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Upload app.py
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app.py
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@@ -64,7 +64,7 @@ def predict(img) -> Tuple[Dict, float]:
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### 4. Gradio app ###
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# Create title, description and article strings
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-
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#description = "A ResNet50 feature extractor computer vision model to classify funduscopic images."
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#article = "Created with the help from [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
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@@ -77,6 +77,7 @@ demo = gr.Interface(fn=predict, # mapping function from input to output
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outputs=[gr.Label(num_top_classes=10, label="Predictions"), # what are the outputs?
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gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
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# Create examples list from "examples/" directory
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examples=example_list,
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css="""
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.gradio-container {background-color: #0B0F19}
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### 4. Gradio app ###
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# Create title, description and article strings
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title = "Retinal Disease Detection"
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#description = "A ResNet50 feature extractor computer vision model to classify funduscopic images."
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#article = "Created with the help from [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
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outputs=[gr.Label(num_top_classes=10, label="Predictions"), # what are the outputs?
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gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
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# Create examples list from "examples/" directory
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title=title,
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examples=example_list,
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css="""
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.gradio-container {background-color: #0B0F19}
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