Update app.py

app.py

@@ -33,8 +33,8 @@ enhancement_type = st.sidebar.selectbox(
     ["Invert", "High Pass Filter", "Unsharp Masking", "Histogram Equalization", "CLAHE"]
 )
 
-# H = 224
-# W = 224
+H = 224
+W = 224
 
 @st.cache_resource
 def load_model_detection():
@@ -52,14 +52,14 @@ def load_model_detection():
     )
     return model
 
-# def preprocess_image(image):
+def preprocess_image(image):
 #     """ Preprocess the image to the required size and normalization. """
 #     W = 320
 #     H = 320
-#     image = cv2.resize(image, (W, H))
-#     image = (image - 127.5) / 127.5  # Normalize to [-1, +1]
-#     image = np.expand_dims(image, axis=0).astype(np.float32)
-#     return image
+    image = cv2.resize(image, (W, H))
+    image = (image - 127.5) / 127.5  # Normalize to [-1, +1]
+    image = np.expand_dims(image, axis=0).astype(np.float32)
+    return image
 
 
 
@@ -79,7 +79,20 @@ model_detection = load_model_detection()
 
 @st.cache_resource
 def load_gradcam_model():
-    model = keras.models.load_model('./model_renamed.h5')
+    base_model = DenseNet121(weights="./densenet.hdf5",
+                    include_top=False)
+    x = base_model.output
+    
+    # add a global spatial average pooling layer
+    x = GlobalAveragePooling2D()(x)
+
+    # and a logistic layer
+    predictions = Dense(len(labels), activation="sigmoid")(x)
+
+    model = Model(inputs=base_model.input, outputs=predictions)
+    model.compile(optimizer='adam', loss="categorical_crossentropy")
+    model.load_weights("./model_renamed.h5")
+    
     return model