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Download app.py from bortle/moon-detector: direct link, hf CLI and curl.
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https://huggingface.co/spaces/bortle/moon-detector/resolve/main/app.py
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hf download hf://spaces/bortle/moon-detector/app.py
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curl -L -o app.py https://huggingface.co/spaces/bortle/moon-detector/resolve/main/app.py
800 Bytes
| import gradio as gr | |
| from transformers import pipeline | |
| from PIL import Image | |
| model_pipeline = pipeline(task="image-classification", model="bortle/moon-detector-v5.a") | |
| def predict(image): | |
| # Resize the image to have width 1080 while keeping aspect ratio | |
| width = 1080 | |
| ratio = width / image.width | |
| height = int(image.height * ratio) | |
| resized_image = image.resize((width, height)) | |
| # Perform predictions | |
| predictions = model_pipeline(resized_image) | |
| # Return predictions as a dictionary | |
| return {p["label"]: p["score"] for p in predictions} | |
| # Define the Gradio Interface | |
| gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="pil", label="Upload image"), | |
| outputs=gr.Label(num_top_classes=5), | |
| title="Moon Detector", | |
| allow_flagging="manual", | |
| ).launch() |