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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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from langchain_community.vectorstores import Chroma |
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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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st.set_page_config(page_title="PDF CHATBOT", layout="wide") |
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st.markdown(""" |
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## Document Genie: Get instant insights from your Documents |
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This chatbot is built using the Retrieval-Augmented Generation (RAG) framework, leveraging Google's Generative AI model Gemini-PRO. It processes uploaded PDF documents by breaking them down into manageable chunks, creates a searchable vector store, and generates accurate answers to user queries. This advanced approach ensures high-quality, contextually relevant responses for an efficient and effective user experience. |
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### How It Works |
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Follow these simple steps to interact with the chatbot: |
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1. **Upload Your Documents**: The system accepts multiple PDF files at once, analyzing the content to provide comprehensive insights. |
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2. **Ask a Question**: After processing the documents, ask any question related to the content of your uploaded documents for a precise answer. |
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""") |
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def get_pdf(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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GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") |
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def response_generate(text,query): |
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text_splitter = RecursiveCharacterTextSplitter( |
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chunk_size=500, |
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chunk_overlap=20, |
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separators=["\n\n","\n"," ",".",","]) |
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chunks=text_splitter.split_text(text) |
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embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") |
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db = Chroma.from_documents(chunks, embeddings) |
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retriever = db.as_retriever() |
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qa = RetrievalQA.from_chain_type(llm = GoogleGenerativeAI(model="gemini-pro", google_api_key=GOOGLE_API_KEY ), chain_type='stuff', retriever=retriever) |
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return qa.run(query_text) |
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def main(): |
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st.header("Chat with your pdf💁") |
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query = st.text_input("Ask a Question from the PDF Files", key="query") |
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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, key="pdf_uploader") |
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if st.button("Submit & Process", key="process_button"): |
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with st.spinner("Processing..."): |
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raw_text = get_pdf(pdf_docs) |
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response = response_generate(raw_text,query) |
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st.success("Done") |
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st.write("Reply: ", response) |
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if __name__ == "__main__": |
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main() |
