Update app.py

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
@@ -8,59 +8,74 @@ from timeit import default_timer as timer
 from typing import Tuple, Dict
 
 # Setup class names
-with open("class_names.txt", "r") as f:
-    class_names = [food_name.strip() for food_name in f.readlines()]
+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 ###    
 
-# 2. Model and transforms preparation
-# Create model and transforms
-effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=len(class_names))
+# Create model
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=101, # could also use len(class_names)
+)
 
 # Load saved weights
 effnetb2.load_state_dict(
-    torch.load(f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
-               map_location=torch.device("cpu")) # Load to CPU
+    torch.load(
+        f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
 )
-# 3. Predict function
+
+### 3. Predict function ###
+
+# Create predict function
 def predict(img) -> Tuple[Dict, float]:
-    # Start a timer
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
+    # Start the timer
     start_time = timer()
-    # Transform the input image for use with EffNetB2
-    img = effnetb2_transforms(img).unsqueeze(0)  # unsqueeze = add batch dimension on 0th index
-    # Put model into eval mode, make prediction
+    
+    # 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 transformed image through the model and turn the prediction logits into probability
+        # 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
+    
+    # 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 pred time
-    end_time = timer()
-    pred_time = round(end_time-start_time, 4)
-    # Return pred dict and pred time
+    
+    # Calculate the prediction time
+    pred_time = round(timer() - start_time, 5)
+    
+    # Return the prediction dictionary and prediction time 
     return pred_labels_and_probs, pred_time
 
-#4. Gradio app ###
+### 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)"
+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")]
 
-# 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=5, 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)
+# Create Gradio interface 
+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,
+)
 
-# Launch the demo!
+# Launch the app!
 demo.launch()