app.py

import tensorflow as tf

inception_net = tf.keras.applications.M

import requests

respuesta  = requests.get("https://git.io/JJkYN")
etiquetas = respuesta.text.split("\n")
# Obteniendo las labels de "https://git.io/JJkYN"

def clasifica_imagen(inp):
  inp = inp.reshape((-1,224,224,3))
  inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
  prediction = inception_net.predict(inp).flatten()
  confidences = {etiquetas[i]: float(prediction[i]) for i in range(1000)}
  return confidences

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("Este es el segundo demo con Blocks")
  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")
    
    with gr.TabItem("Clasificacion de imagenes"):
      with gr.Row():
        imagen = gr.Image(shape=(224,224))
        label1 = gr.Label(num_top_classes=3)
      b3 = gr.Button("clasifica")


    b1.click(audio_a_text, inputs = audio, outputs = transcripcion)
    b2.click(texto_a_sentimiento, inputs = texto, outputs = label)
    b3.click(clasifica_imagen, inputs = imagen, outputs=label1)


demo.launch()