Update app.py

app.py

@@ -1,7 +1,31 @@
 import streamlit as st
 from keras.datasets import mnist
-option = st.selectbox("Select Your Dataset",["Hand Writen Digit Dataset"])
+from keras.models import Sequential,Model
+from keras.layers import Conv2D,MaxPooling2D,AveragePooling2D,InputLayer,Dense
+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(x_train.shape)
-    
+    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))
+    for i in range(6):
+        axs[i].imshow(
+            m.fit_transform(model.layers[0].weights[0][:, :, :, i][:, :, 0]),
+            cmap="gray"
+        )
+        axs[i].axis("off")  # remove axes for cleaner look
+    
+    st.pyplot(fig)
+