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app.py

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+# # The 'app.py' file will have four major parts:
+# # 1. Imports and class names setup
+# # 2. Model and transforms preparation
+# # 3. Predict function ('Predict()')
+# # 4. Gradio app - our Gradio interface + launch command
+#
+# # # 1. Imports and class names setup
+import gradio as gr
+import os
+import torch
+
+from model import create_vit_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+# Setup class names
+class_names = ["pizza", "steak", "sushi"]
+
+# # # 2. Model and transforms preparation
+# Create vit model
+vit_b_16, vit_b_16_transforms = create_vit_model(
+    num_classes=len(class_names),
+)
+# Load saved weights
+vit_b_16.load_state_dict(
+    torch.load(
+        f="09_pretrained_vit_b_16_feature_extractor_foodvision_mini_v2.pth",
+        map_location=torch.device("cpu"),    # load to CPU
+    )
+)
+# # # 3. 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 = vit_b_16_transforms(img).unsqueeze(0)
+
+    # Put model into evaluation mode and turn on inference mode
+    vit_b_16.eval()
+
+    with torch.inference_mode():
+        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+        pred_probs = torch.softmax(vit_b_16(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
+
+# # # 4. Gradio app ###
+
+# # # Create title, description and article strings
+title = "FoodVision Mini V2 🍕🥩🍣"
+description = "An ViT Base 16 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 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=3, label="Predictions"),
+                             gr.Number(label="Prediction time (s)")],  # our fn has two outputs, therefore we have two outputs
+                    # Create the Gradio demo
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article)
+# Launch the demo!
+demo.launch()