import datetime import os import gradio as gr import langchain import pickle from langchain.vectorstores import Weaviate from langchain import OpenAI from chain import get_new_chain1 def get_faiss_store(): with open("docs.pkl", 'rb') as f: faiss_store = pickle.load(f) return faiss_store def set_openai_api_key(api_key, agent): if api_key: os.environ["OPENAI_API_KEY"] = api_key vectorstore = get_faiss_store() rephraser_llm = OpenAI(model_name="text-davinci-003", temperature=0) final_output_llm = OpenAI(model_name="text-davinci-003", temperature=0, max_tokens=-1) qa_chain = get_new_chain1(vectorstore, rephraser_llm, final_output_llm) os.environ["OPENAI_API_KEY"] = "" return qa_chain def chat(inp, history, agent): history = history or [] if agent is None: history.append((inp, "Please paste your OpenAI key to use")) return history, history print("\n==== date/time: " + str(datetime.datetime.now()) + " ====") print("inp: " + inp) history = history or [] output = agent({"question": inp, "chat_history": history}) answer = output["answer"] history.append((inp, answer)) print(history) return history, history block = gr.Blocks(css=".gradio-container {background-color: lightgray}") with block: with gr.Row(): gr.Markdown("
Ask questions about the Hugging Face Transformers Library
") openai_api_key_textbox = gr.Textbox( placeholder="Paste your OpenAI API key (sk-...)", show_label=False, lines=1, type="password", ) chatbot = gr.Chatbot() with gr.Row(): message = gr.Textbox( label="What's your question?", placeholder="What's the answer to life, the universe, and everything?", lines=1, ) submit = gr.Button(value="Send", variant="secondary").style(full_width=False) gr.Examples( examples=[ "How do I install transformers?", "How do I load pretrained instances with an AutoClass?", "How do I fine-tune a pretrained model?", ], inputs=message, ) gr.HTML( """ This simple application uses Langchain, an LLM, and FAISS to do Q&A over the Hugging Face Documentation.""" ) gr.HTML( "