Update app.py

app.py

@@ -14,18 +14,35 @@ NUM_CLASSES=6
 
 model=load_model('best_model.h5')
 
+# def classify_image(inp):
+#   np.random.seed(143)
+#   inp = inp.reshape((-1, HEIGHT,WIDTH, 3))
+#   inp = tf.keras.applications.nasnet.preprocess_input(inp) 
+#   prediction = model.predict(inp)
+#   ###label = dict((v,k) for k,v in labels.items())
+#   predicted_class_indices=np.argmax(prediction,axis=1)
+#   result = {}
+#   for i in range(len(predicted_class_indices)):
+#       if predicted_class_indices[i] < NUM_CLASSES:
+#           result[labels[predicted_class_indices[i]]]= float(predicted_class_indices[i])
+#   return result 
+
+
 def classify_image(inp):
-  np.random.seed(143)
-  inp = inp.reshape((-1, HEIGHT,WIDTH, 3))
-  inp = tf.keras.applications.nasnet.preprocess_input(inp) 
-  prediction = model.predict(inp)
-  #label = dict((v,k) for k,v in labels.items())
-  predicted_class_indices=np.argmax(prediction,axis=1)
-  result = {}
-  for i in range(len(predicted_class_indices)):
-      if predicted_class_indices[i] < NUM_CLASSES:
-          result[labels[predicted_class_indices[i]]]= float(predicted_class_indices[i])
-  return result 
+    np.random.seed(143)
+    inp = inp.reshape((-1, HEIGHT, WIDTH, 3))
+    inp = tf.keras.applications.nasnet.preprocess_input(inp)
+    prediction = model.predict(inp)
+    predicted_class_indices = np.argmax(prediction, axis=1)
+    result = {}
+    for i in range(len(predicted_class_indices)):
+        if predicted_class_indices[i] < NUM_CLASSES:
+            try:
+                label = labels[predicted_class_indices[i]]
+                result[label] = float(predicted_class_indices[i])
+            except KeyError:
+                print(f"KeyError: Label not found for index {predicted_class_indices[i]}")
+    return result