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Upload app.py
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app.py
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import streamlit as st
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from PyPDF2 import PdfReader
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from langchain.text_splitter 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.prompts import PromptTemplate
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from dotenv import load_dotenv
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load_dotenv()
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genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))#config api
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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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def get_text_chunks(text):
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text_splitter=RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
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chunks=text_splitter.split_text(text)
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return chunks
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def get_vector_store(text_chunks):
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embeddings=GoogleGenerativeAIEmbeddings(model="models/embedding-001")
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vector_store=FAISS.from_texts(text_chunks,embedding=embeddings)
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vector_store.save_local("faiss_index")
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def get_conversational_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",
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temperature=0.3)
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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 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)
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docs = new_db.similarity_search(user_question)
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chain = get_conversational_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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print(response)
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st.write("Reply: ", response["output_text"])
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def main():
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st.set_page_config("Chat PDF")
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st.header("Chat with PDF using Gemini💁")
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user_question = st.text_input("Ask a Question from the PDF Files")
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if user_question:
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user_input(user_question)
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with st.sidebar:
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st.title("Menu:")
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pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
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if st.button("Submit & Process"):
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with st.spinner("Processing..."):
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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.success("Done")
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if __name__ == "__main__":
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main()
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