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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "81598ea8-8e97-4ad7-a45f-bd928d0ef416",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"from ultralytics import YOLO\n",
"import cv2\n",
"import os\n",
"\n",
"def predict_image(image_input):\n",
" image = cv2.imread(image_input)\n",
" # load model\n",
" model = YOLO(\"best.pt\")\n",
" #run predict\n",
" outputs = model.predict(source=image_input)\n",
" results = output[0].cpu().numpy()\n",
" for i, det in enumerate(results.boxes.xyxy):\n",
" cv2.rectangle(image, (int(det[0]), int(det[1]), int(det[2]), int(det[3]),\n",
" color=(0, 0, 255), thickness=2, lineType=cv2.Line_AA)\n",
" return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
"\n",
"inputs_image = [gr.components.Image(type=\"filepath\", label=\"Input Image\")]\n",
"outputs_image = [gr.components.Image(type=\"numpy\", label=\"Output Image\")]\n",
"\n",
"interface_image = gr.Interface(fn = predict_image, inputs=inputs_image, outputs=outputs_image, \n",
" title=\"Fire & Smoke Detector\", cache_examples=False)\n",
"\n",
"interface_image.launch(Debug=True)\n"
]
}
],
"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": "3.9.18"
}
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"nbformat_minor": 5
}
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