import os import datetime import pickle import gradio as gr import langchain from langchain.llms import HuggingFaceHub from chain import get_new_chain api_token = os.environ["HF_TOKEN"] def get_faiss_store(): with open("docs.pkl", "rb") as f: faiss_store = pickle.load(f) return faiss_store def load_model(): print(langchain.__file__) vectorstore = get_faiss_store() flan_ul = HuggingFaceHub(repo_id="google/flan-ul2", model_kwargs={"temperature":0.1, "max_new_tokens":200}, huggingfacehub_api_token=api_token) qa_chain = get_new_chain(vectorstore, flan_ul) return qa_chain def chat(inp, agent): result = [] if agent is None: result.append((inp, "Please wait for model to load (3-5 seconds)")) return result print("\n==== date/time: " + str(datetime.datetime.now()) + " ====") print("inp: " + inp) result = [] output = agent({"question": inp}) answer = output["answer"] result.append((inp, answer)) print(result) return result block = gr.Blocks(css=".gradio-container {background-color: lightgray}") with block: with gr.Row(): gr.Markdown("

PotterChat

Ask questions about the Harry Potter Books, Powered by Flan-UL2

") chatbot = gr.Chatbot() with gr.Row(): message = gr.Textbox( label="What's your question?", placeholder="Who was Harry's godfather?", lines=1, ) submit = gr.Button(value="Send", variant="secondary").style(full_width=False) gr.Examples( examples=[ "Which house in Hogwarts was Harry in?", "Who were Harry's best friends?", "Who taught Potions at Hogwarts?", ], inputs=message, ) gr.HTML( """ This simple application uses Langchain, an open-source LLM, and FAISS to do Q&A over the Harry Potter books.""" ) gr.HTML( "
Powered by Hugging Face 🤗 and LangChain 🦜️🔗
" ) # state = gr.State() agent_state = gr.State() block.load(load_model, inputs=None, outputs=[agent_state]) # submit.click(chat, inputs=[message, state, agent_state], outputs=[chatbot, state]) # message.submit(chat, inputs=[message, state, agent_state], outputs=[chatbot, state]) submit.click(chat, inputs=[message, agent_state], outputs=[chatbot]) message.submit(chat, inputs=[message, agent_state], outputs=[chatbot]) block.launch(debug=True)