app.py

# 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.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"))






# 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)
#     vector_store.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_store(text_chunks)
#                 st.success("Done")



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


import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
import os

# Load API key
load_dotenv()
genai_key = os.getenv("GOOGLE_API_KEY")

# Constants for cost calculation
EMBEDDING_COST_PER_1000_TOKENS = 0.0002  # USD
LM_COST_PER_1000_TOKENS = 0.0001  # USD


def get_pdf_text(pdf_docs):
    """Extract text from uploaded PDF documents."""
    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):
    """Split the extracted text into chunks for embedding."""
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
    chunks = text_splitter.split_text(text)
    return chunks


def calculate_cost(tokens, rate_per_1000):
    """Calculate cost based on tokens and rate."""
    return (tokens / 1000) * rate_per_1000


def get_vector_store(text_chunks):
    """Generate embeddings and store in FAISS."""
    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
    vector_store.save_local("faiss_index")

    # Calculate embedding cost
    total_tokens = sum(len(chunk.split()) for chunk in text_chunks)
    embedding_cost = calculate_cost(total_tokens, EMBEDDING_COST_PER_1000_TOKENS)
    return embedding_cost


def get_conversational_chain():
    """Set up the conversational chain."""
    prompt_template = """
    Answer the question as detailed as possible from the provided context. If the answer is not in
    the context, respond with "answer is not available in the context".\n\n
    Context:\n {context}?\n
    Question:\n {question}\n
    Answer:
    """
    model = ChatGoogleGenerativeAI(model="gemini-1.5-flash", 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 process_user_question(user_question):
    """Process user question and calculate costs."""
    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
    vector_store = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
    docs = vector_store.similarity_search(user_question)

    # Token estimation for retrieval
    retrieval_tokens = sum(len(doc.page_content.split()) for doc in docs)

    chain = get_conversational_chain()
    response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)

    # Token estimation for inference
    input_tokens = sum(len(doc.page_content.split()) for doc in docs) + len(user_question.split())
    output_tokens = len(response["output_text"].split())

    # Cost calculation
    retrieval_cost = calculate_cost(retrieval_tokens, EMBEDDING_COST_PER_1000_TOKENS)
    inference_cost = calculate_cost(input_tokens + output_tokens, LM_COST_PER_1000_TOKENS)

    total_cost = retrieval_cost + inference_cost

    # Output the results
    st.write("Response:", response["output_text"])
    st.write(f"Embedding Cost: ${retrieval_cost:.4f}")
    st.write(f"Language Model Cost: ${inference_cost:.4f}")
    st.write(f"Total Query Cost: ${total_cost:.4f}")


def main():
    """Streamlit app entry point."""
    st.set_page_config("Chat PDF Cost Calculator")
    st.header("Chat with PDF using Gemini 💁 (Cost Included)")

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

    if user_question:
        process_user_question(user_question)

    with st.sidebar:
        st.title("Menu:")
        pdf_docs = st.file_uploader("Upload your PDF Files and Click Submit & Process", 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)
                embedding_cost = get_vector_store(text_chunks)
                st.success(f"Processing Done! Embedding Cost: ${embedding_cost:.4f}")


if __name__ == "__main__":
    main()