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app.py
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@@ -37,6 +37,16 @@ def gradio_inference(
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height=1024
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"""Wrapper function for Gradio interface"""
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# Use temporary directory
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with tempfile.TemporaryDirectory() as tmp_dir:
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# Save inputs to temp directory
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@@ -86,6 +96,14 @@ with gr.Blocks() as demo:
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# gr.Video("example/github.mp4", label="Demo Video: How to use the tool")
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with gr.Column():
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with gr.Row():
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with gr.Column():
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image_input = gr.ImageMask(
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height=1024
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):
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"""Wrapper function for Gradio interface"""
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# Check if mask has been drawn
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if image_data is None or "layers" not in image_data or not image_data["layers"]:
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raise gr.Error("Please draw a mask over the clothing area before generating!")
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# Check if mask is empty (all black)
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mask = image_data["layers"][0]
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mask_array = np.array(mask)
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if np.all(mask_array < 10):
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raise gr.Error("The mask is empty! Please draw over the clothing area you want to replace.")
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# Use temporary directory
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with tempfile.TemporaryDirectory() as tmp_dir:
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# Save inputs to temp directory
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# gr.Video("example/github.mp4", label="Demo Video: How to use the tool")
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with gr.Column():
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gr.Markdown("""
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### ⚠️ Important:
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1. Click 'Edit' on the Model Image
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2. Draw a mask over the clothing area you want to replace
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3. Click 'Save' when done
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4. Then click 'Generate Try-On'
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""")
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with gr.Row():
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with gr.Column():
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image_input = gr.ImageMask(
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