Update app.py

app.py

@@ -1,105 +1,5 @@
-# import streamlit as st 
-# import  numpy as np 
-# import matplotlib.pyplot as plt
-# import keras
-# from sklearn.preprocessing import MinMaxScaler
-# from keras.models import Model
-# from keras.layers import Conv2D
-# import cv2
-# import tensorflow as tf 
-
-# 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']
-# # selected_option = st.selectbox('Choose an option:', options)
-
-# st.sidebar.title("Controls")
-# uploaded_img = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-# options = ['1st Convolution', '2nd Convolution', '3rd Convolution']
-# selected_option = st.sidebar.selectbox('Select convolution layer:', options)
-
-
-# conv_layers = [layer for layer in model.layers if isinstance(layer, Conv2D)]
-# layer_ind = options.index(selected_option)
-# selected_layer = conv_layers[layer_ind]
-
-# fig = plt.figure(figsize=(12, 4))
-
-# #layer_ind = options.index(selected_option)
-# # selected_layer = conv_layers[layer_ind]
-# scaler = MinMaxScaler()
-
-# # for i in range(3):
-# for j in range(6):
-#     layer=selected_layer
-#     weights=layer.get_weights()[0][:,:,0,j]
-#     norm_weights = scaler.fit_transform(weights)
-#     plt.subplot(2,3,j+1)
-#     plt.imshow(norm_weights,cmap='gray')
-#     plt.title(f"Filters {j+1}")
-#     plt.axis('off')
-# plt.tight_layout()
-# st.pyplot(fig)
-
-
-# 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_container_width =True, channels="GRAY")
-
-#     #layer_ind = options.index(selected_option)
-#     # selected_layer = conv_layers[layer_ind]
-
-#     #func_model = Model(inputs = model.layers[0].input, outputs = model.selected_layer.output)
-#     func_model = Model(inputs = model.layers[0].input, outputs = selected_layer.output)
-
-
-#     fm = func_model.predict(input_img)
-#     fm = fm[0]
-
-#     if layer_ind == 0:
-#         fig1 = plt.figure(figsize=(12, 4))
-#         for i in range(6):
-#             plt.subplot(2, 3, i + 1)
-#             plt.imshow(fm[:, :, i], cmap='gray')
-#             plt.title(f"Feature Map {i+1}")
-#             plt.axis('off')
-    
-#     elif layer_ind == 1:
-#         fig1 = plt.figure(figsize=(25, 15))
-#         for i in range(16):
-#             plt.subplot(2, 8, i + 1)
-#             plt.imshow(fm[:, :, i], cmap='gray')
-#             plt.title(f"Feature Map {i+1}")
-#             plt.axis('off')
-    
-#     elif layer_ind == 2:
-#         fig1 = plt.figure(figsize=(100, 50))
-#         for i in range(120):
-#             plt.subplot(12,10, i + 1)
-#             plt.imshow(fm[:, :, i],cmap="gray")
-#             plt.title(f"Feature Map {i+1}")
-#             plt.axis('off')
-
-#     plt.tight_layout()
-#     st.pyplot(fig1)
-
-
-
-
-
-
 import streamlit as st 
-import numpy as np 
+import  numpy as np 
 import matplotlib.pyplot as plt
 import keras
 from sklearn.preprocessing import MinMaxScaler
@@ -108,76 +8,90 @@ from keras.layers import Conv2D
 import cv2
 import tensorflow as tf 
 
-# -----------------------------
-# Helper function to normalize feature maps
-def normalize_feature_map(fm):
-    fm_min = np.min(fm)
-    fm_max = np.max(fm)
-    if fm_max - fm_min == 0:
-        return np.zeros_like(fm)
-    return (fm - fm_min) / (fm_max - fm_min)
-# -----------------------------
-
-# Load model
 model = keras.models.load_model("model.keras")
 
-# Sidebar UI
+# uploaded_img = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+# options = ['1st Convolution', '2nd Convolution', '3rd Convolution']
+# selected_option = st.selectbox('Choose an option:', options)
+
 st.sidebar.title("Controls")
 uploaded_img = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 options = ['1st Convolution', '2nd Convolution', '3rd Convolution']
 selected_option = st.sidebar.selectbox('Select convolution layer:', options)
 
-# Extract Conv2D layers from model
+
 conv_layers = [layer for layer in model.layers if isinstance(layer, Conv2D)]
 layer_ind = options.index(selected_option)
 selected_layer = conv_layers[layer_ind]
 
-# Show filters (kernels) from selected layer
 fig = plt.figure(figsize=(12, 4))
+
+#layer_ind = options.index(selected_option)
+# selected_layer = conv_layers[layer_ind]
 scaler = MinMaxScaler()
 
+# for i in range(3):
 for j in range(6):
-    weights = selected_layer.get_weights()[0][:, :, 0, j]
+    layer=selected_layer
+    weights=layer.get_weights()[0][:,:,0,j]
     norm_weights = scaler.fit_transform(weights)
-    plt.subplot(2, 3, j + 1)
-    plt.imshow(norm_weights, cmap='gray')
-    plt.title(f"Filter {j+1}")
+    plt.subplot(2,3,j+1)
+    plt.imshow(norm_weights,cmap='gray')
+    plt.title(f"Filters {j+1}")
     plt.axis('off')
-
 plt.tight_layout()
 st.pyplot(fig)
 
-# Process uploaded image
+
 if uploaded_img is not None:
-    # Read image from uploader
+    #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_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)
+    
+    
 
-    # Normalize input image
-    img_norm = img_resized.astype('float32') / 255.0
-    input_img = img_norm.reshape(1, 28, 28, 1)
+    st.image(img_resized, caption="Uploaded Image (Resized to 28x28)", use_container_width =True, channels="GRAY")
 
-    # Display resized input
-    st.image(img_resized, caption="Uploaded Image (Resized to 28x28)", use_container_width=True, channels="GRAY")
+    #layer_ind = options.index(selected_option)
+    # selected_layer = conv_layers[layer_ind]
 
-    # Create intermediate model to get output from selected conv layer
-    func_model = Model(inputs=model.layers[0].input, outputs=selected_layer.output)
-    fm = func_model.predict(input_img)[0]  # shape: (H, W, C)
+    #func_model = Model(inputs = model.layers[0].input, outputs = model.selected_layer.output)
+    func_model = Model(inputs = model.layers[0].input, outputs = selected_layer.output)
 
-    # Display feature maps
-    num_feature_maps = fm.shape[-1]
 
-    # Automatically adjust grid size
-    cols = 8 if layer_ind == 1 else (10 if layer_ind == 2 else 3)
-    rows = int(np.ceil(num_feature_maps / cols))
+    fm = func_model.predict(input_img)
+    fm = fm[0]
 
-    fig1 = plt.figure(figsize=(cols * 3, rows * 3))
-    for i in range(num_feature_maps):
-        plt.subplot(rows, cols, i + 1)
-        plt.imshow(normalize_feature_map(fm[:, :, i]), cmap='gray')
-        plt.title(f"Feature Map {i+1}")
-        plt.axis('off')
+    if layer_ind == 0:
+        fig1 = plt.figure(figsize=(12, 4))
+        for i in range(6):
+            plt.subplot(2, 3, i + 1)
+            plt.imshow(fm[:, :, i], cmap='gray')
+            plt.title(f"Feature Map {i+1}")
+            plt.axis('off')
+    
+    elif layer_ind == 1:
+        fig1 = plt.figure(figsize=(25, 15))
+        for i in range(16):
+            plt.subplot(2, 8, i + 1)
+            plt.imshow(fm[:, :, i], cmap='gray')
+            plt.title(f"Feature Map {i+1}")
+            plt.axis('off')
+    
+    elif layer_ind == 2:
+        fig1 = plt.figure(figsize=(100, 50))
+        for i in range(120):
+            plt.subplot(12,10, i + 1)
+            plt.imshow(fm[:, :, i],cmap="gray")
+            plt.title(f"Feature Map {i+1}")
+            plt.axis('off')
 
     plt.tight_layout()
     st.pyplot(fig1)
+
+
+