Update app.py

app.py

@@ -3,26 +3,17 @@ import  numpy as np
 import matplotlib.pyplot as plt
 import keras
 from sklearn.preprocessing import MinMaxScaler
-from keras.models import Sequential,Model
-from keras.layers import Input,Dense,Conv2D,AveragePooling2D,Flatten
-
-
 
 model = keras.models.load_model("model.keras")
 
 uploaded_img = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
 options = ['1st Convolution', '2nd Convolution', '3rd Convolution']
-
-# Create the selectbox
-selected_option = st.selectbox(
-    'Choose an option:',  
-     options             
-)
+selected_option = st.selectbox('Choose an option:', options)
 
 conv_layers = [layer for layer in model.layers if isinstance(layer, Conv2D)]
 
-plt.figure(figsize=(12, 4))
+fig = plt.figure(figsize=(12, 4))
 scaler = MinMaxScaler()
 
 # for i in range(3):
@@ -32,9 +23,35 @@ for j in range(6):
     norm_weights = scaler.fit_transform(weights)
     plt.subplot(2,3,j+1)
     plt.imshow(norm_weights,cmap='gray')
-    #plt.title(f"Conv Layer")
-    plt.title(f"Filter {j}")
+    plt.title(f"Conv Layer")
     plt.axis('off')
 plt.tight_layout()
-st.pyplot(plt.gcf())
-
+st.pyplot(fig)
+#plt.show();
+
+if uploaded_img is not None:
+    #st.image(uploaded_img, caption="Uploaded Image", use_column_width=True)
+    file_bytes = np.frombuffer(uploaded_img.read(), np.uint8)
+    img = cv2.imdecode(file_bytes, cv2.IMREAD_GRAYSCALE)
+    img_resized = cv2.resize(img,(28,28),interpolation=cv2.INTER_AREA)
+    #img_norm = img_resized.astype('float32') / 255.0
+    input_img = img_resized.reshape(1,28,28,1)
+    st.image(img_resized, caption="Uploaded Image (Resized to 28x28)", use_column_width=True, channels="GRAY")
+
+    layer_ind = options.index(selected_option)
+    selected_layer = conv_layers[layer_ind]
+
+    func_model = Model(inputs = model.input, outputs = selected_layer.output)
+
+    fm = func_model.predict(input_img)
+    fm = fm[0]
+
+    fig1 = plt.figure(figsize=(12, 4))
+    for i in range(6):
+        plt.subplot(2, 3, i + 1)
+        plt.imshow(feature_maps[:, :, i], cmap='gray')
+        plt.title(f"Feature Map {i}")
+        plt.axis('off')
+
+    plt.tight_layout()
+    st.pyplot(fig1)