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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
+
+imatge_entrada = gr.Image(type='pil')
+etiqueta = gr.Textbox(label='Això és...')
+percentatge = gr.Textbox(label='Probabilitat:')
+
+demo = gr.Interface(
+    fn=pred,
+    inputs=imatge_entrada,
+    outputs=[etiqueta, percentatge],
+    allow_flagging="never",
+    css="footer {visibility: hidden}",
+    theme=gr.themes.Soft(),
+    examples=["1024_2000.jpeg"])
+
+demo.launch(debug=True)

keras_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e62a1d8282fe5bf29d366211cae043b3b31982566f1670b64a4037b5513bad3
+size 2453432

labels.txt

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+0 Class 1
+1 Class 2

requirements.txt

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