update description

app.py

@@ -14,7 +14,7 @@ class_names = ["pizza", "steak", "sushi"]
 
 # Create VIT model
 vit, vit_transforms = create_vit_model(
-    num_classes=3, # len(class_names) would also work
+    num_classes=3,  # len(class_names) would also work
 )
 
 # Load saved weights
@@ -28,6 +28,8 @@ vit.load_state_dict(
 ### 3. Predict function ###
 
 # Create predict function
+
+
 def predict(img) -> Tuple[Dict, float]:
     """Transforms and performs a prediction on img and returns prediction and time taken.
     """
@@ -44,7 +46,8 @@ def predict(img) -> Tuple[Dict, float]:
         pred_probs = torch.softmax(vit(img), dim=1)
 
     # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
-    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+    pred_labels_and_probs = {class_names[i]: float(
+        pred_probs[0][i]) for i in range(len(class_names))}
 
     # Calculate the prediction time
     pred_time = round(timer() - start_time, 5)
@@ -54,19 +57,21 @@ def predict(img) -> Tuple[Dict, float]:
 
 ### 4. Gradio app ###
 
+
 # Create title, description and article strings
 title = "FoodVision Mini 🍕🥩🍣"
-description = "An vit feature extractor computer vision model to classify images of food as pizza, steak or sushi."
-article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
+description = "A ViT-B/16 feature extractor (ViT for short) computer vision model to classify images of food as pizza, steak or sushi."
+article = "Created based on [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")]
 
 # Create the Gradio demo
-demo = gr.Interface(fn=predict, # mapping function from input to output
-                    inputs=gr.Image(type="pil"), # what are the inputs?
-                    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
+demo = gr.Interface(fn=predict,  # mapping function from input to output
+                    inputs=gr.Image(type="pil"),  # what are the inputs?
+                    outputs=[gr.Label(num_top_classes=3, label="Predictions"),  # what are the outputs?
+                             # our fn has two outputs, therefore we have two outputs
+                             gr.Number(label="Prediction time (s)")],
                     # Create examples list from "examples/" directory
                     examples=example_list,
                     title=title,