import gradio as gr import PyPDF2 from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores.faiss import FAISS from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain import OpenAI, VectorDBQA import os openai_api_key = os.environ["OPENAI_API_KEY"] def pdf_to_text(pdf_file, query): # Open the PDF file in binary mode with open(pdf_file.name, 'rb') as pdf_file: # Create a PDF reader object pdf_reader = PyPDF2.PdfReader(pdf_file) # Create an empty string to store the text text = "" # Loop through each page of the PDF for page_num in range(len(pdf_reader.pages)): # Get the page object page = pdf_reader.pages[page_num] # Extract the texst from the page and add it to the text variable text += page.extract_text() #embedding step text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.split_text(text) embeddings = OpenAIEmbeddings() #vector store vectorstore = FAISS.from_texts(texts, embeddings) #inference qa = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type="stuff", vectorstore=vectorstore) return qa.run(query) # Define the Gradio interface pdf_input = [gr.inputs.File(label="PDF File")] query_input = gr.inputs.Textbox(label="Query") outputs = gr.outputs.Textbox(label="Chatbot Response") interface = gr.Interface(fn=pdf_to_text, inputs=[pdf_input, query_input]) # Run the interface interface.launch(debug = True)