import spaces import gradio as gr from transformers import pipeline pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog") @spaces.GPU def predict(input_img): predictions = pipeline(input_img) return input_img, {p["label"]: p["score"] for p in predictions} _HEADER_ = '''

Toon3D: Seeing Cartoons from a New Perspective

**Toon3D** lifts cartoons into 3D via aligning and warping backprojected monocular depth predictions.. Project page @ https://toon3d.studio/ **Important Notes:** - Our demo can export a .obj mesh with vertex colors or a .glb mesh now. If you prefer to export a .obj mesh with a **texture map**, please refer to our Github Repo. - The 3D mesh generation results highly depend on the quality of generated multi-view images. Please try a different **seed value** if the result is unsatisfying (Default: 42). ''' gradio_app = gr.Interface( predict, inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"), outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)], title="Toon3D", ) with gr.Blocks() as demo: gr.Markdown(_HEADER_) with gr.Row(variant="panel"): with gr.Column(): with gr.Row(): input = gr.File(file_count="directory") if __name__ == "__main__": gradio_app.launch()