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| import gradio as gr | |
| from transformers import pipeline | |
| classifier = pipeline("image-classification", model="Asseh/Ball_Classification") | |
| def predict(input_img): | |
| predictions = pipe(input_img) | |
| return input_img, {p["label"]: p["score"] for p in predictions} | |
| gradio_app = gr.Interface( | |
| predict, | |
| inputs = gr.Image(label = "Select Image", sources=['upload', 'webcam'], type="pil"), | |
| outputs = [gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=15)], | |
| title="Which type of ball" | |
| ) | |
| if __name__ == "__main__": | |
| gradio_app.launch() |