Upload 5 files

app.py

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+import gradio as gr
+import tensorflow as tf
+
+model = tf.keras.models.load_model('model.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=3),
+    live=True
+)
+iface.launch()
+    
+
+# ref : https://www.youtube.com/watch?v=3DGLznJorT8    
+

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1f16fa06c2945927a6f6579d2829f900518e9c944cdee40303a599d3999ccf8e
+size 1172728

modelgen.py

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+import tensorflow as tf
+from tensorflow.keras import layers,models
+(train_images,train_labels),(test_images,test_labels) = tf.keras.datasets.mnist.load_data()
+
+train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32')/255
+test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32')/255
+
+train_labels = tf.keras.utils.to_categorical(train_labels)
+print('train_labels')
+test_labels = tf.keras.utils.to_categorical(test_labels)
+
+model = models.Sequential()
+model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1)))
+model.add(layers.MaxPooling2D(2,2))
+model.add(layers.Conv2D(64,(3,3),activation='relu'))
+model.add(layers.MaxPooling2D(2,2))
+model.add(layers.Conv2D(64,(3,3),activation='relu'))
+model.add(layers.Flatten())
+model.add(layers.Dense(64,activation='relu'))
+model.add(layers.Dense(10,activation='softmax'))
+
+model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
+model.fit(train_images,train_labels,epochs=5,batch_size=64,validation_split=0.1)
+
+model.save('model.h5')
+
+print('Here am I')

requirements.txt

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+tensorflow==2.10.1
+gradio==3.50.2