| import gradio as gr | |
| from transformers import pipeline | |
| pipeline = pipeline(task="image-classification", model="bortle/astrophotography-object-classifier-alpha5") | |
| def predict(image): | |
| predictions = pipeline(image) | |
| return {p["label"]: p["score"] for p in predictions} | |
| def process_image(image): | |
| width = 1080 | |
| ratio = width / image.width | |
| height = int(image.height * ratio) | |
| resized_image = image.resize((width, height)) | |
| return resized_image | |
| gr.Interface( | |
| predict, | |
| fn=process_image, | |
| inputs=gr.Image(type="pil", label="Upload Astrophotography image"), | |
| outputs=gr.Label(num_top_classes=5), | |
| title="Astrophotography Object Classifier", | |
| allow_flagging="manual", | |
| examples=["examples/Andromeda.jpg", "examples/Heart.jpg", "examples/Pleiades.jpg", "examples/Rosette.jpg", "examples/Moon.jpg", "examples/GreatHercules.jpg", "examples/Leo-Triplet.jpg", "examples/Crab.jpg", "examples/North-America.jpg", "examples/Horsehead-Flame.jpg", "examples/Pinwheel.jpg", "examples/Saturn.jpg"], | |
| cache_examples=True | |
| ).launch() |