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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_ = '''
<h2>Toon3D: Seeing Cartoons from a New Perspective</h2>
Toon3D lifts cartoons into 3D via aligning and warping backprojected monocular depth predictions.<br>
Project page @ <a href='https://toon3d.studio/' target='_blank'>https://toon3d.studio/</a>
**Important Notes:**
- TODO 1
- TODO 2
'''
def check_input_images(input_images):
if input_images is None:
raise gr.Error("No images uploaded!")
def process_images(input_images):
for image in input_images:
print(image)
return input_images
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_images = gr.File(label="Upload Images", file_count="multiple", file_types=[".jpg", "jpeg", "png"])
with gr.Row():
process_data_button = gr.Button("Process Data", elem_id="process_data", variant="primary")
with gr.Row():
processed_data_zip = gr.File(label="Processed Data", file_count="single", file_types=[".zip"])
with gr.Column():
with gr.Row():
labeled_data = gr.File(label="Labeled Points", file_count="single", file_types=[".json"])
# mv_images = gr.State()
process_data_button.click(fn=check_input_images, inputs=[input_images]).success(
fn=process_images,
inputs=[input_images],
outputs=[processed_data_zip],
)
if __name__ == "__main__":
demo.launch()
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