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requirements.txt

@@ -1,2 +1,2 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@de997e67c9a7feb9e2eccebf92969366dbd67eba#subdirectory=client/python
-https://gradio-builds.s3.amazonaws.com/de997e67c9a7feb9e2eccebf92969366dbd67eba/gradio-4.39.0-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@9b42ba8f1006c05d60a62450d3036ce0d6784f86#subdirectory=client/python
+https://gradio-builds.s3.amazonaws.com/9b42ba8f1006c05d60a62450d3036ce0d6784f86/gradio-4.39.0-py3-none-any.whl

run.ipynb

@@ -1 +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(), \"image\")\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"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(), \"image\")\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -3,8 +3,8 @@ import gradio as gr
 
 def sepia(input_img):
     sepia_filter = np.array([
-        [0.393, 0.769, 0.189], 
-        [0.349, 0.686, 0.168], 
+        [0.393, 0.769, 0.189],
+        [0.349, 0.686, 0.168],
         [0.272, 0.534, 0.131]
     ])
     sepia_img = input_img.dot(sepia_filter.T)