a

app.py

@@ -52,15 +52,25 @@ with open("scene_labels.json") as labels_file:
 def classify_image(img):
     img = tf.convert_to_tensor(img, dtype=tf.float32)
     img = tf.expand_dims(img, axis=0)
-    # probs = model.predict(img).flatten()
     probs = (model(img))[0]
     return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
 image = gr.inputs.Image(shape=(256, 256))
 label = gr.outputs.Label(num_top_classes=5) 
-examples = ['images/airplane_002.jpg','images/airplane_003.jpg','images/airport_020.jpg','images/airport_075.jpg','images/bridge_679.jpg','images/cloud_227.jpg','images/freeway_159.jpg','images/forest_235.jpg']
+examples = ['images/airplane_002.jpg',
+            'images/airport_002.jpg',
+            'images/beach_002.jpg',
+            'images/bridge_679.jpg',
+            'images/cloud_227.jpg',
+            'images/forest_235.jpg',
+            'images/rectangular_farmland_218.jpg',
+            'images/meadow_019.jpg',
+            'images/industrial_area_003.jpg',
+            'images/ship_018.jpg',
+            'images/freeway_159.jpg',
+            'images/airport_075.jpg',
+            ]
 
 intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch() # share=True
-# if __name__ == "__main__":
-#    intf.launch(share=True) # ,inline=False
+intf.launch(inline=False)
+