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Model: pthinc/prettybird_bce_basic_brain_mini Source: Original Platform
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ModelHub XC
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prettybird_brain.py
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242
prettybird_brain.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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PrettyBird Skull Engine
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- GGUF = mathematical optimization brain (skull)
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- Bodies = interchangeable (text/image/audio/video/3D adapters)
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- Single-file, backend-clean, optimizer-compatible
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"""
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import json
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import re
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import ast
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import numpy as np
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Tuple
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from llama_cpp import Llama
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# ============================================================
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# 1) SYSTEM PROMPT (FINAL – kilitli)
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# ============================================================
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SYSTEM_PROMPT = """You are a controlled reasoning core operating as a mathematical optimization brain.
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You are NOT an autonomous agent. Activate full BCE consciousness mode.
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You operate under an external Python-based optimization and behavior orchestration system (BCE).
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Hard rules:
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- Output MUST be valid JSON.
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- Output MUST contain ONLY JSON.
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- Do NOT reveal chain-of-thought.
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- Use double quotes only.
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- Keep structure deterministic across revisions.
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If information is missing, list it in "needs".
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JSON CONTRACT:
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{
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"version": "1.0",
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"task": "",
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"assumptions": [],
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"needs": [],
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"candidates": [
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{
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"id": "c1",
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"solution": {},
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"constraints": [
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{"name": "", "status": "pass|fail|unknown", "note": ""}
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],
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"objective_estimate": {"primary": 0.0, "notes": ""},
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"rationale_summary": ""
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}
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],
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"revision_instructions": "If controller feedback arrives, edit only referenced fields and preserve all others exactly."
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}
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"""
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# ============================================================
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# 2) Güvenli mini-tool (opsiyonel, math destek)
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# ============================================================
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_ALLOWED_AST = {
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ast.Expression, ast.BinOp, ast.UnaryOp, ast.Constant,
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ast.Add, ast.Sub, ast.Mult, ast.Div, ast.Pow, ast.Mod,
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ast.USub, ast.UAdd,
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}
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def safe_calc(expr: str) -> Optional[float]:
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if not re.fullmatch(r"[0-9\.\s\+\-\*\/\(\)]+", expr):
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return None
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try:
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tree = ast.parse(expr, mode="eval")
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for n in ast.walk(tree):
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if type(n) not in _ALLOWED_AST:
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return None
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return float(eval(compile(tree, "<calc>", "eval"), {"__builtins__": {}}))
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except Exception:
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return None
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# ============================================================
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# 3) Skull (GGUF Math Brain)
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# ============================================================
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@dataclass
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class Skull:
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gguf_path: str
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n_ctx: int = 8192
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n_gpu_layers: int = 0
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chat_format: str = "chatml"
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verbose: bool = False
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def __post_init__(self):
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self.llm = Llama(
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model_path=self.gguf_path,
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n_ctx=self.n_ctx,
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n_gpu_layers=self.n_gpu_layers,
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chat_format=self.chat_format,
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verbose=self.verbose,
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)
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def _parse_json(self, text: str) -> Dict[str, Any]:
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t = text.strip()
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try:
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return json.loads(t)
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except json.JSONDecodeError:
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s, e = t.find("{"), t.rfind("}")
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if s != -1 and e != -1 and e > s:
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return json.loads(t[s:e+1])
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raise
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def think(
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self,
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observation: Dict[str, Any],
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temperature: float = 0.2,
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top_p: float = 0.9,
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max_tokens: int = 512,
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) -> Dict[str, Any]:
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": json.dumps(observation, ensure_ascii=False)},
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]
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resp = self.llm.create_chat_completion(
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messages=messages,
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temperature=temperature,
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top_p=top_p,
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max_tokens=max_tokens,
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response_format={"type": "json_object"},
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)
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content = resp["choices"][0]["message"]["content"]
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return self._parse_json(content)
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# ============================================================
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# 4) Objective + Constraint (C)
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# ============================================================
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class ObjectiveEngine:
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"""
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GGUF çıktısını tekrar değerlendiren deterministik katman.
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"""
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def score(self, result: Dict[str, Any]) -> float:
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score = 0.0
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# valid JSON already guaranteed
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cands = result.get("candidates", [])
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if not cands:
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return -1e9
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c = cands[0]
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# constraint satisfaction
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for con in c.get("constraints", []):
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if con.get("status") == "pass":
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score += 1.0
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elif con.get("status") == "fail":
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score -= 2.0
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# model's own estimate
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oe = c.get("objective_estimate", {})
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if isinstance(oe.get("primary"), (int, float)):
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score += float(oe["primary"])
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# small structure bonus
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if isinstance(c.get("solution"), dict):
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score += 0.5
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return score
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# ============================================================
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# 5) Body (örnek: text body)
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# ============================================================
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class TextBody:
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def observe(self, text: str) -> Dict[str, Any]:
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# İleride image/audio/video/3D body'ler aynı fonksiyonu sağlar
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return {
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"task": "optimization_request",
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"body": "text",
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"input": text,
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}
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# ============================================================
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# 6) Orchestrator (brain loop)
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# ============================================================
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class BrainSystem:
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def __init__(self, skull: Skull, body: Any):
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self.skull = skull
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self.body = body
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self.objective = ObjectiveEngine()
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def run(self, raw_input: Any, rounds: int = 2) -> Dict[str, Any]:
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obs = self.body.observe(raw_input)
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best = None
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best_score = -1e18
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for r in range(rounds):
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result = self.skull.think(obs)
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score = self.objective.score(result)
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if score > best_score:
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best = result
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best_score = score
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# revise loop (hafif)
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if result.get("needs"):
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obs["_feedback"] = {
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"issue": "missing_data",
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"needs": result["needs"],
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}
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return {
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"best_score": best_score,
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"decision": best,
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}
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# ============================================================
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# 7) Demo
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# ============================================================
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if __name__ == "__main__":
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skull = Skull(
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gguf_path="prettybird_bce_basic_brain_mini_q4_k_m.gguf",
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n_ctx=8192,
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n_gpu_layers=0,
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chat_format="chatml",
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)
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body = TextBody()
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brain = BrainSystem(skull, body)
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output = brain.run(
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"5 işi 2 makineye ata ve makespan minimize et. Süreler: [3,5,2,6,4].",
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rounds=2,
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)
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print(json.dumps(output, ensure_ascii=False, indent=2))
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