test

app.py

@@ -18,7 +18,7 @@ with open(output_path,'r') as file:
     LABELS = [x.strip() for x in file.readlines()]
 
 num_classes = 4000
-IMG_SIZE = 224
+IMG_SIZE = 400
 
 def _normalize_img(img):
     img = tf.cast(img, tf.float32)/255.   # All images will be rescaled by 1./255
@@ -35,10 +35,9 @@ def predict_fn(img):
      temp = model.predict(x)
      
      idx = np.argsort(np.squeeze(temp))[::-1]
-     sorted_labels = [LABELS[i] for i in idx]
-     sorted_values = [temp[0][i] for i in idx]
+     top3_value = np.asarray([temp[0][i] for i in idx[0:3]])
+     top3_idx = idx[0:3]
      
-     return {label: str(value) for label, value in zip(sorted_labels, sorted_values)}
-
+     return {LABELS[i]:str(v) for i,v in zip(top3_idx,top3_value)}
      
 gr.Interface(predict_fn, gr.Image(type='pil'), outputs='label', examples=path,).launch()