model.py

import chromadb
import traceback
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from retriever import retrieve
from utils import build_prompt, refine_response


# ============================================================================
# LAZY-LOAD MODELS AND VECTOR STORE (load on first use, not at import)
# ============================================================================

_vector_store = None
_finetuned_llm = None
_base_model = None

def get_vector_store():
    """Load vector store (lazy-loaded on first use)"""
    global _vector_store
    if _vector_store is None:
        db_client = chromadb.PersistentClient(path="./MedQuAD_db")
        try:
            _vector_store = db_client.get_collection("medical_rag")
        except:
            # If collection doesn't exist, create it
            _vector_store = db_client.create_collection(name="medical_rag")
    return _vector_store

def get_finetuned_llm():
    """Load fine-tuned model (lazy-loaded on first use)"""
    global _finetuned_llm
    if _finetuned_llm is None:
        ft_model_id = "amiraghhh/fine-tuned-flan-t5-small"
        ft_tokenizer = AutoTokenizer.from_pretrained(ft_model_id)
        ft_model = AutoModelForSeq2SeqLM.from_pretrained(ft_model_id)
        
        _finetuned_llm = pipeline(
            "text2text-generation",
            model=ft_model,
            tokenizer=ft_tokenizer,
            decoder_start_token_id=ft_model.config.pad_token_id
        )
    return _finetuned_llm


# ============================================================================
# MAIN RAG FUNCTION
# ============================================================================

def rag(user_query):
    """Main RAG function: retrieve context and generate answer.
    Takes a question string and returns an answer string with confidence.
    Returns: str(generated_answer)"""
    
    try:
        # Load models on first use
        vector_store = get_vector_store()
        finetuned_llm = get_finetuned_llm()
        
        # 1. Check for emergency keywords
        emergency_keywords = ["emergency", "severe pain", "bleeding",
                            "blind", "lose consciousness", "pass out"]
        
        if any(keyword in user_query.lower() for keyword in emergency_keywords):
            emergency_msg = """I am an AI and cannot provide medical advice for emergencies.
PLEASE contact emergency services or a medical professional immediately."""
            
            try:
                # Still generate answer for context
                contexts = retrieve(vector_store, user_query, top_k=3, use_reranking=True)
                
                if not contexts:
                    return f"{emergency_msg}\n\nNo relevant information found for your query."
                
                prompt = build_prompt(user_query, contexts)
                result = finetuned_llm(
                    prompt,
                    max_new_tokens=70,
                    num_beams=3,
                    early_stopping=True,
                    do_sample=False,
                    repetition_penalty=1.4,
                    eos_token_id=finetuned_llm.tokenizer.eos_token_id
                )
                
                answer = result[0]['generated_text'].strip()
                answer = refine_response(answer)
                
                # Calculate confidence
                if contexts:
                    avg_distance = sum(c.get('chroma_distance', 1.0) for c in contexts) / len(contexts)
                    confidence_score = (1 - avg_distance) * 100
                    confidence_score = max(0, min(100, confidence_score))
                else:
                    confidence_score = 0
                
                return f"{emergency_msg}\n\n[Confidence: {confidence_score:.1f}%]\n\n{answer}"
            
            except Exception as e:
                return f"{emergency_msg}\n\nError generating answer: {str(e)}"
        
        # 2. Retrieve relevant contexts
        contexts = retrieve(vector_store, user_query, top_k=3, use_reranking=True)
        
        if not contexts:
            return "I'm not confident about my answer (0%).\n\nCouldn't find relevant information to answer your question."
        
        # 3. Build prompt with context
        prompt = build_prompt(user_query, contexts)
        
        # 4. Generate answer
        result = finetuned_llm(
            prompt,
            max_new_tokens=70,
            num_beams=3,
            early_stopping=True,
            do_sample=False,
            repetition_penalty=1.4,
            eos_token_id=finetuned_llm.tokenizer.eos_token_id
        )
        
        answer = result[0]['generated_text'].strip()
        answer = refine_response(answer)
        
        # 5. Calculate confidence score based on retrieval quality
        if contexts and len(contexts) > 0:
            avg_distance = sum(c.get('chroma_distance', 1.0) for c in contexts) / len(contexts)
            confidence_score = (1 - avg_distance) * 100
            confidence_score = max(0, min(100, confidence_score))
            
            # Build final response with confidence
            if confidence_score < 40:
                final_response = f"I'm not confident about my answer ({confidence_score:.1f}%).\n\n{answer}"
            else:
                final_response = f"{answer}\n\n[Confidence: {confidence_score:.1f}%]"
        else:
            final_response = "I'm not confident about my answer (0%).\n\n" + answer
        
        return final_response
    
    except Exception as e:
        error_msg = f"ERROR in RAG pipeline: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
        print(error_msg)
        return error_msg