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app.py

@@ -25,6 +25,7 @@ class_names = ['CRVO',
 resnet50, resnet50_transforms = create_resnet50_model(
     num_classes=len(class_names), # actual value would also work
 )
+resnet50.fc = nn.Linear(2048, 10)
 
 # Load saved weights
 resnet50.load_state_dict(
@@ -34,6 +35,7 @@ resnet50.load_state_dict(
     )
 )
 
+
 ### 3. Predict function ###
 
 # Create predict function
@@ -64,9 +66,9 @@ def predict(img) -> Tuple[Dict, float]:
 ### 4. Gradio app ###
 
 # Create title, description and article strings
-#title = "Retinal Disease Detection"
-#description = "A ResNet50 feature extractor computer vision model to classify funduscopic images."
-#article = "Created with the help from [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
+title = "DeepFundus 👀"
+description = "A ResNet50 feature extractor computer vision model to classify funduscopic images."
+article = "Created with the help from [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
 
 # Create examples list from "examples/" directory
 example_list = [["examples/" + example] for example in os.listdir("examples")]
@@ -77,12 +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
-                    #title=title,
-                    examples=example_list,
-                    css="""
-                    .gradio-container {background-color: #0B0F19}
-                    .mx-auto {background-color: #0B0F19}
-                    """)
+                    examples=example_list, 
+                    title=title,
+                    description=description,
+                    article=article)
 
 # Launch the demo!
-demo.launch()
+demo.launch()

model.py

@@ -20,6 +20,7 @@ def create_resnet50_model(num_classes:int=10, # 4
     weights = torchvision.models.ResNet50_Weights.DEFAULT
     transforms = weights.transforms()
     model = torchvision.models.resnet50(weights=weights)
+    model.fc = nn.Linear(2048, 10)
 
     # 4. Freeze all layers in base model
     for param in model.parameters():

pretrained_resnet50_feature_extractor_drappcompressed.pth

@@ -1,3 +1,3 @@
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-size 102643061
+oid sha256:d945c30b1571afcba5b07e2bca41a6c744730d5d655ec8735dfab39dac5622c9
+size 94528949