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app.py

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+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("/content/keras_model.h5", compile=False)
+class_names = open("/content/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=['/content/ampollaaa.jpg','/content/vidreeeee.jpg','/content/cartrooo1.jpg'])
+
+demo.launch(debug=True)

keras_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:46400f3ec395f14170acb8c160f2ace1124dfaaa40626d246e0443a93462e730
+size 2456608

labels.txt

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+0 Vidre
+1 Paper
+2 Cartró
+3 Metall
+4 Plàstic

requirements.txt

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+numpy
+gradio
+pillow
+keras
+tensorflow == 2.12