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_text(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 Sentimientos y Tabs")
  with gr.Tabs():
    with gr.TabItem("escribiendo en Español"):
      with gr.Row():
        audio = gr.Audio(source="microphone", type="filepath")
        escrito = gr.Textbox()
      b1 = gr.Button("Escribe lo hablado")

    with gr.TabItem("Grado de Satisfaccion"):
      with gr.Row():
        texto=gr.Textbox()
        label=gr.Label()
      b2=gr.Button("¿Como se Sintio?")

    b1.click(audio_a_text, inputs=audio, outputs=escrito)
    b2.click(texto_a_sentimiento, inputs=texto, outputs=label)

demo.launch()