app.py

# import os
# import logging
# from dotenv import load_dotenv
# import streamlit as st
# from PyPDF2 import PdfReader
# from langchain.text_splitter import CharacterTextSplitter
# # from langchain.embeddings import HuggingFaceInstructEmbeddings
# from langchain_cohere import CohereEmbeddings
# from langchain.vectorstores import FAISS
# from langchain.memory import ConversationBufferMemory
# from langchain.chains import ConversationalRetrievalChain
# # from langchain.llms import Ollama
# from langchain_groq import ChatGroq

# # Load environment variables
# load_dotenv()

# # Set up logging
# logging.basicConfig(
#     level=logging.INFO,
#     format='%(asctime)s - %(levelname)s - %(message)s'
# )

# # Function to extract text from PDF files
# def get_pdf_text(pdf_docs):
#     text = ""
#     for pdf in pdf_docs:
#         pdf_reader = PdfReader(pdf)
#         for page in pdf_reader.pages:
#             text += page.extract_text()
#     return text

# # Function to split the extracted text into chunks
# def get_text_chunks(text):
#     text_splitter = CharacterTextSplitter(
#         separator="\n",
#         chunk_size=1000,
#         chunk_overlap=200,
#         length_function=len
#     )
#     chunks = text_splitter.split_text(text)
#     return chunks

# # Function to create a FAISS vectorstore
# # def get_vectorstore(text_chunks):
# #     embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
# #     vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
# #     return vectorstore

# def get_vectorstore(text_chunks):
#     cohere_api_key = os.getenv("COHERE_API_KEY")
#     embeddings = CohereEmbeddings(model="embed-english-v3.0", cohere_api_key=cohere_api_key)
#     vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
#     return vectorstore

# # Function to set up the conversational retrieval chain
# def get_conversation_chain(vectorstore):
#     try:
#         # llm = Ollama(model="llama3.2:1b")
#         llm = ChatGroq(model="llama-3.3-70b-versatile", temperature=0.5)
#         memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
        
#         conversation_chain = ConversationalRetrievalChain.from_llm(
#             llm=llm,
#             retriever=vectorstore.as_retriever(),
#             memory=memory
#         )
        
#         logging.info("Conversation chain created successfully.")
#         return conversation_chain
#     except Exception as e:
#         logging.error(f"Error creating conversation chain: {e}")
#         st.error("An error occurred while setting up the conversation chain.")

# # Handle user input
# def handle_userinput(user_question):
#     if st.session_state.conversation is not None:
#         response = st.session_state.conversation({'question': user_question})
#         st.session_state.chat_history = response['chat_history']

#         for i, message in enumerate(st.session_state.chat_history):
#             if i % 2 == 0:
#                 st.write(f"*User:* {message.content}")
#             else:
#                 st.write(f"*Bot:* {message.content}")
#     else:
#         st.warning("Please process the documents first.")

# # Main function to run the Streamlit app
# def main():
#     load_dotenv()
#     st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")

#     if "conversation" not in st.session_state:
#         st.session_state.conversation = None
#     if "chat_history" not in st.session_state:
#         st.session_state.chat_history = None

#     st.header("Chat with multiple PDFs :books:")
#     user_question = st.text_input("Ask a question about your documents:")
#     if user_question:
#         handle_userinput(user_question)

#     with st.sidebar:
#         st.subheader("Your documents")
#         pdf_docs = st.file_uploader(
#             "Upload your PDFs here and click on 'Process'", accept_multiple_files=True
#         )
#         if st.button("Process"):
#             with st.spinner("Processing..."):
#                 raw_text = get_pdf_text(pdf_docs)
#                 text_chunks = get_text_chunks(raw_text)
#                 vectorstore = get_vectorstore(text_chunks)
#                 st.session_state.conversation = get_conversation_chain(vectorstore)

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







import streamlit as st
import os
from dotenv import load_dotenv
import PyPDF2
import requests
import cohere
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_cohere import CohereEmbeddings

# Load environment variables
load_dotenv()
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
COHERE_API_KEY = os.getenv("COHERE_API_KEY")

# Initialize Cohere client
co = cohere.Client(COHERE_API_KEY)

# Configure Streamlit
st.set_page_config(page_title="RAG Chatbot with Gemini & Cohere")
st.title("🤖 Multi-Model RAG Chatbot")

# Initialize session state
if "messages" not in st.session_state:
    st.session_state.messages = []
if "vector_store" not in st.session_state:
    st.session_state.vector_store = None

# File upload and processing
uploaded_file = st.file_uploader("Upload a PDF document", type="pdf")

if uploaded_file and not st.session_state.vector_store:
    # Process PDF
    pdf_reader = PyPDF2.PdfReader(uploaded_file)
    text = ""
    for page in pdf_reader.pages:
        text += page.extract_text()

    # Split text
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200
    )
    chunks = text_splitter.split_text(text)

    # Create embeddings and vector store
    embeddings = CohereEmbeddings(
        cohere_api_key=COHERE_API_KEY,
        model="embed-english-v3.0",
        user_agent="rag-chatbot-v1"
    )
    st.session_state.vector_store = FAISS.from_texts(
        texts=chunks,
        embedding=embeddings
    )

# Display chat messages
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Query expansion function
def expand_query(query):
    prompt = f"""Generate 3 query variations that help answer: {query}
    Format as numbered bullet points:"""

    response = co.generate(
        prompt=prompt,
        max_tokens=100,
        temperature=0.7
    )
    expanded_queries = [query] + [q.split(". ")[1] for q in response.generations[0].text.split("\n") if q]
    return expanded_queries

# Gemini API call
def generate_with_gemini(context, query):
    url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key={GEMINI_API_KEY}"

    system_prompt = f"""You're an expert assistant. Use this context to answer:
    {context}

    Apply Chain of Abstraction and Grounding (CAG):
    1. Identify key concepts
    2. Create abstract relationships
    3. Ground in specific examples
    4. Synthesize final answer"""

    headers = {"Content-Type": "application/json"}
    data = {
        "contents": [{
            "parts": [{
                "text": f"{system_prompt}\n\nQuestion: {query}"
            }]
        }]
    }

    response = requests.post(url, json=data, headers=headers)
    return response.json()["candidates"][0]["content"]["parts"][0]["text"]

# Chat input
if prompt := st.chat_input("Ask about the document"):
    st.session_state.messages.append({"role": "user", "content": prompt})

    with st.chat_message("user"):
        st.markdown(prompt)

    # Query expansion
    expanded_queries = expand_query(prompt)

    # Retrieve documents
    docs = []
    for query in expanded_queries:
        docs.extend(st.session_state.vector_store.similarity_search(query, k=2))

    # Generate response
    context = "\n\n".join([doc.page_content for doc in docs])
    response = generate_with_gemini(context, prompt)

    with st.chat_message("assistant"):
        st.markdown(response)

    st.session_state.messages.append({"role": "assistant", "content": response})