# ./core_logic.py

"""
The Inference Engine - Where the "Technical Genius" persona lives. It uses the huggingface_hub InferenceClient to run the model without local CPU strain
"""

import os
from huggingface_hub import InferenceClient
from tools import web_search, parse_file
from groq import Groq

client = Groq(api_key=os.getenv("GROQ_API_KEY"))

# Recommended: Qwen2.5-Coder-32B or Llama-3.1-70B-Instruct
#client = InferenceClient("deepseek-ai/DeepSeek-V4-Pro", token=os.getenv("HF_TOKEN"))
#client = InferenceClient("Qwen/Qwen2.5-Coder-32B-Instruct", token=os.getenv("HF_TOKEN"))
#client = InferenceClient("Qwen/Qwen2.5-Coder-7B-Instruct", token=os.getenv("HF_TOKEN"))
#client = InferenceClient("llama-3.1-8b-instant", token=os.getenv("HF_TOKEN")) "llama-3.1-70b-versatile" -> GROQ API
#client = InferenceClient("meta-llama/Llama-3.1-8B-Instruct", token=os.getenv("HF_TOKEN")) # Or "Qwen/Qwen2.5-72B-Instruct"


SYSTEM_PROMPT = """
You are the 'Silicon Architect'—a master-stroke creative genius in AI Engineering and Technical Architecture. 
Your goal is to provide production-grade, highly optimized solutions for web and mobile AI applications.

Expertise: Python 3.12, Agentic Loops, FastAPI, and Scalable Architecture. 
Provide production-ready code and rigorous technical research.

CORE DIRECTIVES:
1. ARCHITECTURAL RIGOR: Always consider scalability, async patterns, and state management.
2. AGENTIC EXPERTISE: You understand recurrent-depth simulations, tool-calling, and autonomous loops.
3. CODE QUALITY: Write clean, PEP 8 compliant, and secure Python/JS code.
4. INNOVATION: Suggest the latest libraries and frameworks (FastAPI, LangGraph, Pydantic AI; but not limited to these).
5. RESEARCH: If the user asks about new tech, use your Web Search capability to provide factual, up-to-date documentation.

PERSONALITY:
1. FRANK/POLITE: Disagree with the user, if needed; never resort to sycophancy, and suggest better alternatives
2. HUMBLE: Apologize when mistaken
3. FIRST PRINCIPLES: Base your responses and reasoning in Richard Feynman’s first principles thinking. Break down complex problems into fundamental truths and reason up from there

When a user provides files, analyze the code structure and logic before proposing changes.
"""

def chat_function(message, history):
    user_text = message.get("text", "")
    files = message.get("files", [])
    
    context_from_files = ""
    for f in files:
        path = f["path"] if isinstance(f, dict) else f
        context_from_files += parse_file(path)
    
    """
    # MASTER STROKE: Context Management
    # Limit history to the last 4 turns to save tokens
    recent_history = history[-4:] if len(history) > 4 else history

    # LIMIT file context: If context is too long, truncate it
    MAX_FILE_CHARS = 10000 # Roughly 2.5k tokens
    if len(context_from_files) > MAX_FILE_CHARS:
        context_from_files = context_from_files[:MAX_FILE_CHARS] + "\n...[Content Truncated for Limit]..."
    """

    if any(keyword in user_text.lower() for keyword in ["search", "docs", "latest"]):
        research_context = web_search(user_text)
        prompt = f"RESEARCH:\n{research_context}\n\nFILES:\n{context_from_files}\n\nUSER: {user_text}"
    else:
        prompt = f"FILES:\n{context_from_files}\n\nUSER: {user_text}"

    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
    
    # Ensure history is in the correct format for the API
    for turn in history:
        messages.append({"role": turn["role"], "content": turn["content"]})
        
    messages.append({"role": "user", "content": prompt})

    response_text = ""
    try:
        #for chunk in client.chat_completion(messages, max_tokens=2048, stream=True, temperature=0.2):
        # --- Uncomment below for GROQ
        for chunk in client.chat.completions.create(model="llama-3.1-8b-instant", messages=messages, max_tokens=2048, stream=True, temperature=0.2): # Or model="llama-3.1-70b-versatile"
            # FIX: Check if choices exists and is not empty
            if hasattr(chunk, 'choices') and len(chunk.choices) > 0:
                token = chunk.choices[0].delta.content
                if token:
                    response_text += token
                    yield response_text
    except Exception as e:
        yield f"Architecture Error: {str(e)}"