app.py

import os
os.system('pip install gradio==2.3.5b0')
os.system('pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+${CUDA}.html')


import gradio as gr

from transformers import pipeline

import pandas as pd


table = pd.DataFrame()
tqa = pipeline(task="table-question-answering", model="google/tapas-base-finetuned-wtq")


def chat(message):
    history = gr.get_state() or []


    global table
    
    if message.startswith('http'):
      table = pd.read_csv(message)
      table = table.astype(str)
      response = 'thank you for the dataset... now you can ask questions about it'

    elif table.empty:
      response = 'Hi! Please send a url of a dataset in csv format. Then ask as many questions as you want about it. If you want to talk about another dataset, just send a new link.'
    
    else:
      response = tqa(table=table, query=message)["answer"]



    history.append((message, response))
    gr.set_state(history)
    html = "<div class='chatbot'>"
    for user_msg, resp_msg in history:
        html += f"<div class='user_msg'>{user_msg}</div>"
        html += f"<div class='resp_msg'>{resp_msg}</div>"
    html += "</div>"
    return html

iface = gr.Interface(chat, "text", "html", css="""
    .chatbox {display:flex;flex-direction:column}
    .user_msg, .resp_msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%}
    .user_msg {background-color:cornflowerblue;color:white;align-self:start}
    .resp_msg {background-color:lightgray;align-self:self-end}
""", allow_screenshot=False, allow_flagging=False)
if __name__ == "__main__":
    iface.launch(debug=True)