app.py

import gradio as gr
import numpy as np
import tensorflow as tf 

def clasifica_imagen(inp):
  inp = inp.resize((224,224))
  inp = np.asarray(inp)[:,:,:3]
  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(len(etiquetas)-1)}
  return confidences

demo=gr.Interface(fn= clasifica_imagen,
                  inputs=gr.Image(type='pil',height=200, width = 200),
                  outputs = gr.Label(num_top_classes = 3)
                  )
demo.launch(share=True)