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{
"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()"
]
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"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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}
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