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app.py

@@ -9,13 +9,14 @@ from typing import Tuple, Dict
 
 # Setup class names
 class_names = ['CRVO',
+  'Choroidal Nevus',
   'Diabetic Retinopathy',
   'Laser Spots',
   'Macular Degeneration',
   'Myelinated Nerve Fiber',
   'Normal',
   'Pathological Mypoia',
-  'Retinitis Pigmentosa']
+  'Retinitis Pigmentosa'])
 
 ### 2. Model and transforms preparation ###
 
@@ -72,7 +73,7 @@ example_list = [["examples/" + example] for example in os.listdir("examples")]
 # Create the Gradio demo
 demo = gr.Interface(fn=predict, # mapping function from input to output
                     inputs=gr.Image(type="pil"), # what are the inputs?
-                    outputs=[gr.Label(num_top_classes=8, label="Predictions"), # what are the outputs?
+                    outputs=[gr.Label(num_top_classes=9, label="Predictions"), # what are the outputs?
                              gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
                     # Create examples list from "examples/" directory
                     examples=example_list, 

model.py

@@ -3,7 +3,7 @@ import torchvision
 
 from torch import nn
 
-def create_resnet50_model(num_classes:int=8, # 4
+def create_resnet50_model(num_classes:int=9, # 4
                           seed:int=42):
     """Creates an ResNet50 feature extractor model and transforms.