Update app.py

app.py

@@ -5,22 +5,26 @@ import numpy as np
 from tensorflow.keras.models import load_model
 import tensorflow_addons as tfa
 import os
-from tensorflow.keras.layers import *
-import torch.cuda
-import datetime, os
+import numpy as np
+
 
-labels= {'Subway': 0, 'Starbucks': 1,'McDonalds': 2,'Burger King': 3,'KFC': 4,'Other': 5}
+labels= { 0:, 'Starbucks': 1,'McDonalds': 2,'Burger King': 3,'KFC': 4,'Other': 5}
 HEIGHT,WIDTH=224,224
-model=load_model('best_model.h5')
 NUM_CLASSES=6
+categories= ('Starbucks','McDonalds','Burger King','KFC','Other')
 
+model=load_model('best_model.h5')
 
 def classify_image(inp):
   inp = inp.reshape((-1, HEIGHT,WIDTH, 3))
-  #inp = tf.keras.applications.nasnet.preprocess_input(inp) 
-  prediction = model.predict(inp).flatten()
-  return {labels[i]: float(prediction[i]) for i in range(NUM_CLASSES)}
+  inp = tf.keras.applications.nasnet.preprocess_input(inp) 
+  prediction = model.predict(inp)
+  labels = dict((v,k) for k,v in labels.items())
+  predicted_class_indices=np.argmax(prediction,axis=1)
+  return {labels[i]: float(predicted_class_indices[i]) for i in range(NUM_CLASSES)}
+
 
+    
 image = gr.Image(shape=(HEIGHT,WIDTH),label='Input')
 label = gr.Label()