Commit 2: Add 50 file(s)
Browse files
demos/image_editor_events/run.ipynb
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{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_editor_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "\n", "def predict(im):\n", " return im[\"composite\"]\n", "\n", "def verify_clear(im):\n", " return int(not np.any(im[
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{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_editor_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "\n", "\n", "def predict(im):\n", " return im[\"composite\"]\n", "\n", "\n", "def verify_clear(im):\n", " print(im)\n", " return int(not np.any(im[\"composite\"])), im[\"composite\"]\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Group():\n", " with gr.Row():\n", " im = gr.ImageEditor(\n", " type=\"numpy\",\n", " crop_size=\"1:1\",\n", " elem_id=\"image_editor\",\n", " )\n", " im_preview = gr.Image()\n", " with gr.Group():\n", " with gr.Row():\n", "\n", " n_upload = gr.Label(\n", " 0,\n", " label=\"upload\",\n", " elem_id=\"upload\",\n", " )\n", " n_change = gr.Label(\n", " 0,\n", " label=\"change\",\n", " elem_id=\"change\",\n", " )\n", " n_input = gr.Label(\n", " 0,\n", " label=\"input\",\n", " elem_id=\"input\",\n", " )\n", " n_apply = gr.Label(\n", " 0,\n", " label=\"apply\",\n", " elem_id=\"apply\",\n", " )\n", " cleared_properly = gr.Number(label=\"cleared properly\")\n", " clear_btn = gr.Button(\"Clear Button\", elem_id=\"clear\")\n", "\n", " im.upload(\n", " lambda x: int(x) + 1, outputs=n_upload, inputs=n_upload, show_progress=\"hidden\"\n", " )\n", " im.change(\n", " lambda x: int(x) + 1, outputs=n_change, inputs=n_change, show_progress=\"hidden\"\n", " )\n", " im.input(\n", " lambda x: int(x) + 1, outputs=n_input, inputs=n_input, show_progress=\"hidden\"\n", " )\n", " im.apply(\n", " lambda x: int(x) + 1, outputs=n_apply, inputs=n_apply, show_progress=\"hidden\"\n", " )\n", " im.change(predict, outputs=im_preview, inputs=im, show_progress=\"hidden\")\n", " clear_btn.click(\n", " lambda: None,\n", " None,\n", " im,\n", " ).then(verify_clear, inputs=im, outputs=[cleared_properly, im])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
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demos/image_editor_events/run.py
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import gradio as gr
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import numpy as np
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def predict(im):
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return im["composite"]
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def verify_clear(im):
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with gr.Blocks() as demo:
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with gr.Group():
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lambda: None,
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None,
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im,
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).then(verify_clear,
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inputs=im,
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outputs=[cleared_properly, im])
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if __name__ == "__main__":
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demo.launch()
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import gradio as gr
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import numpy as np
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def predict(im):
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return im["composite"]
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def verify_clear(im):
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print(im)
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return int(not np.any(im["composite"])), im["composite"]
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with gr.Blocks() as demo:
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with gr.Group():
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lambda: None,
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None,
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im,
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).then(verify_clear, inputs=im, outputs=[cleared_properly, im])
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if __name__ == "__main__":
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demo.launch()
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