Update app.py

app.py

@@ -233,14 +233,15 @@ def grad_cam(input_model, img_array, cls, layer_name):
 
 # Compute Grad-CAM
 def compute_gradcam(model, img_path, layer_name='bn'):
-    base_model  = keras.applications.DenseNet121(weights = './densenet.hdf5', include_top = False)
-    x = base_model.output
-    x = keras.layers.GlobalAveragePooling2D()(x)
-    predictions = keras.layers.Dense(14, activation = "sigmoid")(x)
-    model_gradcam = keras.Model(inputs=base_model.input, outputs=predictions)
-    model_gradcam.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
-              loss='sparse_categorical_crossentropy')
-    model.load_weights('./pretrained_model.h5')
+    # base_model  = keras.applications.DenseNet121(weights = './densenet.hdf5', include_top = False)
+    # x = base_model.output
+    # x = keras.layers.GlobalAveragePooling2D()(x)
+    # predictions = keras.layers.Dense(14, activation = "sigmoid")(x)
+    # model_gradcam = keras.Model(inputs=base_model.input, outputs=predictions)
+    # model_gradcam.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
+    #           loss='sparse_categorical_crossentropy')
+    # model.load_weights('./pretrained_model.h5')
+    model_gradcam = keras.models.load_model('./gradcam_model.h5')
     
     preprocessed_input = load_image(img_path)
     predictions = model_gradcam.predict(preprocessed_input)