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# Load model directly
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
import torch
import gradio as gr
extractor = AutoFeatureExtractor.from_pretrained("ayoubkirouane/VIT_Beans_Leaf_Disease_Classifier")
model = AutoModelForImageClassification.from_pretrained("ayoubkirouane/VIT_Beans_Leaf_Disease_Classifier")
labels = ['angular_leaf_spot', 'bean_rust', 'healthy']
def classify(im):
features = 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
interface = gr.Interface(
classify,
inputs='image',
outputs='label',
)
interface.launch(share=True , debug=True)
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