fix issue 2

app.py

@@ -7,24 +7,30 @@ from timeit import default_timer as timer
 from typing import Tuple, Dict
 
 # Setup class names
-class_names = ['pizza', 'steak', 'sushi']
+class_names = ["pizza", "steak", "sushi"]
 
 ### 2. Model and transforms preparation ###
+
 # Create EffNetB2 model
-effnetb2, effnetb2_transforms = create_effnetb2_model(len(class_names))
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=3,  # len(class_names) would also work
+)
 
 # Load saved weights
 effnetb2.load_state_dict(
     torch.load(
-        f='09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth',
-        map_location=torch.device('cpu')
+        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
+        map_location=torch.device("cpu"),  # load to CPU
     )
 )
 
+
 ### 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."""
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
     # Start the timer
     start_time = timer()
 
@@ -37,7 +43,7 @@ def predict(img) -> Tuple[Dict, float]:
         # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
         pred_probs = torch.softmax(effnetb2(img), dim=1)
 
-    # Create a prediction label and prediction probability dictionary for each prediction class
+    # 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))}
 
     # Calculate the prediction time
@@ -46,29 +52,28 @@ def predict(img) -> Tuple[Dict, float]:
     # Return the prediction dictionary and prediction time
     return pred_labels_and_probs, pred_time
 
+
+### 4. Gradio app ###
+
 # Create title, description and article strings
 title = "FoodVision Mini 🍕🥩🍣"
 description = "An EfficientNetB2 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/)."
 
-# Create examples list from 'examples/' directory
-example_list = [['examples/' + example] for example in os.listdir('examples')]
-
-# Create gradio demo
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type='pil'),
-    outputs=[
-        gr.Label(num_top_classes=3, label='Predictions'),
-        gr.Number(label='Prediction time (s)')
-    ],
-    examples=example_list,
-    title=title,
-    description=description,
-    article=article
-)
-
-# Launch the demo
+# 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
+                    # Create examples list from "examples/" directory
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
 demo.launch()
-
-