Update app.py

app.py

@@ -8,64 +8,78 @@ 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 ### 
-# Create model and transforms
-effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=101)
+### 2. Model and transforms preparation ###
+
+# 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.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 ###
 
+# Create predict function
 def predict(img) -> Tuple[Dict, float]:
-  # Start a timer
-  start_time = timer()
+    """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)
 
-  # Transform the input image for use with EffNetB2
-  img = effnetb2_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index
+    # 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)
 
-  # Put model into eval mode, make prediction
-  effnetb2.eval()
-  with torch.inference_mode():
-    # Pass transformed image through the model and turn the prediction logits into probaiblities
-    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))
+    }
 
-  # Create a prediction label and prediction probability dictionary
-  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)
 
-  # Calculate pred time
-  end_time = timer()
-  pred_time = round(end_time - start_time, 4)
+    # Return the prediction dictionary and prediction time
+    return pred_labels_and_probs, pred_time
 
-  # Return pred dict and pred time
-  return pred_labels_and_probs, pred_time
 
 ### 4. Gradio app ###
 
-# Create title, description and article
-title = "FoodVision BIG 🍔👁💪"
-description = "An [EfficientNetB2 feature extractor]"
-#(https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) computer vision model to classify images [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/#11-turning-our-foodvision-big-model-into-a-deployable-app)."
+# Create title, description and article strings
+title = "FoodVision 101 🍔👁"
+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 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, # maps inputs to outputs
-                    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!
-demo.launch() 
+# 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 app!
+demo.launch()