app.py

import gradio as gr
from keras.models import load_model  # TensorFlow is required for Keras to work
from PIL import Image, ImageOps  # Install pillow instead of PIL
import numpy as np

model = load_model("keras_model.h5", compile=False)
class_names = open("labels.txt", "r").readlines()

def pred(img):
  data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
  image = img
  size = (224, 224)
  image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
  image_array = np.asarray(image)
  normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
  data[0] = normalized_image_array
  prediction = model.predict(data)
  index = np.argmax(prediction)
  class_name = class_names[index]
  confidence_score = prediction[0][index]

  return class_name[2:], confidence_score

in_img = gr.Image(type="pil")
etiqueta = gr.Textbox(label='Això és...')
percentatge = gr.Textbox(label='robabilitat:')

demo = gr.Interface(
  fn=pred,
  inputs=in_img,
  outputs=[etiqueta, percentatge],
  allow_flagging="never",
  css='footer {visibility: hidden}',
  theme=gr.themes.Monochrome(),
  examples=['ampollaaa.jpg','cartrooo1.jpg'])

demo.launch(debug=True)