Add application file

app.py

@@ -52,22 +52,14 @@ 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)
-    # pred,idx,probs = model.predict(img)
     probs = model.predict(img).flatten()
-    # vector = np.vectorize(np.float_)
-    # probs = vector(probs)
     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=45)
+label = gr.outputs.Label(num_top_classes=5) # 45
 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']
 
 intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, capture_session=True, interpretation="default", examples=examples)
 
 if __name__ == "__main__":
-   intf.launch(share=True,inline=False)
-# def classify_image(img):
-#     arr = np.expand_dims(img, axis=0)
-#     arr = tf.keras.applications.mobilenet.preprocess_input(arr)
-#     prediction = model.predict(arr).flatten()
-#     return {labels[i]: float(prediction[i]) for i in range(45)}
+   intf.launch(share=True) # ,inline=False