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| import torch | |
| import os | |
| import gradio as gr | |
| from model import create_vit | |
| from timeit import default_timer as timer | |
| from typing import Tuple, Dict | |
| class_names = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash'] | |
| vit, vit_transform = create_vit(output_classes=len(class_names)) | |
| vit.load_state_dict(torch.load(f="vit_b_16_dout0.3_10epochs.pth", map_location=torch.device("cpu"))) | |
| def predict(img) -> Tuple[Dict, float]: | |
| start_time = timer() | |
| img = vit_transform(img).unsqueeze(0) | |
| vit.eval() | |
| with torch.inference_mode(): | |
| pred_probs = torch.softmax(vit(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 | |
| title = "Garbage Sense" | |
| description = "A vision transformer trained to classify garbage into 6 categories on [trashnet](https://github.com/garythung/trashnet)." | |
| article = "" | |
| example_list = [["examples/" + example] for example in os.listdir("examples")] | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="pil"), | |
| outputs=[ | |
| gr.Label(num_top_classes=6, label="Predictions"), | |
| gr.Number(label="Prediction time (s)"), | |
| ], | |
| examples=example_list, | |
| title=title, | |
| description=description | |
| ) | |
| demo.launch() |