Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+# Load the model
+model = tf.keras.saving.load_model('/content/mnist_trained_model_2(acc-97%).h5')
+
+# Classification prediction function
+labels = [0,1,2,3,4,5,6,7,8,9]
+def classify_image(image):
+  prediction = model.predict(image.reshape(-1,784)/255).flatten()
+  confidences = {labels[i]: float(prediction[i]) for i in range(10)}
+  return confidences
+
+# Interface
+label = gr.outputs.Label(num_top_classes=3)
+interface = gr.Interface(fn=classify_image, 
+             inputs="sketchpad",
+             outputs=label,
+             capture_session="True")
+
+# Launch
+interface.launch(inline = False)