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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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from langchain_google_genai import GoogleGenerativeAIEmbeddings |
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import google.generativeai as genai |
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from langchain.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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import os |
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st.set_page_config(page_title="Document Genie", 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. **Enter Your API Key**: You'll need a Google API key for the chatbot to access Google's Generative AI models. Obtain your API key https://makersuite.google.com/app/apikey. |
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2. **Upload Your Documents**: The system accepts multiple PDF files at once, analyzing the content to provide comprehensive insights. |
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3. **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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api_key = st.text_input("Enter your Google API Key:", type="password", key="api_key_input") |
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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, api_key): |
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embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=api_key) |
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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", temperature=0.3, google_api_key=api_key) |
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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, api_key): |
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embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=api_key) |
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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({"input_documents": docs, "question": user_question}, return_only_outputs=True) |
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st.write("Reply: ", response["output_text"]) |
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def main(): |
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st.header("AI clone chatbot💁") |
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user_question = st.text_input("Ask a Question from the PDF Files", key="user_question") |
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if user_question and api_key: |
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user_input(user_question, api_key) |
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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, key="pdf_uploader") |
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if st.button("Submit & Process", key="process_button") and api_key: |
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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, api_key) |
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st.success("Done") |
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if __name__ == "__main__": |
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main() |
