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app.py

@@ -3,7 +3,6 @@ import gradio as gr
 import os
 import torch
 
-from torch import nn
 from model import create_resnet50_model
 from timeit import default_timer as timer
 from typing import Tuple, Dict
@@ -17,7 +16,7 @@ class_names = ['CRVO',
   'Macular Hole',
   'Myelinated Nerve Fiber',
   'Normal',
-  'Pathological Myopia',
+  'Pathological Mypoia',
   'Retinitis Pigmentosa']
 
 ### 2. Model and transforms preparation ###
@@ -28,7 +27,7 @@ resnet50, resnet50_transforms = create_resnet50_model(
 )
 resnet50.fc = nn.Linear(2048, 10)
 
-# Load the saved weights
+# Load saved weights
 resnet50.load_state_dict(
     torch.load(
         f="pretrained_resnet50_feature_extractor_drappcompressed.pth",
@@ -80,7 +79,10 @@ demo = gr.Interface(fn=predict, # mapping function from input to output
                     outputs=[gr.Label(num_top_classes=3, 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)
+                    examples=example_list) 
+                    #title=title,
+                    #description=description,
+                    #article=article)
 
 # Launch the demo!
 demo.launch()

pretrained_resnet50_feature_extractor_drappcompressed.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d945c30b1571afcba5b07e2bca41a6c744730d5d655ec8735dfab39dac5622c9
+oid sha256:441fc37b888752f96603186b5073023065c3699fa5b04aa58354474a09d43bae
 size 94528949