app.py

import gradio as gr
from transformers import pipeline

trans = pipeline("automatic-speech-recognition", model = "facebook/wav2vec2-large-xlsr-53-spanish")
classificador = 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 classificador(text)[0]["label"]



demo = gr.Blocks()

with demo:
  gr.Markdown("Second demo with Blocks")
  with gr.Tabs():
    with gr.TabItem("Transcribe audio"):
      with gr.Row():
        audio = gr.Audio(source="microphone", type = "filepath")
        transcription = gr.Textbox()
      b1 = gr.Button("Transcribe Audio")
      #b1.click(fn=)
    with gr.TabItem("Sentiment Analysis"):
      with gr.Row():
        text = gr.Textbox()
        label = gr.Label()
      b2 = gr.Button('Classify')
    
    b1.click(audio_a_texto, inputs = audio, outputs = transcription)
    b2.click(texto_a_sentimiento, inputs = text, outputs = label)

demo.launch()