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import streamlit as st
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from PyPDF2 import PdfReader
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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import os
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from langchain_google_genai import GoogleGenerativeAIEmbeddings
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import google.generativeai as genai
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from langchain_community.vectorstores import FAISS
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from langchain_google_genai import ChatGoogleGenerativeAI
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from langchain.chains.question_answering import load_qa_chain
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from langchain_core.prompts import PromptTemplate
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from dotenv import load_dotenv
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load_dotenv()
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if 'processed' not in st.session_state:
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st.session_state.processed = False
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genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
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@st.cache_data
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def get_pdf_text(pdf_docs):
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text=""
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for pdf in pdf_docs:
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pdf_reader = PdfReader(pdf)
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for page in pdf_reader.pages:
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text+= page.extract_text()
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return text
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@st.cache_data
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def get_text_chunks(text):
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500)
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chunks = text_splitter.split_text(text)
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return chunks
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@st.cache_data
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def get_vector_store(chunks):
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embeddings=GoogleGenerativeAIEmbeddings(model="models/embedding-001")
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vector_store = FAISS.from_texts(chunks, embedding=embeddings)
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vector_store.save_local("faiss_index")
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@st.cache_resource
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def get_conversation_chain():
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prompt_template = """
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Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
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provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
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Context:\n {context}?\n
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Question: \n{question}\n
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Answer:
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"""
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model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.4)
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prompt=PromptTemplate(template=prompt_template, input_variables=["context", "question"])
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chain=load_qa_chain(model,chain_type="stuff",prompt=prompt)
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return chain
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def process_pdfs(pdf_docs):
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raw_text = get_pdf_text(pdf_docs)
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text_chunks = get_text_chunks(raw_text)
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get_vector_store(text_chunks)
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st.session_state.processed = True
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return "PDFs processed successfully!"
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def user_input(user_question):
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embeddings=GoogleGenerativeAIEmbeddings(model="models/embedding-001")
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new_db=FAISS.load_local("faiss_index", embeddings,allow_dangerous_deserialization=True)
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docs=new_db.similarity_search(user_question)
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chain=get_conversation_chain()
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response=chain(
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{"input_documents":docs, "question":user_question},
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return_only_outputs=True
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)
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return response["output_text"]
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def main():
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st.title("Chat with multiple PDFs")
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tab1, tab2 = st.tabs(["Upload PDFs", "Chat"])
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with tab1:
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pdf_docs = st.file_uploader("Upload your PDF files", type=['pdf'], accept_multiple_files=True)
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if st.button("Process"):
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with st.spinner("Processing PDFs..."):
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status = process_pdfs(pdf_docs)
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st.success(status)
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with tab2:
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if not st.session_state.processed:
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st.warning("Please upload and process PDFs first")
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else:
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user_question = st.text_input("Ask a question from the PDF files")
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if st.button("Submit"):
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with st.spinner("Generating response..."):
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response = user_input(user_question)
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st.write(response)
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if __name__=="__main__":
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main()
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