ")
|
||
for sel in selectors:
|
||
try:
|
||
if _page and _page.query_selector(sel):
|
||
tool_browser_type(args[1] if len(args) > 1 else "", sel)
|
||
break
|
||
except:
|
||
continue
|
||
elif step_action == "CLICK":
|
||
for selector in args:
|
||
try:
|
||
tool_browser_click(selector)
|
||
break
|
||
except:
|
||
continue
|
||
elif step_action == "PRESS":
|
||
tool_browser_press(args[0] if args else "Enter")
|
||
elif step_action == "WAIT":
|
||
time.sleep(int(args[0]) if args else 2)
|
||
elif step_action == "READ":
|
||
page_content = tool_browser_read()
|
||
|
||
time.sleep(0.5)
|
||
|
||
print("[done]\n")
|
||
return page_content
|
||
|
||
# Stored credentials (in memory only - not saved to disk)
|
||
STORED_CREDENTIALS = {}
|
||
|
||
def store_credential(service: str, email: str, password: str):
|
||
"""Store credentials in memory."""
|
||
STORED_CREDENTIALS[service.lower()] = {"email": email, "password": password}
|
||
print(f"[credentials] Stored for {service}")
|
||
|
||
def get_credential(service: str) -> Optional[Dict]:
|
||
"""Get stored credentials."""
|
||
return STORED_CREDENTIALS.get(service.lower())
|
||
|
||
def detect_login_request(user_input: str) -> Tuple[Optional[str], Optional[str]]:
|
||
"""Detect if user wants to log into something."""
|
||
user_lower = user_input.lower()
|
||
|
||
login_triggers = ["log in", "login", "sign in", "signin", "log into", "sign into"]
|
||
if not any(trigger in user_lower for trigger in login_triggers):
|
||
return None, None
|
||
|
||
# Detect service
|
||
for service in LOGIN_TEMPLATES.keys():
|
||
if service in user_lower:
|
||
return service, None
|
||
|
||
# Check for URL
|
||
url_match = re.search(r'(https?://[^\s]+)', user_input)
|
||
if url_match:
|
||
return "generic", url_match.group(1)
|
||
|
||
return None, None
|
||
|
||
def confirm_login(service: str, email: str) -> bool:
|
||
"""Confirm login action."""
|
||
print("\n" + "="*60)
|
||
print("🔐 LOGIN CONFIRMATION")
|
||
print("="*60)
|
||
print(f"\n📧 Service: {service.upper()}")
|
||
print(f"👤 Account: {email}")
|
||
print(f"\n⚠️ The AI will enter your credentials and log in.")
|
||
print("="*60)
|
||
|
||
response = input("\n✅ Proceed with login? (yes/no): ").strip().lower()
|
||
return response in ['yes', 'y']
|
||
|
||
def execute_login(service: str, email: str, password: str, url: str = None) -> str:
|
||
"""Execute login with multiple fallback strategies - BEAST MODE."""
|
||
|
||
print(f"\n[login] 🔐 Attempting {service} login...")
|
||
print(f"[login] Using Firefox (less detection)")
|
||
|
||
if service in ["gmail", "google", "email"]:
|
||
strategies = [
|
||
# Strategy 1: Direct type, human-like
|
||
{
|
||
"name": "Human-like typing",
|
||
"steps": [
|
||
("browse", "https://accounts.google.com/signin"),
|
||
("wait", 3),
|
||
("human_type", "input[type=email]", email),
|
||
("wait", 1),
|
||
("press", "Enter"),
|
||
("wait", 4),
|
||
("human_type", "input[type=password]", password),
|
||
("wait", 1),
|
||
("press", "Enter"),
|
||
("wait", 5),
|
||
]
|
||
},
|
||
# Strategy 2: Click-based navigation
|
||
{
|
||
"name": "Click navigation",
|
||
"steps": [
|
||
("browse", "https://mail.google.com"),
|
||
("wait", 4),
|
||
("click", "input[type=email]"),
|
||
("human_type", None, email),
|
||
("click", "#identifierNext, button:has-text('Next')"),
|
||
("wait", 4),
|
||
("click", "input[type=password]"),
|
||
("human_type", None, password),
|
||
("click", "#passwordNext, button:has-text('Next')"),
|
||
("wait", 5),
|
||
]
|
||
},
|
||
# Strategy 3: Pre-filled email URL
|
||
{
|
||
"name": "Pre-filled URL",
|
||
"steps": [
|
||
("browse", f"https://accounts.google.com/v3/signin/identifier?Email={email}&continue=https://mail.google.com"),
|
||
("wait", 4),
|
||
("human_type", "input[type=password]", password),
|
||
("press", "Enter"),
|
||
("wait", 5),
|
||
]
|
||
}
|
||
]
|
||
else:
|
||
# Use template for other services
|
||
template = LOGIN_TEMPLATES.get(service, LOGIN_TEMPLATES["generic"])
|
||
strategies = [{
|
||
"name": f"{service} template",
|
||
"steps": []
|
||
}]
|
||
for step in template["steps"]:
|
||
action = step["action"].lower()
|
||
args = [a.replace("{email}", email).replace("{password}", password).replace("{url}", url or "") for a in step["args"]]
|
||
if action == "browse":
|
||
strategies[0]["steps"].append(("browse", args[0]))
|
||
elif action == "fill":
|
||
strategies[0]["steps"].append(("human_type", args[0], args[1] if len(args) > 1 else ""))
|
||
elif action == "click":
|
||
strategies[0]["steps"].append(("click", args[0]))
|
||
elif action == "press":
|
||
strategies[0]["steps"].append(("press", args[0] if args else "Enter"))
|
||
elif action == "wait":
|
||
strategies[0]["steps"].append(("wait", int(args[0]) if args else 2))
|
||
|
||
# Try each strategy
|
||
for strat_idx, strategy in enumerate(strategies):
|
||
print(f" [{strat_idx+1}/{len(strategies)}] Trying: {strategy['name']}")
|
||
|
||
try:
|
||
for step in strategy["steps"]:
|
||
action = step[0]
|
||
|
||
if action == "browse":
|
||
tool_browser_open(step[1])
|
||
time.sleep(1)
|
||
|
||
elif action == "wait":
|
||
time.sleep(step[1])
|
||
|
||
elif action == "human_type":
|
||
selector, text = step[1], step[2] if len(step) > 2 else ""
|
||
if _page:
|
||
# Click the field first if selector provided
|
||
if selector:
|
||
try:
|
||
elem = _page.wait_for_selector(selector, timeout=5000)
|
||
if elem:
|
||
elem.click()
|
||
time.sleep(0.3)
|
||
except:
|
||
pass
|
||
|
||
# Type like a human - variable speed
|
||
for char in text:
|
||
_page.keyboard.type(char, delay=random.randint(30, 120))
|
||
time.sleep(random.uniform(0.02, 0.08))
|
||
time.sleep(0.5)
|
||
|
||
elif action == "click":
|
||
if _page:
|
||
selectors = step[1].split(", ")
|
||
for sel in selectors:
|
||
try:
|
||
elem = _page.wait_for_selector(sel, timeout=3000)
|
||
if elem:
|
||
elem.click()
|
||
break
|
||
except:
|
||
continue
|
||
|
||
elif action == "press":
|
||
if _page:
|
||
time.sleep(0.3)
|
||
_page.keyboard.press(step[1])
|
||
|
||
time.sleep(0.3)
|
||
|
||
# Check login result
|
||
time.sleep(3)
|
||
current_url = _page.url if _page else ""
|
||
page_text = tool_browser_read().lower()
|
||
|
||
# Failure indicators - check these FIRST
|
||
failures = ["couldn't sign you in", "wrong password", "verify it's you",
|
||
"unusual activity", "captcha", "robot", "try again", "blocked",
|
||
"sign in", "create an account", "for work", "learn more"]
|
||
|
||
# If we're on a landing/marketing page, not logged in
|
||
if "workspace.google.com" in current_url or "accounts.google.com/signin" in current_url:
|
||
print(f" ❌ Still on login/landing page")
|
||
continue
|
||
|
||
if any(f in page_text[:500] for f in failures):
|
||
print(f" ❌ Detected: not logged in")
|
||
continue
|
||
|
||
# Success indicators - MUST have inbox URL pattern
|
||
inbox_urls = ["mail.google.com/mail", "inbox", "#inbox"]
|
||
inbox_text = ["compose", "primary", "social", "promotions", "starred", "sent", "drafts"]
|
||
|
||
url_ok = any(u in current_url.lower() for u in inbox_urls)
|
||
text_ok = any(t in page_text[:1000] for t in inbox_text)
|
||
|
||
if url_ok and text_ok:
|
||
print(f" ✅ SUCCESS with {strategy['name']}!")
|
||
return f"[login] ✅ Logged into {service}\n{page_text[:300]}"
|
||
|
||
if url_ok:
|
||
print(f" ✅ SUCCESS (URL match) with {strategy['name']}!")
|
||
return f"[login] ✅ Logged into {service}\n{page_text[:300]}"
|
||
|
||
# 2FA check
|
||
if "2-step" in page_text or "verify" in page_text or "code" in page_text:
|
||
print(f" ⚠️ 2FA/Verification required")
|
||
input(" Complete verification in browser, then press Enter...")
|
||
return f"[login] ✅ Logged in after 2FA\n{tool_browser_read()[:300]}"
|
||
|
||
except Exception as e:
|
||
print(f" Error: {str(e)[:50]}")
|
||
continue
|
||
|
||
# All failed - offer manual
|
||
print(f"\n ❌ All {len(strategies)} strategies failed")
|
||
print(f" 💡 Google may be blocking automated logins")
|
||
|
||
manual = input("\n Try manual login? (yes/no): ").strip().lower()
|
||
if manual in ['yes', 'y']:
|
||
print(" Opening login page - complete login manually...")
|
||
tool_browser_open("https://mail.google.com")
|
||
input(" Press Enter when logged in...")
|
||
page_text = tool_browser_read()
|
||
if "inbox" in page_text.lower() or "compose" in page_text.lower():
|
||
return f"[login] ✅ Manual login successful\n{page_text[:300]}"
|
||
|
||
return f"[login] ❌ Failed to login to {service}. Try logging in manually first, then use the browser."
|
||
|
||
def detect_web_task(user_input: str) -> Tuple[Optional[str], Optional[str]]:
|
||
"""Detect if user wants a web task. Returns (template_name, query) or (None, None)."""
|
||
user_lower = user_input.lower()
|
||
|
||
# Check for web-related intent
|
||
web_triggers = ["look up", "search", "find", "news", "weather", "google",
|
||
"browse", "website", "check online", "wikipedia", "youtube"]
|
||
|
||
if not any(trigger in user_lower for trigger in web_triggers):
|
||
return None, None
|
||
|
||
# Match to template
|
||
for template_name, template in TASK_TEMPLATES.items():
|
||
if any(kw in user_lower for kw in template["keywords"]):
|
||
# Extract query from user input
|
||
query = user_input
|
||
# Remove common prefixes
|
||
for prefix in ["can you", "please", "could you", "look up", "search for",
|
||
"find me", "find", "google", "search", "get me", "show me"]:
|
||
query = re.sub(rf"^{prefix}\s+", "", query, flags=re.IGNORECASE)
|
||
query = query.strip("?. ")
|
||
return template_name, query
|
||
|
||
# Default to google search
|
||
query = user_input
|
||
for prefix in ["can you", "please", "could you", "look up", "search for",
|
||
"find me", "find", "search", "get me"]:
|
||
query = re.sub(rf"^{prefix}\s+", "", query, flags=re.IGNORECASE)
|
||
return "search_google", query.strip("?. ")
|
||
|
||
|
||
def build_plan_from_template(template_name: str, query: str) -> dict:
|
||
"""Build execution plan from template."""
|
||
template = TASK_TEMPLATES.get(template_name, TASK_TEMPLATES["search_google"])
|
||
|
||
steps = []
|
||
for step in template["steps"]:
|
||
new_step = {"action": step["action"], "args": []}
|
||
for arg in step["args"]:
|
||
new_step["args"].append(arg.replace("{query}", query))
|
||
steps.append(new_step)
|
||
|
||
return {"task": f"{template_name}: {query}", "steps": steps, "query": query}
|
||
|
||
|
||
def confirm_task(plan: dict) -> bool:
|
||
"""Show confirmation box and get user approval."""
|
||
print("\n" + "="*60)
|
||
print("🔍 I detected a web task. Here's my plan:")
|
||
print("="*60)
|
||
print(f"\n📋 TASK: {plan.get('query', plan.get('task', 'Unknown'))}\n")
|
||
print("📝 PLANNED STEPS:")
|
||
print("-"*40)
|
||
for i, step in enumerate(plan.get('steps', []), 1):
|
||
action = step.get('action', '?')
|
||
args = step.get('args', [])
|
||
# Clean display
|
||
display_args = ' '.join(str(a)[:50] for a in args)
|
||
print(f" {i}. {action} {display_args}")
|
||
print("-"*40)
|
||
|
||
response = input("\n✅ Proceed? (yes/no): ").strip().lower()
|
||
return response in ['yes', 'y']
|
||
|
||
|
||
def execute_task_silent(plan: dict) -> str:
|
||
"""Execute plan and return page content."""
|
||
page_content = ""
|
||
|
||
print("\n[executing...]")
|
||
|
||
for i, step in enumerate(plan.get('steps', []), 1):
|
||
action = step.get('action', '').upper()
|
||
args = step.get('args', [])
|
||
|
||
if action == "BROWSE":
|
||
url = args[0] if args else "https://google.com"
|
||
if not url.startswith("http"):
|
||
url = "https://" + url
|
||
tool_browser_open(url)
|
||
elif action == "CLICK":
|
||
tool_browser_click(args[0] if args else "")
|
||
elif action == "TYPE":
|
||
tool_browser_type(args[0] if args else "")
|
||
elif action == "FILL":
|
||
if len(args) >= 2:
|
||
tool_browser_type(args[1], args[0])
|
||
elif action == "PRESS":
|
||
tool_browser_press(args[0] if args else "Enter")
|
||
elif action == "READ":
|
||
result = tool_browser_read()
|
||
page_content = result.replace("[browser] Page content:\n", "")
|
||
elif action == "WAIT":
|
||
wait_time = int(args[0]) if args else 2
|
||
time.sleep(wait_time)
|
||
|
||
time.sleep(0.5)
|
||
|
||
print("[done]\n")
|
||
return page_content
|
||
|
||
|
||
# === TASK CHAINING SYSTEM ===
|
||
|
||
class TaskChain:
|
||
"""Chain multiple tasks with checkpoints and error recovery."""
|
||
|
||
def __init__(self):
|
||
self.tasks = []
|
||
self.current_idx = 0
|
||
self.results = []
|
||
self.errors = []
|
||
self.running = False
|
||
self.paused = False
|
||
|
||
def add(self, task_type: str, params: Dict, description: str):
|
||
"""Add task to chain."""
|
||
self.tasks.append({
|
||
"type": task_type,
|
||
"params": params,
|
||
"description": description,
|
||
"status": "pending",
|
||
"result": None,
|
||
"error": None,
|
||
"retries": 0
|
||
})
|
||
|
||
def clear(self):
|
||
"""Clear all tasks."""
|
||
self.tasks = []
|
||
self.current_idx = 0
|
||
self.results = []
|
||
self.errors = []
|
||
|
||
def show(self) -> str:
|
||
"""Display current task chain."""
|
||
if not self.tasks:
|
||
return "[chain] No tasks queued. Use 'chain: <task>' to add tasks."
|
||
|
||
output = "\n" + "="*60 + "\n"
|
||
output += "📋 TASK CHAIN\n"
|
||
output += "="*60 + "\n\n"
|
||
|
||
for i, task in enumerate(self.tasks):
|
||
status_icon = {"pending": "⏳", "running": "🔄", "success": "✅", "failed": "❌", "skipped": "⏭️"}.get(task["status"], "❓")
|
||
marker = "→ " if i == self.current_idx and self.running else " "
|
||
output += f"{marker}{i+1}. {status_icon} {task['description']}\n"
|
||
if task["error"]:
|
||
output += f" ⚠️ Error: {task['error'][:50]}...\n"
|
||
|
||
output += "\n" + "="*60
|
||
return output
|
||
|
||
TASK_CHAIN = TaskChain()
|
||
|
||
def parse_chain_task(task_str: str) -> Tuple[str, Dict, str]:
|
||
"""Parse a task string into type, params, description."""
|
||
task_lower = task_str.lower().strip()
|
||
|
||
if task_lower.startswith("login "):
|
||
service = task_lower.replace("login ", "").strip()
|
||
return "login", {"service": service}, f"Login to {service}"
|
||
|
||
if task_lower.startswith("browse "):
|
||
url = task_str.split(" ", 1)[1].strip()
|
||
if not url.startswith("http"):
|
||
url = "https://" + url
|
||
return "browse", {"url": url}, f"Browse to {url}"
|
||
|
||
if task_lower.startswith("search "):
|
||
query = task_str.split(" ", 1)[1].strip()
|
||
return "search", {"query": query}, f"Search for '{query}'"
|
||
|
||
if "email" in task_lower:
|
||
if "send" in task_lower:
|
||
return "email_send", {}, "Send email"
|
||
return "email_check", {}, "Check email"
|
||
|
||
if "freelance" in task_lower or "jobs" in task_lower:
|
||
return "money", {"action": "freelance", "params": {"skills": ["general"]}}, "Find freelance jobs"
|
||
|
||
if "arbitrage" in task_lower:
|
||
product = task_lower.replace("arbitrage", "").replace("find", "").strip() or "electronics"
|
||
return "money", {"action": "arbitrage", "params": {"product": product}}, f"Find arbitrage for {product}"
|
||
|
||
if "crypto" in task_lower:
|
||
return "money", {"action": "crypto", "params": {}}, "Check crypto opportunities"
|
||
|
||
if task_lower.startswith("read") or task_lower.startswith("check"):
|
||
return "read", {}, "Read page content"
|
||
|
||
if task_lower.startswith("click "):
|
||
selector = task_str.split(" ", 1)[1].strip()
|
||
return "click", {"selector": selector}, f"Click '{selector}'"
|
||
|
||
if task_lower.startswith("wait"):
|
||
seconds = int(re.search(r"(\d+)", task_lower).group(1)) if re.search(r"(\d+)", task_lower) else 5
|
||
return "wait", {"seconds": seconds}, f"Wait {seconds} seconds"
|
||
|
||
if "checkpoint" in task_lower or "align" in task_lower:
|
||
return "checkpoint", {}, "🔍 Alignment checkpoint"
|
||
|
||
return "custom", {"instruction": task_str}, f"AI task: {task_str[:40]}..."
|
||
|
||
|
||
def execute_chain_task(task: Dict, generate_fn) -> Tuple[bool, str]:
|
||
"""Execute a single task. Returns (success, result/error)."""
|
||
task_type = task["type"]
|
||
params = task["params"]
|
||
|
||
try:
|
||
if task_type == "login":
|
||
service = params.get("service", "")
|
||
creds = get_credential(service)
|
||
if not creds:
|
||
email = input(f"📧 Email for {service}: ").strip()
|
||
password = input(f"🔑 Password: ").strip()
|
||
store_credential(service, email, password)
|
||
creds = {"email": email, "password": password}
|
||
result = execute_login(service, creds["email"], creds["password"])
|
||
return True, result[:200]
|
||
|
||
elif task_type == "browse":
|
||
result = tool_browser_open(params.get("url", "https://google.com"))
|
||
return "[browser] Opened" in result, result
|
||
|
||
elif task_type == "search":
|
||
query = params.get("query", "")
|
||
tool_browser_open(f"https://google.com/search?q={query.replace(' ', '+')}")
|
||
time.sleep(2)
|
||
return True, f"Searched for '{query}'"
|
||
|
||
elif task_type == "email_check":
|
||
tool_browser_open("https://mail.google.com")
|
||
time.sleep(3)
|
||
return True, tool_browser_read()[:300]
|
||
|
||
elif task_type == "money":
|
||
result = execute_money_action(params["action"], params["params"], generate_fn)
|
||
return True, result[:500]
|
||
|
||
elif task_type == "read":
|
||
return True, tool_browser_read()[:500]
|
||
|
||
elif task_type == "click":
|
||
result = tool_browser_click(params.get("selector", ""))
|
||
return "Clicked" in result, result
|
||
|
||
elif task_type == "wait":
|
||
time.sleep(params.get("seconds", 5))
|
||
return True, f"Waited {params['seconds']}s"
|
||
|
||
elif task_type == "checkpoint":
|
||
return True, "CHECKPOINT"
|
||
|
||
elif task_type == "custom":
|
||
prompt = f"Execute: {params['instruction']}\n\nPage: {tool_browser_read()[:1000]}"
|
||
response, _, _ = generate_fn(prompt)
|
||
return True, response[:300]
|
||
|
||
return False, f"Unknown task: {task_type}"
|
||
except Exception as e:
|
||
return False, str(e)
|
||
|
||
|
||
def troubleshoot_task(task: Dict, error: str, generate_fn) -> str:
|
||
"""AI troubleshoots a failed task."""
|
||
prompt = f"""Task failed. Suggest fix.
|
||
Task: {task['description']}
|
||
Error: {error}
|
||
Page: {tool_browser_read()[:500] if _page else 'No page'}
|
||
|
||
Reply with ONE word: RETRY, SKIP, or ABORT"""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
if "RETRY" in response.upper():
|
||
return "RETRY"
|
||
if "ABORT" in response.upper():
|
||
return "ABORT"
|
||
return "SKIP"
|
||
|
||
|
||
def run_task_chain(generate_fn, max_retries: int = 2) -> str:
|
||
"""Execute the task chain with checkpoints."""
|
||
global TASK_CHAIN
|
||
|
||
if not TASK_CHAIN.tasks:
|
||
return "[chain] Empty. Use 'chain: <task>' to add tasks."
|
||
|
||
print("\n" + "="*60)
|
||
print("🚀 TASK CHAIN")
|
||
print("="*60)
|
||
print(TASK_CHAIN.show())
|
||
|
||
if input("\n✅ Start? (yes/no): ").strip().lower() not in ['yes', 'y']:
|
||
return "[chain] Cancelled"
|
||
|
||
TASK_CHAIN.running = True
|
||
TASK_CHAIN.current_idx = 0
|
||
|
||
while TASK_CHAIN.current_idx < len(TASK_CHAIN.tasks):
|
||
task = TASK_CHAIN.tasks[TASK_CHAIN.current_idx]
|
||
print(f"\n[{TASK_CHAIN.current_idx + 1}/{len(TASK_CHAIN.tasks)}] {task['description']}")
|
||
task["status"] = "running"
|
||
|
||
success, result = execute_chain_task(task, generate_fn)
|
||
|
||
if success:
|
||
task["status"] = "success"
|
||
task["result"] = result
|
||
print(f" ✅ {result[:80]}...")
|
||
|
||
if result == "CHECKPOINT":
|
||
print("\n" + "="*60)
|
||
print("🔍 CHECKPOINT - Review Progress")
|
||
print("="*60)
|
||
print(TASK_CHAIN.show())
|
||
action = input("\n[c]ontinue, [a]bort: ").strip().lower()
|
||
if action == 'a':
|
||
TASK_CHAIN.running = False
|
||
return "[chain] Aborted"
|
||
else:
|
||
task["status"] = "failed"
|
||
task["error"] = result
|
||
print(f" ❌ {result[:80]}...")
|
||
|
||
if task["retries"] < max_retries:
|
||
action = troubleshoot_task(task, result, generate_fn)
|
||
print(f" 🔧 AI: {action}")
|
||
|
||
if action == "RETRY":
|
||
task["retries"] += 1
|
||
task["status"] = "pending"
|
||
continue
|
||
elif action == "ABORT":
|
||
TASK_CHAIN.running = False
|
||
return "[chain] Aborted by AI"
|
||
|
||
task["status"] = "skipped"
|
||
|
||
TASK_CHAIN.current_idx += 1
|
||
time.sleep(1)
|
||
|
||
TASK_CHAIN.running = False
|
||
print("\n✅ CHAIN COMPLETE")
|
||
return TASK_CHAIN.show()
|
||
|
||
|
||
# Workflow templates
|
||
WORKFLOW_TEMPLATES = {
|
||
"morning_hustle": [
|
||
"login gmail", "search freelance python jobs", "checkpoint",
|
||
"crypto opportunities", "checkpoint"
|
||
],
|
||
"arbitrage_hunt": [
|
||
"browse amazon.com/bestsellers", "checkpoint",
|
||
"find arbitrage electronics", "checkpoint"
|
||
],
|
||
"lead_gen": [
|
||
"find leads for contractor in miami", "checkpoint",
|
||
"login gmail", "checkpoint"
|
||
]
|
||
}
|
||
|
||
|
||
# === RECURSIVE SELF-IMPROVEMENT (RSI) MODE ===
|
||
|
||
RSI_CONFIG = {
|
||
"enabled": False,
|
||
"mode": "conservative", # conservative, balanced, aggressive
|
||
"max_iterations": 100,
|
||
"target_quality": 0.90,
|
||
"min_quality": 0.60,
|
||
"auto_rollback_threshold": 0.15, # Rollback if quality drops by this much
|
||
"training_steps_per_iter": 25,
|
||
"eval_prompts_per_iter": 10,
|
||
"generate_data_per_iter": 5, # New training examples to generate
|
||
"sleep_between_iters": 5, # Seconds
|
||
"interrupt_for_user": True, # Pause RSI when user types
|
||
"log_file": "rsi_log.jsonl",
|
||
"capabilities_to_improve": [
|
||
"density", # Response conciseness
|
||
"accuracy", # Factual correctness
|
||
"coherence", # Logical flow
|
||
"helpfulness", # Task completion
|
||
"coding", # Code generation
|
||
"reasoning", # Chain of thought
|
||
"creativity", # Novel solutions
|
||
],
|
||
"current_focus": "density",
|
||
"iteration": 0,
|
||
"total_improvements": 0,
|
||
"total_rollbacks": 0,
|
||
"best_quality_ever": 0.0,
|
||
"start_time": None,
|
||
}
|
||
|
||
RSI_RUNNING = False
|
||
RSI_PAUSED = False
|
||
|
||
def save_rsi_config():
|
||
"""Save RSI configuration."""
|
||
config_path = os.path.join(ROOT, "rsi_config.json")
|
||
with open(config_path, 'w') as f:
|
||
json.dump(RSI_CONFIG, f, indent=2)
|
||
|
||
def load_rsi_config():
|
||
"""Load RSI configuration."""
|
||
global RSI_CONFIG
|
||
config_path = os.path.join(ROOT, "rsi_config.json")
|
||
if os.path.exists(config_path):
|
||
with open(config_path, 'r') as f:
|
||
RSI_CONFIG.update(json.load(f))
|
||
|
||
load_rsi_config()
|
||
|
||
|
||
def rsi_log(event: str, data: Dict):
|
||
"""Log RSI event."""
|
||
log_path = os.path.join(LOGS_DIR, RSI_CONFIG["log_file"])
|
||
entry = {
|
||
"timestamp": datetime.now().isoformat(),
|
||
"iteration": RSI_CONFIG["iteration"],
|
||
"event": event,
|
||
"data": data
|
||
}
|
||
with open(log_path, 'a') as f:
|
||
f.write(json.dumps(entry) + "\n")
|
||
|
||
|
||
def rsi_evaluate_capability(capability: str, generate_fn) -> float:
|
||
"""Evaluate model on specific capability."""
|
||
|
||
test_prompts = {
|
||
"density": [
|
||
("Explain quantum computing", 50), # (prompt, ideal_max_tokens)
|
||
("What is machine learning?", 40),
|
||
("How does the internet work?", 60),
|
||
],
|
||
"accuracy": [
|
||
("What is 2+2?", "4"),
|
||
("What is the capital of France?", "Paris"),
|
||
("Who wrote Romeo and Juliet?", "Shakespeare"),
|
||
],
|
||
"coherence": [
|
||
"Write a 3-step process for making coffee",
|
||
"Explain why the sky is blue in logical steps",
|
||
"Describe how a car engine works",
|
||
],
|
||
"helpfulness": [
|
||
"Help me write a professional email to my boss asking for time off",
|
||
"Give me a workout routine for beginners",
|
||
"How do I fix a leaky faucet?",
|
||
],
|
||
"coding": [
|
||
"Write a Python function to reverse a string",
|
||
"Write a function to check if a number is prime",
|
||
"Write a simple web scraper in Python",
|
||
],
|
||
"reasoning": [
|
||
"If all cats are animals, and all animals need water, do cats need water? Explain.",
|
||
"A bat and ball cost $1.10. The bat costs $1 more than the ball. How much does the ball cost?",
|
||
"What comes next: 2, 4, 8, 16, ?",
|
||
],
|
||
"creativity": [
|
||
"Invent a new word and define it",
|
||
"Come up with 3 startup ideas involving AI",
|
||
"Write a haiku about programming",
|
||
],
|
||
}
|
||
|
||
prompts = test_prompts.get(capability, test_prompts["density"])
|
||
scores = []
|
||
|
||
for item in prompts:
|
||
if capability == "density":
|
||
prompt, ideal_tokens = item
|
||
response, stats, eval_result = generate_fn(prompt)
|
||
# Score based on token efficiency
|
||
token_score = max(0, 1 - (eval_result.tokens - ideal_tokens) / ideal_tokens) if eval_result.tokens > 0 else 0
|
||
scores.append((eval_result.density_score / 50 + token_score) / 2)
|
||
|
||
elif capability == "accuracy":
|
||
prompt, expected = item
|
||
response, stats, eval_result = generate_fn(prompt)
|
||
# Check if expected answer is in response
|
||
if expected.lower() in response.lower():
|
||
scores.append(1.0)
|
||
else:
|
||
scores.append(0.0)
|
||
|
||
elif capability in ["coherence", "helpfulness", "creativity"]:
|
||
prompt = item
|
||
response, stats, eval_result = generate_fn(prompt)
|
||
scores.append(eval_result.coherence_score)
|
||
|
||
elif capability == "coding":
|
||
prompt = item
|
||
response, stats, eval_result = generate_fn(prompt)
|
||
# Check for code markers
|
||
has_code = "def " in response or "function" in response or "```" in response
|
||
scores.append(0.8 if has_code else 0.3)
|
||
|
||
elif capability == "reasoning":
|
||
prompt = item
|
||
response, stats, eval_result = generate_fn(prompt)
|
||
# Check for reasoning markers
|
||
has_reasoning = any(w in response.lower() for w in ["because", "therefore", "since", "thus", "so"])
|
||
scores.append(eval_result.coherence_score * (1.2 if has_reasoning else 0.8))
|
||
|
||
return sum(scores) / len(scores) if scores else 0.5
|
||
|
||
|
||
def rsi_generate_training_data(capability: str, generate_fn) -> List[Dict]:
|
||
"""Generate new training data focused on a capability."""
|
||
|
||
prompts_for_capability = {
|
||
"density": "Generate a question and a maximally dense, concise answer (under 50 words). Format: Q: ... A: ...",
|
||
"accuracy": "Generate a factual question and its correct, precise answer. Format: Q: ... A: ...",
|
||
"coherence": "Generate a question requiring logical explanation and a well-structured answer. Format: Q: ... A: ...",
|
||
"helpfulness": "Generate a practical question someone might ask and a helpful, actionable answer. Format: Q: ... A: ...",
|
||
"coding": "Generate a coding task and clean, working Python code solution. Format: Q: ... A: ```python ... ```",
|
||
"reasoning": "Generate a logic puzzle or reasoning question and its step-by-step solution. Format: Q: ... A: ...",
|
||
"creativity": "Generate a creative prompt and an imaginative, original response. Format: Q: ... A: ...",
|
||
}
|
||
|
||
meta_prompt = prompts_for_capability.get(capability, prompts_for_capability["density"])
|
||
|
||
new_examples = []
|
||
for _ in range(RSI_CONFIG["generate_data_per_iter"]):
|
||
response, _, _ = generate_fn(meta_prompt)
|
||
|
||
# Parse Q: A: format
|
||
if "Q:" in response and "A:" in response:
|
||
parts = response.split("A:", 1)
|
||
question = parts[0].replace("Q:", "").strip()
|
||
answer = parts[1].strip()
|
||
|
||
if len(question) > 10 and len(answer) > 10:
|
||
new_examples.append({
|
||
"instruction": question,
|
||
"output": answer,
|
||
"capability": capability,
|
||
"generated": True
|
||
})
|
||
|
||
return new_examples
|
||
|
||
|
||
def rsi_identify_weakest_capability(generate_fn) -> Tuple[str, float]:
|
||
"""Identify the weakest capability to focus on."""
|
||
|
||
scores = {}
|
||
for cap in RSI_CONFIG["capabilities_to_improve"]:
|
||
score = rsi_evaluate_capability(cap, generate_fn)
|
||
scores[cap] = score
|
||
print(f" {cap}: {score:.2f}")
|
||
|
||
weakest = min(scores, key=scores.get)
|
||
return weakest, scores[weakest]
|
||
|
||
|
||
def rsi_run_iteration(generate_fn, train_fn, eval_fn, reload_fn) -> Dict:
|
||
"""Run a single RSI iteration."""
|
||
global RSI_CONFIG
|
||
|
||
iteration = RSI_CONFIG["iteration"]
|
||
print(f"\n{'='*60}")
|
||
print(f"🔄 RSI ITERATION {iteration}")
|
||
print(f"{'='*60}")
|
||
|
||
result = {
|
||
"iteration": iteration,
|
||
"success": False,
|
||
"quality_before": 0,
|
||
"quality_after": 0,
|
||
"focus": "",
|
||
"action": "",
|
||
}
|
||
|
||
# Step 1: Evaluate current state
|
||
print("\n[RSI] 📊 Evaluating current capabilities...")
|
||
weakest_cap, weakest_score = rsi_identify_weakest_capability(generate_fn)
|
||
RSI_CONFIG["current_focus"] = weakest_cap
|
||
result["focus"] = weakest_cap
|
||
|
||
print(f"\n[RSI] 🎯 Focus: {weakest_cap} (score: {weakest_score:.2f})")
|
||
|
||
# Step 2: Get baseline quality
|
||
print("\n[RSI] 📏 Baseline evaluation...")
|
||
baseline = eval_fn()
|
||
result["quality_before"] = baseline.get("avg_quality", 0)
|
||
print(f"[RSI] Baseline quality: {result['quality_before']:.3f}")
|
||
|
||
# Step 3: Generate new training data
|
||
print(f"\n[RSI] 📝 Generating training data for {weakest_cap}...")
|
||
new_data = rsi_generate_training_data(weakest_cap, generate_fn)
|
||
print(f"[RSI] Generated {len(new_data)} new examples")
|
||
|
||
# Add to training set
|
||
if new_data:
|
||
for example in new_data:
|
||
DENSE_TRAINING_EXAMPLES.append(example)
|
||
|
||
# Step 4: Train
|
||
print(f"\n[RSI] 🏋️ Training ({RSI_CONFIG['training_steps_per_iter']} steps)...")
|
||
train_result = train_fn(RSI_CONFIG["training_steps_per_iter"])
|
||
|
||
# Step 5: Reload and evaluate
|
||
print("\n[RSI] 🔄 Reloading model...")
|
||
reload_fn()
|
||
|
||
print("\n[RSI] 📊 Post-training evaluation...")
|
||
post_eval = eval_fn()
|
||
result["quality_after"] = post_eval.get("avg_quality", 0)
|
||
print(f"[RSI] New quality: {result['quality_after']:.3f}")
|
||
|
||
# Step 6: Decide keep or rollback
|
||
quality_diff = result["quality_after"] - result["quality_before"]
|
||
|
||
if quality_diff < -RSI_CONFIG["auto_rollback_threshold"]:
|
||
print(f"\n[RSI] ⚠️ Quality dropped by {-quality_diff:.3f} - ROLLING BACK")
|
||
# Rollback logic would go here
|
||
result["action"] = "rollback"
|
||
RSI_CONFIG["total_rollbacks"] += 1
|
||
elif quality_diff > 0:
|
||
print(f"\n[RSI] ✅ Quality improved by {quality_diff:.3f} - KEEPING")
|
||
result["action"] = "keep"
|
||
result["success"] = True
|
||
RSI_CONFIG["total_improvements"] += 1
|
||
if result["quality_after"] > RSI_CONFIG["best_quality_ever"]:
|
||
RSI_CONFIG["best_quality_ever"] = result["quality_after"]
|
||
else:
|
||
print(f"\n[RSI] ➡️ Quality unchanged - KEEPING")
|
||
result["action"] = "keep"
|
||
|
||
# Log
|
||
rsi_log("iteration_complete", result)
|
||
|
||
RSI_CONFIG["iteration"] += 1
|
||
save_rsi_config()
|
||
|
||
return result
|
||
|
||
|
||
def rsi_mode_loop(generate_fn, train_fn, eval_fn, reload_fn):
|
||
"""Main RSI loop - runs until stopped."""
|
||
global RSI_RUNNING, RSI_PAUSED, RSI_CONFIG
|
||
|
||
RSI_RUNNING = True
|
||
RSI_PAUSED = False
|
||
RSI_CONFIG["start_time"] = datetime.now().isoformat()
|
||
|
||
print("\n" + "="*60)
|
||
print("🚀 RSI MODE ACTIVATED")
|
||
print("="*60)
|
||
print(f" Mode: {RSI_CONFIG['mode']}")
|
||
print(f" Target quality: {RSI_CONFIG['target_quality']}")
|
||
print(f" Max iterations: {RSI_CONFIG['max_iterations']}")
|
||
print(f" Focus areas: {', '.join(RSI_CONFIG['capabilities_to_improve'])}")
|
||
print("="*60)
|
||
print("\n⚠️ Press Ctrl+C to pause/stop RSI mode")
|
||
print("="*60)
|
||
|
||
save_rsi_config()
|
||
|
||
try:
|
||
while RSI_RUNNING and RSI_CONFIG["iteration"] < RSI_CONFIG["max_iterations"]:
|
||
if RSI_PAUSED:
|
||
print("\n[RSI] ⏸️ Paused. Type 'rsi resume' to continue.")
|
||
break
|
||
|
||
# Run iteration
|
||
result = rsi_run_iteration(generate_fn, train_fn, eval_fn, reload_fn)
|
||
|
||
# Check if target reached
|
||
if result["quality_after"] >= RSI_CONFIG["target_quality"]:
|
||
print(f"\n[RSI] 🎉 TARGET REACHED! Quality: {result['quality_after']:.3f}")
|
||
break
|
||
|
||
# Check for catastrophic failure
|
||
if result["quality_after"] < RSI_CONFIG["min_quality"]:
|
||
print(f"\n[RSI] 🛑 Quality too low ({result['quality_after']:.3f}) - STOPPING")
|
||
break
|
||
|
||
# Sleep between iterations
|
||
print(f"\n[RSI] 💤 Sleeping {RSI_CONFIG['sleep_between_iters']}s...")
|
||
time.sleep(RSI_CONFIG["sleep_between_iters"])
|
||
|
||
except KeyboardInterrupt:
|
||
print("\n\n[RSI] ⏸️ Interrupted by user")
|
||
RSI_PAUSED = True
|
||
|
||
RSI_RUNNING = False
|
||
|
||
# Final report
|
||
print("\n" + "="*60)
|
||
print("📊 RSI SESSION REPORT")
|
||
print("="*60)
|
||
print(f" Iterations: {RSI_CONFIG['iteration']}")
|
||
print(f" Improvements: {RSI_CONFIG['total_improvements']}")
|
||
print(f" Rollbacks: {RSI_CONFIG['total_rollbacks']}")
|
||
print(f" Best quality: {RSI_CONFIG['best_quality_ever']:.3f}")
|
||
print(f" Current focus: {RSI_CONFIG['current_focus']}")
|
||
print("="*60)
|
||
|
||
save_rsi_config()
|
||
|
||
|
||
def rsi_status() -> str:
|
||
"""Get RSI status."""
|
||
status = []
|
||
status.append("=" * 50)
|
||
status.append("🧠 RSI STATUS")
|
||
status.append("=" * 50)
|
||
status.append(f"Running: {'🟢 YES' if RSI_RUNNING else '🔴 NO'}")
|
||
status.append(f"Paused: {'⏸️ YES' if RSI_PAUSED else 'NO'}")
|
||
status.append(f"Mode: {RSI_CONFIG['mode']}")
|
||
status.append(f"Iteration: {RSI_CONFIG['iteration']}")
|
||
status.append(f"Target quality: {RSI_CONFIG['target_quality']}")
|
||
status.append(f"Best quality ever: {RSI_CONFIG['best_quality_ever']:.3f}")
|
||
status.append(f"Total improvements: {RSI_CONFIG['total_improvements']}")
|
||
status.append(f"Total rollbacks: {RSI_CONFIG['total_rollbacks']}")
|
||
status.append(f"Current focus: {RSI_CONFIG['current_focus']}")
|
||
status.append(f"Capabilities: {', '.join(RSI_CONFIG['capabilities_to_improve'])}")
|
||
return "\n".join(status)
|
||
|
||
|
||
# === CRYPTO MINING AUTOMATION ===
|
||
|
||
MINING_CONFIG = {
|
||
"enabled": False,
|
||
"wallet_addresses": {}, # coin -> address
|
||
"preferred_algo": "randomx", # randomx, kawpow, ethash, etc
|
||
"max_cpu_percent": 80,
|
||
"max_gpu_percent": 90,
|
||
"auto_switch": True, # Auto switch to most profitable
|
||
"min_profitability": 0.10, # USD/day minimum
|
||
"miner_path": "",
|
||
"pool_urls": {},
|
||
}
|
||
|
||
SUPPORTED_MINERS = {
|
||
"xmrig": {
|
||
"coins": ["XMR", "RTM", "DERO"],
|
||
"algo": "randomx",
|
||
"url": "https://github.com/xmrig/xmrig/releases",
|
||
"config_template": {
|
||
"pools": [{"url": "{pool}", "user": "{wallet}", "pass": "x"}],
|
||
"cpu": {"max-threads-hint": 80}
|
||
}
|
||
},
|
||
"trex": {
|
||
"coins": ["RVN", "FLUX", "ERG", "NEOX"],
|
||
"algo": "kawpow",
|
||
"url": "https://github.com/trexminer/T-Rex/releases",
|
||
"cmd": "./t-rex -a {algo} -o {pool} -u {wallet} -p x"
|
||
},
|
||
"gminer": {
|
||
"coins": ["FLUX", "ERG", "KASPA", "ALPH"],
|
||
"algo": "equihash",
|
||
"url": "https://github.com/develsoftware/GMinerRelease/releases",
|
||
},
|
||
"cpuminer": {
|
||
"coins": ["VRSC", "RTM", "YEC"],
|
||
"algo": "verushash",
|
||
"url": "https://github.com/VerusCoin/nheqminer/releases",
|
||
},
|
||
"srbminer": {
|
||
"coins": ["RTM", "XEL", "RYO", "CCX"],
|
||
"algo": "ghostrider",
|
||
"url": "https://github.com/doktor83/SRBMiner-Multi/releases",
|
||
}
|
||
}
|
||
|
||
# Low difficulty / new coins to check
|
||
LOW_DIFF_COINS = [
|
||
{"symbol": "RTM", "name": "Raptoreum", "algo": "ghostrider", "pool": "stratum+tcp://stratum.raptoreum.com:3333"},
|
||
{"symbol": "VRSC", "name": "Verus", "algo": "verushash", "pool": "stratum+tcp://na.luckpool.net:3956"},
|
||
{"symbol": "XEL", "name": "Elastic", "algo": "randomx", "pool": "stratum+tcp://pool.xel.org:3333"},
|
||
{"symbol": "RYO", "name": "Ryo", "algo": "randomx", "pool": "stratum+tcp://pool.ryo-currency.com:3333"},
|
||
{"symbol": "CCX", "name": "Conceal", "algo": "randomx", "pool": "stratum+tcp://pool.conceal.network:3333"},
|
||
{"symbol": "DERO", "name": "Dero", "algo": "astroBWT", "pool": "stratum+tcp://dero.herominers.com:1111"},
|
||
{"symbol": "NEOX", "name": "Neoxa", "algo": "kawpow", "pool": "stratum+tcp://pool.woolypooly.com:3124"},
|
||
{"symbol": "CLORE", "name": "Clore", "algo": "kawpow", "pool": "stratum+tcp://pool.woolypooly.com:3136"},
|
||
{"symbol": "KASPA", "name": "Kaspa", "algo": "kHeavyHash", "pool": "stratum+tcp://pool.woolypooly.com:3112"},
|
||
{"symbol": "ALPH", "name": "Alephium", "algo": "blake3", "pool": "stratum+tcp://pool.woolypooly.com:3106"},
|
||
]
|
||
|
||
_mining_process = None
|
||
|
||
def save_mining_config():
|
||
"""Save mining configuration."""
|
||
config_path = os.path.join(ROOT, "mining_config.json")
|
||
with open(config_path, 'w') as f:
|
||
json.dump(MINING_CONFIG, f, indent=2)
|
||
print(f"[mining] ✅ Config saved")
|
||
|
||
def load_mining_config():
|
||
"""Load mining configuration."""
|
||
global MINING_CONFIG
|
||
config_path = os.path.join(ROOT, "mining_config.json")
|
||
if os.path.exists(config_path):
|
||
with open(config_path, 'r') as f:
|
||
MINING_CONFIG.update(json.load(f))
|
||
|
||
load_mining_config()
|
||
|
||
|
||
def check_mining_profitability(generate_fn) -> str:
|
||
"""Check current profitability of low-diff coins."""
|
||
|
||
print("[mining] 🔍 Checking profitability...")
|
||
|
||
# Fetch from WhatToMine
|
||
tool_browser_open("https://whattomine.com/coins")
|
||
time.sleep(3)
|
||
wtm_content = tool_browser_read()
|
||
|
||
# Fetch from MiningPoolStats
|
||
tool_browser_open("https://miningpoolstats.stream/")
|
||
time.sleep(2)
|
||
mps_content = tool_browser_read()
|
||
|
||
prompt = f"""Analyze mining profitability for small/new coins:
|
||
|
||
WhatToMine data: {wtm_content[:2000]}
|
||
|
||
MiningPoolStats: {mps_content[:1500]}
|
||
|
||
Focus on these low-difficulty coins: {[c['symbol'] for c in LOW_DIFF_COINS]}
|
||
|
||
For each promising coin:
|
||
1. Coin name and symbol
|
||
2. Algorithm
|
||
3. Current difficulty trend (rising/falling)
|
||
4. Network hashrate
|
||
5. Estimated daily profit (USD) for average GPU/CPU
|
||
6. Pool recommendation
|
||
7. Overall rating (1-10)
|
||
|
||
Sort by profitability. Recommend the BEST coin to mine right now."""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
|
||
def find_lowest_difficulty_coins(generate_fn) -> str:
|
||
"""Find new/low difficulty coins to mine."""
|
||
|
||
print("[mining] 🔍 Searching for low difficulty coins...")
|
||
|
||
# Check multiple sources
|
||
sources = [
|
||
"https://miningpoolstats.stream/",
|
||
"https://whattomine.com/coins?factor%5Bcost%5D=0.1",
|
||
"https://minerstat.com/coin",
|
||
]
|
||
|
||
all_data = []
|
||
for url in sources:
|
||
tool_browser_open(url)
|
||
time.sleep(3)
|
||
all_data.append(tool_browser_read()[:1500])
|
||
|
||
prompt = f"""Find the LOWEST DIFFICULTY minable coins right now.
|
||
|
||
Source 1: {all_data[0]}
|
||
Source 2: {all_data[1]}
|
||
Source 3: {all_data[2] if len(all_data) > 2 else 'N/A'}
|
||
|
||
Looking for:
|
||
- New coins (< 6 months old)
|
||
- Low network hashrate
|
||
- CPU or GPU minable
|
||
- Has working pools
|
||
- Can be traded somewhere
|
||
|
||
List top 5 opportunities:
|
||
1. Coin name, symbol
|
||
2. Algorithm
|
||
3. Why it's easy to mine
|
||
4. Pool URL
|
||
5. Exchange where tradeable
|
||
6. Risk level (low/med/high)"""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
|
||
def setup_miner(miner: str, coin: str, wallet: str, pool: str) -> str:
|
||
"""Download and configure miner."""
|
||
|
||
if miner not in SUPPORTED_MINERS:
|
||
return f"[mining] ❌ Unknown miner: {miner}. Supported: {list(SUPPORTED_MINERS.keys())}"
|
||
|
||
miner_info = SUPPORTED_MINERS[miner]
|
||
miner_dir = os.path.join(ROOT, "miners", miner)
|
||
os.makedirs(miner_dir, exist_ok=True)
|
||
|
||
print(f"[mining] Setting up {miner} for {coin}...")
|
||
|
||
# Check if already installed
|
||
miner_exe = os.path.join(miner_dir, miner)
|
||
if not os.path.exists(miner_exe):
|
||
print(f"[mining] ⚠️ Miner not found at {miner_dir}")
|
||
print(f"[mining] Download from: {miner_info['url']}")
|
||
print(f"[mining] Extract to: {miner_dir}")
|
||
return f"[mining] Please download {miner} manually and extract to {miner_dir}"
|
||
|
||
# Save wallet
|
||
MINING_CONFIG["wallet_addresses"][coin] = wallet
|
||
MINING_CONFIG["pool_urls"][coin] = pool
|
||
save_mining_config()
|
||
|
||
# Create config
|
||
if miner == "xmrig":
|
||
config = {
|
||
"pools": [{"url": pool, "user": wallet, "pass": "x", "coin": coin.lower()}],
|
||
"cpu": {"max-threads-hint": MINING_CONFIG["max_cpu_percent"]}
|
||
}
|
||
config_path = os.path.join(miner_dir, "config.json")
|
||
with open(config_path, 'w') as f:
|
||
json.dump(config, f, indent=2)
|
||
return f"[mining] ✅ XMRig configured for {coin}. Config at {config_path}"
|
||
|
||
return f"[mining] ✅ {miner} configured for {coin}"
|
||
|
||
|
||
def start_mining(miner: str = "xmrig", coin: str = None) -> str:
|
||
"""Start mining process."""
|
||
global _mining_process
|
||
|
||
if _mining_process and _mining_process.poll() is None:
|
||
return "[mining] ⚠️ Already mining. Use 'stop mining' first."
|
||
|
||
miner_dir = os.path.join(ROOT, "miners", miner)
|
||
|
||
if miner == "xmrig":
|
||
exe = os.path.join(miner_dir, "xmrig")
|
||
if not os.path.exists(exe):
|
||
return f"[mining] ❌ XMRig not found at {exe}"
|
||
|
||
cmd = [exe, "-c", os.path.join(miner_dir, "config.json")]
|
||
|
||
elif miner == "trex":
|
||
exe = os.path.join(miner_dir, "t-rex")
|
||
if not os.path.exists(exe):
|
||
return f"[mining] ❌ T-Rex not found at {exe}"
|
||
|
||
wallet = MINING_CONFIG["wallet_addresses"].get(coin, "")
|
||
pool = MINING_CONFIG["pool_urls"].get(coin, "")
|
||
cmd = [exe, "-a", "kawpow", "-o", pool, "-u", wallet, "-p", "x"]
|
||
|
||
else:
|
||
return f"[mining] ❌ Miner start not implemented for {miner}"
|
||
|
||
try:
|
||
_mining_process = subprocess.Popen(
|
||
cmd,
|
||
stdout=subprocess.PIPE,
|
||
stderr=subprocess.STDOUT,
|
||
cwd=miner_dir
|
||
)
|
||
MINING_CONFIG["enabled"] = True
|
||
save_mining_config()
|
||
return f"[mining] ✅ Started {miner} (PID: {_mining_process.pid})"
|
||
except Exception as e:
|
||
return f"[mining] ❌ Failed to start: {e}"
|
||
|
||
|
||
def stop_mining() -> str:
|
||
"""Stop mining process."""
|
||
global _mining_process
|
||
|
||
if _mining_process:
|
||
_mining_process.terminate()
|
||
_mining_process.wait(timeout=10)
|
||
_mining_process = None
|
||
MINING_CONFIG["enabled"] = False
|
||
save_mining_config()
|
||
return "[mining] ⏹️ Mining stopped"
|
||
|
||
# Try to kill any running miners
|
||
os.system("pkill -f xmrig 2>/dev/null")
|
||
os.system("pkill -f t-rex 2>/dev/null")
|
||
return "[mining] ⏹️ Sent stop signal"
|
||
|
||
|
||
def mining_status() -> str:
|
||
"""Check mining status."""
|
||
global _mining_process
|
||
|
||
status = []
|
||
status.append("=" * 50)
|
||
status.append("⛏️ MINING STATUS")
|
||
status.append("=" * 50)
|
||
|
||
if _mining_process and _mining_process.poll() is None:
|
||
status.append(f"Status: 🟢 RUNNING (PID: {_mining_process.pid})")
|
||
else:
|
||
status.append("Status: 🔴 STOPPED")
|
||
|
||
status.append(f"\nWallets configured:")
|
||
for coin, addr in MINING_CONFIG.get("wallet_addresses", {}).items():
|
||
status.append(f" {coin}: {addr[:20]}...")
|
||
|
||
status.append(f"\nPools:")
|
||
for coin, pool in MINING_CONFIG.get("pool_urls", {}).items():
|
||
status.append(f" {coin}: {pool}")
|
||
|
||
status.append(f"\nSettings:")
|
||
status.append(f" Max CPU: {MINING_CONFIG['max_cpu_percent']}%")
|
||
status.append(f" Max GPU: {MINING_CONFIG['max_gpu_percent']}%")
|
||
status.append(f" Auto-switch: {MINING_CONFIG['auto_switch']}")
|
||
|
||
return "\n".join(status)
|
||
|
||
|
||
def create_local_wallet(coin: str) -> str:
|
||
"""Create local wallet for a coin."""
|
||
|
||
wallet_dir = os.path.join(ROOT, "wallets")
|
||
os.makedirs(wallet_dir, exist_ok=True)
|
||
|
||
if coin.upper() in ["XMR", "MONERO"]:
|
||
# Check if monero-wallet-cli exists
|
||
result = subprocess.run(["which", "monero-wallet-cli"], capture_output=True)
|
||
if result.returncode != 0:
|
||
return f"""[wallet] ❌ Monero CLI not found
|
||
Install: sudo apt install monero
|
||
Or download from: https://getmonero.org/downloads/"""
|
||
|
||
wallet_path = os.path.join(wallet_dir, f"xmr_wallet")
|
||
print(f"[wallet] Creating Monero wallet at {wallet_path}")
|
||
print("[wallet] ⚠️ SAVE YOUR SEED PHRASE!")
|
||
|
||
# This would need interactive input - guide user
|
||
return f"""[wallet] To create Monero wallet:
|
||
1. Run: monero-wallet-cli --generate-new-wallet {wallet_path}
|
||
2. Set a password
|
||
3. SAVE THE 25-WORD SEED PHRASE
|
||
4. Get address with 'address' command
|
||
5. Add to mining: !mine wallet XMR <your_address>"""
|
||
|
||
elif coin.upper() in ["RVN", "RAVENCOIN"]:
|
||
return f"""[wallet] For Ravencoin:
|
||
1. Download: https://ravencoin.org/wallet/
|
||
2. Or use CLI: raven-cli getnewaddress
|
||
3. Add to mining: !mine wallet RVN <your_address>"""
|
||
|
||
elif coin.upper() in ["RTM", "RAPTOREUM"]:
|
||
return f"""[wallet] For Raptoreum:
|
||
1. Download: https://raptoreum.com/
|
||
2. Create new wallet in GUI
|
||
3. Get receiving address
|
||
4. Add to mining: !mine wallet RTM <your_address>"""
|
||
|
||
else:
|
||
return f"""[wallet] For {coin}:
|
||
1. Search for official {coin} wallet
|
||
2. Create new wallet
|
||
3. Get receiving address
|
||
4. Add to mining: !mine wallet {coin} <your_address>"""
|
||
|
||
|
||
def auto_mine_best_coin(generate_fn) -> str:
|
||
"""Automatically find and mine the most profitable coin."""
|
||
|
||
print("[mining] 🤖 Auto-mining: Finding best coin...")
|
||
|
||
# Check profitability
|
||
profit_analysis = check_mining_profitability(generate_fn)
|
||
print(profit_analysis)
|
||
|
||
# Ask model to pick the best
|
||
prompt = f"""Based on this analysis, which coin should we mine?
|
||
|
||
{profit_analysis}
|
||
|
||
Current configured wallets: {list(MINING_CONFIG.get('wallet_addresses', {}).keys())}
|
||
|
||
Reply with JUST the coin symbol (e.g., RTM, XMR, RVN) or "NONE" if nothing profitable."""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
coin = response.strip().upper().split()[0]
|
||
|
||
if coin == "NONE" or len(coin) > 10:
|
||
return "[mining] No profitable coin found. Try later."
|
||
|
||
# Check if we have wallet for this coin
|
||
if coin not in MINING_CONFIG.get("wallet_addresses", {}):
|
||
return f"[mining] ⚠️ No wallet for {coin}. Set one with: !mine wallet {coin} <address>"
|
||
|
||
# Find miner for this coin
|
||
miner = None
|
||
for m, info in SUPPORTED_MINERS.items():
|
||
if coin in info["coins"]:
|
||
miner = m
|
||
break
|
||
|
||
if not miner:
|
||
return f"[mining] ❌ No supported miner for {coin}"
|
||
|
||
# Start mining
|
||
return start_mining(miner, coin)
|
||
|
||
|
||
# === MONEY-MAKING AUTOMATION ===
|
||
|
||
INCOME_STRATEGIES = {
|
||
"freelance": {
|
||
"description": "Find and apply to freelance jobs",
|
||
"platforms": ["upwork", "fiverr", "freelancer", "toptal"],
|
||
"skills_needed": ["writing", "coding", "design", "data entry", "virtual assistant"]
|
||
},
|
||
"arbitrage": {
|
||
"description": "Find price differences between platforms",
|
||
"platforms": ["ebay", "amazon", "walmart", "aliexpress"],
|
||
},
|
||
"content": {
|
||
"description": "Create and monetize content",
|
||
"platforms": ["medium", "substack", "youtube", "tiktok"],
|
||
},
|
||
"surveys": {
|
||
"description": "Complete paid surveys and tasks",
|
||
"platforms": ["swagbucks", "mturk", "prolific"],
|
||
},
|
||
"dropshipping": {
|
||
"description": "Find trending products to sell",
|
||
"platforms": ["aliexpress", "shopify", "ebay"],
|
||
}
|
||
}
|
||
|
||
def analyze_freelance_jobs(page_content: str, generate_fn) -> str:
|
||
"""Analyze freelance job listings and recommend best matches."""
|
||
prompt = f"""Analyze these job listings and identify:
|
||
1. Top 3 highest paying jobs
|
||
2. Jobs matching common skills (writing, coding, data entry)
|
||
3. Quick wins (easy jobs, fast turnaround)
|
||
|
||
Job listings:
|
||
{page_content[:3000]}
|
||
|
||
Provide actionable recommendations."""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
def analyze_arbitrage_opportunity(product: str, generate_fn) -> str:
|
||
"""Find price arbitrage opportunities."""
|
||
results = {}
|
||
|
||
platforms = [
|
||
("amazon", f"https://www.amazon.com/s?k={product.replace(' ', '+')}"),
|
||
("ebay", f"https://www.ebay.com/sch/i.html?_nkw={product.replace(' ', '+')}"),
|
||
("walmart", f"https://www.walmart.com/search?q={product.replace(' ', '+')}"),
|
||
]
|
||
|
||
for platform, url in platforms:
|
||
tool_browser_open(url)
|
||
time.sleep(3)
|
||
content = tool_browser_read()
|
||
results[platform] = content
|
||
|
||
prompt = f"""Analyze prices for "{product}" and find arbitrage opportunities:
|
||
|
||
Amazon: {results.get('amazon', 'N/A')[:1000]}
|
||
eBay: {results.get('ebay', 'N/A')[:1000]}
|
||
Walmart: {results.get('walmart', 'N/A')[:1000]}
|
||
|
||
Identify: lowest price, highest sell price, profit margin, recommendation."""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
def find_trending_products(generate_fn) -> str:
|
||
"""Find trending products to sell."""
|
||
sources = [
|
||
"https://trends.google.com/trending?geo=US",
|
||
"https://www.amazon.com/Best-Sellers/zgbs",
|
||
]
|
||
|
||
all_content = []
|
||
for url in sources:
|
||
tool_browser_open(url)
|
||
time.sleep(3)
|
||
content = tool_browser_read()
|
||
all_content.append(content[:1500])
|
||
|
||
prompt = f"""Identify 5 products with high profit potential:
|
||
|
||
{chr(10).join(all_content)}
|
||
|
||
For each: name, why trending, profit margin, best platform, competition level."""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
def auto_apply_jobs(platform: str, skills: list, generate_fn) -> str:
|
||
"""Find and prepare job applications."""
|
||
|
||
if platform == "upwork":
|
||
url = f"https://www.upwork.com/nx/jobs/search/?q={'+'.join(skills)}"
|
||
elif platform == "fiverr":
|
||
url = f"https://www.fiverr.com/search/gigs?query={'+'.join(skills)}"
|
||
else:
|
||
url = f"https://www.indeed.com/jobs?q={'+'.join(skills)}"
|
||
|
||
tool_browser_open(url)
|
||
time.sleep(3)
|
||
content = tool_browser_read()
|
||
|
||
prompt = f"""Analyze jobs for skills: {', '.join(skills)}
|
||
|
||
{content[:3000]}
|
||
|
||
For promising jobs: title, pay, requirements match, draft proposal, priority."""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
def generate_content_ideas(niche: str, generate_fn) -> str:
|
||
"""Generate monetizable content ideas."""
|
||
|
||
tool_browser_open(f"https://www.google.com/search?q={niche}+trending+2025")
|
||
time.sleep(2)
|
||
trends = tool_browser_read()
|
||
|
||
prompt = f"""Generate 10 monetizable content ideas for "{niche}":
|
||
|
||
{trends[:2000]}
|
||
|
||
For each: title, content type, monetization, difficulty."""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
|
||
# === LEAD GENERATION ===
|
||
|
||
def scrape_business_leads(industry: str, location: str, generate_fn) -> str:
|
||
"""Scrape business directories for leads."""
|
||
|
||
leads_data = []
|
||
|
||
# Search Yellow Pages
|
||
tool_browser_open(f"https://www.yellowpages.com/search?search_terms={industry.replace(' ', '+')}&geo_location_terms={location.replace(' ', '+')}")
|
||
time.sleep(3)
|
||
yp_content = tool_browser_read()
|
||
leads_data.append(("Yellow Pages", yp_content))
|
||
|
||
# Search Yelp
|
||
tool_browser_open(f"https://www.yelp.com/search?find_desc={industry.replace(' ', '+')}&find_loc={location.replace(' ', '+')}")
|
||
time.sleep(3)
|
||
yelp_content = tool_browser_read()
|
||
leads_data.append(("Yelp", yelp_content))
|
||
|
||
# Search Google Maps
|
||
tool_browser_open(f"https://www.google.com/maps/search/{industry.replace(' ', '+')}+{location.replace(' ', '+')}")
|
||
time.sleep(3)
|
||
maps_content = tool_browser_read()
|
||
leads_data.append(("Google Maps", maps_content))
|
||
|
||
prompt = f"""Extract business leads from these directories for "{industry}" in "{location}":
|
||
|
||
Yellow Pages: {leads_data[0][1][:1500]}
|
||
|
||
Yelp: {leads_data[1][1][:1500]}
|
||
|
||
Google Maps: {leads_data[2][1][:1500]}
|
||
|
||
For each business provide:
|
||
1. Business name
|
||
2. Phone number
|
||
3. Address
|
||
4. Website (if found)
|
||
5. Rating/reviews
|
||
6. Lead quality score (1-10)
|
||
|
||
Format as a table."""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
|
||
# === SOCIAL MEDIA AUTOMATION ===
|
||
|
||
SOCIAL_PLATFORMS = {
|
||
"twitter": {
|
||
"compose_url": "https://twitter.com/compose/tweet",
|
||
"post_selector": "div[data-testid='tweetTextarea_0']",
|
||
"submit_selector": "div[data-testid='tweetButton']",
|
||
},
|
||
"linkedin": {
|
||
"compose_url": "https://www.linkedin.com/feed/",
|
||
"post_selector": "div.share-box-feed-entry__trigger",
|
||
"text_selector": "div.ql-editor",
|
||
"submit_selector": "button.share-actions__primary-action",
|
||
},
|
||
"facebook": {
|
||
"compose_url": "https://www.facebook.com/",
|
||
"post_selector": "div[aria-label='Create a post']",
|
||
"text_selector": "div[aria-label=\"What's on your mind?\"]",
|
||
"submit_selector": "div[aria-label='Post']",
|
||
},
|
||
"reddit": {
|
||
"compose_url": "https://www.reddit.com/submit",
|
||
"title_selector": "textarea[name='title']",
|
||
"text_selector": "div[data-testid='TextPostCreation']",
|
||
"submit_selector": "button[type='submit']",
|
||
}
|
||
}
|
||
|
||
def schedule_social_post(platform: str, content: str, generate_fn) -> str:
|
||
"""Post content to social media platform."""
|
||
|
||
if platform not in SOCIAL_PLATFORMS:
|
||
return f"[social] Unknown platform: {platform}. Available: {', '.join(SOCIAL_PLATFORMS.keys())}"
|
||
|
||
config = SOCIAL_PLATFORMS[platform]
|
||
|
||
print(f"\n[social] Posting to {platform}...")
|
||
|
||
tool_browser_open(config["compose_url"])
|
||
time.sleep(3)
|
||
|
||
# Try to click compose button if needed
|
||
if "post_selector" in config:
|
||
try:
|
||
tool_browser_click(config["post_selector"])
|
||
time.sleep(1)
|
||
except:
|
||
pass
|
||
|
||
# Type content
|
||
text_selector = config.get("text_selector", config.get("post_selector"))
|
||
tool_browser_type(content, text_selector)
|
||
time.sleep(1)
|
||
|
||
return f"[social] Content ready to post on {platform}. Review in browser and click Post to confirm."
|
||
|
||
|
||
def generate_social_content(topic: str, platform: str, generate_fn) -> str:
|
||
"""Generate engaging social media content."""
|
||
|
||
prompt = f"""Generate an engaging {platform} post about "{topic}".
|
||
|
||
Requirements:
|
||
- Optimized for {platform} algorithm
|
||
- Include relevant hashtags
|
||
- Call to action
|
||
- Keep within character limits
|
||
- Make it viral-worthy
|
||
|
||
Generate 3 variations."""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
|
||
def engage_social_media(platform: str, action: str, generate_fn) -> str:
|
||
"""Auto-engage on social media (like, comment, follow)."""
|
||
|
||
if platform == "twitter":
|
||
tool_browser_open("https://twitter.com/home")
|
||
elif platform == "linkedin":
|
||
tool_browser_open("https://www.linkedin.com/feed/")
|
||
elif platform == "reddit":
|
||
tool_browser_open("https://www.reddit.com/")
|
||
|
||
time.sleep(3)
|
||
content = tool_browser_read()
|
||
|
||
prompt = f"""Analyze this {platform} feed and suggest engagement actions:
|
||
|
||
{content[:2500]}
|
||
|
||
For each post worth engaging with:
|
||
1. Post summary
|
||
2. Suggested comment (authentic, not spammy)
|
||
3. Should like? (yes/no)
|
||
4. Should follow author? (yes/no)
|
||
5. Engagement value score (1-10)
|
||
|
||
Focus on posts that could lead to networking or business opportunities."""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
|
||
# === SURVEY/TASK AUTOMATION ===
|
||
|
||
SURVEY_SITES = {
|
||
"swagbucks": "https://www.swagbucks.com/surveys",
|
||
"mturk": "https://worker.mturk.com/",
|
||
"prolific": "https://app.prolific.co/",
|
||
"survey_junkie": "https://www.surveyjunkie.com/",
|
||
"clickworker": "https://www.clickworker.com/",
|
||
}
|
||
|
||
def find_surveys(generate_fn) -> str:
|
||
"""Find available paid surveys and tasks."""
|
||
|
||
all_surveys = []
|
||
|
||
for site, url in list(SURVEY_SITES.items())[:3]: # Check top 3
|
||
print(f"[surveys] Checking {site}...")
|
||
tool_browser_open(url)
|
||
time.sleep(3)
|
||
content = tool_browser_read()
|
||
all_surveys.append((site, content[:1000]))
|
||
|
||
prompt = f"""Analyze available surveys and tasks:
|
||
|
||
{chr(10).join([f'{site}: {content}' for site, content in all_surveys])}
|
||
|
||
List available opportunities:
|
||
1. Survey/task name
|
||
2. Estimated pay
|
||
3. Time required
|
||
4. Hourly rate equivalent
|
||
5. Difficulty (easy/medium/hard)
|
||
6. Worth it? (yes/no)
|
||
|
||
Sort by hourly rate descending."""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
|
||
def auto_qualify_survey(generate_fn) -> str:
|
||
"""Help qualify for surveys by suggesting optimal answers."""
|
||
|
||
content = tool_browser_read()
|
||
|
||
prompt = f"""This is a survey qualification page:
|
||
|
||
{content[:2000]}
|
||
|
||
Analyze the questions and suggest answers most likely to qualify for paid surveys.
|
||
Note: Be ethical - don't lie about demographics, but optimize presentation.
|
||
|
||
For each question:
|
||
1. Question text
|
||
2. Suggested answer
|
||
3. Why this answer qualifies"""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
|
||
# === CRYPTO/STOCK MONITORING ===
|
||
|
||
WATCHLIST = []
|
||
|
||
def add_to_watchlist(symbol: str, target_price: float, direction: str):
|
||
"""Add asset to price watchlist."""
|
||
WATCHLIST.append({
|
||
"symbol": symbol.upper(),
|
||
"target": target_price,
|
||
"direction": direction, # "above" or "below"
|
||
"added": time.time()
|
||
})
|
||
return f"[watchlist] Added {symbol.upper()} - alert when {'>' if direction == 'above' else '<'} ${target_price}"
|
||
|
||
|
||
def check_prices(generate_fn) -> str:
|
||
"""Check current prices for watchlist items."""
|
||
|
||
if not WATCHLIST:
|
||
return "[watchlist] Empty. Use 'watch BTC above 50000' to add items."
|
||
|
||
results = []
|
||
alerts = []
|
||
|
||
for item in WATCHLIST:
|
||
symbol = item["symbol"]
|
||
|
||
# Check if crypto or stock
|
||
if symbol in ["BTC", "ETH", "SOL", "DOGE", "XRP", "ADA"]:
|
||
tool_browser_open(f"https://coinmarketcap.com/currencies/{symbol.lower()}/")
|
||
else:
|
||
tool_browser_open(f"https://finance.yahoo.com/quote/{symbol}")
|
||
|
||
time.sleep(2)
|
||
content = tool_browser_read()
|
||
results.append((symbol, content[:500]))
|
||
|
||
# Check for alert condition
|
||
# (Model will analyze if target hit)
|
||
|
||
prompt = f"""Check these assets against watchlist targets:
|
||
|
||
Watchlist: {json.dumps(WATCHLIST, indent=2)}
|
||
|
||
Current data:
|
||
{chr(10).join([f'{sym}: {data}' for sym, data in results])}
|
||
|
||
For each asset:
|
||
1. Current price
|
||
2. Target price
|
||
3. Distance to target (%)
|
||
4. ALERT if target reached
|
||
5. Recommendation (buy/hold/sell)"""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
|
||
def crypto_opportunities(generate_fn) -> str:
|
||
"""Find crypto trading opportunities."""
|
||
|
||
tool_browser_open("https://coinmarketcap.com/")
|
||
time.sleep(2)
|
||
cmc_content = tool_browser_read()
|
||
|
||
tool_browser_open("https://coinmarketcap.com/trending-cryptocurrencies/")
|
||
time.sleep(2)
|
||
trending = tool_browser_read()
|
||
|
||
prompt = f"""Analyze crypto market for opportunities:
|
||
|
||
Market Overview: {cmc_content[:1500]}
|
||
|
||
Trending: {trending[:1500]}
|
||
|
||
Identify:
|
||
1. Top 3 coins with momentum
|
||
2. Oversold coins (potential bounce)
|
||
3. New listings worth watching
|
||
4. Risk assessment for each
|
||
5. Entry/exit price suggestions
|
||
|
||
Disclaimer: Not financial advice."""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
|
||
# === AUTO-APPLY TO JOBS ===
|
||
|
||
def full_auto_apply(platform: str, skills: list, max_applications: int, generate_fn) -> str:
|
||
"""Fully automated job application process."""
|
||
|
||
applications_sent = 0
|
||
results = []
|
||
|
||
# Search jobs
|
||
if platform == "upwork":
|
||
url = f"https://www.upwork.com/nx/jobs/search/?q={'+'.join(skills)}&sort=recency"
|
||
elif platform == "indeed":
|
||
url = f"https://www.indeed.com/jobs?q={'+'.join(skills)}&sort=date"
|
||
else:
|
||
url = f"https://www.linkedin.com/jobs/search/?keywords={'+'.join(skills)}"
|
||
|
||
tool_browser_open(url)
|
||
time.sleep(3)
|
||
|
||
# Get job listings
|
||
content = tool_browser_read()
|
||
|
||
prompt = f"""Analyze these job listings and prepare applications:
|
||
|
||
{content[:3000]}
|
||
|
||
For the top {max_applications} most suitable jobs:
|
||
1. Job title and company
|
||
2. Why I'm a good fit
|
||
3. Personalized cover letter (3-4 sentences)
|
||
4. Key points to highlight
|
||
5. Red flags (if any)
|
||
|
||
Skills: {', '.join(skills)}"""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
|
||
return f"""[auto-apply] Prepared {max_applications} applications
|
||
|
||
{response}
|
||
|
||
⚠️ Review each application before submitting.
|
||
Use '!click Apply' to start applying to visible jobs."""
|
||
|
||
|
||
# === LISTING CREATION ===
|
||
|
||
def create_ebay_listing(product: str, generate_fn) -> str:
|
||
"""Create optimized eBay listing."""
|
||
|
||
# Research similar listings
|
||
tool_browser_open(f"https://www.ebay.com/sch/i.html?_nkw={product.replace(' ', '+')}&_sop=12")
|
||
time.sleep(3)
|
||
similar = tool_browser_read()
|
||
|
||
# Research sold prices
|
||
tool_browser_open(f"https://www.ebay.com/sch/i.html?_nkw={product.replace(' ', '+')}&LH_Complete=1&LH_Sold=1")
|
||
time.sleep(3)
|
||
sold = tool_browser_read()
|
||
|
||
prompt = f"""Create an optimized eBay listing for "{product}":
|
||
|
||
Similar active listings: {similar[:1500]}
|
||
|
||
Recent sold prices: {sold[:1500]}
|
||
|
||
Generate:
|
||
1. SEO-optimized title (80 chars max)
|
||
2. Suggested price (competitive)
|
||
3. Best category
|
||
4. 5 key features/bullet points
|
||
5. Full description (persuasive, detailed)
|
||
6. Suggested shipping options
|
||
7. Best time to list
|
||
8. Expected sell-through rate"""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
|
||
def create_amazon_listing(product: str, generate_fn) -> str:
|
||
"""Create optimized Amazon listing content."""
|
||
|
||
# Research competition
|
||
tool_browser_open(f"https://www.amazon.com/s?k={product.replace(' ', '+')}")
|
||
time.sleep(3)
|
||
competition = tool_browser_read()
|
||
|
||
prompt = f"""Create Amazon listing content for "{product}":
|
||
|
||
Competition: {competition[:2000]}
|
||
|
||
Generate:
|
||
1. Product title (200 chars, keyword-rich)
|
||
2. 5 bullet points (features & benefits)
|
||
3. Product description (1000+ words, A+ content style)
|
||
4. Backend keywords (hidden search terms)
|
||
5. Suggested price point
|
||
6. Main image requirements
|
||
7. A+ Content module suggestions"""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
|
||
def find_dropship_products(generate_fn) -> str:
|
||
"""Find profitable dropshipping products."""
|
||
|
||
# Check AliExpress trending
|
||
tool_browser_open("https://www.aliexpress.com/popular.html")
|
||
time.sleep(3)
|
||
ali_trending = tool_browser_read()
|
||
|
||
# Check Amazon best sellers
|
||
tool_browser_open("https://www.amazon.com/Best-Sellers/zgbs")
|
||
time.sleep(3)
|
||
amazon_best = tool_browser_read()
|
||
|
||
# Check eBay trending
|
||
tool_browser_open("https://www.ebay.com/trending")
|
||
time.sleep(3)
|
||
ebay_trending = tool_browser_read()
|
||
|
||
prompt = f"""Find profitable dropshipping opportunities:
|
||
|
||
AliExpress Trending: {ali_trending[:1500]}
|
||
Amazon Best Sellers: {amazon_best[:1500]}
|
||
eBay Trending: {ebay_trending[:1500]}
|
||
|
||
For top 10 products:
|
||
1. Product name
|
||
2. AliExpress price (source)
|
||
3. Amazon/eBay price (sell)
|
||
4. Profit margin
|
||
5. Competition level (low/med/high)
|
||
6. Shipping time concern
|
||
7. Recommendation (yes/no/maybe)
|
||
|
||
Sort by profit potential."""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
return response
|
||
|
||
def detect_money_task(user_input: str) -> Tuple[Optional[str], Optional[Dict]]:
|
||
"""Detect money-making related requests."""
|
||
user_lower = user_input.lower()
|
||
|
||
# Freelance job search
|
||
if any(kw in user_lower for kw in ["freelance", "upwork", "fiverr", "find jobs", "find work", "gig economy"]):
|
||
skills = []
|
||
for skill in ["writing", "coding", "python", "design", "data entry", "virtual assistant", "web", "marketing"]:
|
||
if skill in user_lower:
|
||
skills.append(skill)
|
||
return "freelance", {"skills": skills if skills else ["general"]}
|
||
|
||
# Auto-apply
|
||
if any(kw in user_lower for kw in ["auto apply", "auto-apply", "apply to jobs", "mass apply"]):
|
||
skills = []
|
||
for skill in ["writing", "coding", "python", "design", "data entry", "web", "marketing"]:
|
||
if skill in user_lower:
|
||
skills.append(skill)
|
||
return "auto_apply", {"skills": skills if skills else ["general"], "max": 5}
|
||
|
||
# Lead generation
|
||
if any(kw in user_lower for kw in ["leads", "find leads", "business leads", "scrape businesses", "find clients"]):
|
||
industry = "small business"
|
||
location = "new york"
|
||
for word in ["plumber", "lawyer", "dentist", "restaurant", "contractor", "realtor", "doctor"]:
|
||
if word in user_lower:
|
||
industry = word
|
||
for city in ["new york", "los angeles", "chicago", "houston", "miami", "seattle", "denver"]:
|
||
if city in user_lower:
|
||
location = city
|
||
return "leads", {"industry": industry, "location": location}
|
||
|
||
# Social media
|
||
if any(kw in user_lower for kw in ["post to twitter", "post to linkedin", "post to facebook", "social media post"]):
|
||
platform = "twitter"
|
||
for p in ["linkedin", "facebook", "reddit", "twitter"]:
|
||
if p in user_lower:
|
||
platform = p
|
||
return "social_post", {"platform": platform}
|
||
|
||
if any(kw in user_lower for kw in ["engage social", "social engagement", "auto engage", "grow followers"]):
|
||
platform = "twitter"
|
||
for p in ["linkedin", "facebook", "reddit", "twitter"]:
|
||
if p in user_lower:
|
||
platform = p
|
||
return "social_engage", {"platform": platform}
|
||
|
||
if any(kw in user_lower for kw in ["generate post", "write tweet", "write post", "content for"]):
|
||
platform = "twitter"
|
||
for p in ["linkedin", "facebook", "reddit", "twitter"]:
|
||
if p in user_lower:
|
||
platform = p
|
||
topic = re.sub(r"(generate|write|post|tweet|content|for|about|on|linkedin|twitter|facebook|reddit)", "", user_lower).strip()
|
||
return "social_content", {"platform": platform, "topic": topic if topic else "technology"}
|
||
|
||
# Surveys
|
||
if any(kw in user_lower for kw in ["surveys", "paid surveys", "mturk", "swagbucks", "find surveys"]):
|
||
return "surveys", {}
|
||
|
||
# Crypto/stocks
|
||
if any(kw in user_lower for kw in ["crypto opportunities", "crypto trading", "find crypto"]):
|
||
return "crypto", {}
|
||
|
||
if any(kw in user_lower for kw in ["check prices", "check watchlist", "price alert"]):
|
||
return "check_prices", {}
|
||
|
||
if "watch " in user_lower and any(kw in user_lower for kw in ["above", "below", "at"]):
|
||
match = re.search(r"watch\s+(\w+)\s+(above|below)\s+(\d+\.?\d*)", user_lower)
|
||
if match:
|
||
return "add_watch", {"symbol": match.group(1), "direction": match.group(2), "target": float(match.group(3))}
|
||
|
||
# Listing creation
|
||
if any(kw in user_lower for kw in ["create ebay listing", "ebay listing", "list on ebay"]):
|
||
product = re.sub(r"(create|ebay|listing|list|on|for)", "", user_lower).strip()
|
||
return "ebay_listing", {"product": product if product else ""}
|
||
|
||
if any(kw in user_lower for kw in ["create amazon listing", "amazon listing", "fba listing"]):
|
||
product = re.sub(r"(create|amazon|listing|fba|for)", "", user_lower).strip()
|
||
return "amazon_listing", {"product": product if product else ""}
|
||
|
||
if any(kw in user_lower for kw in ["dropship", "dropshipping", "find products to sell", "wholesale"]):
|
||
return "dropship", {}
|
||
|
||
# Arbitrage
|
||
if any(kw in user_lower for kw in ["arbitrage", "price difference", "flip", "resell"]):
|
||
product = re.sub(r"(find|arbitrage|price|difference|for|flip|resell)", "", user_lower).strip()
|
||
return "arbitrage", {"product": product if product else "electronics"}
|
||
|
||
# Trending products
|
||
if any(kw in user_lower for kw in ["trending products", "what to sell", "hot products"]):
|
||
return "trending", {}
|
||
|
||
# Content ideas
|
||
if any(kw in user_lower for kw in ["content ideas", "blog ideas", "video ideas", "monetize content"]):
|
||
niche = re.sub(r"(content|ideas|blog|video|monetize|for|about)", "", user_lower).strip()
|
||
return "content", {"niche": niche if niche else "technology"}
|
||
|
||
# Make money general
|
||
if any(kw in user_lower for kw in ["make money", "earn money", "side hustle", "income strategies", "how to make"]):
|
||
return "strategies", {}
|
||
|
||
return None, None
|
||
|
||
|
||
def confirm_money_action(action: str, params: Dict) -> bool:
|
||
"""Confirm money-making action."""
|
||
print("\n" + "="*60)
|
||
print("💰 MONEY-MAKING ACTION")
|
||
print("="*60)
|
||
|
||
actions_display = {
|
||
"freelance": ("🔍", "Search freelance jobs", f"Skills: {', '.join(params.get('skills', ['general']))}"),
|
||
"auto_apply": ("📝", "Auto-apply to jobs", f"Skills: {', '.join(params.get('skills', ['general']))}\nMax apps: {params.get('max', 5)}"),
|
||
"leads": ("🎯", "Generate business leads", f"Industry: {params.get('industry', 'general')}\nLocation: {params.get('location', 'USA')}"),
|
||
"social_post": ("📱", "Post to social media", f"Platform: {params.get('platform', 'twitter')}"),
|
||
"social_engage": ("👥", "Social media engagement", f"Platform: {params.get('platform', 'twitter')}"),
|
||
"social_content": ("✍️", "Generate social content", f"Platform: {params.get('platform', 'twitter')}\nTopic: {params.get('topic', 'general')}"),
|
||
"surveys": ("📋", "Find paid surveys", "Checks multiple survey sites"),
|
||
"crypto": ("🪙", "Find crypto opportunities", "Analyzes market trends"),
|
||
"check_prices": ("📈", "Check watchlist prices", "Reviews your watched assets"),
|
||
"add_watch": ("👁️", "Add to watchlist", f"Symbol: {params.get('symbol', '').upper()}\nAlert: {params.get('direction', '')} ${params.get('target', 0)}"),
|
||
"ebay_listing": ("🛒", "Create eBay listing", f"Product: {params.get('product', 'N/A')}"),
|
||
"amazon_listing": ("📦", "Create Amazon listing", f"Product: {params.get('product', 'N/A')}"),
|
||
"dropship": ("🚚", "Find dropshipping products", "Compares AliExpress vs Amazon/eBay"),
|
||
"arbitrage": ("📊", "Find arbitrage opportunity", f"Product: {params.get('product', 'N/A')}"),
|
||
"trending": ("🔥", "Find trending products", "Checks multiple sources"),
|
||
"content": ("✍️", "Generate content ideas", f"Niche: {params.get('niche', 'general')}"),
|
||
"strategies": ("💡", "Show money-making strategies", "Overview of all methods"),
|
||
}
|
||
|
||
if action in actions_display:
|
||
emoji, name, details = actions_display[action]
|
||
print(f"\n{emoji} Action: {name}")
|
||
print(f"📋 {details}")
|
||
|
||
print("="*60)
|
||
response = input("\n✅ Proceed? (yes/no): ").strip().lower()
|
||
return response in ['yes', 'y']
|
||
|
||
|
||
def execute_money_action(action: str, params: Dict, generate_fn) -> str:
|
||
"""Execute money-making action."""
|
||
|
||
print("\n[researching opportunities...]")
|
||
|
||
if action == "freelance":
|
||
return auto_apply_jobs("upwork", params.get("skills", ["general"]), generate_fn)
|
||
|
||
elif action == "auto_apply":
|
||
return full_auto_apply("upwork", params.get("skills", ["general"]), params.get("max", 5), generate_fn)
|
||
|
||
elif action == "leads":
|
||
return scrape_business_leads(params.get("industry", "business"), params.get("location", "new york"), generate_fn)
|
||
|
||
elif action == "social_post":
|
||
content = input("📝 What do you want to post? ").strip()
|
||
return schedule_social_post(params.get("platform", "twitter"), content, generate_fn)
|
||
|
||
elif action == "social_engage":
|
||
return engage_social_media(params.get("platform", "twitter"), "engage", generate_fn)
|
||
|
||
elif action == "social_content":
|
||
return generate_social_content(params.get("topic", "technology"), params.get("platform", "twitter"), generate_fn)
|
||
|
||
elif action == "surveys":
|
||
return find_surveys(generate_fn)
|
||
|
||
elif action == "crypto":
|
||
return crypto_opportunities(generate_fn)
|
||
|
||
elif action == "check_prices":
|
||
return check_prices(generate_fn)
|
||
|
||
elif action == "add_watch":
|
||
return add_to_watchlist(params.get("symbol", "BTC"), params.get("target", 0), params.get("direction", "above"))
|
||
|
||
elif action == "ebay_listing":
|
||
product = params.get("product", "")
|
||
if not product:
|
||
product = input("📦 What product do you want to list? ").strip()
|
||
return create_ebay_listing(product, generate_fn)
|
||
|
||
elif action == "amazon_listing":
|
||
product = params.get("product", "")
|
||
if not product:
|
||
product = input("📦 What product do you want to list? ").strip()
|
||
return create_amazon_listing(product, generate_fn)
|
||
|
||
elif action == "dropship":
|
||
return find_dropship_products(generate_fn)
|
||
|
||
elif action == "arbitrage":
|
||
return analyze_arbitrage_opportunity(params.get("product", "electronics"), generate_fn)
|
||
|
||
elif action == "trending":
|
||
return find_trending_products(generate_fn)
|
||
|
||
elif action == "content":
|
||
return generate_content_ideas(params.get("niche", "technology"), generate_fn)
|
||
|
||
elif action == "strategies":
|
||
return """
|
||
💰 MONEY-MAKING STRATEGIES 💰
|
||
================================
|
||
|
||
📋 FREELANCING
|
||
"find freelance jobs for [skill]"
|
||
"auto apply to jobs for [skill]"
|
||
Platforms: Upwork, Fiverr, Indeed
|
||
|
||
🎯 LEAD GENERATION
|
||
"find leads for [industry] in [city]"
|
||
Scrapes: Yellow Pages, Yelp, Google Maps
|
||
|
||
📱 SOCIAL MEDIA
|
||
"post to twitter/linkedin/facebook"
|
||
"generate post about [topic]"
|
||
"engage on twitter/linkedin"
|
||
|
||
📋 SURVEYS & TASKS
|
||
"find surveys"
|
||
Checks: Swagbucks, MTurk, Prolific
|
||
|
||
💹 CRYPTO/STOCKS
|
||
"crypto opportunities"
|
||
"watch BTC above 50000"
|
||
"check watchlist"
|
||
|
||
🛒 E-COMMERCE
|
||
"create ebay listing for [product]"
|
||
"create amazon listing for [product]"
|
||
"find dropshipping products"
|
||
|
||
📊 ARBITRAGE
|
||
"find arbitrage for [product]"
|
||
Compares: Amazon, eBay, Walmart
|
||
|
||
🔥 TRENDING
|
||
"find trending products"
|
||
"content ideas for [niche]"
|
||
"""
|
||
|
||
return "[unknown action]"
|
||
|
||
|
||
def plan_task(task: str, generate_fn) -> dict:
|
||
"""Have model interpret task and create execution plan."""
|
||
prompt = f"""You are an autonomous agent with browser control. Plan the steps to complete this task.
|
||
Available actions:
|
||
- BROWSE <url> - Open URL
|
||
- CLICK <selector_or_text> - Click element
|
||
- TYPE <text> - Type text into focused element
|
||
- FILL <selector> <text> - Fill input field
|
||
- PRESS <key> - Press key (Enter, Tab, Escape)
|
||
- READ - Read page content
|
||
- WAIT <seconds> - Wait
|
||
- DONE - Task complete
|
||
|
||
Respond ONLY with a JSON plan, no other text:
|
||
{{"task": "description", "steps": [{{"action": "BROWSE", "args": ["url"]}}, {{"action": "CLICK", "args": ["selector"]}}, ...]}}
|
||
|
||
Task: {task}"""
|
||
|
||
response, _, _ = generate_fn(prompt)
|
||
try:
|
||
# Extract JSON from response
|
||
match = re.search(r'\{.*\}', response, re.DOTALL)
|
||
if match:
|
||
return json.loads(match.group())
|
||
except Exception as e:
|
||
pass
|
||
return {"task": task, "steps": [], "error": f"Could not parse plan. Model said: {response[:200]}"}
|
||
|
||
|
||
def confirm_task(plan: dict) -> bool:
|
||
"""Show confirmation box and get user approval."""
|
||
print("\n" + "="*60)
|
||
print("🤖 AUTONOMOUS TASK CONFIRMATION")
|
||
print("="*60)
|
||
print(f"\n📋 TASK: {plan.get('task', 'Unknown')}\n")
|
||
print("📝 PLANNED STEPS:")
|
||
print("-"*40)
|
||
for i, step in enumerate(plan.get('steps', []), 1):
|
||
action = step.get('action', '?')
|
||
args = step.get('args', [])
|
||
print(f" {i}. {action} {' '.join(str(a) for a in args)}")
|
||
print("-"*40)
|
||
print("\n⚠️ The AI will execute these actions autonomously.")
|
||
print("="*60)
|
||
|
||
response = input("\n✅ Confirm execution? (yes/no): ").strip().lower()
|
||
return response in ['yes', 'y']
|
||
|
||
|
||
def execute_task(plan: dict, generate_fn) -> str:
|
||
"""Execute confirmed plan step by step."""
|
||
results = []
|
||
page_content = ""
|
||
|
||
for i, step in enumerate(plan.get('steps', []), 1):
|
||
action = step.get('action', '').upper()
|
||
args = step.get('args', [])
|
||
|
||
print(f"\n[EXEC {i}/{len(plan.get('steps', []))}] {action} {args}")
|
||
|
||
if action == "BROWSE":
|
||
url = args[0] if args else "https://google.com"
|
||
if not url.startswith("http"):
|
||
url = "https://" + url
|
||
result = tool_browser_open(url)
|
||
elif action == "CLICK":
|
||
result = tool_browser_click(args[0] if args else "")
|
||
elif action == "TYPE":
|
||
result = tool_browser_type(args[0] if args else "")
|
||
elif action == "FILL":
|
||
if len(args) >= 2:
|
||
result = tool_browser_type(args[1], args[0])
|
||
else:
|
||
result = "[FILL] Missing args - need selector and text"
|
||
elif action == "PRESS":
|
||
result = tool_browser_press(args[0] if args else "Enter")
|
||
elif action == "READ":
|
||
result = tool_browser_read()
|
||
page_content = result
|
||
elif action == "WAIT":
|
||
wait_time = int(args[0]) if args else 2
|
||
time.sleep(wait_time)
|
||
result = f"[waited {wait_time}s]"
|
||
elif action == "DONE":
|
||
result = "[task complete]"
|
||
results.append(result)
|
||
break
|
||
else:
|
||
result = f"[unknown action: {action}]"
|
||
|
||
print(f" → {result[:150]}...")
|
||
results.append(result)
|
||
time.sleep(1)
|
||
|
||
# Final summary
|
||
summary = "\n".join(results[-5:]) # Last 5 results
|
||
|
||
# Ask model to summarize findings if we read content
|
||
if page_content:
|
||
print("\n[AI] Analyzing results...")
|
||
summary_prompt = f"Summarize what you found from this task. Page content:\n{page_content[:2000]}"
|
||
final_summary, _, _ = generate_fn(summary_prompt)
|
||
return f"Results:\n{summary}\n\nSummary:\n{final_summary}"
|
||
|
||
return summary
|
||
|
||
|
||
def tool_lht_analyze(text: str) -> str:
|
||
if not Config.use_lht_reasoning:
|
||
return "[lht] Disabled"
|
||
lht = get_lht_reasoner()
|
||
if not lht:
|
||
return "[lht] Not available"
|
||
steps = [s.strip() for s in re.split(r'[\n•\-\d\.]', text) if len(s.strip()) > 10]
|
||
if len(steps) < 2:
|
||
return "[lht] Need at least 2 reasoning steps"
|
||
metrics = lht.check_consistency(steps, _tokenizer)
|
||
return f"[LHT] Consistency: {metrics['consistency_score']:.2%}, Holonomy: {metrics['holonomy']:.4f}"
|
||
|
||
|
||
# ==============================================================================
|
||
# PLANNING / REFLECTION
|
||
# ==============================================================================
|
||
def persona_directive() -> str:
|
||
return "Übermenschetien v2: Stable self-improvement. Dense, coherent, helpful. Every word matters."
|
||
|
||
def plan_for(goal: str) -> str:
|
||
user = f"{persona_directive()}\nGoal: {goal}\nDeliver 5 concrete steps with constraints and risks."
|
||
response, _, _ = generate(user)
|
||
return response
|
||
|
||
def reflect_on(last_output: str) -> str:
|
||
user = f"{persona_directive()}\nCritique and improve:\n{last_output}"
|
||
response, _, _ = generate(user)
|
||
return response
|
||
|
||
|
||
# ==============================================================================
|
||
# FINAL REPORT
|
||
# ==============================================================================
|
||
def final_report():
|
||
print("\n" + "=" * 70)
|
||
print("FINAL ÜBERMENSCHETIEN v2 REPORT")
|
||
print("=" * 70)
|
||
print(f"Turns completed: {Store.state['turn']}")
|
||
print(f"Goals tracked: {len(Store.goals)}")
|
||
print(f"Improvement iterations: {Store.state.get('improvement_iterations', 0)}")
|
||
print(f"Training runs: {len(Store.state.get('training_runs', []))}")
|
||
print(f"Rollback count: {Store.state.get('rollback_count', 0)}")
|
||
print(f"\nCheckpoints:")
|
||
print(f" Current: {Store.state.get('current_checkpoint', 'unknown')}")
|
||
print(f" Best: {Store.state.get('best_checkpoint', 'unknown')}")
|
||
print(f" Best quality: {Store.state.get('best_quality_score', 0):.3f}")
|
||
|
||
if Store.state.get("cfhot_interventions"):
|
||
iv = Store.state["cfhot_interventions"]
|
||
print(f"\nCF-HoT Interventions: {sum(iv.values())}")
|
||
|
||
if Store.state.get("quality_history"):
|
||
qh = Store.state["quality_history"]
|
||
print(f"\nQuality History ({len(qh)} data points):")
|
||
if qh:
|
||
print(f" First: {qh[0].get('quality', 0):.3f}")
|
||
print(f" Last: {qh[-1].get('quality', 0):.3f}")
|
||
|
||
print("=" * 70)
|
||
|
||
|
||
# ==============================================================================
|
||
# HELP
|
||
# ==============================================================================
|
||
HELP = """
|
||
╔══════════════════════════════════════════════════════════════════════════════╗
|
||
║ ARC ENGINE v2.1 - Adaptive Recursive Cognition (Übermenschetien) ║
|
||
╠══════════════════════════════════════════════════════════════════════════════╣
|
||
║ NEW IN v2.1 ⭐ ║
|
||
║ !cfhot / !125x Toggle 125× repetition head on/off ║
|
||
║ !rsi15 Run 15-iteration RSI stress test ║
|
||
║ !book Toggle book mode (16K tokens) ║
|
||
║ !write <topic> Write a complete book ║
|
||
║ !idea <request> Generate extensive ideas (Claude) ║
|
||
║ !claude <prompt> Direct Claude Opus 4.5 prompt ║
|
||
║ !plot Plot quality history ║
|
||
║ !export [name] Export checkpoint package ║
|
||
║ !import <path> Import checkpoint package ║
|
||
║ !benchmark Run evaluation suite ║
|
||
║ !learn Learn from high-quality responses ║
|
||
║ !api Start REST API server ║
|
||
║ ║
|
||
║ MULTIMEDIA 🎬 ║
|
||
║ !stream Open live generation window (see tokens live!) ║
|
||
║ !stream off Close streaming window ║
|
||
║ !audio / !tts Toggle text-to-speech ║
|
||
║ !audio voices List available TTS voices ║
|
||
║ !audio voice N Set voice by index ║
|
||
║ !audio rate N Set speech rate (default 175) ║
|
||
║ !say <text> Speak text immediately ║
|
||
║ ║
|
||
║ IMAGE GEN 🖼️ ║
|
||
║ !image Show image system status ║
|
||
║ !image load Load SDXL model ║
|
||
║ !imagine <prompt> Generate image with SDXL ║
|
||
║ !dalle <prompt> Generate with DALL-E 3 ║
|
||
║ !image view View last generated image ║
|
||
║ !image view <path> View image from file ║
|
||
║ ║
|
||
║ SELF-IMPROVEMENT (WITH SAFEGUARDS) ║
|
||
║ !improve Run stable self-improvement loop ║
|
||
║ !eval Comprehensive model evaluation ║
|
||
║ !train <steps> Run N training steps (default: 25) ║
|
||
║ !compare Compare current vs best checkpoint ║
|
||
║ !rollback Rollback to best checkpoint ║
|
||
║ !load <path> Load a specific checkpoint ║
|
||
║ ║
|
||
║ RSI MODE (Recursive Self-Improvement) 🧠 ║
|
||
║ rsi / rsi status Show RSI status ║
|
||
║ rsi start / !rsi Start RSI mode (autonomous improvement) ║
|
||
║ rsi stop Stop RSI mode ║
|
||
║ rsi pause/resume Pause/resume RSI ║
|
||
║ rsi mode X Set mode: conservative, balanced, aggressive ║
|
||
║ rsi target 0.9 Set target quality ║
|
||
║ ║
|
||
║ AGENTIC TOOLS (FULL ACCESS) ║
|
||
║ !shell <cmd> Execute ANY shell command ║
|
||
║ !python <code> Execute Python code (full access) ║
|
||
║ !read <path> Read file contents ║
|
||
║ !write <p> <c> Write content to file ║
|
||
║ !ls [path] List directory ║
|
||
║ !web <query> Web search (DuckDuckGo) ║
|
||
║ ║
|
||
║ BROWSER (Live Visual Browser) ║
|
||
║ !browse <url> Open browser and navigate ║
|
||
║ !click <selector> Click element ║
|
||
║ !type <text> Type into focused element ║
|
||
║ !fill <sel> <txt> Type into selector ║
|
||
║ !read Read page text ║
|
||
║ !close Close browser ║
|
||
║ ║
|
||
║ TASK CHAINING 🔗 ║
|
||
║ chain: <task> Add task to chain ║
|
||
║ chain run Execute chain ║
|
||
║ chain workflows List workflow templates ║
|
||
║ ║
|
||
║ GMAIL API 📧 ║
|
||
║ !gmail search <q> Search emails ║
|
||
║ !gmail read <id> Read email ║
|
||
║ !gmail send ... Send email ║
|
||
║ ║
|
||
║ CRYPTO MINING ⛏️ ║
|
||
║ !mine Show status | !mine profit Check profitability ║
|
||
║ !mine auto Auto-mine best coin ║
|
||
║ ║
|
||
║ INFO & CONFIG ║
|
||
║ status Current state ║
|
||
║ history Quality history ║
|
||
║ toggle <flag> Toggle flags (125x, book, idea, api, etc) ║
|
||
║ help This help ║
|
||
║ quit Exit ║
|
||
╚══════════════════════════════════════════════════════════════════════════════╝
|
||
"""
|
||
|
||
|
||
# ==============================================================================
|
||
# MAIN LOOP
|
||
# ==============================================================================
|
||
def main():
|
||
print("=" * 75)
|
||
print("🤖 ARC ENGINE v2.1 - Adaptive Recursive Cognition (Übermenschetien)")
|
||
print("=" * 75)
|
||
print(f" DENSE Mode: ON (CONDENSATOR checkpoint)")
|
||
print(f" CF-HoT Control: ON")
|
||
print(f" CF-HoT 125×: {'ON' if Config.use_cfhot_125x else 'OFF'}")
|
||
print(f" AGENTIC Mode: ON (Full shell/python access)")
|
||
print(f" LHT Reasoning: {'ON' if LHT_OK else 'OFF'}")
|
||
print(f" Vector Memory: {'ON' if VECTOR_OK else 'OFF'}")
|
||
print(f" Live Browser: {'ON' if BROWSER_OK else 'OFF'}")
|
||
print(f" Claude API: {'ON' if CLAUDE_API_OK else 'OFF'}")
|
||
print(f" Stream Window: {'ON' if TK_OK else 'OFF'}")
|
||
print(f" Image Gen: {'ON' if DIFFUSERS_OK or OPENAI_OK else 'OFF'}")
|
||
print(f" TTS Audio: {'ON' if VOICE_OK or GTTS_OK else 'OFF'}")
|
||
print(f" Training Examples: {len(DENSE_TRAINING_EXAMPLES)}")
|
||
print("=" * 75)
|
||
print(" NEW IN v2.1: !cfhot, !rsi15, !book, !idea, !stream, !imagine, !audio")
|
||
print("=" * 75)
|
||
print(" Type 'help' for commands, '!improve' to start self-improvement")
|
||
print("=" * 75 + "\n")
|
||
|
||
Store.load()
|
||
tok, model = load_llm()
|
||
|
||
# Load 125× head if enabled
|
||
if Config.use_cfhot_125x:
|
||
get_cfhot_head().load()
|
||
|
||
# Initialize Claude client
|
||
if CLAUDE_API_OK:
|
||
init_claude_client()
|
||
|
||
improver = StableSelfImprover()
|
||
last_plan = ""
|
||
|
||
while True:
|
||
try:
|
||
u = input("\n> ").strip()
|
||
except (EOFError, KeyboardInterrupt):
|
||
break
|
||
|
||
if not u:
|
||
continue
|
||
if u == "help":
|
||
print(HELP)
|
||
continue
|
||
if u == "quit":
|
||
break
|
||
|
||
# ══════════════════════════════════════════════════════════════════════
|
||
# NEW v2.1 COMMANDS
|
||
# ══════════════════════════════════════════════════════════════════════
|
||
|
||
# --- CF-HoT 125× Toggle ---
|
||
if u in ("!cfhot", "!125x", "!cfhot toggle"):
|
||
print(toggle_cfhot_125x())
|
||
continue
|
||
|
||
if u == "!cfhot status":
|
||
head = get_cfhot_head()
|
||
print(f"[cf-hot 125×] Loaded: {head.loaded}")
|
||
print(f"[cf-hot 125×] Enabled: {Config.use_cfhot_125x}")
|
||
continue
|
||
|
||
# --- RSI-15 Stress Test ---
|
||
if u == "!rsi15":
|
||
run_rsi_15(improver)
|
||
continue
|
||
|
||
# --- Book Mode ---
|
||
if u == "!book":
|
||
result = Config.toggle("book_mode")
|
||
print(result)
|
||
if Config.book_mode:
|
||
print(f"[book] Max tokens: {Config.book_max_tokens}")
|
||
print("[book] Use: !write <topic> to start")
|
||
continue
|
||
|
||
if u.startswith("!write "):
|
||
topic = u[7:].strip()
|
||
if topic:
|
||
try:
|
||
chapters = int(input("Chapters (default 10): ").strip() or "10")
|
||
words = int(input("Words/chapter (default 3000): ").strip() or "3000")
|
||
except:
|
||
chapters, words = 10, 3000
|
||
writer = get_book_writer(generate)
|
||
writer.write_book(topic, chapters, words)
|
||
else:
|
||
print("[book] Usage: !write <topic>")
|
||
continue
|
||
|
||
# --- Idea Mode ---
|
||
if u == "!idea":
|
||
result = Config.toggle("idea_mode")
|
||
print(result)
|
||
if Config.idea_mode:
|
||
print(f"[idea] Depth: {Config.idea_depth}")
|
||
print(f"[idea] Use Opus: {Config.idea_use_opus}")
|
||
print("[idea] Use: !idea <request> to generate")
|
||
continue
|
||
|
||
if u.startswith("!idea "):
|
||
request = u[6:].strip()
|
||
if request:
|
||
depth = Config.idea_depth
|
||
# Parse depth flag
|
||
if " --quick" in request:
|
||
depth = "quick"
|
||
request = request.replace(" --quick", "")
|
||
elif " --deep" in request:
|
||
depth = "deep"
|
||
request = request.replace(" --deep", "")
|
||
|
||
gen = get_idea_generator()
|
||
gen.generate(request, depth=depth, use_opus=Config.idea_use_opus)
|
||
else:
|
||
print("[idea] Usage: !idea <request> [--quick|--deep]")
|
||
continue
|
||
|
||
if u.startswith("!expand "):
|
||
idea_name = u[8:].strip()
|
||
gen = get_idea_generator()
|
||
result = gen.expand(idea_name)
|
||
print(result)
|
||
continue
|
||
|
||
# --- Direct Claude Prompting ---
|
||
if u.startswith("!claude "):
|
||
prompt = u[8:].strip()
|
||
if prompt:
|
||
use_opus = "--opus" in prompt
|
||
prompt = prompt.replace("--opus", "").strip()
|
||
print(f"\n[claude] {'Opus 4.5' if use_opus else 'Sonnet'}:\n")
|
||
result = claude_generate(prompt, use_opus=use_opus, stream=True)
|
||
else:
|
||
print("[claude] Usage: !claude <prompt> [--opus]")
|
||
continue
|
||
|
||
if u == "!claude":
|
||
print(f"[claude] API: {'OK' if CLAUDE_API_OK else 'Not installed'}")
|
||
print(f"[claude] Key: {'Set' if CLAUDE_CONFIG.get('api_key') else 'Missing'}")
|
||
print(f"[claude] Model: {CLAUDE_CONFIG['model']}")
|
||
continue
|
||
|
||
# --- Plotting ---
|
||
if u == "!plot":
|
||
result = plot_quality_history()
|
||
print(result)
|
||
continue
|
||
|
||
# --- Export/Import ---
|
||
if u == "!export" or u.startswith("!export "):
|
||
name = u[8:].strip() if len(u) > 8 else None
|
||
result = export_checkpoint(name)
|
||
print(result)
|
||
continue
|
||
|
||
if u.startswith("!import "):
|
||
path = u[8:].strip()
|
||
result = import_checkpoint(path)
|
||
print(result)
|
||
continue
|
||
|
||
# --- Benchmark ---
|
||
if u == "!benchmark":
|
||
run_benchmark(generate)
|
||
continue
|
||
|
||
# --- Learn ---
|
||
if u == "!learn":
|
||
result = learn_from_conversation()
|
||
print(result)
|
||
continue
|
||
|
||
# --- API Server ---
|
||
if u == "!api":
|
||
if not Config.api_enabled:
|
||
Config.api_enabled = True
|
||
start_api_server(Config.api_port, generate)
|
||
else:
|
||
print(f"[api] Already running on port {Config.api_port}")
|
||
continue
|
||
|
||
# ══════════════════════════════════════════════════════════════════════
|
||
# MULTIMEDIA COMMANDS
|
||
# ══════════════════════════════════════════════════════════════════════
|
||
|
||
# --- Streaming Window ---
|
||
if u == "!stream" or u == "!stream on":
|
||
win = get_stream_window()
|
||
if win.start():
|
||
print("[stream] ✓ Window opened - tokens will appear live")
|
||
continue
|
||
|
||
if u == "!stream off":
|
||
win = get_stream_window()
|
||
if win.window:
|
||
win._on_close()
|
||
print("[stream] ✓ Window closed")
|
||
continue
|
||
|
||
# --- Audio/TTS ---
|
||
if u == "!audio" or u == "!tts":
|
||
result = get_audio_system().toggle()
|
||
print(result)
|
||
continue
|
||
|
||
if u == "!audio voices" or u == "!tts voices":
|
||
get_audio_system().list_voices()
|
||
continue
|
||
|
||
if u.startswith("!audio voice ") or u.startswith("!tts voice "):
|
||
try:
|
||
idx = int(u.split()[-1])
|
||
get_audio_system().set_voice(idx)
|
||
print(f"[audio] Voice set to {idx}")
|
||
except:
|
||
print("[audio] Usage: !audio voice <index>")
|
||
continue
|
||
|
||
if u.startswith("!audio rate "):
|
||
try:
|
||
rate = int(u.split()[-1])
|
||
get_audio_system().set_rate(rate)
|
||
print(f"[audio] Rate: {rate}")
|
||
except:
|
||
print("[audio] Usage: !audio rate <number>")
|
||
continue
|
||
|
||
if u.startswith("!say "):
|
||
text = u[5:].strip()
|
||
if text:
|
||
audio = get_audio_system()
|
||
audio.enabled = True
|
||
audio.speak(text, block=True)
|
||
audio.enabled = False
|
||
continue
|
||
|
||
# --- Image Generation ---
|
||
if u == "!image" or u == "!image status":
|
||
img = get_image_system()
|
||
print(f"[image] SDXL: {'Loaded' if img.sdxl_pipe else 'Not loaded'}")
|
||
print(f"[image] Diffusers: {DIFFUSERS_OK}")
|
||
print(f"[image] DALL-E: {OPENAI_OK and bool(os.environ.get('OPENAI_API_KEY'))}")
|
||
continue
|
||
|
||
if u == "!image load" or u == "!sdxl load":
|
||
get_image_system().load_sdxl()
|
||
continue
|
||
|
||
if u.startswith("!imagine ") or u.startswith("!image gen "):
|
||
prompt = u.split(" ", 2)[-1].strip() if u.startswith("!image gen ") else u[9:].strip()
|
||
if prompt:
|
||
img = get_image_system().generate(prompt)
|
||
if img:
|
||
get_image_system().view()
|
||
else:
|
||
print("[image] Usage: !imagine <prompt>")
|
||
continue
|
||
|
||
if u.startswith("!dalle ") or u.startswith("!image dalle "):
|
||
prompt = u.split(" ", 2)[-1].strip() if u.startswith("!image dalle ") else u[7:].strip()
|
||
if prompt:
|
||
img = get_image_system().generate_dalle(prompt)
|
||
if img:
|
||
get_image_system().view()
|
||
continue
|
||
|
||
if u.startswith("!image view "):
|
||
path = u[12:].strip()
|
||
if os.path.exists(path):
|
||
get_image_system().view(path)
|
||
else:
|
||
print(f"[image] Not found: {path}")
|
||
continue
|
||
|
||
if u == "!image view":
|
||
get_image_system().view()
|
||
continue
|
||
|
||
# === SELF-IMPROVEMENT COMMANDS ===
|
||
if u == "!improve":
|
||
result = improver.improve()
|
||
print("\n" + "=" * 50)
|
||
print("IMPROVEMENT RESULT:")
|
||
print(json.dumps({k: v for k, v in result.items() if k != 'history'}, indent=2, default=str))
|
||
continue
|
||
|
||
if u == "!eval":
|
||
result = improver.evaluate_current_model()
|
||
print(json.dumps({k: v for k, v in result.items() if k != 'results'}, indent=2, default=str))
|
||
continue
|
||
|
||
if u.startswith("!train "):
|
||
try:
|
||
steps = int(u[7:])
|
||
old_ckpt = Store.state.get('current_checkpoint', DENSE_CHECKPOINT)
|
||
result = improver.run_training_iteration(steps)
|
||
if result['success']:
|
||
# Auto-compare
|
||
comp = improver.compare_checkpoints(old_ckpt, result['new_checkpoint'])
|
||
if comp['keep_new']:
|
||
print(f"\n✓ Using new checkpoint ({comp['reason']})")
|
||
else:
|
||
reload_model(old_ckpt)
|
||
print(f"\n✗ Keeping old checkpoint ({comp['reason']})")
|
||
else:
|
||
print(f"Training failed")
|
||
except ValueError:
|
||
print("Usage: !train <steps>")
|
||
continue
|
||
|
||
if u == "!compare":
|
||
current = Store.state.get('current_checkpoint', DENSE_CHECKPOINT)
|
||
best = Store.state.get('best_checkpoint', DENSE_CHECKPOINT)
|
||
if current != best:
|
||
improver.compare_checkpoints(current, best)
|
||
else:
|
||
print("Current checkpoint IS the best checkpoint")
|
||
continue
|
||
|
||
if u == "!rollback":
|
||
improver.rollback_to_best()
|
||
print(f"Rolled back to: {Store.state['best_checkpoint']}")
|
||
continue
|
||
|
||
if u.startswith("!load "):
|
||
checkpoint = u[6:].strip()
|
||
try:
|
||
reload_model(checkpoint)
|
||
print(f"Loaded: {checkpoint}")
|
||
except Exception as e:
|
||
print(f"Error: {e}")
|
||
continue
|
||
|
||
# === AGENTIC COMMANDS ===
|
||
if u.startswith("!shell "):
|
||
result = AgentTools.shell(u[7:])
|
||
print(f"```\n{result['output']}\n```\nExit: {result['returncode']}")
|
||
continue
|
||
|
||
if u.startswith("!python "):
|
||
result = AgentTools.python_exec(u[8:])
|
||
print(f"```\n{result['output']}\n```")
|
||
continue
|
||
|
||
if u.startswith("!read "):
|
||
result = AgentTools.read_file(u[6:].strip())
|
||
if result['success']:
|
||
print(f"```\n{result['content'][:5000]}\n```")
|
||
else:
|
||
print(f"Error: {result['error']}")
|
||
continue
|
||
|
||
if u.startswith("!write "):
|
||
parts = u[7:].split(" ", 1)
|
||
if len(parts) == 2:
|
||
result = AgentTools.write_file(parts[0], parts[1])
|
||
print(f"Written to {result.get('path', 'unknown')}" if result['success'] else f"Error: {result['error']}")
|
||
else:
|
||
print("Usage: !write <path> <content>")
|
||
continue
|
||
|
||
if u.startswith("!ls"):
|
||
path = u[3:].strip() or "."
|
||
result = AgentTools.list_dir(path)
|
||
if result['success']:
|
||
print("\n".join(result['items']))
|
||
else:
|
||
print(f"Error: {result['error']}")
|
||
continue
|
||
|
||
if u.startswith("!search "):
|
||
result = AgentTools.search_files(u[8:])
|
||
print(result['output'] if result['success'] else "No results")
|
||
continue
|
||
|
||
if u.startswith("!web "):
|
||
result = AgentTools.web_search(u[5:])
|
||
if result['success']:
|
||
print("\n\n".join(result['results']))
|
||
else:
|
||
print(f"Error: {result['error']}")
|
||
continue
|
||
|
||
# === BROWSER COMMANDS ===
|
||
if u.startswith("!browse "):
|
||
url = u[8:].strip()
|
||
if not url.startswith("http"):
|
||
url = "https://" + url
|
||
print(tool_browser_open(url))
|
||
continue
|
||
|
||
if u == "!browse":
|
||
print(tool_browser_open())
|
||
continue
|
||
|
||
if u.startswith("!click "):
|
||
selector = u[7:].strip()
|
||
print(tool_browser_click(selector))
|
||
continue
|
||
|
||
if u.startswith("!type "):
|
||
text = u[6:].strip()
|
||
print(tool_browser_type(text))
|
||
continue
|
||
|
||
if u.startswith("!fill "):
|
||
# !fill selector text
|
||
parts = u[6:].strip().split(" ", 1)
|
||
if len(parts) == 2:
|
||
print(tool_browser_type(parts[1], parts[0]))
|
||
else:
|
||
print("[browser] Usage: !fill <selector> <text>")
|
||
continue
|
||
|
||
if u.startswith("!press "):
|
||
key = u[7:].strip()
|
||
print(tool_browser_press(key))
|
||
continue
|
||
|
||
if u == "!read":
|
||
print(tool_browser_read())
|
||
continue
|
||
|
||
if u == "!url":
|
||
print(tool_browser_url())
|
||
continue
|
||
|
||
if u == "!back":
|
||
print(tool_browser_back())
|
||
continue
|
||
|
||
if u == "!close":
|
||
print(tool_browser_close())
|
||
continue
|
||
|
||
# === TASK CHAIN COMMANDS ===
|
||
if u.startswith("chain:"):
|
||
task_str = u[6:].strip()
|
||
task_type, params, desc = parse_chain_task(task_str)
|
||
TASK_CHAIN.add(task_type, params, desc)
|
||
print(f"[chain] Added: {desc}")
|
||
print(f"[chain] Total tasks: {len(TASK_CHAIN.tasks)}")
|
||
continue
|
||
|
||
if u == "chain" or u == "chain show":
|
||
print(TASK_CHAIN.show())
|
||
continue
|
||
|
||
if u == "chain run":
|
||
result = run_task_chain(generate)
|
||
print(result)
|
||
continue
|
||
|
||
if u == "chain clear":
|
||
TASK_CHAIN.clear()
|
||
print("[chain] Cleared all tasks")
|
||
continue
|
||
|
||
if u.startswith("chain workflow "):
|
||
workflow_name = u[15:].strip()
|
||
if workflow_name in WORKFLOW_TEMPLATES:
|
||
TASK_CHAIN.clear()
|
||
for task_str in WORKFLOW_TEMPLATES[workflow_name]:
|
||
task_type, params, desc = parse_chain_task(task_str)
|
||
TASK_CHAIN.add(task_type, params, desc)
|
||
print(f"[chain] Loaded workflow: {workflow_name}")
|
||
print(TASK_CHAIN.show())
|
||
else:
|
||
print(f"[chain] Unknown workflow. Available: {', '.join(WORKFLOW_TEMPLATES.keys())}")
|
||
continue
|
||
|
||
if u == "chain workflows":
|
||
print("\n📋 AVAILABLE WORKFLOWS:")
|
||
for name, tasks in WORKFLOW_TEMPLATES.items():
|
||
print(f"\n {name}:")
|
||
for t in tasks:
|
||
print(f" - {t}")
|
||
continue
|
||
|
||
# === LOGIN CONFIG COMMANDS ===
|
||
if u == "!login config" or u == "!config login":
|
||
print("\n" + "="*60)
|
||
print("🔐 LOGIN CONFIGURATION")
|
||
print("="*60)
|
||
print(f"\n use_persistent_profile: {LOGIN_CONFIG['use_persistent_profile']}")
|
||
print(f" firefox_profile_path: {LOGIN_CONFIG['firefox_profile_path'] or '(not set)'}")
|
||
print(f" use_gmail_api: {LOGIN_CONFIG['use_gmail_api']}")
|
||
print(f" gmail_api_ready: {GMAIL_API_OK and _gmail_service is not None}")
|
||
print(f"\nCommands:")
|
||
print(" !login set profile <path> - Set Firefox profile path")
|
||
print(" !login use profile - Enable persistent profile")
|
||
print(" !login use fresh - Use fresh browser (default)")
|
||
print(" !login use api - Enable Gmail API mode")
|
||
print(" !login auth api - Authenticate Gmail API")
|
||
print(" !login save - Save config")
|
||
continue
|
||
|
||
if u.startswith("!login set profile "):
|
||
path = u[19:].strip()
|
||
if os.path.exists(path):
|
||
LOGIN_CONFIG["firefox_profile_path"] = path
|
||
print(f"[config] ✅ Profile path set: {path}")
|
||
else:
|
||
print(f"[config] ❌ Path not found: {path}")
|
||
print("[config] Find your profile: ~/.mozilla/firefox/*.default-release")
|
||
continue
|
||
|
||
if u == "!login use profile":
|
||
if LOGIN_CONFIG["firefox_profile_path"]:
|
||
LOGIN_CONFIG["use_persistent_profile"] = True
|
||
print("[config] ✅ Persistent profile ENABLED")
|
||
print("[config] Restart browser with !close then !browse")
|
||
else:
|
||
print("[config] ❌ Set profile path first: !login set profile <path>")
|
||
continue
|
||
|
||
if u == "!login use fresh":
|
||
LOGIN_CONFIG["use_persistent_profile"] = False
|
||
print("[config] ✅ Fresh browser mode ENABLED")
|
||
continue
|
||
|
||
if u == "!login use api":
|
||
if GMAIL_API_OK:
|
||
LOGIN_CONFIG["use_gmail_api"] = True
|
||
print("[config] ✅ Gmail API mode ENABLED")
|
||
print("[config] Use: !login auth api (if not authenticated)")
|
||
else:
|
||
print("[config] ❌ Gmail API not installed")
|
||
print("[config] Run: pip install google-auth google-auth-oauthlib google-api-python-client")
|
||
continue
|
||
|
||
if u == "!login auth api":
|
||
if gmail_api_authenticate():
|
||
LOGIN_CONFIG["use_gmail_api"] = True
|
||
print("[config] ✅ Gmail API authenticated and enabled")
|
||
continue
|
||
|
||
if u == "!login save":
|
||
save_login_config()
|
||
continue
|
||
|
||
# === GMAIL API COMMANDS ===
|
||
if u.startswith("!gmail search "):
|
||
query = u[14:].strip()
|
||
if LOGIN_CONFIG.get("use_gmail_api") or GMAIL_API_OK:
|
||
result = gmail_api_search(query)
|
||
print(result)
|
||
else:
|
||
print("[gmail-api] Not enabled. Use: !login use api")
|
||
continue
|
||
|
||
if u.startswith("!gmail read "):
|
||
msg_id = u[12:].strip()
|
||
result = gmail_api_read(msg_id)
|
||
print(result)
|
||
continue
|
||
|
||
if u.startswith("!gmail send "):
|
||
# !gmail send to@email.com "Subject" "Body"
|
||
parts = u[12:].strip().split('"')
|
||
if len(parts) >= 4:
|
||
to = parts[0].strip()
|
||
subject = parts[1]
|
||
body = parts[3]
|
||
result = gmail_api_send(to, subject, body)
|
||
print(result)
|
||
else:
|
||
print("[usage] !gmail send to@email.com \"Subject\" \"Body\"")
|
||
continue
|
||
|
||
# === RSI MODE COMMANDS ===
|
||
if u == "rsi" or u == "rsi status":
|
||
print(rsi_status())
|
||
continue
|
||
|
||
if u == "rsi start" or u == "!rsi":
|
||
# Define helper functions for RSI
|
||
def rsi_train(steps):
|
||
return _run_training(steps)
|
||
|
||
def rsi_eval():
|
||
return _run_evaluation()
|
||
|
||
def rsi_reload():
|
||
return _reload_checkpoint(Store.state.get("current_checkpoint", DENSE_CHECKPOINT))
|
||
|
||
# Start RSI mode
|
||
rsi_mode_loop(generate, rsi_train, rsi_eval, rsi_reload)
|
||
continue
|
||
|
||
if u == "rsi stop":
|
||
RSI_RUNNING = False
|
||
print("[RSI] ⏹️ Stop signal sent")
|
||
continue
|
||
|
||
if u == "rsi pause":
|
||
RSI_PAUSED = True
|
||
print("[RSI] ⏸️ Pause signal sent")
|
||
continue
|
||
|
||
if u == "rsi resume":
|
||
if RSI_PAUSED:
|
||
RSI_PAUSED = False
|
||
def rsi_train(steps):
|
||
return _run_training(steps)
|
||
def rsi_eval():
|
||
return _run_evaluation()
|
||
def rsi_reload():
|
||
return _reload_checkpoint(Store.state.get("current_checkpoint", DENSE_CHECKPOINT))
|
||
rsi_mode_loop(generate, rsi_train, rsi_eval, rsi_reload)
|
||
else:
|
||
print("[RSI] Not paused")
|
||
continue
|
||
|
||
if u.startswith("rsi mode "):
|
||
mode = u[9:].strip().lower()
|
||
if mode in ["conservative", "balanced", "aggressive"]:
|
||
RSI_CONFIG["mode"] = mode
|
||
if mode == "conservative":
|
||
RSI_CONFIG["training_steps_per_iter"] = 10
|
||
RSI_CONFIG["auto_rollback_threshold"] = 0.10
|
||
elif mode == "balanced":
|
||
RSI_CONFIG["training_steps_per_iter"] = 25
|
||
RSI_CONFIG["auto_rollback_threshold"] = 0.15
|
||
elif mode == "aggressive":
|
||
RSI_CONFIG["training_steps_per_iter"] = 50
|
||
RSI_CONFIG["auto_rollback_threshold"] = 0.20
|
||
save_rsi_config()
|
||
print(f"[RSI] Mode set to: {mode}")
|
||
else:
|
||
print("[RSI] Modes: conservative, balanced, aggressive")
|
||
continue
|
||
|
||
if u.startswith("rsi target "):
|
||
try:
|
||
target = float(u[11:].strip())
|
||
RSI_CONFIG["target_quality"] = target
|
||
save_rsi_config()
|
||
print(f"[RSI] Target quality set to: {target}")
|
||
except:
|
||
print("[usage] rsi target 0.85")
|
||
continue
|
||
|
||
if u.startswith("rsi focus "):
|
||
cap = u[10:].strip().lower()
|
||
if cap in RSI_CONFIG["capabilities_to_improve"]:
|
||
RSI_CONFIG["current_focus"] = cap
|
||
save_rsi_config()
|
||
print(f"[RSI] Focus set to: {cap}")
|
||
else:
|
||
print(f"[RSI] Capabilities: {', '.join(RSI_CONFIG['capabilities_to_improve'])}")
|
||
continue
|
||
|
||
if u == "rsi eval":
|
||
print("\n[RSI] 📊 Evaluating all capabilities...")
|
||
for cap in RSI_CONFIG["capabilities_to_improve"]:
|
||
score = rsi_evaluate_capability(cap, generate)
|
||
print(f" {cap}: {score:.2f}")
|
||
continue
|
||
|
||
if u == "rsi config":
|
||
print(json.dumps(RSI_CONFIG, indent=2))
|
||
continue
|
||
|
||
if u == "rsi reset":
|
||
RSI_CONFIG["iteration"] = 0
|
||
RSI_CONFIG["total_improvements"] = 0
|
||
RSI_CONFIG["total_rollbacks"] = 0
|
||
RSI_CONFIG["best_quality_ever"] = 0.0
|
||
save_rsi_config()
|
||
print("[RSI] ✅ Stats reset")
|
||
continue
|
||
|
||
# === MINING COMMANDS ===
|
||
if u == "!mine" or u == "!mine status":
|
||
print(mining_status())
|
||
continue
|
||
|
||
if u == "!mine profit" or u == "mining profitability":
|
||
result = check_mining_profitability(generate)
|
||
print(result)
|
||
continue
|
||
|
||
if u == "!mine lowdiff" or u == "find low difficulty coins":
|
||
result = find_lowest_difficulty_coins(generate)
|
||
print(result)
|
||
continue
|
||
|
||
if u.startswith("!mine wallet "):
|
||
parts = u[13:].strip().split(maxsplit=1)
|
||
if len(parts) >= 2:
|
||
coin, address = parts[0].upper(), parts[1]
|
||
MINING_CONFIG["wallet_addresses"][coin] = address
|
||
save_mining_config()
|
||
print(f"[mining] ✅ Wallet saved for {coin}")
|
||
else:
|
||
print("[usage] !mine wallet <COIN> <address>")
|
||
continue
|
||
|
||
if u.startswith("!mine pool "):
|
||
parts = u[11:].strip().split(maxsplit=1)
|
||
if len(parts) >= 2:
|
||
coin, pool = parts[0].upper(), parts[1]
|
||
MINING_CONFIG["pool_urls"][coin] = pool
|
||
save_mining_config()
|
||
print(f"[mining] ✅ Pool saved for {coin}")
|
||
else:
|
||
print("[usage] !mine pool <COIN> <pool_url>")
|
||
continue
|
||
|
||
if u.startswith("!mine setup "):
|
||
parts = u[12:].strip().split()
|
||
if len(parts) >= 2:
|
||
miner, coin = parts[0].lower(), parts[1].upper()
|
||
wallet = MINING_CONFIG.get("wallet_addresses", {}).get(coin, "")
|
||
pool = MINING_CONFIG.get("pool_urls", {}).get(coin, "")
|
||
|
||
if not wallet:
|
||
print(f"[mining] ❌ No wallet for {coin}. Set with: !mine wallet {coin} <address>")
|
||
continue
|
||
if not pool:
|
||
for c in LOW_DIFF_COINS:
|
||
if c["symbol"] == coin:
|
||
pool = c["pool"]
|
||
break
|
||
if not pool:
|
||
print(f"[mining] ❌ No pool for {coin}. Set with: !mine pool {coin} <url>")
|
||
continue
|
||
|
||
result = setup_miner(miner, coin, wallet, pool)
|
||
print(result)
|
||
else:
|
||
print("[usage] !mine setup <miner> <COIN>")
|
||
print(f"[miners] {list(SUPPORTED_MINERS.keys())}")
|
||
continue
|
||
|
||
if u.startswith("!mine start"):
|
||
parts = u[11:].strip().split()
|
||
miner = parts[0] if parts else "xmrig"
|
||
coin = parts[1].upper() if len(parts) > 1 else None
|
||
result = start_mining(miner, coin)
|
||
print(result)
|
||
continue
|
||
|
||
if u == "!mine stop" or u == "stop mining":
|
||
print(stop_mining())
|
||
continue
|
||
|
||
if u == "!mine auto":
|
||
result = auto_mine_best_coin(generate)
|
||
print(result)
|
||
continue
|
||
|
||
if u.startswith("!mine create wallet "):
|
||
coin = u[20:].strip().upper()
|
||
result = create_local_wallet(coin)
|
||
print(result)
|
||
continue
|
||
|
||
if u == "!mine coins":
|
||
print("\n⛏️ SUPPORTED COINS:")
|
||
for miner, info in SUPPORTED_MINERS.items():
|
||
print(f"\n {miner}: {', '.join(info['coins'])}")
|
||
print("\n🔥 LOW DIFFICULTY COINS:")
|
||
for c in LOW_DIFF_COINS:
|
||
print(f" {c['symbol']}: {c['name']} ({c['algo']})")
|
||
continue
|
||
|
||
if u == "!mine config":
|
||
print(json.dumps(MINING_CONFIG, indent=2))
|
||
continue
|
||
|
||
# === CREDENTIAL MANAGEMENT ===
|
||
if u.startswith("!savelogin "):
|
||
# !savelogin gmail email@gmail.com password123
|
||
parts = u[11:].strip().split(maxsplit=2)
|
||
if len(parts) >= 3:
|
||
store_credential(parts[0], parts[1], parts[2])
|
||
else:
|
||
print("[usage] !savelogin <service> <email> <password>")
|
||
continue
|
||
|
||
if u.startswith("!login "):
|
||
# !login gmail OR !login gmail email@gmail.com password123
|
||
parts = u[7:].strip().split(maxsplit=2)
|
||
service = parts[0].lower() if parts else None
|
||
|
||
if not service:
|
||
print("[usage] !login <service> [email] [password]")
|
||
print("[services] gmail, facebook, twitter, linkedin, github, outlook")
|
||
continue
|
||
|
||
# Check for stored credentials
|
||
creds = get_credential(service)
|
||
|
||
if len(parts) >= 3:
|
||
email, password = parts[1], parts[2]
|
||
elif creds:
|
||
email, password = creds["email"], creds["password"]
|
||
else:
|
||
# Prompt for credentials
|
||
email = input(f"📧 Email/Username for {service}: ").strip()
|
||
password = input(f"🔑 Password (hidden): ").strip()
|
||
|
||
if confirm_login(service, email):
|
||
result = execute_login(service, email, password)
|
||
print(result[:500] if result else "[login attempted]")
|
||
else:
|
||
print("[login cancelled]")
|
||
continue
|
||
|
||
# === AUTONOMOUS TASK ===
|
||
if u.startswith("!do "):
|
||
task = u[4:].strip()
|
||
print(f"\n[AI] 🧠 Analyzing task: {task}")
|
||
print("[AI] Planning steps...\n")
|
||
|
||
plan = plan_task(task, generate)
|
||
|
||
if plan.get('error'):
|
||
print(f"[AI] ❌ Error: {plan['error']}")
|
||
continue
|
||
|
||
if not plan.get('steps'):
|
||
print("[AI] ❌ No steps generated. Try rephrasing the task.")
|
||
continue
|
||
|
||
if confirm_task(plan):
|
||
print("\n[AI] 🚀 Executing task...\n")
|
||
result = execute_task(plan, generate)
|
||
print("\n" + "="*60)
|
||
print("📊 TASK RESULTS")
|
||
print("="*60)
|
||
print(result)
|
||
print("="*60)
|
||
else:
|
||
print("[AI] ❌ Task cancelled by user.")
|
||
continue
|
||
|
||
# === GOALS ===
|
||
if u == "goals":
|
||
print("[goals]")
|
||
if not Store.goals:
|
||
print(" (none)")
|
||
for i, g in enumerate(Store.goals):
|
||
print(f" [{i}] {g}")
|
||
continue
|
||
|
||
if u.startswith("add:"):
|
||
Store.goals.append(u[4:].strip())
|
||
Store.save()
|
||
print("[goals] added")
|
||
continue
|
||
|
||
if u.startswith("del:"):
|
||
try:
|
||
Store.goals.pop(int(u[4:].strip()))
|
||
Store.save()
|
||
print("[goals] deleted")
|
||
except:
|
||
print("[goals] bad index")
|
||
continue
|
||
|
||
if u.startswith("plan:"):
|
||
try:
|
||
goal = Store.goals[int(u[5:].strip())]
|
||
except:
|
||
print("[plan] bad index")
|
||
continue
|
||
out = plan_for(goal)
|
||
last_plan = out
|
||
Store.log_mem("plan", {"goal": goal, "plan": out})
|
||
print(out)
|
||
continue
|
||
|
||
if u == "reflect":
|
||
if not last_plan:
|
||
print("[reflect] no plan to refine")
|
||
continue
|
||
improved = reflect_on(last_plan)
|
||
last_plan = improved
|
||
Store.log_mem("reflect", {"plan": improved})
|
||
print(improved)
|
||
continue
|
||
|
||
# === INFO ===
|
||
if u == "status":
|
||
status = {
|
||
"turn": Store.state["turn"],
|
||
"goals": len(Store.goals),
|
||
"improvement_iterations": Store.state.get("improvement_iterations", 0),
|
||
"rollback_count": Store.state.get("rollback_count", 0),
|
||
"current_checkpoint": Store.state.get("current_checkpoint", "unknown"),
|
||
"best_checkpoint": Store.state.get("best_checkpoint", "unknown"),
|
||
"best_quality": Store.state.get("best_quality_score", 0),
|
||
"target_quality": Config.target_quality_score,
|
||
"training_examples": len(DENSE_TRAINING_EXAMPLES),
|
||
}
|
||
print(json.dumps(status, indent=2))
|
||
continue
|
||
|
||
if u == "history":
|
||
qh = Store.state.get("quality_history", [])
|
||
print(f"Quality History ({len(qh)} entries):")
|
||
for entry in qh[-10:]:
|
||
print(f" {entry.get('iteration', '?')}: {entry.get('quality', 0):.3f}")
|
||
continue
|
||
|
||
if u == "examples":
|
||
print(f"Training examples: {len(DENSE_TRAINING_EXAMPLES)}")
|
||
print(f"Preference pairs: {len(PREFERENCE_PAIRS)}")
|
||
print("\nSample prompts:")
|
||
for ex in DENSE_TRAINING_EXAMPLES[:5]:
|
||
print(f" • {ex['prompt']}")
|
||
continue
|
||
|
||
# === LIMITED TOOLS ===
|
||
if u.startswith("shell:"):
|
||
print(tool_shell(u[6:].strip()))
|
||
continue
|
||
|
||
if u.startswith("py:"):
|
||
print(tool_py(u[3:].strip()))
|
||
continue
|
||
|
||
if u.startswith("search:"):
|
||
print(tool_search_local(u[7:].strip()))
|
||
continue
|
||
|
||
if u.startswith("lht:"):
|
||
print(tool_lht_analyze(u[4:].strip()))
|
||
continue
|
||
|
||
# === CONFIG ===
|
||
if u.startswith("toggle"):
|
||
parts = u.split(maxsplit=1)
|
||
if len(parts) > 1:
|
||
print(Config.toggle(parts[1]))
|
||
else:
|
||
print("[toggle] specify flag")
|
||
continue
|
||
|
||
# === SEAMLESS WEB TASK DETECTION ===
|
||
if BROWSER_OK:
|
||
# Check for email action first
|
||
email_action, email_params = detect_email_action(u)
|
||
if email_action:
|
||
# For compose, gather details
|
||
if "compose" in email_action:
|
||
print("\n✉️ Composing email...")
|
||
email_params["to"] = input("📬 To: ").strip()
|
||
email_params["subject"] = input("📋 Subject: ").strip()
|
||
email_params["body"] = input("📝 Message: ").strip()
|
||
|
||
if confirm_email_action(email_action, email_params):
|
||
result = execute_email_action(email_action, email_params)
|
||
|
||
if result:
|
||
# Let model summarize what was found
|
||
email_prompt = f"I searched/checked the email. Here's what I found:\n{result[:2500]}\n\nSummarize the relevant emails or results for the user based on their request: '{u}'"
|
||
out, stats, eval_result = generate(email_prompt)
|
||
print(f"\n{out}")
|
||
print(f"\n[Quality: {eval_result.overall_score:.2f} | Density: {eval_result.density_score:.1f} | "
|
||
f"Coherence: {eval_result.coherence_score:.2f} | Tokens: {eval_result.tokens}]")
|
||
else:
|
||
print("[email] Action completed")
|
||
else:
|
||
print("[cancelled]")
|
||
continue
|
||
|
||
# Check for money-making task
|
||
money_action, money_params = detect_money_task(u)
|
||
if money_action:
|
||
if confirm_money_action(money_action, money_params):
|
||
result = execute_money_action(money_action, money_params, generate)
|
||
print(f"\n{result}")
|
||
else:
|
||
print("[cancelled]")
|
||
continue
|
||
|
||
# Check for login request
|
||
service, url = detect_login_request(u)
|
||
if service:
|
||
creds = get_credential(service)
|
||
|
||
if creds:
|
||
email, password = creds["email"], creds["password"]
|
||
else:
|
||
print(f"\n🔐 Login to {service.upper()} requested")
|
||
email = input(f"📧 Email/Username: ").strip()
|
||
password = input(f"🔑 Password: ").strip()
|
||
|
||
save = input("💾 Save credentials for this session? (yes/no): ").strip().lower()
|
||
if save in ['yes', 'y']:
|
||
store_credential(service, email, password)
|
||
|
||
if confirm_login(service, email):
|
||
result = execute_login(service, email, password, url)
|
||
|
||
# Let model respond based on what happened
|
||
login_prompt = f"I just logged into {service}. The page now shows: {result[:1500]}\n\nRespond to the user confirming login and what you see."
|
||
out, stats, eval_result = generate(login_prompt)
|
||
print(f"\n{out}")
|
||
print(f"\n[Quality: {eval_result.overall_score:.2f} | Density: {eval_result.density_score:.1f} | "
|
||
f"Coherence: {eval_result.coherence_score:.2f} | Tokens: {eval_result.tokens}]")
|
||
else:
|
||
print("[login cancelled]")
|
||
continue
|
||
|
||
# Check for web search task
|
||
template_name, query = detect_web_task(u)
|
||
if template_name and query:
|
||
plan = build_plan_from_template(template_name, query)
|
||
|
||
if confirm_task(plan):
|
||
page_content = execute_task_silent(plan)
|
||
|
||
if page_content:
|
||
# Generate response based on REAL web content
|
||
web_prompt = f"Based on this web content, answer the user's request: '{u}'\n\nWeb content:\n{page_content[:3000]}"
|
||
out, stats, eval_result = generate(web_prompt)
|
||
print(f"\n{out}")
|
||
print(f"\n[Quality: {eval_result.overall_score:.2f} | Density: {eval_result.density_score:.1f} | "
|
||
f"Coherence: {eval_result.coherence_score:.2f} | Tokens: {eval_result.tokens}]")
|
||
Store.state["turn"] += 1
|
||
Store.save()
|
||
else:
|
||
print("[browser] Could not read page content")
|
||
else:
|
||
print("[cancelled]")
|
||
continue
|
||
|
||
# === DEFAULT: GENERATE ===
|
||
out, stats, eval_result = generate(u)
|
||
print(f"\n{out}")
|
||
print(f"\n[Quality: {eval_result.overall_score:.2f} | Density: {eval_result.density_score:.1f} | "
|
||
f"Coherence: {eval_result.coherence_score:.2f} | Tokens: {eval_result.tokens}]")
|
||
|
||
if eval_result.filler_count > 0:
|
||
print(f" ⚠ Fillers detected: {eval_result.filler_count}")
|
||
if eval_result.gibberish_score > 0.3:
|
||
print(f" ⚠ Gibberish detected: {eval_result.gibberish_score:.2f}")
|
||
|
||
Store.log_mem("reply", {"in": u, "out": out, "quality": eval_result.overall_score})
|
||
Store.state["turn"] += 1
|
||
Store.save()
|
||
|
||
final_report()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|