app.py

# import streamlit as st
# import faiss
# import pickle
# import numpy as np
# import torch
# from transformers import T5Tokenizer, T5ForConditionalGeneration
# from sentence_transformers import SentenceTransformer

# # Load LLM model (local folder)
# @st.cache_resource
# def load_llm():
#     model_path = "./Generator_Model"
#     tokenizer = T5Tokenizer.from_pretrained(model_path)
#     model = T5ForConditionalGeneration.from_pretrained(model_path)
#     return tokenizer, model

# # Load embedding model (local folder)
# @st.cache_resource
# def load_embedding_model():
#     embed_model_path = "./Embedding_Model1"
#     return SentenceTransformer(embed_model_path)

# # Load FAISS index and embeddings
# @st.cache_resource
# def load_faiss():
#     faiss_index = faiss.read_index("faiss_index_file.index")
#     data = np.load("embeddings_file.npy", allow_pickle=True)
#     return faiss_index, data


# # Search function
# def search(query, embed_model, index, data):
#     query_embedding = embed_model.encode([query]).astype('float32')
#     _, I = index.search(query_embedding, k=5)  # Top 5 results
#     results = [data['texts'][i] for i in I[0] if i != -1]
#     return results

# # Generate response using LLM
# def generate_response(context, query, tokenizer, model):
#     input_text = f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"
#     inputs = tokenizer.encode(input_text, return_tensors="pt")
#     outputs = model.generate(inputs, max_length=512, do_sample=True, temperature=0.7)
#     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
#     return response

# # Streamlit App
# def main():
#     st.title("Local LLM + FAISS + Embedding Search App")
#     st.markdown("🔍 Ask a question, and get context-aware answers!")

#     # Load everything once
#     tokenizer, llm_model = load_llm()
#     embed_model = load_embedding_model()
#     faiss_index, data = load_faiss()

#     query = st.text_input("Enter your query:")

#     if query:
#         with st.spinner("Processing..."):
#             # Search relevant contexts
#             contexts = search(query, embed_model, faiss_index, data)
#             combined_context = " ".join(contexts)

#             # Generate answer
#             response = generate_response(combined_context, query, tokenizer, llm_model)

#             st.subheader("Response:")
#             st.write(response)

#             st.subheader("Top Retrieved Contexts:")
#             for idx, ctx in enumerate(contexts, 1):
#                 st.markdown(f"**{idx}.** {ctx}")

# if __name__ == "__main__":
#     main()



###########################
import os
import streamlit as st
import faiss
import pickle
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModel, T5Tokenizer, T5ForConditionalGeneration,AutoModelForSeq2SeqLM
 

# Paths (everything is local now)
FAISS_INDEX_PATH = "faiss_index_file.index"
TEXTS_PATH = "texts.pkl"
EMBEDDINGS_PATH = "embeddings_file.npy"

# EMBEDDING_MODEL_NAME = "Ah1111/Embedding_Model"
# GENERATOR_MODEL_NAME = "Ah1111/Generator_Model"

# Load generator model (T5)
@st.cache_resource
def load_llm():
    tokenizer = T5Tokenizer.from_pretrained("Ah1111/Generator_Model")
    model = T5ForConditionalGeneration.from_pretrained("Ah1111/Generator_Model")
    return tokenizer, model

    # model_name = "google/flan-t5-base"

    # tokenizer = AutoTokenizer.from_pretrained(model_name)
    # model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
    # return tokenizer, model

# Load embedding model (custom Hugging Face model)
@st.cache_resource
def load_embedding_model():
    tokenizer = AutoTokenizer.from_pretrained("Ah1111/Embedding_Model")
    model = AutoModel.from_pretrained("Ah1111/Embedding_Model")
    return tokenizer, model

# Load FAISS index and texts
@st.cache_resource
def load_faiss():
    faiss_index = faiss.read_index(FAISS_INDEX_PATH)
    with open(TEXTS_PATH, "rb") as f:
        data = pickle.load(f)
    embeddings = np.load(EMBEDDINGS_PATH, allow_pickle=True)
    return faiss_index, data, embeddings

# Function to encode query using the embedding model
def encode_query(query, tokenizer, model):
    inputs = tokenizer(query, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        embeddings = model(**inputs).last_hidden_state.mean(dim=1)
    return embeddings.cpu().numpy()

# Search top-k contexts
def search(query, tokenizer, model, index, data, k=5):
    query_embedding = encode_query(query, tokenizer, model).astype('float32')
    _, I = index.search(query_embedding, k)
    results = [data[i] for i in I[0] if i != -1]
    return results

# Generate response using generator model
def generate_response(context, query, tokenizer, model):
    input_text = f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"
    inputs = tokenizer.encode(input_text, return_tensors="pt", truncation=True)
    outputs = model.generate(inputs, max_length=512, do_sample=True, temperature=0.7)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Streamlit app
def main():
    st.set_page_config(page_title="Clinical QA with RAG", page_icon="🩺")
    st.title("🔎 Clinical QA System (RAG + FAISS + T5)")

    st.markdown(
        """
        Enter your **clinical question** below.  
        The system will retrieve relevant context and generate an informed answer using a local model. 🚀
        """
    )

    # Load models and files
    embed_tokenizer, embed_model = load_embedding_model()
    gen_tokenizer, gen_model = load_llm()
    faiss_index, data, embeddings = load_faiss()

    query = st.text_input("💬 Your Question:")

    if query:
        with st.spinner("🔍 Retrieving and Generating..."):
            contexts = search(query, embed_tokenizer, embed_model, faiss_index, data)
            combined_context = " ".join(contexts)
            response = generate_response(combined_context, query, gen_tokenizer, gen_model)

            st.success("✅ Answer Ready!")
            st.subheader("📄 Response:")
            st.write(response)

if __name__ == "__main__":
    main()