Upload 2.py

2.py

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+import numpy as np
+import gradio as gr
+import tensorflow as tf
+
+model = tf.keras.models.load_model('model1.h5')
+
+def recognize_digit(image):
+    if image is not None:
+        image = image.reshape((1,28,28,1)).astype('float32') / 255
+        
+        prediction = model.predict(image)
+        
+        return {str(i): float(prediction[0][i]) for i in range(10)}
+    else:
+        return ''
+    
+iface = gr.Interface(
+    fn=recognize_digit,
+    inputs=gr.Image(shape=(28,28), image_mode='L', invert_colors=True, source='canvas'),
+    outputs=gr.Label(num_top_classes=10),
+    live=True
+)
+
+iface.launch(share=True)