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app.py

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+import cv2
+import gradio as gr
+import tensorflow as tf
+
+title = "Welcome on your first sketch recognition app!"
+
+
+head = (
+  "<center>"
+  "<img src='file/mnist-classes.png' width=400>"
+  "The robot was trained to classify numbers (from 0 to 9). To test it, write your number in the space provided."
+  "</center>"
+)
+
+ref = "Find the whole code [here](https://github.com/ovh/ai-training-examples/tree/main/apps/gradio/sketch-recognition)."
+
+img_size = 28
+labels = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
+
+model = tf.keras.models.load_model("number_recognition_model_colab.keras")
+
+
+def predict(img):
+
+  img = cv2.resize(img, (img_size, img_size))
+  img = img.reshape(1, img_size, img_size, 1)
+
+  preds = model.predict(img)[0]
+
+  return {label: float(pred) for label, pred in zip(labels, preds)}
+
+label = gr.outputs.Label(num_top_classes=3)
+
+
+
+interface = gr.Interface(fn=predict, inputs="sketchpad", outputs=label, title=title, description=head, article=ref)
+interface.launch()

requirements.txt

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+gradio==3.0.10
+tensorflow~=2.18.0
+opencv-python-headless
+numpy~=2.0.2
+opencv-python~=4.10.0.84
+pillow~=11.0.0