Upload app.py

app.py

@@ -64,9 +64,9 @@ def predict(img) -> Tuple[Dict, float]:
 ### 4. Gradio app ###
 
 # Create title, description and article strings
-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/)."
+#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")]
@@ -80,7 +80,10 @@ demo = gr.Interface(fn=predict, # mapping function from input to output
                     examples=example_list, 
                     title=title,
                     description=description,
-                    article=article)
+                    article=article,
+                    css="""
+                    .gradio-container {background-color: #000029}
+                    """)
 
 # Launch the demo!
-demo.launch()
+demo.launch()