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| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| 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 | |
| import os | |
| st.set_page_config(page_title="Document Genie", layout="wide") | |
| st.markdown(""" | |
| ## Document Genie: Get instant insights from your Documents | |
| 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. | |
| ### How It Works | |
| Follow these simple steps to interact with the chatbot: | |
| 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. | |
| 2. **Upload Your Documents**: The system accepts multiple PDF files at once, analyzing the content to provide comprehensive insights. | |
| 3. **Ask a Question**: After processing the documents, ask any question related to the content of your uploaded documents for a precise answer. | |
| """) | |
| # This is the first API key input; no need to repeat it in the main function. | |
| api_key = st.text_input("Enter your Google API Key:", type="password", key="api_key_input") | |
| 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, api_key): | |
| embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=api_key) | |
| 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, google_api_key=api_key) | |
| 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, api_key): | |
| embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=api_key) | |
| new_db = FAISS.load_local("faiss_index", embeddings) | |
| docs = new_db.similarity_search(user_question) | |
| chain = get_conversational_chain() | |
| response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True) | |
| st.write("Reply: ", response["output_text"]) | |
| def main(): | |
| st.header("AI clone chatbot💁") | |
| user_question = st.text_input("Ask a Question from the PDF Files", key="user_question") | |
| if user_question and api_key: # Ensure API key and user question are provided | |
| user_input(user_question, api_key) | |
| 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, key="pdf_uploader") | |
| if st.button("Submit & Process", key="process_button") and api_key: # Check if API key is provided before processing | |
| with st.spinner("Processing..."): | |
| raw_text = get_pdf_text(pdf_docs) | |
| text_chunks = get_text_chunks(raw_text) | |
| get_vector_store(text_chunks, api_key) | |
| st.success("Done") | |
| if __name__ == "__main__": | |
| main() |