| import torch |
| from transformers import AutoModelForImageClassification, AutoFeatureExtractor |
| import gradio as gr |
|
|
| model_id = f'palbee/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(debug=False, share=False) |
