Upload with huggingface_hub

requirements.txt

@@ -1,2 +1,2 @@
 
-https://gradio-main-build.s3.amazonaws.com/8edc91ff4b45981ad3e96288b404f9143115b178/gradio-3.10.1-py3-none-any.whl
+https://gradio-main-build.s3.amazonaws.com/4f24e146dac42a85de708d147f488fdf418d4a7f/gradio-3.10.1-py3-none-any.whl

run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: sepia_filter"]}, {"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 numpy as np\n", "import gradio as gr\n", "\n", "def sepia(input_img):\n", "    sepia_filter = np.array([\n", "        [0.393, 0.769, 0.189], \n", "        [0.349, 0.686, 0.168], \n", "        [0.272, 0.534, 0.131]\n", "    ])\n", "    sepia_img = input_img.dot(sepia_filter.T)\n", "    sepia_img /= sepia_img.max()\n", "    return sepia_img\n", "\n", "demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), \"image\")\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}