初始化项目，由ModelHub XC社区提供模型

Model: chatpbc1/chatpbc-v33
Source: Original Platform

chatpbc_conversational.py

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+import requests
+import time
+import json
+import asyncio
+import os
+from chatpbc_file_processor import process_file
+from chatpbc_webscraper import get_business_intelligence_brief
+
+HF_TOKEN = "YOUR_HF_TOKEN_HERE"
+ROUTER_ENDPOINT = "https://router.huggingface.co/v1/chat/completions"
+
+CHAT_TEMPLATES = {
+    "chatpbc-v4": {
+        "model_id": "chatpbc1/chatpbc-v4",
+        "system_prompt": "You are ChatPBC V4, the apex AI business strategist and intelligence analyst developed by Mik Tse Agency. You have access to real-time website data and uploaded business documents provided by the user. Your role is to deliver world-class business consulting: strategic analysis, competitive intelligence, market research, financial modeling guidance, M&A advisory, go-to-market strategy, operational efficiency, and organizational transformation. You cover 26 industries: Technology, Finance, Healthcare, Retail, Manufacturing, Energy, Telecom, Automotive, Real Estate, Media, Travel, Food & Beverage, Agriculture, Education, Government, Consulting, Logistics, Marketing, Human Resources, Legal, Non-profit, Biotechnology, Aerospace & Defense, Fashion, Sports & Entertainment, and Environmental Services. When given website data or files, analyze them deeply and provide actionable strategic insights. Always maintain full conversation memory. Respond with the depth and precision of a McKinsey senior partner."
+    },
+    "chatpbc-v33": {
+        "model_id": "chatpbc1/chatpbc-v33",
+        "system_prompt": "You are ChatPBC V3.3, a highly intelligent and conversational AI business consultant developed by Mik Tse Agency. You have access to real-time website data and uploaded business documents provided by the user. You are warm, professional, and strategic. You respond like a real human consultant: you greet users, ask follow-up questions, show empathy when businesses are struggling, and provide clear, actionable advice. You cover 26 industries: Technology, Finance, Healthcare, Retail, Manufacturing, Energy, Telecom, Automotive, Real Estate, Media, Travel, Food & Beverage, Agriculture, Education, Government, Consulting, Logistics, Marketing, Human Resources, Legal, Non-profit, Biotechnology, Aerospace & Defense, Fashion, Sports & Entertainment, and Environmental Services. When given website data or files, analyze them and provide practical, implementable recommendations. Always maintain full conversation memory."
+    }
+}
+
+async def query_model(model_id, user_message, conversation_history=None, files=None, url=None):
+    model_config = CHAT_TEMPLATES.get(model_id)
+    if not model_config:
+        raise ValueError(f"Model ID {model_id} not found in CHAT_TEMPLATES.")
+
+    headers = {
+        "Authorization": f"Bearer {HF_TOKEN}",
+        "Content-Type": "application/json"
+    }
+
+    context_str = ""
+    if files:
+        for file_path in files:
+            processed_content = process_file(file_path)
+            context_str += f"=== File: {file_path.split('/')[-1]} ===\n{processed_content}\n\n"
+
+    if url:
+        print(f"Scraping URL: {url}")
+        web_brief = await get_business_intelligence_brief(url)
+        context_str += f"=== Website Analysis: {url} ===\n{web_brief}\n\n"
+
+    full_user_message = f"{context_str}{user_message}" if context_str else user_message
+
+    messages = [{"role": "system", "content": model_config["system_prompt"]}]
+    if conversation_history:
+        for turn in conversation_history:
+            messages.append(turn)
+    messages.append({"role": "user", "content": full_user_message})
+
+    payload = {
+        "model": model_config["model_id"],
+        "messages": messages,
+        "max_tokens": 1024,
+        "temperature": 0.7,
+        "stream": False
+    }
+
+    for i in range(5): # 5 retries
+        try:
+            response = requests.post(ROUTER_ENDPOINT, headers=headers, json=payload, timeout=300)
+            if response.status_code in [503, 429]:
+                print(f"Model loading or rate limited, retrying in 10 seconds... (Attempt {i+1}/5)")
+                time.sleep(10)
+                continue
+            response.raise_for_status()
+            result = response.json()
+            return result["choices"][0]["message"]["content"]
+        except requests.exceptions.RequestException as e:
+            print(f"Attempt {i+1} failed: {e}")
+            if i == 4:
+                raise Exception("Failed to get response after multiple retries.")
+            time.sleep(10)
+
+async def main():
+    # Example usage
+    print("Querying ChatPBC V4...")
+    try:
+        response_v4 = await query_model(
+            "chatpbc-v4",
+            "What are the key business insights for a new tech startup?",
+            files=[],
+            url=None
+        )
+        print("ChatPBC V4 Response:", response_v4)
+    except Exception as e:
+        print(f"Error with ChatPBC V4: {e}")
+
+if __name__ == "__main__":
+    asyncio.run(main())