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import gradio as gr |
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from PIL import Image |
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from transformers import pipeline |
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classifier = pipeline("image-classification", model="Docty/mangoes") |
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def classify_image(img): |
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if not isinstance(img, Image.Image): |
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img = Image.fromarray(img) |
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results = classifier(img) |
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return {res["label"]: float(res["score"]) for res in results} |
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theme = gr.themes.Soft( |
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primary_hue="blue", |
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secondary_hue="lime", |
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neutral_hue="slate" |
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) |
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with gr.Blocks(theme=theme) as demo: |
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gr.Markdown("## Mango Image Classifier") |
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gr.Markdown("Upload an image of a mango to classify it using a fine-tuned model.") |
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with gr.Row(): |
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image_input = gr.Image(type="pil", label="Upload Mango Image") |
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label_output = gr.Label(num_top_classes=3, label="Predictions") |
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classify_btn = gr.Button("Classify Image", variant="primary") |
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gr.Examples( |
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examples=[ |
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"0.jpg", |
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"1.jpg", |
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"2.jpg", |
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"3.jpg", |
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"4.jpg", |
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"5.jpg", |
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"6.jpg", |
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"7.jpg" |
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], |
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inputs=image_input, |
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outputs=label_output, |
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fn=classify_image, |
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cache_examples=False |
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) |
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classify_btn.click(fn=classify_image, inputs=image_input, outputs=label_output) |
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demo.launch(share=True) |
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