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| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain_community.llms import OpenAI | |
| from langchain_community.vectorstores import FAISS | |
| from langchain_community.callbacks import get_openai_callback | |
| from langchain.chains.question_answering import load_qa_chain | |
| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| import re | |
| ##! Converting PDF to text ## | |
| # def read_pdf(pdf): | |
| # pdf_reader = PdfReader(pdf) | |
| # text = "" | |
| # for page in pdf_reader.pages: | |
| # text += page.extract_text() | |
| # return text | |
| ##! Saving vectorestore locally ## | |
| # def save_vectorstore(title, vector_store): | |
| # title = re.sub('\s+', '-', title) | |
| # title = re.sub('[^a-zA-Z0-9\-]+', '', title) | |
| # vector_store.save_local(f"./vectordb/{title[:35]}") | |
| # print(f"{title} stored!") | |
| # return True | |
| ##* Load vectorstore ## | |
| def load_vectorstore(name, embeddings): | |
| vector_store = FAISS.load_local(f"./vectordb/{name}/", embeddings=embeddings, allow_dangerous_deserialization=True) | |
| return vector_store | |
| def main(): | |
| st.header('Chat with PDF 💬') | |
| # pdf = st.file_uploader("Upload PDF", type='pdf') | |
| embeddings = OpenAIEmbeddings() | |
| vector_store = '' | |
| if 'clicked' not in st.session_state: | |
| st.session_state.clicked = False | |
| def click_button(): | |
| st.session_state.clicked = True | |
| # st.button('Load', on_click=click_button) | |
| with st.container(border=True): | |
| st.markdown(''' | |
| *Disclaimer: Section for uploading the PDF file has been removed as the API calls for OpenAI are not free. I've included few pdfs for Q&A. You can access the source code and enable the section for uploading PDFs.* | |
| ''') | |
| ##! Converting text to word Embeddings ## | |
| # if st.session_state.clicked: | |
| # if pdf is not None: | |
| # text = read_pdf(pdf) | |
| # text_splitter = RecursiveCharacterTextSplitter( | |
| # chunk_size=1000, | |
| # chunk_overlap=200, | |
| # length_function=len | |
| # ) | |
| # chunks = text_splitter.split_text(text=text) | |
| # vector_store = FAISS.from_texts(chunks, embedding=embeddings) | |
| # if save_btn: | |
| # save_vectorstore(pdf.name[:-4], vector_store) | |
| option = st.selectbox(label="Select the PDF: ", options=['Budget Speech 2024', 'The 100 Page Machine Learning Book'], index=None) | |
| query = st.text_input("Ask questions from your PDF file:") | |
| if query and not option: | |
| st.warning("Please Select a PDF") | |
| if option: | |
| title = re.sub('\s+', '-', option) | |
| title = re.sub('[^a-zA-Z0-9\-]+', '', title) | |
| vector_store = load_vectorstore(title, embeddings) | |
| if query: | |
| docs = vector_store.similarity_search(query=query, k=3) | |
| llm = OpenAI(temperature=0) | |
| chain = load_qa_chain(llm=llm, chain_type="stuff") | |
| with get_openai_callback() as cb: | |
| response = chain.invoke({"input_documents": docs, "question":query}) | |
| print(cb) | |
| st.write(response["output_text"]) | |
| if __name__ == '__main__': | |
| main() |