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 audio2text(audio):
  text = trans(audio)["text"]
  return text

def text2sentiment(text):
  return clasificador(text)[0]["label"]

demo=gr.Blocks()
with demo:
  gr.Markdown("Gradio + Blocks + TabItem")
  with gr.Tabs():
    with gr.TabItem("Transcribe audio en espanol"):
      with gr.Row():
        audio=gr.Audio(sources="microphone",type="filepath")
        transcripcion=gr.Textbox()
      b1=gr.Button("Transcribe")

    with gr.TabItem("Analisis de sentimiento en espanol"):
      with gr.Row():
        text=gr.Textbox()
        label=gr.Label()
      b2=gr.Button("Sentimiento")

      b1.click(audio2text,inputs=audio,outputs=transcripcion)
      b2.click(text2sentiment,inputs=text,outputs=label)

demo.launch()