app.py

import streamlit as st
from PyPDF2 import PdfReader
import docx2txt
import json
import pandas as pd
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
from langchain_google_genai import GoogleGenerativeAIEmbeddings
import google.generativeai as genai
from langchain.vectorstores import FAISS
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv

# Step 2: Load environment variable
load_dotenv()
api_key = os.getenv("GOOGLE_API_KEY")

# Step 3: Configure Google_API
genai.configure(api_key=api_key)

# Step 4: Function to read files and extract text
def extract_text(file):
    text = ""
    if file.name.endswith(".pdf"):
        pdf_reader = PdfReader(file)
        for page in pdf_reader.pages:
            text += page.extract_text()
    elif file.name.endswith(".docx"):
        text = docx2txt.process(file)
    elif file.name.endswith(".txt"):
        text = file.read().decode("utf-8")
    elif file.name.endswith(".csv"):
        df = pd.read_csv(file)
        text = df.to_string()
    elif file.name.endswith(".xlsx"):
        df = pd.read_excel(file)
        text = df.to_string()
    elif file.name.endswith(".json"):
        data = json.load(file)
        text = json.dumps(data, indent=4)
    return text

# Step 5: Function to convert text into chunks
def get_text_chunks(text):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
    chunks = text_splitter.split_text(text)
    return chunks

# Step 6: Function for converting chunks into embeddings and saving the FAISS index
def get_vector_store(text_chunks):
    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
    
    # Ensure the directory exists
    if not os.path.exists("faiss_index"):
        os.makedirs("faiss_index")
        
    vector_store.save_local("faiss_index")
    print("FAISS index saved successfully.")

# Step 7: Function to implement Gemini-Pro Model
def get_conversational_chain():
    prompt_template = """
    Answer the question as detailed as possible from the provided context. If the answer is not in
    the provided context, just say, "The answer is not available in the context." Do not provide a wrong answer.\n\n
    Context:\n {context}\n
    Question: \n{question}\n

    Answer:
    """
    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
    return chain

# Step 8: Function to take inputs from user and generate response
def user_input(user_question):
    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
    docs = new_db.similarity_search(user_question)
    chain = get_conversational_chain()
    response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
    return response["output_text"]

# Step 9: Streamlit App
def main():
    st.set_page_config(page_title="RAG Chatbot")
    st.header("Chat with Multiple Files using RAG and Gemini ")

    user_question = st.text_input("Ask a Question")

    if user_question:
        with st.spinner("Processing your question..."):
            response = user_input(user_question)
            st.write("Reply: ", response)

    with st.sidebar:
        st.title("Upload Files:")
        uploaded_files = st.file_uploader("Upload your files", accept_multiple_files=True, type=["pdf", "docx", "txt", "csv", "xlsx", "json"])
        if st.button("Submit & Process"):
            if uploaded_files:
                with st.spinner("Processing files..."):
                    combined_text = ""
                    for file in uploaded_files:
                        combined_text += extract_text(file) + "\n"
                    text_chunks = get_text_chunks(combined_text)
                    get_vector_store(text_chunks)
                    st.success("Files processed and indexed successfully!")
            else:
                st.error("Please upload at least one file.")

if __name__ == "__main__":
    main()