app.py

import os
import pickle
import numpy as np
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from transformers import (
    AutoTokenizer, 
    AutoModelForSeq2SeqLM, 
    AutoModelForTokenClassification,
    AutoModelForCausalLM,
    pipeline
)
from sentence_transformers import SentenceTransformer, CrossEncoder
from sklearn.metrics.pairwise import cosine_similarity
from bs4 import BeautifulSoup
import nltk
import torch
import pandas as pd

# Initialize FastAPI app
app = FastAPI()

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Global variables for models and data
models = {}
data = {}

class QueryRequest(BaseModel):
    query: str
    language_code: int = 0

def init_nltk():
    """Initialize NLTK resources"""
    try:
        nltk.download('punkt', quiet=True)
        return True
    except Exception as e:
        print(f"Error initializing NLTK: {e}")
        return False

def load_models():
    """Initialize all required models"""
    try:
        print("Loading models...")
        
        # Set device
        device = "cuda" if torch.cuda.is_available() else "cpu"
        print(f"Device set to use {device}")
        
        # Embedding models
        models['embedding'] = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
        models['cross_encoder'] = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2', max_length=512)
        
        # Translation models
        models['ar_to_en_tokenizer'] = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ar-en")
        models['ar_to_en_model'] = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-ar-en")
        models['en_to_ar_tokenizer'] = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ar")
        models['en_to_ar_model'] = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-ar")
        
        # NER model
        models['bio_tokenizer'] = AutoTokenizer.from_pretrained("blaze999/Medical-NER")
        models['bio_model'] = AutoModelForTokenClassification.from_pretrained("blaze999/Medical-NER")
        models['ner_pipeline'] = pipeline("ner", model=models['bio_model'], tokenizer=models['bio_tokenizer'])
        
        # LLM model
        model_name = "M4-ai/Orca-2.0-Tau-1.8B"
        models['llm_tokenizer'] = AutoTokenizer.from_pretrained(model_name)
        models['llm_model'] = AutoModelForCausalLM.from_pretrained(model_name)
        
        print("Models loaded successfully")
        return True
    except Exception as e:
        print(f"Error loading models: {e}")
        return False

import pickle
import numpy as np
import os
from typing import Dict, Optional

def load_embeddings(embeddings_path: str = 'embeddings.pkl') -> Optional[Dict[str, np.ndarray]]:
    """
    Load embeddings from a pickle file containing a dictionary of numpy arrays.
    
    Args:
        embeddings_path (str): Path to the pickle file containing embeddings
        
    Returns:
        Optional[Dict[str, np.ndarray]]: Dictionary of embeddings or None if loading fails
    """
    if not os.path.exists(embeddings_path):
        print(f"Error: {embeddings_path} not found")
        return None
        
    try:
        with open(embeddings_path, 'rb') as f:
            embeddings = pickle.load(f)
            
        # Validate the loaded data
        if not isinstance(embeddings, dict):
            print(f"Error: Expected dict, got {type(embeddings)}")
            return None
            
        # Convert values to numpy arrays if they aren't already
        for key in embeddings:
            if not isinstance(embeddings[key], np.ndarray):
                embeddings[key] = np.array(embeddings[key])
        
        # Print sample for verification
        sample_key = next(iter(embeddings))
        print(f"Data type: {type(embeddings)}")
        print(f"First few keys and values:")
        print(f"Key: {sample_key}, Value: {embeddings[sample_key][:20]}")  # Show first 20 values
        print(f"Successfully loaded {len(embeddings)} embeddings")
        
        return embeddings
        
    except Exception as e:
        print(f"Error loading embeddings: {str(e)}")
        return None

def load_documents_data():
    """Load document data with error handling"""
    try:
        print("Loading documents data...")
        docs_path = 'finalcleaned_excel_file.xlsx'
        
        if not os.path.exists(docs_path):
            print(f"Error: {docs_path} not found")
            return False
            
        data['df'] = pd.read_excel(docs_path)
        print(f"Successfully loaded {len(data['df'])} document records")
        return True
    except Exception as e:
        print(f"Error loading documents data: {e}")
        data['df'] = pd.DataFrame()
        return False

def load_data():
    """Load all required data"""
    embeddings_success = load_embeddings()
    documents_success = load_documents_data()
    
    if not embeddings_success:
        print("Warning: Failed to load embeddings, falling back to basic functionality")
    if not documents_success:
        print("Warning: Failed to load documents data, falling back to basic functionality")
        
    return True

def translate_text(text, source_to_target='ar_to_en'):
    """Translate text between Arabic and English"""
    try:
        if source_to_target == 'ar_to_en':
            tokenizer = models['ar_to_en_tokenizer']
            model = models['ar_to_en_model']
        else:
            tokenizer = models['en_to_ar_tokenizer']
            model = models['en_to_ar_model']
            
        inputs = tokenizer(text, return_tensors="pt", truncation=True)
        outputs = model.generate(**inputs)
        return tokenizer.decode(outputs[0], skip_special_tokens=True)
    except Exception as e:
        print(f"Translation error: {e}")
        return text

def extract_entities(text):
    """Extract medical entities from text using NER"""
    try:
        results = models['ner_pipeline'](text)
        return list({result['word'] for result in results if result['entity'].startswith("B-")})
    except Exception as e:
        print(f"Error extracting entities: {e}")
        return []

def generate_answer(query, context, max_length=860, temperature=0.2):
    """Generate answer using LLM"""
    try:
        prompt = f"""
        As a medical expert, please provide a clear and accurate answer to the following question based solely on the provided context.
        
        Context: {context}
        
        Question: {query}
        
        Answer: Let me help you with accurate information from reliable medical sources."""

        inputs = models['llm_tokenizer'](prompt, return_tensors="pt", truncation=True)
        
        with torch.no_grad():
            outputs = models['llm_model'].generate(
                inputs.input_ids,
                max_length=max_length,
                num_return_sequences=1,
                temperature=temperature,
                do_sample=True,
                top_p=0.9,
                pad_token_id=models['llm_tokenizer'].eos_token_id
            )
        
        response = models['llm_tokenizer'].decode(outputs[0], skip_special_tokens=True)
        
        if "Answer:" in response:
            response = response.split("Answer:")[-1].strip()
        
        sentences = nltk.sent_tokenize(response)
        if sentences:
            return " ".join(sentences)
        return response

    except Exception as e:
        print(f"Error generating answer: {e}")
        return "I apologize, but I'm unable to generate an answer at this time. Please try again later."

def query_embeddings(query_embedding, n_results=5):
    """Find relevant documents using embedding similarity"""
    if not data['embeddings']:
        return []
        
    try:
        doc_ids = list(data['embeddings'].keys())
        doc_embeddings = np.array(list(data['embeddings'].values()))
        similarities = cosine_similarity(query_embedding, doc_embeddings).flatten()
        top_indices = similarities.argsort()[-n_results:][::-1]
        return [(doc_ids[i], similarities[i]) for i in top_indices]
    except Exception as e:
        print(f"Error in query_embeddings: {e}")
        return []

def retrieve_document_text(doc_id):
    """Retrieve document text from HTML file"""
    try:
        file_path = os.path.join('downloaded_articles', doc_id)
        if not os.path.exists(file_path):
            print(f"Warning: Document file not found: {file_path}")
            return ""
            
        with open(file_path, 'r', encoding='utf-8') as file:
            soup = BeautifulSoup(file, 'html.parser')
            return soup.get_text(separator=' ', strip=True)
    except Exception as e:
        print(f"Error retrieving document {doc_id}: {e}")
        return ""

def rerank_documents(query, doc_texts):
    """Rerank documents using cross-encoder"""
    try:
        pairs = [(query, doc) for doc in doc_texts]
        scores = models['cross_encoder'].predict(pairs)
        return scores
    except Exception as e:
        print(f"Error reranking documents: {e}")
        return np.zeros(len(doc_texts))

@app.get("/health")
async def health_check():
    """Health check endpoint"""
    status = {
        'status': 'healthy',
        'models_loaded': bool(models),
        'embeddings_loaded': bool(data.get('embeddings')),
        'documents_loaded': not data.get('df', pd.DataFrame()).empty
    }
    return status

@app.post("/api/query")
async def process_query(request: QueryRequest):
    """Main query processing endpoint"""
    try:
        query_text = request.query
        language_code = request.language_code

        if not models or not data.get('embeddings'):
            raise HTTPException(
                status_code=503,
                detail="The system is currently initializing. Please try again in a few minutes."
            )

        try:
            if language_code == 0:
                query_text = translate_text(query_text, 'ar_to_en')

            query_embedding = models['embedding'].encode([query_text])
            relevant_docs = query_embeddings(query_embedding)
            
            if not relevant_docs:
                return {
                    'answer': 'No relevant information found. Please try a different query.',
                    'success': True
                }

            doc_texts = [retrieve_document_text(doc_id) for doc_id, _ in relevant_docs]
            doc_texts = [text for text in doc_texts if text.strip()]
            
            if not doc_texts:
                return {
                    'answer': 'Unable to retrieve relevant documents. Please try again.',
                    'success': True
                }

            rerank_scores = rerank_documents(query_text, doc_texts)
            ranked_texts = [text for _, text in sorted(zip(rerank_scores, doc_texts), reverse=True)]
            
            context = " ".join(ranked_texts[:3])
            answer = generate_answer(query_text, context)
            
            if language_code == 0:
                answer = translate_text(answer, 'en_to_ar')

            return {
                'answer': answer,
                'success': True
            }

        except Exception as e:
            print(f"Error processing query: {e}")
            raise HTTPException(
                status_code=500,
                detail="An error occurred while processing your query"
            )

    except Exception as e:
        print(f"Error in process_query: {e}")
        raise HTTPException(
            status_code=500,
            detail=str(e)
        )

# Initialize application
print("Initializing application...")
init_success = init_nltk() and load_models() and load_data()

if not init_success:
    print("Warning: Application initialized with partial functionality")

# For running locally
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)