Update app-gradcam.py

app-gradcam.py

@@ -1,71 +1,71 @@
-import streamlit as st
-import tensorflow as tf
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import cv2
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
-
-class GradCAM(object):
-    
-    def __init__(self, model, alpha=0.8, beta=0.3):
-        self.model = model
-        self.alpha = alpha
-        self.beta = beta
-
-    def apply_heatmap(self, heatmap, image): 
-        heatmap = cv2.resize(heatmap, image.shape[:-1]) 
-        heatmap = cv2.applyColorMap(np.uint8(255 * heatmap), cv2.COLORMAP_JET)   
-        superimposed_img = cv2.addWeighted(np.array(image).astype(np.float32), self.alpha, 
-                                           np.array(heatmap).astype(np.float32), self.beta, 0)
-        return np.array(superimposed_img).astype(np.uint8)
-    
-    def gradCAM(self, x_test=None, name='block5_conv3', index_class=0):
-        with tf.GradientTape() as tape:
-            last_conv_layer = self.model.get_layer(name) 
-            grad_model = tf.keras.Model([self.model.input], [self.model.output, last_conv_layer.output])
-            model_out, last_conv_layer = grad_model(np.expand_dims(x_test, axis=0))   
-            class_out = model_out[:, index_class]  
-            grads = tape.gradient(class_out, last_conv_layer)   
-            pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
-            last_conv_layer = last_conv_layer[0]
-            heatmap = last_conv_layer @ pooled_grads[..., tf.newaxis]
-            heatmap = tf.squeeze(heatmap)
-        heatmap = np.maximum(heatmap, 0)
-        heatmap /= np.max(heatmap)    
-        heatmap = np.array(heatmap) 
-        return self.apply_heatmap(heatmap, x_test)
-
-# Streamlit app
-st.title("Grad-CAM Visualization")
-
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-if uploaded_file is not None:
-    try:
-        # Load the uploaded image
-        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
-        img = cv2.imdecode(file_bytes, 1)
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        
-        st.image(img, caption='Uploaded Image.', use_column_width=True)
-
-        # Preprocess the image for the model (assuming the model expects 224x224 images)
-        img_resized = cv2.resize(img, (224, 224))
-        img_array = np.expand_dims(img_resized, axis=0)
-
-        # Load the model
-        model_path = './model/model_renamed.h5'  # Update this path to your model's path
-        model = tf.keras.models.load_model(model_path)
-
-        # Initialize GradCAM
-        grad_cam = GradCAM(model)
-
-        # Compute GradCAM heatmap
-        heatmap_img = grad_cam.gradCAM(img_array[0])
-
-        # Display the GradCAM heatmap
-        st.image(heatmap_img, caption='Grad-CAM Heatmap.', use_column_width=True)
-
-    except Exception as e:
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import cv2
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
+
+class GradCAM(object):
+    
+    def __init__(self, model, alpha=0.8, beta=0.3):
+        self.model = model
+        self.alpha = alpha
+        self.beta = beta
+
+    def apply_heatmap(self, heatmap, image): 
+        heatmap = cv2.resize(heatmap, image.shape[:-1]) 
+        heatmap = cv2.applyColorMap(np.uint8(255 * heatmap), cv2.COLORMAP_JET)   
+        superimposed_img = cv2.addWeighted(np.array(image).astype(np.float32), self.alpha, 
+                                           np.array(heatmap).astype(np.float32), self.beta, 0)
+        return np.array(superimposed_img).astype(np.uint8)
+    
+    def gradCAM(self, x_test=None, name='block5_conv3', index_class=0):
+        with tf.GradientTape() as tape:
+            last_conv_layer = self.model.get_layer(name) 
+            grad_model = tf.keras.Model([self.model.input], [self.model.output, last_conv_layer.output])
+            model_out, last_conv_layer = grad_model(np.expand_dims(x_test, axis=0))   
+            class_out = model_out[:, index_class]  
+            grads = tape.gradient(class_out, last_conv_layer)   
+            pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
+            last_conv_layer = last_conv_layer[0]
+            heatmap = last_conv_layer @ pooled_grads[..., tf.newaxis]
+            heatmap = tf.squeeze(heatmap)
+        heatmap = np.maximum(heatmap, 0)
+        heatmap /= np.max(heatmap)    
+        heatmap = np.array(heatmap) 
+        return self.apply_heatmap(heatmap, x_test)
+
+# Streamlit app
+st.title("Grad-CAM Visualization")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    try:
+        # Load the uploaded image
+        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+        img = cv2.imdecode(file_bytes, 1)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        
+        st.image(img, caption='Uploaded Image.', use_column_width=True)
+
+        # Preprocess the image for the model (assuming the model expects 224x224 images)
+        img_resized = cv2.resize(img, (224, 224))
+        img_array = np.expand_dims(img_resized, axis=0)
+
+        # Load the model
+        model_path = 'model_renamed.h5'  # Update this path to your model's path
+        model = tf.keras.models.load_model(model_path)
+
+        # Initialize GradCAM
+        grad_cam = GradCAM(model)
+
+        # Compute GradCAM heatmap
+        heatmap_img = grad_cam.gradCAM(img_array[0])
+
+        # Display the GradCAM heatmap
+        st.image(heatmap_img, caption='Grad-CAM Heatmap.', use_column_width=True)
+
+    except Exception as e:
         st.error(f"Error: {e}")