app.py

import transformers
import pickle
import os
import numpy as np
import torchvision
import nltk
import torch
import pandas as pd
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from transformers import (
    AutoTokenizer,
    AutoModelForSeq2SeqLM,
    AutoModelForTokenClassification,
    AutoModelForCausalLM,
    pipeline, 
    Qwen2Tokenizer, 
    BartForConditionalGeneration
)
from sentence_transformers import SentenceTransformer, CrossEncoder, util
from sklearn.metrics.pairwise import cosine_similarity
from bs4 import BeautifulSoup
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file 
from typing import List, Dict, Optional
from safetensors.numpy import load_file
from safetensors.torch import safe_open
nltk.download('punkt_tab')

# 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

class MedicalProfile(BaseModel):
    chronic_conditions: List[str]
    symptoms: List[str]
    food_restrictions: List[str]
    mental_conditions: List[str]
    daily_symptoms: List[str]

class ChatQuery(BaseModel):
    query: str
    conversation_id: str

class ChatMessage(BaseModel):
    role: str
    content: str
    timestamp: str

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_model'] = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
        models['cross_encoder'] = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2', max_length=512)
        models['semantic_model'] = SentenceTransformer('all-MiniLM-L6-v2')

        # 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")

        #Attention model
        models['att_tokenizer'] = AutoTokenizer.from_pretrained("facebook/bart-base")
        models['att_model'] = BartForConditionalGeneration.from_pretrained("facebook/bart-base")
        
        # 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

def load_embeddings() -> Optional[Dict[str, np.ndarray]]:
    try:
        # Locate or download embeddings file
        embeddings_path = 'embeddings.safetensors'
        if not os.path.exists(embeddings_path):
            print("File not found locally. Attempting to download from Hugging Face Hub...")
            embeddings_path = hf_hub_download(
                repo_id=os.environ.get('HF_SPACE_ID', 'thechaiexperiment/TeaRAG'),
                filename="embeddings.safetensors",
                repo_type="space"
            )
        
        # Initialize a dictionary to store embeddings
        embeddings = {}

        # Open the safetensors file
        with safe_open(embeddings_path, framework="pt") as f:
            keys = f.keys()
            print(f"Available keys in the .safetensors file: {list(keys)}")  # Debugging info

            # Iterate over the keys and load tensors
            for key in keys:
                try:
                    tensor = f.get_tensor(key)
                    if not isinstance(tensor, torch.Tensor):
                        raise TypeError(f"Value for key {key} is not a valid PyTorch tensor.")
                    
                    # Convert tensor to NumPy array
                    embeddings[key] = tensor.numpy()
                except Exception as key_error:
                    print(f"Failed to process key {key}: {key_error}")

        if embeddings:
            print("Embeddings successfully loaded.")
        else:
            print("No embeddings could be loaded. Please check the file format and content.")
        
        return embeddings

    except Exception as e:
        print(f"Error loading embeddings: {e}")
        return None


def load_documents_data(folder_path='downloaded_articles/downloaded_articles'):
    """Load document data from HTML articles in a specified folder."""
    try:
        print("Loading documents data...")
        # Check if the folder exists
        if not os.path.exists(folder_path) or not os.path.isdir(folder_path):
            print(f"Error: Folder '{folder_path}' not found")
            return False
        # List all HTML files in the folder
        html_files = [f for f in os.listdir(folder_path) if f.endswith('.html')]
        if not html_files:
            print(f"No HTML files found in folder '{folder_path}'")
            return False
        documents = []
        # Iterate through each HTML file and parse the content
        for file_name in html_files:
            file_path = os.path.join(folder_path, file_name)
            try:
                with open(file_path, 'r', encoding='utf-8') as file:
                    # Parse the HTML file
                    soup = BeautifulSoup(file, 'html.parser')
                    # Extract text content (or customize this as per your needs)
                    text = soup.get_text(separator='\n').strip()
                    documents.append({"file_name": file_name, "content": text})
            except Exception as e:
                print(f"Error reading file {file_name}: {e}")
        # Convert the list of documents to a DataFrame
        data['df'] = pd.DataFrame(documents)
        
        if data['df'].empty:
            print("No valid documents loaded.")
            return False
        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

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

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 embed_query_text(query_text):
    embedding = models['embedding_model']
    query_embedding = embedding.encode([query_text])
    return query_embedding

def query_embeddings(query_embedding, embeddings_data=None, n_results=5):
    embeddings_data = load_embeddings()
    if not embeddings_data:
        print("No embeddings data available.")
        return []
    try:
        doc_ids = list(embeddings_data.keys())
        doc_embeddings = np.array(list(embeddings_data.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_texts(doc_ids, folder_path='downloaded_articles/downloaded_articles'):
    texts = []
    for doc_id in doc_ids:
        file_path = os.path.join(folder_path, doc_id)
        try:
            # Check if the file exists
            if not os.path.exists(file_path):
                print(f"Warning: Document file not found: {file_path}")
                texts.append("")
                continue
            # Read and parse the HTML file
            with open(file_path, 'r', encoding='utf-8') as file:
                soup = BeautifulSoup(file, 'html.parser')
                text = soup.get_text(separator=' ', strip=True)
                texts.append(text)
        except Exception as e:
            print(f"Error retrieving document {doc_id}: {e}")
            texts.append("")
    return texts


def rerank_documents(query, document_ids, document_texts, cross_encoder_model):
    try:
        # Prepare pairs for the cross-encoder
        pairs = [(query, doc) for doc in document_texts]
        # Get scores using the cross-encoder model
        scores = cross_encoder_model.predict(pairs)
        # Combine scores with document IDs and texts
        scored_documents = list(zip(scores, document_ids, document_texts))
        # Sort by scores in descending order
        scored_documents.sort(key=lambda x: x[0], reverse=True)
        # Print reranked results
        print("Reranked results:")
        for idx, (score, doc_id, doc) in enumerate(scored_documents):
            print(f"Rank {idx + 1} (Score: {score:.4f}, Document ID: {doc_id})")
        return scored_documents
    except Exception as e:
        print(f"Error reranking documents: {e}")
        return []

def extract_entities(text, ner_pipeline=None):
    try:
        # Use the provided pipeline or default to the model dictionary
        if ner_pipeline is None:
            ner_pipeline = models['ner_pipeline']
        # Perform NER using the pipeline
        ner_results = ner_pipeline(text)
        # Extract unique entities that start with "B-"
        entities = {result['word'] for result in ner_results if result['entity'].startswith("B-")}
        return list(entities)
    except Exception as e:
        print(f"Error extracting entities: {e}")
        return []

def match_entities(query_entities, sentence_entities):
    try:
        query_set, sentence_set = set(query_entities), set(sentence_entities)
        matches = query_set.intersection(sentence_set)
        return len(matches)
    except Exception as e:
        print(f"Error matching entities: {e}")
        return 0

def extract_relevant_portions(document_texts, query, max_portions=3, portion_size=1, min_query_words=1):
    relevant_portions = {}
    # Extract entities from the query
    #ner_biobert = models['ner_pipeline']
    query_entities = extract_entities(query)
    print(f"Extracted Query Entities: {query_entities}")
    for doc_id, doc_text in enumerate(document_texts):
        sentences = nltk.sent_tokenize(doc_text)  # Split document into sentences
        doc_relevant_portions = []
        # Extract entities from the entire document
        doc_entities = extract_entities(doc_text, ner_biobert)
        print(f"Document {doc_id} Entities: {doc_entities}")
        for i, sentence in enumerate(sentences):
            # Extract entities from the sentence
            sentence_entities = extract_entities(sentence, ner_biobert)
            # Compute relevance score
            relevance_score = match_entities(query_entities, sentence_entities)
            # Select sentences with at least `min_query_words` matching entities
            if relevance_score >= min_query_words:
                start_idx = max(0, i - portion_size // 2)
                end_idx = min(len(sentences), i + portion_size // 2 + 1)
                portion = " ".join(sentences[start_idx:end_idx])
                doc_relevant_portions.append(portion)
            if len(doc_relevant_portions) >= max_portions:
                break
        # Fallback: Include most entity-dense sentences if no relevant portions were found
        if not doc_relevant_portions and len(doc_entities) > 0:
            print(f"Fallback: Selecting sentences with most entities for Document {doc_id}")
            sorted_sentences = sorted(sentences, key=lambda s: len(extract_entities(s, ner_biobert)), reverse=True)
            for fallback_sentence in sorted_sentences[:max_portions]:
                doc_relevant_portions.append(fallback_sentence)
        # Add the extracted portions to the result dictionary
        relevant_portions[f"Document_{doc_id}"] = doc_relevant_portions
    return relevant_portions

def remove_duplicates(selected_parts):
    unique_sentences = set()
    unique_selected_parts = []
    for sentence in selected_parts:
        if sentence not in unique_sentences:
            unique_selected_parts.append(sentence)
            unique_sentences.add(sentence)
    return unique_selected_parts

def extract_entities(text):
    try:
        inputs = biobert_tokenizer(text, return_tensors="pt")
        outputs = biobert_model(**inputs)
        predictions = torch.argmax(outputs.logits, dim=2)

        tokens = biobert_tokenizer.convert_ids_to_tokens(inputs.input_ids[0])
        entities = [
            tokens[i] 
            for i in range(len(tokens)) 
            if predictions[0][i].item() != 0  # Assuming 0 is the label for non-entity
        ]
        return entities
    except Exception as e:
        print(f"Error extracting entities: {e}")
        return []

def enhance_passage_with_entities(passage, entities):
    return f"{passage}\n\nEntities: {', '.join(entities)}"

def create_prompt(question, passage):
    prompt = ("""
    As a medical expert, you are required to answer the following question based only on the provided passage. Do not include any information not present in the passage. Your response should directly reflect the content of the passage. Maintain accuracy and relevance to the provided information.

    Passage: {passage}

    Question: {question}

    Answer:
    """)
    return prompt.format(passage=passage, question=question)

def generate_answer(prompt, max_length=860, temperature=0.2):
    inputs = tokenizer_f(prompt, return_tensors="pt", truncation=True)
    # Start timing
    start_time = time.time()
    # Generate the output
    output_ids = model_f.generate(
        inputs.input_ids,
        max_length=max_length,
        num_return_sequences=1,
        temperature=temperature,
        pad_token_id=tokenizer_f.eos_token_id
    )
    # End timing
    end_time = time.time()
    # Calculate the duration
    duration = end_time - start_time
    # Decode the answer
    answer = tokenizer_f.decode(output_ids[0], skip_special_tokens=True)
    # Extract keywords from the passage and answer
    passage_keywords = set(prompt.lower().split())  # Adjusted to check keywords in the full prompt
    answer_keywords = set(answer.lower().split())
    # Verify if the answer aligns with the passage
    if passage_keywords.intersection(answer_keywords):
        return answer, duration
    else:
        return "Sorry, I can't help with that.", duration
  
def remove_answer_prefix(text):
    prefix = "Answer:"
    if prefix in text:
        return text.split(prefix, 1)[-1].strip()  # Split only once to avoid splitting at other occurrences of "Answer:"
    return text

def remove_incomplete_sentence(text):
    # Check if the text ends with a period
    if not text.endswith('.'):
        # Find the last period or the end of the string
        last_period_index = text.rfind('.')
        if last_period_index != -1:
            # Remove everything after the last period
            return text[:last_period_index + 1].strip()
    return text


language_code = 1
query_text = 'What are symptoms of heart attack ?'        
query_embedding = embed_query_text(query_text)  # Embed the query text
embeddings_data = load_embeddings ()
folder_path = 'downloaded_articles/downloaded_articles'
initial_results = query_embeddings(query_embedding, embeddings_data, n_results=5)
document_ids = [doc_id for doc_id, _ in initial_results]
print(document_ids)
document_ids = [doc_id for doc_id, _ in initial_results]
document_texts = retrieve_document_texts(document_ids, folder_path)

# Rerank the results using the CrossEncoder
cross_encoder = models['cross_encoder']
scores = cross_encoder.predict([(query_text, doc) for doc in document_texts])
scored_documents = list(zip(scores, document_ids, document_texts))
scored_documents.sort(key=lambda x: x[0], reverse=True)
print("Reranked results:")
for idx, (score, doc_id, doc) in enumerate(scored_documents):
    print(f"Rank {idx + 1} (Score: {score:.4f}, Document ID: {doc_id}")
relevant_portions = extract_relevant_portions(document_texts, query_text, max_portions=3, portion_size=1, min_query_words=1)

for doc_id, portions in relevant_portions.items():
    print(f"{doc_id}: {portions}")

flattened_relevant_portions = []
for doc_id, portions in relevant_portions.items():
    flattened_relevant_portions.extend(portions)

# Remove duplicate portions
unique_selected_parts = remove_duplicates(flattened_relevant_portions)

# Combine the unique parts into a single string of context
combined_parts = " ".join(unique_selected_parts)

# Construct context as a list: first the query, then the unique selected portions
context = [query_text] + unique_selected_parts

# Print the context (query + relevant portions)
print(context)
entities = extract_entities(query_text)
passage = enhance_passage_with_entities(combined_parts, entities)
# Generate answer with the enhanced passage
prompt = create_prompt(query_text, passage)
answer, generation_time = generate_answer(prompt)
print(f"\nTime taken to generate the answer: {generation_time:.2f} seconds")
answer_part = answer.split("Answer:")[-1].strip()
cleaned_answer = remove_answer_prefix(answer_part)
final_answer = remove_incomplete_sentence(cleaned_answer)

if language_code == 0:
    final_answer = translate_en_to_ar(final_answer)

if final_answer:
    print("Answer:")
    print(final_answer)
else:
    print("Sorry, I can't help with that.")

    






@app.get("/")
async def root():
    return {"message": "Welcome to the FastAPI application! Use the /health endpoint to check health, and /api/query for processing queries."}

@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/chat")
async def chat_endpoint(chat_query: ChatQuery):
    try:
        query_text = chat_query.query
        
        # Step 1: Embed the query
        query_embedding = embed_query_text(query_text)
        
        # Step 2: Retrieve top results using embeddings similarity
        initial_results = query_embeddings(query_embedding, embeddings_data, n_results=5)
        document_ids = [doc_id for doc_id, _ in initial_results]
        
        # Step 3: Fetch document texts
        document_texts = retrieve_document_texts(document_ids, folder_path)
        
        # Step 4: Re-rank documents (optional, if reranking is used)
        reranked_documents = rerank_documents(query_text, document_ids, document_texts, cross_encoder_model)
        
        # Step 5: Extract relevant portions (if enabled)
        relevant_portions = extract_relevant_portions(
            document_texts,
            query=query_text,
            max_portions=3,
            portion_size=1,
            min_query_words=1
        )
        
        # Step 6: Flatten and clean relevant portions
        flattened_relevant_portions = []
        for doc_id, portions in relevant_portions.items():
            flattened_relevant_portions.extend(portions)
        unique_selected_parts = remove_duplicates(flattened_relevant_portions)
        combined_parts = " ".join(unique_selected_parts)
        
        # Step 7: Extract entities and enhance passage
        entities = extract_entities(query_text)
        passage = enhance_passage_with_entities(combined_parts, entities)
        
        # Step 8: Create prompt and generate answer
        prompt = create_prompt(query_text, passage)
        answer, generation_time = generate_answer(prompt)
        
        # Step 9: Clean the generated answer
        answer_part = answer.split("Answer:")[-1].strip()
        cleaned_answer = remove_answer_prefix(answer_part)
        final_answer = remove_incomplete_sentence(cleaned_answer)
        
        return {
            "response": final_answer,
            "conversation_id": chat_query.conversation_id,
            "success": True
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/api/resources")
async def resources_endpoint(profile: MedicalProfile):
    try:
        context = f"""
        Medical conditions: {', '.join(profile.chronic_conditions)}
        Current symptoms: {', '.join(profile.daily_symptoms)}
        Restrictions: {', '.join(profile.food_restrictions)}
        Mental health: {', '.join(profile.mental_conditions)}
        """
        
        query_embedding = models['embedding'].encode([context])
        relevant_docs = query_embeddings(query_embedding)
        doc_texts = [retrieve_document_text(doc_id) for doc_id, _ in relevant_docs]
        doc_texts = [text for text in doc_texts if text.strip()]
        
        rerank_scores = rerank_documents(context, doc_texts)
        ranked_docs = sorted(zip(relevant_docs, rerank_scores, doc_texts), key=lambda x: x[1], reverse=True)
        
        resources = []
        for (doc_id, _), score, text in ranked_docs[:10]:
            doc_info = data['df'][data['df']['id'] == doc_id].iloc[0]
            resources.append({
                "id": doc_id,
                "title": doc_info['title'],
                "content": text[:200],
                "score": float(score)
            })
        
        return {"resources": resources, "success": True}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/api/recipes")
async def recipes_endpoint(profile: MedicalProfile):
    try:
        recipe_query = f"Recipes and meals suitable for someone with: {', '.join(profile.chronic_conditions + profile.food_restrictions)}"
        
        query_embedding = models['embedding'].encode([recipe_query])
        relevant_docs = query_embeddings(query_embedding)
        doc_texts = [retrieve_document_text(doc_id) for doc_id, _ in relevant_docs]
        doc_texts = [text for text in doc_texts if text.strip()]
        
        rerank_scores = rerank_documents(recipe_query, doc_texts)
        ranked_docs = sorted(zip(relevant_docs, rerank_scores, doc_texts), key=lambda x: x[1], reverse=True)
        
        recipes = []
        for (doc_id, _), score, text in ranked_docs[:10]:
            doc_info = data['df'][data['df']['id'] == doc_id].iloc[0]
            if 'recipe' in text.lower() or 'meal' in text.lower():
                recipes.append({
                    "id": doc_id,
                    "title": doc_info['title'],
                    "content": text[:200],
                    "score": float(score)
                })
        
        return {"recipes": recipes[:5], "success": True}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))



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)