import gradio as gr 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="demo/foodvision_mini/examples", interpretation="default", title=title, description=description, article=article, ) demo.launch()