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| ### 1. Imports and class names setup ### | |
| import gradio as gr | |
| import os | |
| import torch | |
| from model import create_effnetb2_model | |
| 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()] | |
| ### 2. Model and transforms preparation ### | |
| # Create model and transforms | |
| effnetb2, effnetb2_transforms = create_effnetb2_model( | |
| num_classes = 101 | |
| ) | |
| # Load the saved weights | |
| effnetb2.load_state_dict( | |
| torch.load(f = '09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth', | |
| map_location = torch.device('cpu')) | |
| ) | |
| ### 3. Predict function ### | |
| from typing import Tuple, Dict | |
| def predict(img) -> Tuple[Dict, float]: | |
| # Start a timer | |
| start_time = timer() | |
| # Transform the input image for use with EffNetB2 | |
| img = effnetb2_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index | |
| # Put model int eval mode, make predictions | |
| effnetb2.eval() | |
| with torch.inference_mode(): | |
| # Pass transformed image through the model and turn prediction logits into prediction probabilities | |
| 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 pred 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 | |
| ### 4. Gradio app ### | |
| # Create title, description, and article | |
| title = 'FoodVision Big 🍔👀💪' | |
| description = 'An EfficientNetB2 Feature extractor computer vision model to classify images 101 classes of food from the Food101 dataset ' | |
| 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 example list | |
| 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(debug = True) # print errors locally | |