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from transformers import AutoFeatureExtractor, AutoModelForImageClassification |
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import torch |
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import gradio as gr |
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extractor = AutoFeatureExtractor.from_pretrained("ayoubkirouane/VIT_Beans_Leaf_Disease_Classifier") |
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model = AutoModelForImageClassification.from_pretrained("ayoubkirouane/VIT_Beans_Leaf_Disease_Classifier") |
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labels = ['angular_leaf_spot', 'bean_rust', 'healthy'] |
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def classify(im): |
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features = extractor(im, return_tensors='pt') |
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logits = model(features["pixel_values"])[-1] |
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probability = torch.nn.functional.softmax(logits, dim=-1) |
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probs = probability[0].detach().numpy() |
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confidences = {label: float(probs[i]) for i, label in enumerate(labels)} |
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return confidences |
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interface = gr.Interface( |
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classify, |
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inputs='image', |
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outputs='label', |
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) |
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interface.launch(share=True , debug=True) |
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