change some stuff

app.py

@@ -1,65 +1,77 @@
+### 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, TypedDict
+from typing import Tuple, Dict
 
 # Setup class names
 class_names = ["pizza", "steak", "sushi"]
 
-# Create EffNetB2 model instance and transform
-effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=len(class_names))
+### 2. Model and transforms preparation ###
 
-# Load model weights
+# Create EffNetB2 model
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=3, # len(class_names) would also work
+)
+
+# Load saved weights
 effnetb2.load_state_dict(
     torch.load(
-        os.path.join("models", "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
     )
+)
 
-# Predict function
-def predict(img) -> Tuple[Dict, float]:
-  # Start a timer
-  start_time = timer()
-
-  # Transform the input image for use with EffNetB2
-  img = effnetb2_transforms(img).unsqueeze(0)
+### 3. Predict function ###
 
-  # put model into eval mode, make prediction
-  effnetb2.eval()
-  with torch.inference_mode():
-    pred_probs = torch.softmax(effnetb2(img), dim=-1)
-
-  # Create a prediction label and predcition probability
-  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(class_names)}
-
-  # Calculate pred time and pred dict
-  pred_time = round(timer() - start_time, 5)
+# 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))}
+    
+    # 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
 
-  return pred_labels_and_probs, pred_time
+### 4. Gradio app ###
 
-# 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 an example list
+# 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,
-                    inputs=gr.inputs.Image(type="pil"),
-                    outputs=[gr.outputs.Label(num_top_classes=3, label="Predictions"),
-                             gr.outputs.Number(label="Prediction time (s)")],
-                    examples=example_list,
+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(debug=False,
-            share=True)
+demo.launch()