Update app.py

app.py

@@ -4,24 +4,14 @@ from sklearn.preprocessing import MinMaxScaler
 from keras.models import Sequential,Model
 import matplotlib.pyplot as plt
 m = MinMaxScaler()
+from tensorflow import keras
+model = keras.models.load_model('cnn_model.keras')
 from keras.layers import Conv2D,MaxPooling2D,AveragePooling2D,InputLayer,Dense,Flatten
 option = st.sidebar.selectbox("Datasets",["Select dataset","Hand Writen Digit Dataset"])
 if option == "Hand Writen Digit Dataset":
     (x_train,y_train),(x_test,y_test) = mnist.load_data()
     st.write("Successfully Load the Dataset")
 if st.button("Train"):
-    model = Sequential()
-    model.add(InputLayer(shape=(28,28,1)))
-    model.add(Conv2D(filters=6,kernel_size=(5,5),activation="tanh",padding="valid",strides=(1,1)))
-    model.add(AveragePooling2D(pool_size=(2,2),strides=(2,2)))
-    model.add(Conv2D(filters=16,kernel_size=(5,5),activation="tanh",padding="same",strides=(1,1)))
-    model.add(AveragePooling2D(pool_size=(2,2),strides=(2,2)))
-    model.add(Conv2D(filters=120,kernel_size=(5,5),activation="tanh",padding="valid",strides=(1,1)))
-    model.add(Flatten())
-    model.add(Dense(units=84,activation="tanh"))
-    model.add(Dense(10,activation="softmax"))
-    model.compile(optimizer="sgd",loss="sparse_categorical_crossentropy",metrics=["accuracy"])
-    model.fit(x_train,y_train,epochs=10,batch_size=128,validation_split=0.2)
     fig, axs = plt.subplots(6, 1, figsize=(8, 6))
     col1,col2,col3,col4,col5,col6 = st.columns(6)
     with col1: