{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#|default_exp app" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#|export\n", "from fastai.vision.all import *\n", "import gradio as gr" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#|export\n", "\n", "learn = load_learner('food.pkl')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['burger', 'chilly potato', 'chowmein', 'french fry', 'fried rice', 'momos', 'pizza', 'spring roll']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "learn.dls.vocab" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#|export\n", "\n", "categories = ('burger', 'chilly potato', 'chowmein', 'french fry', 'fried rice', 'momos', 'pizza', 'spring roll')\n", "\n", "def classify_image(img):\n", " pred, idx, probs = learn.predict(img)\n", " return dict(zip(categories, map(float, probs)))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\Himz\\anaconda3\\lib\\site-packages\\gradio\\inputs.py:257: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n", " warnings.warn(\n", "c:\\Users\\Himz\\anaconda3\\lib\\site-packages\\gradio\\deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n", " warnings.warn(value)\n", "c:\\Users\\Himz\\anaconda3\\lib\\site-packages\\gradio\\outputs.py:197: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n", " warnings.warn(\n", "c:\\Users\\Himz\\anaconda3\\lib\\site-packages\\gradio\\deprecation.py:40: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n", " warnings.warn(value)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/plain": [] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#|export \n", "image = gr.inputs.Image(shape=(224,224))\n", "label = gr.outputs.Label()\n", "examples = ['chillypotato.jpg', 'friedrice.jpg', 'momos.jpg']\n", "\n", "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n", "intf.launch(inline=False)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Export" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfull\n" ] } ], "source": [ "import nbdev\n", "nbdev.export.nb_export('app.ipynb', './')\n", "print(\"Successfull\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "5a98ca32c900e45ba6fdf802df0386790e8919c5ffd6432e6c5973ff7369f74d" } } }, "nbformat": 4, "nbformat_minor": 2 }