updated the code to run the app.

app.py

@@ -1,60 +1,55 @@
-### 1. Imports and class names setup ###
 import gradio as gr
 import os
 import torch
-
 from model import create_effnetb2_model
 from timeit import default_timer as timer
 from typing import Tuple, Dict
 
-# Setting up the class names
 with open("class_names.txt", "r") as f:
-  class_names = [food.strip() for food in f.readlines()]
+    class_names = [food.strip() for food in f.readlines()]
+
 
-### 2. Model and transforms preparation ###
-# Create model and transforms
-effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=101)
+effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=len(class_names))
 
-# Load the saved Weights
 effnetb2.load_state_dict(
-    torch.load(f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
-               map_location = torch.device("cpu"))
+    torch.load(
+        f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
+        map_location=torch.device("cpu")
+    )
 )
 
-### 3. Predict Function ###
 def predict(img) -> Tuple[Dict, float]:
+    start_time = timer()
+    
+    img = effnetb2_transforms(img).unsqueeze(0)
+    
+    effnetb2.eval()
+    with torch.inference_mode():
+        pred_probs = torch.softmax(effnetb2(img), dim=1)
+    
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+    
+    pred_time = round(timer() - start_time, 4)
+    return pred_labels_and_probs, pred_time
 
-  start_time = timer()
-
-  img = effnetb2_transforms(img).unsqueeze(0) # Unsqueeze == Add batch dimension on 0th index
-
-  effnetb2.eval()
-  with torch.inference_mode():
-    pred_probs = torch.softmax(effnetb2(img), dim = 1)
-
-  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
-
-  end_time = timer()
-  pred_time = round(end_time - start_time, 4)
-
-  return pred_labels_and_probs, pred_time
-
-### 4. Gradio app ###
-# Create title,, description and articcle
 title = "FoodVision Big 🍔👁"
-description = "An [EfficientNetB2 Feature Extractor](https://pytorch.org/vision/stable/models/efficientnet.html) computer vision model to classify [101 classes of food from the Food101 Dataset](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 example list
-example_list = [["examples/" + example] for example in os.listdir("examples")]
-# Create the Gradio demo
-demo =gr.Interface(fn=predict,
-                  inputs=gr.Image(type="pil"),
-                  outputs=[gr.Label(num_top_classes=5, label=("Predicitions")),
-                           gr.Number(label="Prediction Time (s)")],
-                  examples=example_list,
-                  title=title,
-                  description=description,
-                  article=article)
-# Launch the demo!
-demo.launch()
+description = "An EfficientNetB2 feature extractor to classify 101 classes of food from the Food101 dataset."
+article = "Created during PyTorch Model Deployment study."
+
+example_list = [["examples/" + example] for example in os.listdir("examples") if example.endswith(('.png', '.jpg', '.jpeg'))]
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Label(num_top_classes=5, label="Predictions"),
+        gr.Number(label="Prediction Time (s)")
+    ],
+    examples=example_list,
+    title=title,
+    description=description,
+    article=article
+)
+
+if __name__ == "__main__":
+    demo.launch()