Spaces:
Sleeping
Sleeping
| 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 | |
| with open("class_names.txt", "r") as f: | |
| class_names = [food_name.strip() for food_name in f.readlines()] | |
| # Create model | |
| vit, vit_transforms = create_vit_model(num_classes=101) | |
| # Load trained model weights | |
| vit.load_state_dict( | |
| torch.load( | |
| f="pretrained_vit_feature_extractor_food101_finetuned.pth", | |
| map_location=torch.device("cpu") | |
| ) | |
| ) | |
| # 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_transforms(img).unsqueeze(0) | |
| # Put model into evaluation mode and turn on inference mode | |
| vit.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(img), dim=1) | |
| # Create a prediction label and prediction probability dictionary for each prediction class | |
| 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 | |
| # Create title, description and article strings | |
| title = "FoodVision" | |
| description = "A ViT feature extractor computer vision model to classify images of food into [101 different classes](https://huggingface.co/datasets/ethz/food101)." | |
| article = "[Pretrained ViT model](https://arxiv.org/abs/2010.11929) finetuned on Food101 dataset" | |
| # Create examples list from "examples/" directory | |
| example_list = [["examples/" + example] for example in os.listdir("examples")] | |
| # Create Gradio interface | |
| demo = gr.Interface( | |
| fn=predict, | |
| 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 app! | |
| demo.launch() | |