app.py

import gradio as gr
from transformers import pipeline

#trans = pipeline("automatic-speech-recognition", model = "facebook/wav2vec2-large-xlsr-53-spanish")
clasificador = pipeline("text-classification", model = "pysentimiento/robertuito-sentiment-analysis")


#def audio_a_texto(audio):
#    text = trans(audio)["text"]
#    return text


def texto_a_sentimiento(text):
    return clasificador(text)[0]["label"]
    
demo = gr.Blocks()

with demo:
    gr.Markdown("Demo para el taller de FAIaS.")
    with gr.Tabs():
      #with gr.TabItem("Transcribe audio en español"):
      #  with gr.Row():
      #    audio = gr.Audio(source="microphone", type="filepath")
      #    transcripcion = gr.Textbox()
      #  b1 = gr.Button("Transcribe porfa")
    
      with gr.TabItem("Análisis de sentimiento en español"):
        with gr.Row():
          texto = gr.Textbox()
          label = gr.Label()
        b2 = gr.Button("Sentimiento porfa")

    #b1.click(audio_a_texto, inputs=audio, outputs=transcripcion)
    b2.click(texto_a_sentimiento, inputs=texto, outputs=label)

demo.launch()