app.py

import gradio as gr

from db import get_db
from chain import get_chain

import logging
logger = logging.getLogger(__name__)


#logger.info('Instantiating vectordb')
#vectordb = get_db(
#    chunk_size=1000,
#    chunk_overlap=200,
#    model_name = 'intfloat/multilingual-e5-large-instruct',
#)


#logger.info('Instantiating chain')
#chain = get_chain(
#    vectordb,
#    repo_id="HuggingFaceH4/zephyr-7b-beta",
#    task="text-generation",
#    max_new_tokens=512,
#    top_k=30,
#    temperature=0.1,
#    repetition_penalty=1.03,
#    search_type="mmr",
#    k=3,
#    fetch_k=5,
#    template="""Use the following sentences of context to answer the question at the end.
#If you don't know the answer, that is if the answer is not in the context, then just say that you don't know, don't try to make up an answer.
#Always say "Thanks for asking!" at the end of the answer.
#
#{context}
#
#Question: {question}
#Helpful Answer:"""
#)

def respond(
    question,
    _, # Ignore the message history parameter since we are doing one-off invocations
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    print(f'respond called by Gradio ChatInterface with question={question}')
    #return chain.invoke({'question': question})
    return "hello!"


demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


if __name__ == "__main__":
    demo.launch()