Update app.py

app.py

@@ -55,12 +55,10 @@ def predict(img) -> Tuple[Dict, float]:
 ### 4. Gradio app ###
 
 # Create title, description and article strings
-title = "FoodVision Big 🍔👁"
-description = "An EfficientNetB2 feature extractor computer vision model to classify images of food into [101 different classes](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/food101_class_names.txt)."
-article = "Created at [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")]
+title = "Food Classifier"
+description = "An EfficientNetB2 feature extractor computer vision model to classify images of food into 101 classes
+ 
+burger_example_path = "burger.jpg"
 
 # Create Gradio interface 
 demo = gr.Interface(
@@ -70,10 +68,9 @@ demo = gr.Interface(
         gr.Label(num_top_classes=5, label="Predictions"),
         gr.Number(label="Prediction time (s)"),
     ],
-    examples=example_list,
+    examples=[[burger_example_path]],
     title=title,
     description=description,
-    article=article,
 )
 
 # Launch the app!