Update app.py

app.py

@@ -9,6 +9,16 @@ import streamlit as st
 from PIL import Image
 import pandas as pd
 import numpy as np
+from keras import layers  
+import matplotlib.pyplot as plt 
+
+def get_img_array(img_path, target_size):
+    array = keras.utils.img_to_array(img)
+    array = np.expand_dims(array, axis=0)
+    return array
+st.set_page_config(layout="wide")
+
+
 
 model_type = st.sidebar.selectbox(
     'Select Model', ('VGG16', 'VGG19', 'ResNet50V2', 'MobileNetV2'))
@@ -18,7 +28,7 @@ model_type2 = models[model_type]
 
 top_n = st.sidebar.selectbox('Number of Results', (3, 5, 10))
 results = st.sidebar.selectbox('Display Summary', ('No','Yes'))
-
+display = st.sidebar.selectbox('Display Filtered Images', ('No','Yes'))
 
 exec(f'from keras.applications.{model_type2} import {model_type}')
 exec(
@@ -31,7 +41,7 @@ try:
     img = Image.open(img_path)
 except:
     img = Image.open('dog.jpg')
-st.image(img)
+
 
 img = img.resize((224, 224))  # Resize to match VGG16 input size
 x = np.array(img)
@@ -50,11 +60,77 @@ df = df[['Object', 'Percent Certainty']]
 df['Percent Certainty'] = df['Percent Certainty'].apply(
     lambda x: '{:.2%}'.format(x))
 
-st.dataframe(df)
+# with st.container():
 
-if results=='Yes':
-    stringlist = []
-    model.summary(print_fn=lambda x: stringlist.append(x))
-    short_model_summary = "\n".join(stringlist)
-    print(short_model_summary)
-    st.write(short_model_summary)
+with st.container():
+    col1, col2 = st.columns((1,3))
+    with col1:
+        st.image(img,width=400)
+    with col2:
+        st.dataframe(df)
+        
+    
+with st.container():
+    col1, col2 = st.columns((2, 4))
+    if results=='Yes':
+        with col1:
+            stringlist = []
+            model.summary(print_fn=lambda x: stringlist.append(x))
+            short_model_summary = "\n".join(stringlist)
+            print(short_model_summary)
+            st.write(short_model_summary)
+    
+    
+    if display =='Yes':
+        img_tensor = get_img_array(img, target_size=(224, 224))
+        layer_outputs = []
+        layer_names = []
+        for layer in model.layers:
+            if isinstance(layer, (layers.Conv2D, layers.MaxPooling2D)):
+                layer_outputs.append(layer.output)
+                layer_names.append(layer.name)
+        activation_model = keras.Model(inputs=model.input, outputs=layer_outputs)
+        
+        activations = activation_model.predict(img_tensor)
+        
+        first_layer_activation = activations[0]
+        
+        plt.matshow(first_layer_activation[0, :, :, 5], cmap="viridis")
+        
+        images_per_row = 16
+        all_pngs=[]
+        for layer_name, layer_activation in zip(layer_names, activations):
+            n_features = layer_activation.shape[-1]
+            size = layer_activation.shape[1]
+            n_cols = n_features // images_per_row
+            display_grid = np.zeros(((size + 1) * n_cols - 1,
+                                     images_per_row * (size + 1) - 1))
+            for col in range(n_cols):
+                for row in range(images_per_row):
+                    channel_index = col * images_per_row + row
+                    channel_image = layer_activation[0, :, :, channel_index].copy()
+                    if channel_image.sum() != 0:
+                        channel_image -= channel_image.mean()
+                        channel_image /= channel_image.std()
+                        channel_image *= 64
+                        channel_image += 128
+                    channel_image = np.clip(channel_image, 0, 255).astype("uint8")
+                    display_grid[
+                        col * (size + 1): (col + 1) * size + col,
+                        row * (size + 1) : (row + 1) * size + row] = channel_image
+            scale = 1. / size
+            plt.figure(figsize=(scale * display_grid.shape[1],
+                                scale * display_grid.shape[0]))
+            plt.title(layer_name)
+            plt.grid(False)
+            plt.axis("off")
+            plt.imshow(display_grid, aspect="auto", cmap="viridis")
+            
+            filename=f'{layer_name}.png'
+            plt.savefig(f'{layer_name}.png')
+            all_pngs.append(filename)
+        with col2:
+            for i in all_pngs: st.image(i)
+         
+        
+