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| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| import os | |
| from langchain_google_genai import GoogleGenerativeAIEmbeddings | |
| import google.generativeai as genai | |
| from langchain.vectorstores import Pinecone as PC | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| os.getenv("GOOGLE_API_KEY") | |
| os.getenv("PINECONE_API_KEY") | |
| genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) | |
| os.environ['PINECONE_API_KEY'] = os.getenv("PINECONE_API_KEY") | |
| def Pine(): | |
| from pinecone import Pinecone, ServerlessSpec | |
| pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY")) | |
| index_name = "testing" | |
| if index_name not in pc.list_indexes().names(): | |
| pc.create_index( | |
| name=index_name, | |
| dimension=768, | |
| metric="cosine", | |
| spec=ServerlessSpec(cloud='aws', region='us-east-1') | |
| ) | |
| return index_name | |
| 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): | |
| index_name = Pine() | |
| embedding = GoogleGenerativeAIEmbeddings(model="models/embedding-001") | |
| docsearch = PC.from_texts([t for t in text_chunks], embedding, index_name=index_name) | |
| return docsearch | |
| def showman(pdf_docs): | |
| st.header("Chat with PDF") | |
| user_question = st.text_input("Ask a Question from the PDF Files", key="user_question") | |
| ask_another_question = st.button("Ask Another Question",on_click=clear_text) | |
| if user_question and not ask_another_question: | |
| llm = ChatGoogleGenerativeAI(model="models/gemini-1.5-pro-latest", temperature=0.9) | |
| from langchain.chains import RetrievalQA | |
| qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=st.session_state["docsearch"].as_retriever()) | |
| response = qa(user_question) | |
| st.session_state["response"] = response["result"] | |
| st.write("Answer:", st.session_state["response"]) | |
| def clear_text(): | |
| st.session_state["user_question"] = "" | |
| st.session_state["response"] = "" | |
| def show(): | |
| with st.sidebar: | |
| st.title("Menu:") | |
| pdf_docs = st.file_uploader("Upload your PDF Files", accept_multiple_files=True) | |
| st.session_state["pdf_docs"] = pdf_docs if pdf_docs is not None else st.session_state.get("pdf_docs", []) | |
| processed = st.session_state.get("processed", False) | |
| if not processed and pdf_docs: | |
| if st.button("Submit & Process"): | |
| with st.spinner("Processing..."): | |
| raw_text = get_pdf_text(pdf_docs) | |
| text_chunks = get_text_chunks(raw_text) | |
| docsearch = get_vector_store(text_chunks) | |
| st.session_state["docsearch"] = docsearch | |
| st.session_state["processed"] = True | |
| st.success("Done!") | |
| showman(st.session_state["pdf_docs"]) |