app.py

import gradio as gr
from gradio import CSVLogger
from huggingface_hub import Repository as repo
import os
HF_TOKEN = os.getenv('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')#  #'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'
hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "tigo_question_answer")
def question_answer(context, question):
  predictions = model.predict([{"context": context, "qas": [{"question": question, "id": "0",}],}])
  prediccion = predictions[0][0]['answer'][0]
  return prediccion
iface = gr.Interface(fn=question_answer, inputs=["text", "text"], outputs=["text"],
                     allow_flagging="manual", #manual
                     flagging_options=["correcto", "incorrecto"],
                     flagging_dir='flagged',
                     flagging_callback=hf_writer)
iface.launch(enable_queue=True)