Update app.py

app.py

@@ -45,17 +45,11 @@ metrics = [real_dice_coeff, tf.keras.metrics.MeanIoU(num_classes=2, sparse_y_tru
 model = load_model('unet_contrails_model.keras', custom_objects={'dice_loss_plus_5focal_loss': total_loss, 'jaccard_coef': jaccard_coef, 'IOU score' : sm.metrics.IOUScore(threshold=0.9, name="IOU score"), 'Dice Coeficient' : sm.metrics.FScore(threshold=0.6, name="Dice Coeficient")}, compile=False)
 model.compile(metrics=metrics)
 
-images_means = {175.1322: '1205045288117020016.npy',
- 162.1303: '4617259572479165215.npy',
- 146.4613: '2399738000417381513.npy',
- 171.075: '5500775309238786210.npy',
- 152.4325: '3268948859446517114.npy',
- 144.5998: '9000307066571621514.npy',
- 169.5583: '552609781892851211.npy',
- 159.6155: '5663079497093130113.npy',
- 178.3942: '3517995218957041214.npy',
- 168.0662: '4182340004986797719.npy'}
-
+images_means = {}
+for f in os.listdir('images'):
+    if f.endswith('.png'):
+        im = Image.open('images/' + f)
+        images_means[round(np.asarray(im).mean(),4)] = f.split('.')[0] + '.npy'
 
 # def greet(name):
 #     return "Hello " + name + "!!"