| import torch | |
| from transformers import AutoModelForImageClassification, AutoFeatureExtractor | |
| import gradio as gr | |
| model_id = f'omarques/vit-base-patch16-224-finetuned-flower' | |
| labels = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips'] | |
| def classify_image(image): | |
| model = AutoModelForImageClassification.from_pretrained(model_id) | |
| feature_extractor = AutoFeatureExtractor.from_pretrained(model_id) | |
| inp = feature_extractor(image, return_tensors='pt') | |
| outp = model(**inp) | |
| pred = torch.nn.functional.softmax(outp.logits, dim=-1) | |
| preds = pred[0].cpu().detach().numpy() | |
| confidence = {label: float(preds[i]) for i, label in enumerate(labels)} | |
| return confidence | |
| interface = gr.Interface(fn=classify_image, | |
| inputs='image', | |
| examples=['flower-1.jpeg', 'flower-2.jpeg'], | |
| outputs='label').launch()) |