from models.local_llm import run_llm
import logging

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Global conversation memory (in a real app, this should be session-based)
conversation_memory = []

def process_text(input_text: str) -> str:
    """
    Process user input through LLM with conversation memory.
    
    Args:
        input_text: User input text
        
    Returns:
        Generated response
    """
    # Add user input to memory
    conversation_memory.append({"user": input_text})
    
    # Create context from memory (last 5 exchanges for brevity)
    recent_memory = conversation_memory[-10:] if len(conversation_memory) > 10 else conversation_memory
    context = "\n".join([
        f"User: {m['user']}" if 'user' in m else f"Assistant: {m['assistant']}" 
        for m in recent_memory
    ])
    
    # Create prompt with context
    prompt = f"""You are a telecom AI assistant. You help users with questions about telecommunications, 
networks, phones, and related technologies. Be concise and helpful.

Conversation history:
{context}

Please provide your next response:"""
    
    # Get response from LLM
    logger.info(f"Sending prompt to LLM with context length: {len(context)}")
    response = run_llm(prompt)
    
    # Add response to memory
    conversation_memory.append({"assistant": response})
    
    return response

def clear_memory():
    """Clear the conversation memory."""
    global conversation_memory
    conversation_memory = []
    return True