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