| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "af9bff8c", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from fastai.vision.all import*\n", | |
| "import gradio as gr\n", | |
| "learn1 = load_learner('stage1.pkl')\n", | |
| "learn2 = load_learner('stage2.pkl')\n", | |
| "demo = gr.Blocks()\n", | |
| "\n", | |
| "categories1 = 'discarded clothing', 'food waste', 'plastic bags', 'recyc_no_scrap', 'scrap metal piece', 'wood scraps'\n", | |
| "categories2 = 'HDPE container', 'PET plastic bottle', 'aluminium can', 'cardboard', 'glass', 'paper2D', 'paper3D', 'steel and tin cans'\n", | |
| "categories1_str = \"Stage 1 categories: \"+\", \".join(categories1)\n", | |
| "categories2_str = \"Stage 2 categories: \"+\", \".join(categories2)\n", | |
| "placeholder_=\"Stages 1 and 2 of the Recycling Process\\n\"+categories1_str+\"\\n\"+categories2_str\n", | |
| "\n", | |
| "image1 = gr.inputs.Image(shape=(192,192))\n", | |
| "label1 = gr.outputs.Label()\n", | |
| "examples1 = ['stage1ex1_t.jpeg', 'stage1ex2_t.jpeg','stage1ex3_t.jpeg','stage1ex4_t.jpeg', 'stage1ex5_t.jpeg','stage1ex6_t.jpeg']\n", | |
| "\n", | |
| "\n", | |
| "image2 = gr.inputs.Image(shape=(192,192))\n", | |
| "label2 = gr.outputs.Label()\n", | |
| "examples2 = ['stage2ex1_t.jpeg', 'stage2ex2_t.jpeg','stage2ex3_t.jpeg', 'stage2ex4_t.jpeg','stage2ex5_t.jpeg',\n", | |
| " 'stage2ex6_tt.jpeg','stage2ex7_tt.jpeg','stage2ex8_t.jpeg']\n", | |
| "\n", | |
| "\n", | |
| "def classify_stage1(img):\n", | |
| " pred, idx, probs = learn1.predict(img)\n", | |
| " return dict(zip(categories1, map(float,probs)))\n", | |
| "def classify_stage2(img):\n", | |
| " pred, idx, probs = learn2.predict(img)\n", | |
| " return dict(zip(categories2, map(float,probs)))\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "with demo:\n", | |
| " gr.Markdown(placeholder_)\n", | |
| " with gr.Tabs():\n", | |
| " with gr.TabItem(\"Stage 1\"):\n", | |
| " with gr.Row():\n", | |
| " nxt1 = random.choice(examples1)\n", | |
| " stage1_input = gr.Image(nxt1)\n", | |
| " stage1_output = gr.Label()\n", | |
| " \n", | |
| " stage1_button = gr.Button(\"Categorize Stage 1 Item\")\n", | |
| " \n", | |
| " \n", | |
| " \n", | |
| " with gr.TabItem(\"Stage2\"):\n", | |
| " with gr.Row():\n", | |
| " stage2_input = gr.Image(random.choice(examples2))\n", | |
| " stage2_output = gr.Label()\n", | |
| " \n", | |
| " stage2_button = gr.Button(\"Categorize Stage 2 Item\")\n", | |
| "\n", | |
| " stage1_button.click(classify_stage1, inputs=stage1_input, outputs=stage1_output)#, examples = examples1)\n", | |
| " stage2_button.click(classify_stage2, inputs=stage2_input, outputs=stage2_output)#, examples = examples2)\n", | |
| "\n", | |
| "demo.launch()" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3 (ipykernel)", | |
| "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": "3.10.4" | |
| }, | |
| "toc": { | |
| "base_numbering": 1, | |
| "nav_menu": {}, | |
| "number_sections": true, | |
| "sideBar": true, | |
| "skip_h1_title": false, | |
| "title_cell": "Table of Contents", | |
| "title_sidebar": "Contents", | |
| "toc_cell": false, | |
| "toc_position": {}, | |
| "toc_section_display": true, | |
| "toc_window_display": false | |
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| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } | |