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| ### 1. Imports and class names setup ### | |
| import gradio as gr | |
| import os | |
| import torchvision | |
| import torch | |
| from model import create_effnetb2_model | |
| from timeit import default_timer as timer | |
| from typing import Tuple, Dict | |
| # Setup class names | |
| class_names = ["pizza","steak","sushi"] | |
| ### 2. Model and transdorms preparation ### | |
| effnetb2, effnetb2_transforms = create_effnetb2_model() | |
| # Load save weights | |
| effnetb2.load_state_dict(torch.load("09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth", | |
| map_location = torch.device('cpu')# Load the model to the CPU | |
| ) | |
| ) | |
| ### 3. Predict function ### | |
| def predict(img) -> Tuple[Dict, float]: | |
| #Start timer | |
| start_time = timer() | |
| # Transform the input image for use with EffNetB2 | |
| img = effnetb2_transforms(img).unsqueeze(0) | |
| # Put the model in eval mode, make prediction | |
| effnetb2.eval() | |
| with torch.inference_mode(): | |
| # Pass transformed image trough the model abd turn the prediction logits into prediction probs | |
| pred_probs = torch.softmax(effnetb2(img), dim = 1) | |
| # 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 pre time | |
| end_time = timer() | |
| pred_time = round(end_time -start_time, 4) | |
| # return pred dict and pred time | |
| return pred_labels_and_probs, pred_time | |
| # Create example list | |
| example_list = [["examples/"+example] for example in os.listdir("examples")] | |
| example_list | |
| ### 4. 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 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"), # what are the outputs? | |
| gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs | |
| examples=example_list, | |
| title=title, | |
| description=description, | |
| article=article) | |
| # Launch the demo! | |
| demo.launch(debug=False) # generate a publically shareable URL? | |