diff --git "a/Untitled1.ipynb" "b/Untitled1.ipynb"
--- "a/Untitled1.ipynb"
+++ "b/Untitled1.ipynb"
@@ -2,65 +2,262 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 237,
    "id": "2e6185e5",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import AutoFeatureExtractor, AutoModelForImageClassification\n",
+    "import requests\n",
+    "import torch\n",
+    "import base64\n",
+    "import traceback\n",
+    "from ultralyticsplus import YOLO\n",
+    "\n",
+    "from PIL import Image, ImageDraw\n",
+    "from io import BytesIO\n",
+    "\n",
+    "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
+    "extractor = AutoFeatureExtractor.from_pretrained(\"rizvandwiki/gender-classification\")\n",
+    "\n",
+    "model_gender = AutoModelForImageClassification.from_pretrained(\"rizvandwiki/gender-classification\")\n",
+    "model_gender = model_gender.to(device)\n",
+    "\n",
+    "safe_img_base64 = \"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\"\n",
+    "safe_img_bytes = BytesIO(base64.b64decode(safe_img_base64))\n",
+    "safe_img = Image.open(safe_img_bytes)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "id": "273a1e64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load model\n",
+    "model_yolo = YOLO('kadirnar/yolov8n-v8.0')\n",
+    "\n",
+    "image_size = 640\n",
+    "model_yolo.conf = conf_threshold\n",
+    "model_yolo.iou = iou_threshold\n",
+    "\n",
+    "def yolov8(img):\n",
+    "    results = model_yolo.predict(img, imgsz=image_size)\n",
+    "    object_prediction_list = []\n",
+    "#     img1 = ImageDraw.Draw(img)  \n",
+    "    for image_results in results:\n",
+    "        for box in image_results.boxes:\n",
+    "            x1, y1, x2, y2 = (\n",
+    "                int(box.xyxy[0][0]),\n",
+    "                int(box.xyxy[0][1]),\n",
+    "                int(box.xyxy[0][2]),\n",
+    "                int(box.xyxy[0][3]),\n",
+    "            )\n",
+    "            bbox = [x1, y1, x2, y2]\n",
+    "            score = float(box.conf)\n",
+    "#             category_name = model_yolo.model_yolo.names[int(pred[5])]\n",
+    "#             category_id = pred[5]\n",
+    "#             img1.rectangle([(x1,y1), (x2,y2)], outline =\"red\")\n",
+    "            object_prediction_list.append([bbox, score])\n",
+    "\n",
+    "#     return img\n",
+    "    return object_prediction_list\n",
+    "\n",
+    "# response = requests.get('https://i.ytimg.com/vi/_Z2VZ_WpCvc/hqdefault.jpg?sqp=-oaymwEbCKgBEF5IVfKriqkDDggBFQAAiEIYAXABwAEG&rs=AOn4CLCXi7Lk6BjYQzi-25Fki7F_IAajhQ')\n",
+    "# img_bytes = BytesIO(response.content)\n",
+    "# img = Image.open(img_bytes)\n",
+    "\n",
+    "# yolov8(img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "id": "e98d64f0",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Ultralytics YOLOv8.0.8  Python-3.9.13 torch-1.13.1 CUDA:0 (NVIDIA GeForce GTX 1050, 4096MiB)\n",
+      "Fusing layers... \n",
+      "YOLOv8n summary: 168 layers, 3151904 parameters, 0 gradients\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7871\n",
-      "\n",
-      "To create a public link, set `share=True` in `launch()`.\n"
+      "[[[115, 0, 148, 39], 0.37744140625], [[0, 0, 160, 92], 0.310791015625], [[23, 1, 61, 56], 0.27783203125], [[146, 1, 168, 50], 0.269775390625]]\n"
      ]
     },
     {
      "data": {
-      "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7871/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
+      "image/png": "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\n",
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=422x315>"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 24,
+     "execution_count": 264,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "import gradio as gr\n",
-    "import requests\n",
-    "from PIL import Image\n",
-    "from io import BytesIO\n",
-    "\n",
     "def main(url):\n",
-    "    response = requests.get(url)\n",
-    "    img = Image.open(BytesIO(response.content))\n",
-    "    return img\n",
+    "    try:\n",
+    "        if url.startswith('.'):\n",
+    "            img = Image.open(url)\n",
+    "        else:\n",
+    "            response = requests.get(url)\n",
+    "            img_bytes = BytesIO(response.content)\n",
+    "            img = Image.open(img_bytes)\n",
+    "\n",
+    "        if \".svg\" in url:\n",
+    "            return response.content\n",
+    "        \n",
+    "        objects = yolov8(img)\n",
+    "        print(objects)\n",
+    "        for obj in objects:\n",
+    "            left, top, right, bottom = obj[0] # bbox\n",
+    "            crop = img.crop((left, top, right, bottom))\n",
+    "            inputs = extractor(crop, return_tensors=\"pt\").to(device)\n",
+    "\n",
+    "            with torch.no_grad():\n",
+    "                logits = model_gender(**inputs).logits\n",
+    "                logits = logits.softmax(-1)\n",
     "\n",
-    "iface = gr.Interface(\n",
-    "    fn=main,\n",
-    "    inputs=\"text\",\n",
-    "    outputs=\"image\",\n",
-    "    examples=[\"https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Ftse2.mm.bing.net%2Fth%3Fid%3DOIP.3_vWIWfkYD9VVXexuMeRzwHaLQ%26pid%3DApi&f=1&ipt=2affe653b6649aec2aa169f1b267cbb77ac0547559bbb46e25c2f841daa5d7eb&ipo=images\"])\n",
+    "            predicted_label = logits.argmax(-1).item()\n",
+    "            percentage = logits[0][predicted_label]\n",
+    "            label = model_gender.config.id2label[predicted_label]\n",
+    "            \n",
+    "            if label == \"female\" and percentage > 0.79:\n",
+    "                return safe_img\n",
+    "        return img\n",
+    "    except Exception as e:\n",
+    "        print(traceback.format_exc())\n",
+    "        return img\n",
     "\n",
-    "iface.launch()   "
+    "# main(\"./bad3.png\")\n",
+    "main(\"https://i.ytimg.com/vi/_Z2VZ_WpCvc/hqdefault.jpg?sqp=-oaymwEbCKgBEF5IVfKriqkDDggBFQAAiEIYAXABwAEG&rs=AOn4CLCXi7Lk6BjYQzi-25Fki7F_IAajhQ\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e98d64f0",
+   "execution_count": 266,
+   "id": "410be856",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: ultralyticsplus in c:\\users\\user\\anaconda3\\lib\\site-packages (0.0.3)\n",
+      "Collecting ultralyticsplus\n",
+      "  Using cached ultralyticsplus-0.0.14-py3-none-any.whl (11 kB)\n",
+      "Requirement already satisfied: pandas in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralyticsplus) (1.4.4)\n",
+      "Requirement already satisfied: sahi<0.12.0,>=0.11.11 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralyticsplus) (0.11.11)\n",
+      "Requirement already satisfied: fire in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralyticsplus) (0.5.0)\n",
+      "Collecting ultralytics<8.0.9,>=8.0.7\n",
+      "  Using cached ultralytics-8.0.8-py3-none-any.whl (256 kB)\n",
+      "Requirement already satisfied: huggingface-hub in c:\\users\\user\\anaconda3\\lib\\site-packages\\huggingface_hub-0.11.1-py3.8.egg (from ultralyticsplus) (0.11.1)\n",
+      "Requirement already satisfied: requests in c:\\users\\user\\anaconda3\\lib\\site-packages (from sahi<0.12.0,>=0.11.11->ultralyticsplus) (2.28.1)\n",
+      "Requirement already satisfied: terminaltables in c:\\users\\user\\anaconda3\\lib\\site-packages (from sahi<0.12.0,>=0.11.11->ultralyticsplus) (3.1.10)\n",
+      "Requirement already satisfied: click==8.0.4 in c:\\users\\user\\anaconda3\\lib\\site-packages (from sahi<0.12.0,>=0.11.11->ultralyticsplus) (8.0.4)\n",
+      "Requirement already satisfied: tqdm>=4.48.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from sahi<0.12.0,>=0.11.11->ultralyticsplus) (4.64.1)\n",
+      "Requirement already satisfied: shapely>=1.8.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from sahi<0.12.0,>=0.11.11->ultralyticsplus) (2.0.0)\n",
+      "Requirement already satisfied: pillow>=8.2.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from sahi<0.12.0,>=0.11.11->ultralyticsplus) (9.2.0)\n",
+      "Requirement already satisfied: pyyaml in c:\\users\\user\\anaconda3\\lib\\site-packages (from sahi<0.12.0,>=0.11.11->ultralyticsplus) (6.0)\n",
+      "Requirement already satisfied: opencv-python>=4.2.0.32 in c:\\users\\user\\anaconda3\\lib\\site-packages (from sahi<0.12.0,>=0.11.11->ultralyticsplus) (4.7.0.68)\n",
+      "Requirement already satisfied: pybboxes==0.1.6 in c:\\users\\user\\anaconda3\\lib\\site-packages (from sahi<0.12.0,>=0.11.11->ultralyticsplus) (0.1.6)\n",
+      "Requirement already satisfied: colorama in c:\\users\\user\\anaconda3\\lib\\site-packages (from click==8.0.4->sahi<0.12.0,>=0.11.11->ultralyticsplus) (0.4.5)\n",
+      "Requirement already satisfied: numpy in c:\\users\\user\\anaconda3\\lib\\site-packages (from pybboxes==0.1.6->sahi<0.12.0,>=0.11.11->ultralyticsplus) (1.21.5)\n",
+      "Requirement already satisfied: seaborn>=0.11.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.11.2)\n",
+      "Requirement already satisfied: torch>=1.7.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (1.13.1)\n",
+      "Requirement already satisfied: torchvision>=0.8.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.14.1)\n",
+      "Requirement already satisfied: matplotlib>=3.2.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (3.5.2)\n",
+      "Requirement already satisfied: hydra-core>=1.2.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (1.3.1)\n",
+      "Requirement already satisfied: sentry-sdk in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (1.13.0)\n",
+      "Requirement already satisfied: scipy>=1.4.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (1.9.1)\n",
+      "Requirement already satisfied: tensorboard>=2.4.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (2.11.2)\n",
+      "Requirement already satisfied: GitPython>=3.1.24 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (3.1.30)\n",
+      "Requirement already satisfied: psutil in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (5.9.0)\n",
+      "Requirement already satisfied: thop>=0.1.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.1.1.post2209072238)\n",
+      "Requirement already satisfied: ipython in c:\\users\\user\\anaconda3\\lib\\site-packages (from ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (7.31.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas->ultralyticsplus) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas->ultralyticsplus) (2022.1)\n",
+      "Requirement already satisfied: termcolor in c:\\users\\user\\anaconda3\\lib\\site-packages (from fire->ultralyticsplus) (2.2.0)\n",
+      "Requirement already satisfied: six in c:\\users\\user\\anaconda3\\lib\\site-packages (from fire->ultralyticsplus) (1.16.0)\n",
+      "Requirement already satisfied: filelock in c:\\users\\user\\anaconda3\\lib\\site-packages (from huggingface-hub->ultralyticsplus) (3.6.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from huggingface-hub->ultralyticsplus) (4.3.0)\n",
+      "Requirement already satisfied: packaging>=20.9 in c:\\users\\user\\anaconda3\\lib\\site-packages (from huggingface-hub->ultralyticsplus) (21.3)\n",
+      "Requirement already satisfied: gitdb<5,>=4.0.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from GitPython>=3.1.24->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (4.0.10)\n",
+      "Requirement already satisfied: omegaconf<2.4,>=2.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from hydra-core>=1.2.0->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (2.3.0)\n",
+      "Requirement already satisfied: antlr4-python3-runtime==4.9.* in c:\\users\\user\\anaconda3\\lib\\site-packages (from hydra-core>=1.2.0->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (4.9.3)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=3.2.2->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.11.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=3.2.2->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (4.25.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=3.2.2->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (1.4.2)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib>=3.2.2->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (3.0.9)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->sahi<0.12.0,>=0.11.11->ultralyticsplus) (3.3)\n",
+      "Requirement already satisfied: charset-normalizer<3,>=2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->sahi<0.12.0,>=0.11.11->ultralyticsplus) (2.0.4)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->sahi<0.12.0,>=0.11.11->ultralyticsplus) (2022.12.7)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests->sahi<0.12.0,>=0.11.11->ultralyticsplus) (1.26.11)\n",
+      "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (2.16.0)\n",
+      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.6.1)\n",
+      "Requirement already satisfied: setuptools>=41.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (63.4.1)\n",
+      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.4.6)\n",
+      "Requirement already satisfied: grpcio>=1.24.3 in c:\\users\\user\\anaconda3\\lib\\site-packages (from tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (1.51.1)\n",
+      "Requirement already satisfied: absl-py>=0.4 in c:\\users\\user\\anaconda3\\lib\\site-packages (from tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (1.4.0)\n",
+      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (2.0.3)\n",
+      "Requirement already satisfied: wheel>=0.26 in c:\\users\\user\\anaconda3\\lib\\site-packages (from tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.37.1)\n",
+      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\user\\anaconda3\\lib\\site-packages (from tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (3.3.4)\n",
+      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (1.8.1)\n",
+      "Requirement already satisfied: protobuf<4,>=3.9.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (3.20.1)\n",
+      "Requirement already satisfied: pygments in c:\\users\\user\\anaconda3\\lib\\site-packages (from ipython->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (2.11.2)\n",
+      "Requirement already satisfied: pickleshare in c:\\users\\user\\anaconda3\\lib\\site-packages (from ipython->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.7.5)\n",
+      "Requirement already satisfied: backcall in c:\\users\\user\\anaconda3\\lib\\site-packages (from ipython->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.2.0)\n",
+      "Requirement already satisfied: traitlets>=4.2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ipython->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (5.1.1)\n",
+      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ipython->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (3.0.20)\n",
+      "Requirement already satisfied: decorator in c:\\users\\user\\anaconda3\\lib\\site-packages (from ipython->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (5.1.1)\n",
+      "Requirement already satisfied: matplotlib-inline in c:\\users\\user\\anaconda3\\lib\\site-packages (from ipython->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.1.6)\n",
+      "Requirement already satisfied: jedi>=0.16 in c:\\users\\user\\anaconda3\\lib\\site-packages (from ipython->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.18.1)\n",
+      "Requirement already satisfied: smmap<6,>=3.0.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from gitdb<5,>=4.0.1->GitPython>=3.1.24->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (5.0.0)\n",
+      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\user\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (4.9)\n",
+      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.2.8)\n",
+      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (5.2.1)\n",
+      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (1.3.1)\n",
+      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from jedi>=0.16->ipython->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.8.3)\n",
+      "Requirement already satisfied: wcwidth in c:\\users\\user\\anaconda3\\lib\\site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.2.5)\n",
+      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (0.4.8)\n",
+      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->ultralytics<8.0.9,>=8.0.7->ultralyticsplus) (3.2.2)\n",
+      "Installing collected packages: ultralytics, ultralyticsplus\n",
+      "  Attempting uninstall: ultralytics\n",
+      "    Found existing installation: ultralytics 8.0.10\n",
+      "    Uninstalling ultralytics-8.0.10:\n",
+      "      Successfully uninstalled ultralytics-8.0.10\n",
+      "  Attempting uninstall: ultralyticsplus\n",
+      "    Found existing installation: ultralyticsplus 0.0.3\n",
+      "    Uninstalling ultralyticsplus-0.0.3:\n",
+      "      Successfully uninstalled ultralyticsplus-0.0.3\n",
+      "Successfully installed ultralytics-8.0.8 ultralyticsplus-0.0.14\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install --upgrade ultralyticsplus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12273dcf",
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [],
    "source": []
   }