Update app.py

app.py

@@ -32,3 +32,81 @@ print('X_train shape', X_train.shape)
 print('y_train shape', y_train.shape)
 print('X_test shape', X_test.shape)
 print('y_test shape', y_test.shape)
+
+X_train /= 255
+X_test /= 255
+
+X_train = X_train.values.reshape(-1,28,28,1)
+X_test = X_test.values.reshape(-1,28,28,1)
+
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+
+
+print("Training matrix shape", X_train.shape)
+print("Testing matrix shape", X_test.shape)
+
+nb_classes = 10
+
+Y_train = to_categorical(y_train, nb_classes)
+Y_test = to_categorical(y_test, nb_classes)
+
+print(nb_classes)
+print(Y_train.shape)
+print(Y_test.shape)
+
+import sklearn
+from sklearn.model_selection import train_test_split
+
+X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=4)
+
+print(X_train.shape)
+print(Y_train.shape)
+print(X_val.shape)
+print(Y_val.shape)
+
+#Creating CNN model
+
+model = Sequential()
+
+model.add(layers.Conv2D(filters = 80, kernel_size = (5,5),padding = 'Same', activation ='relu', input_shape = (28,28,1)))
+
+model.add(layers.MaxPool2D(pool_size=(2,2)))
+
+model.add(layers.Dropout(0.25))
+
+model.add(layers.Conv2D(filters = 64, kernel_size = (5,5),padding = 'Same', activation ='relu'))
+
+model.add(layers.MaxPool2D(pool_size=(2,2)))
+model.add(layers.Dropout(0.25))
+
+
+model.add(layers.Flatten())
+
+model.add(layers.Dense(128, activation = "relu"))
+
+model.add(layers.Dropout(0.25))
+
+model.add(layers.Dense(10, activation = "softmax"))
+
+model.summary()
+
+from tensorflow.keras.optimizers import SGD
+optimizer = SGD(learning_rate=0.001, momentum=0.30)
+
+model.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"])
+
+history = model.fit( X_train,Y_train, batch_size=64, epochs = 30, validation_data = (X_val, Y_val), verbose = 1)
+
+def predict_image(img):
+  img_3d = img.reshape(-1, 28,28)
+  img_scaled = img_3d/255
+  prediction = model.predict(img_scaled)
+  pred = np.argmax(prediction)
+
+  return pred.item()
+
+
+iface = gr.Interface(predict_image, inputs='sketchpad', outputs='label', title='Digit Recognition Model By Debamrita Paul', description='Draw a single digit(0 to 9)')
+
+iface.launch(share=True)