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import torch | |
from transformers import AutoModelForImageClassification, AutoFeatureExtractor | |
import gradio as gr | |
model_id = f'N0elle-08/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() |