Update app.py

app.py

@@ -1,41 +1,21 @@
 import streamlit as st
 from keras.datasets import mnist
 from sklearn.preprocessing import MinMaxScaler
-from keras.models import Model
+from keras.models import Sequential,Model
 import matplotlib.pyplot as plt
-import numpy as np
-from tensorflow import keras
-from PIL import Image
-
 m = MinMaxScaler()
-model = keras.models.load_model("cnn_lenet.keras")
-
-# Sidebar upload
-uploaded_file = st.sidebar.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-
-# Load MNIST for fallback
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
-
-# Preprocess uploaded image or fallback
-if uploaded_file is not None:
-    image = Image.open(uploaded_file).convert("L")   # grayscale
-    image = image.resize((28, 28))                   # resize to model input
-    img_array = np.array(image) / 255.0
-    img_array = img_array.reshape(1, 28, 28, 1)      # reshape for CNN
-    st.success("✅ Successfully uploaded image")
-    st.image(image, caption="Uploaded Image (28x28)", use_column_width=True)
-else:
-    img_array = x_train[0:1].reshape(1, 28, 28, 1)
-    st.info("ℹ️ No image uploaded, using MNIST sample instead")
-    st.image(x_train[0], caption="MNIST Sample", use_column_width=True)
-
-# Button to visualize
-if st.button("Visualize Layers"):
-    col1, col2, col3, col4, col5, col6 = st.columns(6)
-
+from tensorflow import keras
+model = keras.models.load_model('cnn_lenet.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"):
+    fig, axs = plt.subplots(6, 1, figsize=(8, 6))
+    col1,col2,col3,col4,col5,col6 = st.columns(6)
     with col1:
         st.write("Filters")
-        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]),
@@ -43,57 +23,46 @@ if st.button("Visualize Layers"):
             )
             axs[i].axis("off")
         st.pyplot(fig)
-
     with col2:
         st.write("Conv2D Layer-1")
-        sub = Model(inputs=model.inputs[0], outputs=model.layers[0].output)
-        fmap = sub.predict(img_array)
-        fig, ax = plt.subplots(6, 1, figsize=(8, 6))
+        sub = Model(inputs=model.inputs[0],outputs=model.layers[0].output)
+        fig,ax = plt.subplots(6,1,figsize=(8,6))
         for i in range(6):
-            ax[i].imshow(fmap[0, :, :, i], cmap="gray")
+            ax[i].imshow(sub.predict(x_train[0:1,:].reshape(1,28,28,1))[0,:,:,i],cmap="gray")
             ax[i].axis("off")
         st.pyplot(fig)
-
     with col3:
         st.write("Average Pooling Layer-1")
-        max1 = Model(inputs=model.inputs[0], outputs=model.layers[1].output)
-        fmap = max1.predict(img_array)
-        fig, ax1 = plt.subplots(6, 1, figsize=(8, 6))
+        max1 = Model(inputs=model.inputs[0],outputs=model.layers[1].output)
+        fig,ax1 = plt.subplots(6,1,figsize=(8,6))
         for i in range(6):
-            ax1[i].imshow(fmap[0, :, :, i], cmap="gray")
+            ax1[i].imshow(max1.predict(x_train[0:1,:].reshape(1,28,28,1))[0,:,:,i],cmap="gray")
             ax1[i].axis("off")
         st.pyplot(fig)
-
+    
     with col4:
         st.write("Conv2D Layer-2")
-        sub1 = Model(inputs=model.inputs[0], outputs=model.layers[2].output)
-        fmap = sub1.predict(img_array)
-        fig, ax2 = plt.subplots(16, 1, figsize=(8, 6))
+        sub1 = Model(inputs=model.inputs[0],outputs=model.layers[2].output)
+        fig,ax2 = plt.subplots(16,1,figsize=(8,6))
         for i in range(16):
-            ax2[i].imshow(fmap[0, :, :, i], cmap="gray")
+            ax2[i].imshow(sub1.predict(x_train[0:1,:].reshape(1,28,28,1))[0,:,:,i],cmap="gray")
             ax2[i].axis("off")
         st.pyplot(fig)
-
     with col5:
         st.write("Average Pooling Layer-2")
-        max2 = Model(inputs=model.inputs[0], outputs=model.layers[3].output)
-        fmap = max2.predict(img_array)
-        fig, ax3 = plt.subplots(16, 1, figsize=(8, 6))
+        max2 = Model(inputs=model.inputs[0],outputs=model.layers[3].output)
+        fig,ax3 = plt.subplots(16,1,figsize=(8,6))
         for i in range(16):
-            ax3[i].imshow(fmap[0, :, :, i], cmap="gray")
+            ax3[i].imshow(max2.predict(x_train[0:1,:].reshape(1,28,28,1))[0,:,:,i],cmap="gray")
             ax3[i].axis("off")
         st.pyplot(fig)
-
     with col6:
         st.write("Conv2D Layer-3")
-        sub3 = Model(inputs=model.inputs[0], outputs=model.layers[4].output)
-        fmap = sub3.predict(img_array)
-        fig, ax4 = plt.subplots(30, 4, figsize=(12, 20))  # grid for 120 filters
-        idx = 0
-        for r in range(30):
-            for c in range(4):
-                if idx < 120:
-                    ax4[r, c].imshow(fmap[0, :, :, idx], cmap="gray")
-                    ax4[r, c].axis("off")
-                    idx += 1
+        sub3 = Model(inputs=model.inputs[0],outputs=model.layers[4].output)
+        fig,ax4 = plt.subplots(120,1,figsize=(8,6))
+        for i in range(120):
+            ax4[i].imshow(sub3.predict(x_train[0:1,:].reshape(1,28,28,1))[0,:,:,i],cmap="gray")
+            ax4[i].axis("off")
         st.pyplot(fig)
+    
+