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| from transformers import ViTImageProcessor, ViTForImageClassification | |
| import torch | |
| import gradio as gr | |
| feature_extractor = ViTImageProcessor.from_pretrained("car_scene_model") | |
| model = ViTForImageClassification.from_pretrained("car_scene_model") | |
| labels = ['Exterior', 'Interior', 'Unknown'] | |
| def classify(im): | |
| features = feature_extractor(im, return_tensors='pt') | |
| logits = model(features["pixel_values"])[-1] | |
| probability = torch.nn.functional.softmax(logits, dim=-1) | |
| probs = probability[0].detach().numpy() | |
| confidences = {label: float(probs[i]) for i, label in enumerate(labels)} | |
| return confidences | |
| description = """ | |
| Car scene recognition demo. Upload or drag a .jpg image to test | |
| """ | |
| interface = gr.Interface(fn=classify, | |
| inputs="image", | |
| outputs="label", | |
| title="Car scene recognition", | |
| examples=["crv.jpg", | |
| "cadillac1.jpeg", | |
| "cadillacinterior.jpeg", | |
| "outsidescene.jpg", | |
| "wheel.jpeg", | |
| "crv_inside.jpg", | |
| "chevy_exterior.jpeg", | |
| "lexus_inside.jpeg", | |
| "malibu_interior.jpeg", | |
| "maserati_interior.jpeg", | |
| "highlander_inside.jpeg", | |
| "altima_inside.jpeg", | |
| "altima_outside.jpeg"], | |
| description=description ) | |
| interface.launch() |