import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
from langchain_google_genai import GoogleGenerativeAIEmbeddings
import google.generativeai as genai
from langchain_community.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

# Load environment variables
load_dotenv()
google_api_key = os.getenv("GOOGLE_API_KEY")
if not google_api_key:
    st.error("Google API Key is not set. Please set the GOOGLE_API_KEY in the .env file.")
else:
    genai.configure(api_key=google_api_key)


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


def get_text_chunks(text):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
    chunks = text_splitter.split_text(text)
    return chunks


def get_vector_store(text_chunks):
    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
    os.makedirs("faiss_index", exist_ok=True)
    vector_store.save_local("faiss_index")
    st.write("FAISS index saved successfully!")


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, 
    'Answer is not available in the context.' Do not provide a wrong answer.

    Context: {context}
    Question: {question}
    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


def user_input(user_question):
    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
    try:
        new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
    except Exception as e:
        st.error(f"Error loading FAISS index: {e}")
        return
    docs = new_db.similarity_search(user_question)
    chain = get_conversational_chain()
    response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
    st.write("Reply:", response["output_text"])


def main():
    st.set_page_config(page_title="Chat with PDF using Gemini💁", layout="wide")
    st.header("Chat with your PDF using Gemini 💁")
    
    with st.sidebar:
        st.title("Menu:")
        pdf_docs = st.file_uploader("Upload your PDF files", accept_multiple_files=True)
        if st.button("Submit & Process"):
            if pdf_docs:
                with st.spinner("Processing..."):
                    raw_text = get_pdf_text(pdf_docs)
                    text_chunks = get_text_chunks(raw_text)
                    get_vector_store(text_chunks)
                    st.success("Processing Done")
            else:
                st.error("Please upload PDF files.")

    st.subheader("Ask a question about your PDFs")
    user_question = st.text_input("Type your question here:")
    if user_question:
        with st.spinner("Getting your answer..."):
            user_input(user_question)


if __name__ == "__main__":
    main()