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_dotenv()
# os.getenv("GOOGLE_API_KEY")
# genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

# Extracting the PDFs content...
# We go through each page of each PDF and extract the text of the pages...
def get_pdf_text(pdf_docs):
    text=""
    for pdf in pdf_docs:
        doc = PdfReader(pdf)
        for page in doc.pages:
            text += page.extract_text()
    return text


# Now the next step is to divide the texts to smaller chunks...
def get_text_chunks(text):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
    chunks = text_splitter.split_text(text)
    return chunks


# Nextly, convert these chunks into vectors...
def get_vector_stores(text_chunks):
    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
    vector_stores = FAISS.from_texts(text_chunks, embedding=embeddings)
    vector_stores.save_local("faiss-index")


def get_conversational_chain():

    prompt_template = """
    Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
    provided context just say, "answer is not available in the context", don't provide the 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


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)

    print(response)
    st.write("Reply: ", response["output_text"])


def main():
    st.set_page_config("Chat PDF")
    st.header("Chat with PDF using Gemini💁")

    user_question = st.text_input("Ask a Question from the PDF Files")

    if user_question:
        user_input(user_question)

    with st.sidebar:
        st.title("Menu:")
        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
        if st.button("Submit & Process"):
            with st.spinner("Processing..."):
                raw_text = get_pdf_text(pdf_docs)
                text_chunks = get_text_chunks(raw_text)
                get_vector_stores(text_chunks)
                st.success("Done")


if __name__ == "__main__":
    main()