app.py

import streamlit as st
from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.prompts import PromptTemplate
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.llms import CTransformers
from langchain.chains import RetrievalQA
from huggingface_hub import snapshot_download

DB_FAISS_PATH = './faiss_data'

# Define your custom prompt template for the LLM
custom_prompt_template = """Use the following pieces of information to answer the user's question. If you don't know the answer, just say that you don't know, don't try to make up an answer.
Context: {context}
Question: {question}
Only return the helpful answer below and nothing else.
Helpful answer: """

def set_custom_prompt():
    prompt = PromptTemplate(template=custom_prompt_template, input_variables=['context', 'question'])
    return prompt

# Function to create a retrieval-based QA chain
def retrieval_qa_chain(llm, prompt, db):
    qa_chain = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type='stuff',
        retriever=db.as_retriever(search_kwargs={'k': 2}),
        return_source_documents=True,
        chain_type_kwargs={'prompt': prompt}
    )
    return qa_chain

# Load the LLM model (e.g., LLaMA model from a local path)
def load_llm():
    model_path = "./llama-2-7b-chat.ggmlv3.q4_0.bin"
    llm = CTransformers(
        model=model_path,
        model_type="llama",
        max_new_tokens=1024,
        temperature=0.5
    )
    return llm

# Main chatbot logic
def qa_bot():
    embeddings = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2",
        model_kwargs={'device': 'cpu'}
    )
    db = FAISS.load_local(DB_FAISS_PATH, embeddings, allow_dangerous_deserialization=True)
    llm = load_llm()
    qa_prompt = set_custom_prompt()
    qa = retrieval_qa_chain(llm, qa_prompt, db)
    return qa

# Streamlit main app
def main():
    st.title("RAG")
    
    # Initialize session state for chat history
    if "messages" not in st.session_state:
        st.session_state.messages = []
        
    # Display chat history
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])
    
    # Chat input
    if prompt := st.chat_input("What is your medical query?"):
        # Display user message
        st.session_state.messages.append({"role": "user", "content": prompt})
        with st.chat_message("user"):
            st.markdown(prompt)
            
        # Generate and display assistant response
        with st.chat_message("assistant"):
            with st.spinner("Thinking..."):
                qa_chain = qa_bot()
                response = qa_chain({'query': prompt})
                st.markdown(response["result"])
                st.session_state.messages.append({"role": "assistant", "content": response["result"]})

if __name__ == '__main__':
    main()