import gradio as gr import os import time from langchain.document_loaders import OnlinePDFLoader from langchain.text_splitter import CharacterTextSplitter from langchain.llms import OpenAI from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.chains import ConversationalRetrievalChain def loading_pdf(): return "Loading..." def pdf_changes(pdf_doc, open_ai_key): if openai_key is not None: os.environ['OPENAI_API_KEY'] = open_ai_key loader = OnlinePDFLoader(pdf_doc.name) documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.split_documents(documents) embeddings = OpenAIEmbeddings() db = Chroma.from_documents(texts, embeddings) retriever = db.as_retriever() global qa qa = ConversationalRetrievalChain.from_llm( llm=OpenAI(temperature=0), retriever=retriever, return_source_documents=False) return "Ready" else: return "You forgot OpenAI API key" def add_text(history, text): history = history + [(text, None)] return history, "" def bot(history): response = infer(history[-1][0], history) history[-1][1] = "" for character in response: history[-1][1] += character time.sleep(0.05) yield history def infer(question, history): res = [] for human, ai in history[:-1]: pair = (human, ai) res.append(pair) chat_history = res #print(chat_history) query = question result = qa({"question": query, "chat_history": chat_history}) #print(result) return result["answer"] css=""" #col-container {max-width: 700px; margin-left: auto; margin-right: auto;} """ title = """

Chat with PDF • OpenAI

Upload a .PDF from your computer, click the "Load PDF to LangChain" button,
when everything is ready, you can start asking questions about the pdf ;)
This version is set to store chat history, and uses OpenAI as LLM, don't forget to copy/paste your OpenAI API key

""" with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.HTML(title) with gr.Column(): openai_key = gr.Textbox(label="You OpenAI API key", type="password") pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file") with gr.Row(): langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False) load_pdf = gr.Button("Load pdf to langchain") chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350) question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ") submit_btn = gr.Button("Send Message") load_pdf.click(loading_pdf, None, langchain_status, queue=False) load_pdf.click(pdf_changes, inputs=[pdf_doc, openai_key], outputs=[langchain_status], queue=False) question.submit(add_text, [chatbot, question], [chatbot, question]).then( bot, chatbot, chatbot ) submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then( bot, chatbot, chatbot) demo.launch()