{ "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 } }, "nbformat": 4, "nbformat_minor": 5 }