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app.py

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+import gradio as gr
+import numpy as np
+from tensorflow import keras
+
+model = keras.models.load_model('my_model')
+
+def predict(img):
+  img = np.expand_dims(img, axis=0)
+  return np.argmax(model.predict(img)[0])
+
+demo = gr.Interface(fn=predict, inputs='sketchpad', outputs="text")
+
+demo.launch()

requirements.txt

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+keras
+numpy
+gradio
+huggingface_hub
+tensorflow