{
 "cells": [
  {
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   "execution_count": 5,
   "id": "0db6b22a-5180-45ad-bbcb-aeff29f1af15",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\faiza\\anaconda3\\envs\\hgace\\Lib\\site-packages\\gradio\\interface.py:331: UserWarning: The `allow_flagging` parameter in `Interface` nowtakes a string value ('auto', 'manual', or 'never'), not a boolean. Setting parameter to: 'never'.\n",
      "  warnings.warn(\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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keyboard interruption in main thread... closing server.\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing required libraries\n",
    "from transformers import pipeline\n",
    "import gradio as gr\n",
    "from PIL import Image, ImageDraw\n",
    "\n",
    "# main function for object detection\n",
    "def detector(raw):\n",
    "    # Resize the image\n",
    "    WIDTH = 800\n",
    "    width, height = raw.size\n",
    "    ratio = float(WIDTH) / float(width)\n",
    "    new_h = height * ratio\n",
    "    ip_img = raw.resize((int(WIDTH), int(new_h)), Image.Resampling.LANCZOS)\n",
    "\n",
    "    # load the model pipeline and predict\n",
    "    outs = pipeline(model=\"hustvl/yolos-tiny\")(ip_img)\n",
    "\n",
    "    # draw the image on the canvas\n",
    "    draw = ImageDraw.Draw(ip_img)\n",
    "\n",
    "    # draw the boxes with labels\n",
    "    for object in outs:\n",
    "        score = f\"{object['score']*100:.2f}%\"\n",
    "        label = object['label']\n",
    "        xmin, ymin, xmax, ymax = object['box'].values()\n",
    "        draw.rectangle((xmin, ymin, xmax, ymax), outline='red', width=1)\n",
    "        draw.text((xmin, ymin), f\"{label}: {score}\", fill=\"black\")\n",
    "        \n",
    "    return ip_img\n",
    "\n",
    "demo = gr.Interface(fn=detector, \n",
    "             inputs=gr.Image(type='pil'),\n",
    "             outputs=gr.Image(type='pil'), allow_flagging=False)\n",
    "demo.queue(True)\n",
    "demo.launch(debug=True, inline=False, show_api=False, share=False)"
   ]
  }
 ],
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