app.py

import gradio as gr

from transformers import pipeline

"""pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
def predict(text):
  return pipe(text)[0]["translation_text"]

demo = gr.Interface(
  fn=predict, 
  inputs='text',
  outputs='text',
)"""

pipe = pipeline("sentiment-analysis", model="michellejieli/emotion_text_classifier")

def predict(text):
  return pipe(text)[0]["label"], pipe(text)[0]['score']

demo = gr.Interface(
  fn = predict, 
  inputs = gr.Textbox(placeholder="Text here ..."),
  outputs = ['text', 'number']
)

demo.launch()