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import gradio as gr |
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from transformers import pipeline |
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import json |
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config_path = "KhadijaAsehnoune12/LeafDiseaseDetector/config.json" |
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with open(config_path, "r", encoding="utf-8") as f: |
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config = json.load(f) |
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pipe = pipeline(task="image-classification", model="KhadijaAsehnoune12/LeafDiseaseDetector") |
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def predict(image): |
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predictions = pipe(image) |
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predicted_index = predictions[0]['label'] |
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label_name = config["id2label"][str(predicted_index)] |
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confidence_score = predictions[0]['score'] |
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return f"{label_name} ({confidence_score:.2f})" |
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iface = gr.Interface(fn=predict, |
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inputs=gr.inputs.Image(type="numpy"), |
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outputs="text", |
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title="Orange Disease Image Classification", |
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description="Detect diseases in orange leaves and fruits.", |
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examples=['MoucheB.jpg', 'verdissement.jpg']) |
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iface.launch() |
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