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| import gradio as gr | |
| import os | |
| import torch | |
| from model import create_effnetb2_model | |
| from timeit import default_timer as timer | |
| # Setup class names | |
| class_names = ["pizza", "steak", "sushi"] | |
| # Create model | |
| model, transforms = create_effnetb2_model( | |
| num_classes=3, | |
| ) | |
| # Load saved weights | |
| model.load_state_dict( | |
| torch.load( | |
| f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth", | |
| map_location=torch.device("cpu"), # load to CPU | |
| ) | |
| ) | |
| # Create prediction code | |
| def predict(img): | |
| start_time = timer() | |
| img = transforms(img).unsqueeze(0) | |
| model.eval() | |
| with torch.inference_mode(): | |
| pred_probs = torch.softmax(model(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, 5) | |
| return pred_labels_and_probs, pred_time | |
| # Create Gradio app | |
| 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/)." | |
| example_dir = "demo/examples" | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="pil"), | |
| outputs=[ | |
| gr.Label(num_top_classes=3, label="Predictions"), | |
| gr.Number(label="Prediction time (s)"), | |
| ], | |
| examples=[["examples/" + example] for example in os.listdir("examples")], | |
| interpretation="default", | |
| title=title, | |
| description=description, | |
| article=article, | |
| ) | |
| demo.launch() |