comment out examples

app.py

@@ -1,4 +1,4 @@
-### 1. Imports and class names setup ### 
+### 1. Imports and class names setup ###
 import gradio as gr
 import os
 import torch
@@ -8,14 +8,14 @@ from timeit import default_timer as timer
 from typing import Tuple, Dict
 
 # Setup class names
-with open("class_names.txt", "r") as f: # reading them in from class_names.txt
-    class_names = [food_name.strip() for food_name in  f.readlines()]
-    
-### 2. Model and transforms preparation ###    
+with open("class_names.txt", "r") as f:  # reading them in from class_names.txt
+    class_names = [food_name.strip() for food_name in f.readlines()]
+
+### 2. Model and transforms preparation ###
 
 # Create model
 effnetb2, effnetb2_transforms = create_effnetb2_model(
-    num_classes=101, # could also use len(class_names)
+    num_classes=101,  # could also use len(class_names)
 )
 
 # Load saved weights
@@ -29,41 +29,45 @@ effnetb2.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.
     """
     # Start the timer
     start_time = timer()
-    
+
     # Transform the target image and add a batch dimension
     img = effnetb2_transforms(img).unsqueeze(0)
-    
+
     # Put model into evaluation mode and turn on inference mode
     effnetb2.eval()
     with torch.inference_mode():
         # 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 (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)
-    
-    # Return the prediction dictionary and prediction time 
+
+    # 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 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")]
+# example_list = [["examples/" + example] for example in os.listdir("examples")]
 
-# Create Gradio interface 
+# Create Gradio interface
 demo = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),
@@ -71,7 +75,7 @@ demo = gr.Interface(
         gr.Label(num_top_classes=5, label="Predictions"),
         gr.Number(label="Prediction time (s)"),
     ],
-    examples=example_list,
+    # examples=example_list,
     title=title,
     description=description,
     article=article,