codigo base app pregunta y respuesta

app.py

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+import gradio as gr
+from simpletransformers.t5 import T5Model , T5Args
+
+
+model_args = T5Args()
+model_args.num_train_epochs = 3
+#model_args.no_save = True
+#model_args.evaluate_generated_text = True
+#model_args.evaluate_during_training = True
+#model_args.evaluate_during_training_verbose = True
+model_args.overwrite_output_dir = True
+model_args.fp16 = False
+model_args.use_cuda = False
+model_args.use_multiprocessing = False
+model_args.use_multiprocessing_for_evaluation = False
+model_args.use_multiprocessed_decoding = False
+model_args.learning_rate=0.001
+#model_args.num_beams = 3
+model_args.train_batch_size = 4
+model_args.eval_batch_size = 4
+model_args.adafactor_beta1 = 0
+model_args.length_penalty=1.5
+model_args.max_length=100
+model_args.max_seq_length = 100
+
+
+model = T5Model("mt5", "hackathon-pln-es/itama", args=model_args , use_cuda=False)
+
+
+def predict(input_text):
+  p = model.predict([input_text])[0]
+  return p
+
+
+gr.Interface(
+   fn=predict,
+   inputs=gr.inputs.Textbox(lines=1, label="Pregunta por profesión - {profesión}: {pregunta}"),
+   outputs=[
+     gr.outputs.Textbox(label="Respuesta"),
+     ],
+   theme="peach",
+   title='Modelo predicctivo AMA Reddit',
+   description='Modelo T5 Transformer (mt5-base), utilizando dataset de preguntas y respuestas de AMA Reddit',
+   examples=[
+     'psicologo: cuanto trabajas al año?',
+     'jefe: cuanto trabajas al año?',
+     'profesor: cuando dinero ganas al año?',
+     ],
+   article=article,
+   allow_flagging="manual",
+   #flagging_options=["right translation", "wrong translation", "error", "other"],
+   flagging_dir="logs"
+   ).launch(enable_queue=True)

requirements.txt

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+gradio
+simpletransformers==0.63.6
+torch