add colab notebook

WD_Tagger_Mobile.ipynb

@@ -0,0 +1,436 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [
+        "GAZViC5o2bya",
+        "QwoVd4CE2njF",
+        "8r0qzU2NRoIT",
+        "lgaEjLAo7lMd",
+        "RadVNaev2_mF"
+      ]
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Dependencies"
+      ],
+      "metadata": {
+        "id": "GAZViC5o2bya"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wNCZ04U82IiL",
+        "outputId": "dc277e76-67e7-4781-95a1-123bb139bbf3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting onnxruntime\n",
+            "  Downloading onnxruntime-1.17.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (6.8 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting coloredlogs (from onnxruntime)\n",
+            "  Downloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: flatbuffers in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (23.5.26)\n",
+            "Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (1.25.2)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (23.2)\n",
+            "Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (3.20.3)\n",
+            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (1.12)\n",
+            "Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime)\n",
+            "  Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->onnxruntime) (1.3.0)\n",
+            "Installing collected packages: humanfriendly, coloredlogs, onnxruntime\n",
+            "Successfully installed coloredlogs-15.0.1 humanfriendly-10.0 onnxruntime-1.17.1\n",
+            "Collecting onnx\n",
+            "  Downloading onnx-1.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.7 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.7/15.7 MB\u001b[0m \u001b[31m40.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from onnx) (1.25.2)\n",
+            "Requirement already satisfied: protobuf>=3.20.2 in /usr/local/lib/python3.10/dist-packages (from onnx) (3.20.3)\n",
+            "Installing collected packages: onnx\n",
+            "Successfully installed onnx-1.15.0\n",
+            "Collecting onnxruntime-extensions\n",
+            "  Downloading onnxruntime_extensions-0.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.0 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m21.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: onnxruntime-extensions\n",
+            "Successfully installed onnxruntime-extensions-0.10.1\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install onnxruntime\n",
+        "!pip install onnx\n",
+        "!pip install onnxruntime-extensions"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Download ONNX Model\n",
+        "This downloads [wd-convnext-tagger-v3](https://huggingface.co/SmilingWolf/wd-convnext-tagger-v3) created by [SmilingWolf](https://huggingface.co/SmilingWolf).\n",
+        "\n",
+        "Feel free to use SmilingWolfs other model variants instead.\n",
+        "\n",
+        "The tags and power image is also downloaded for inferencing."
+      ],
+      "metadata": {
+        "id": "QwoVd4CE2njF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!wget https://huggingface.co/SmilingWolf/wd-convnext-tagger-v3/resolve/main/model.onnx?download=true -O model.onnx"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AMF_IIxm2tT_",
+        "outputId": "b8e574b8-8276-4f74-92fa-b3a946e92655"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2024-03-09 05:03:49--  https://huggingface.co/SmilingWolf/wd-convnext-tagger-v3/resolve/main/model.onnx?download=true\n",
+            "Resolving huggingface.co (huggingface.co)... 3.163.189.90, 3.163.189.74, 3.163.189.37, ...\n",
+            "Connecting to huggingface.co (huggingface.co)|3.163.189.90|:443... connected.\n",
+            "HTTP request sent, awaiting response... 302 Found\n",
+            "Location: https://cdn-lfs-us-1.huggingface.co/repos/d8/61/d8612304f05de662484c881a2ac180318d718b820314ffaaa700ef22c267e1a1/02f30d4de9bada756981a11464d13aa206f5e2d4ff6da384511beb812d58b2ca?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27model.onnx%3B+filename%3D%22model.onnx%22%3B&Expires=1710219829&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxMDIxOTgyOX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2Q4LzYxL2Q4NjEyMzA0ZjA1ZGU2NjI0ODRjODgxYTJhYzE4MDMxOGQ3MThiODIwMzE0ZmZhYWE3MDBlZjIyYzI2N2UxYTEvMDJmMzBkNGRlOWJhZGE3NTY5ODFhMTE0NjRkMTNhYTIwNmY1ZTJkNGZmNmRhMzg0NTExYmViODEyZDU4YjJjYT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=NUW35U0E0VvUCTynr4WArU1pgdg-F506HK5TiNnP7IrwbhEJfQpEcJo5CBoz1e4iUprWUCcEZJS0dRCmlGrr0PGYIjKXZ00BE4EiGZyi2vUqdP%7ExxUzWxps6XwEIVGiXc5R9yC%7EQgtd6oSJYQOH4ITBvEoNOJoQUPnjL5m1vk9T8-xHpeAxkHkHeOaF8FjlU5HKvUIc65SlUGirxOsHXl0v8o7sKmYlFs0Nmkoj9MurWKFL0sLFW5XIxkZveAGS9GB2sisitzkc4BUhICqDMSfv5CtlTEhXpgDUGbFo%7EohbeuKkQjIgnSU%7EVdFhDvY7Qew%7E5emodk-508AHvCx-UrA__&Key-Pair-Id=KCD77M1F0VK2B [following]\n",
+            "--2024-03-09 05:03:49--  https://cdn-lfs-us-1.huggingface.co/repos/d8/61/d8612304f05de662484c881a2ac180318d718b820314ffaaa700ef22c267e1a1/02f30d4de9bada756981a11464d13aa206f5e2d4ff6da384511beb812d58b2ca?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27model.onnx%3B+filename%3D%22model.onnx%22%3B&Expires=1710219829&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxMDIxOTgyOX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2Q4LzYxL2Q4NjEyMzA0ZjA1ZGU2NjI0ODRjODgxYTJhYzE4MDMxOGQ3MThiODIwMzE0ZmZhYWE3MDBlZjIyYzI2N2UxYTEvMDJmMzBkNGRlOWJhZGE3NTY5ODFhMTE0NjRkMTNhYTIwNmY1ZTJkNGZmNmRhMzg0NTExYmViODEyZDU4YjJjYT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=NUW35U0E0VvUCTynr4WArU1pgdg-F506HK5TiNnP7IrwbhEJfQpEcJo5CBoz1e4iUprWUCcEZJS0dRCmlGrr0PGYIjKXZ00BE4EiGZyi2vUqdP%7ExxUzWxps6XwEIVGiXc5R9yC%7EQgtd6oSJYQOH4ITBvEoNOJoQUPnjL5m1vk9T8-xHpeAxkHkHeOaF8FjlU5HKvUIc65SlUGirxOsHXl0v8o7sKmYlFs0Nmkoj9MurWKFL0sLFW5XIxkZveAGS9GB2sisitzkc4BUhICqDMSfv5CtlTEhXpgDUGbFo%7EohbeuKkQjIgnSU%7EVdFhDvY7Qew%7E5emodk-508AHvCx-UrA__&Key-Pair-Id=KCD77M1F0VK2B\n",
+            "Resolving cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)... 3.163.189.20, 3.163.189.28, 3.163.189.91, ...\n",
+            "Connecting to cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)|3.163.189.20|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 394990732 (377M) [application/octet-stream]\n",
+            "Saving to: ‘model.onnx’\n",
+            "\n",
+            "model.onnx          100%[===================>] 376.69M  31.6MB/s    in 5.2s    \n",
+            "\n",
+            "2024-03-09 05:03:54 (72.7 MB/s) - ‘model.onnx’ saved [394990732/394990732]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Download Tags / Test Image"
+      ],
+      "metadata": {
+        "id": "8r0qzU2NRoIT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!wget https://huggingface.co/SmilingWolf/wd-convnext-tagger-v3/resolve/main/selected_tags.csv?download=true -O tags.csv\n",
+        "!wget https://huggingface.co/spaces/SmilingWolf/wd-tagger/resolve/main/power.jpg?download=true -O power.jpg"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WPrRzNP-RqKs",
+        "outputId": "a4a5af15-3bf2-4383-d4de-616e85485c20"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2024-03-09 05:03:54--  https://huggingface.co/SmilingWolf/wd-convnext-tagger-v3/resolve/main/selected_tags.csv?download=true\n",
+            "Resolving huggingface.co (huggingface.co)... 3.163.189.90, 3.163.189.74, 3.163.189.37, ...\n",
+            "Connecting to huggingface.co (huggingface.co)|3.163.189.90|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 308468 (301K) [text/plain]\n",
+            "Saving to: ‘tags.csv’\n",
+            "\n",
+            "\rtags.csv              0%[                    ]       0  --.-KB/s               \rtags.csv            100%[===================>] 301.24K  --.-KB/s    in 0.03s   \n",
+            "\n",
+            "2024-03-09 05:03:54 (11.1 MB/s) - ‘tags.csv’ saved [308468/308468]\n",
+            "\n",
+            "--2024-03-09 05:03:55--  https://huggingface.co/spaces/SmilingWolf/wd-tagger/resolve/main/power.jpg?download=true\n",
+            "Resolving huggingface.co (huggingface.co)... 3.163.189.90, 3.163.189.74, 3.163.189.37, ...\n",
+            "Connecting to huggingface.co (huggingface.co)|3.163.189.90|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 91159 (89K) [image/jpeg]\n",
+            "Saving to: ‘power.jpg’\n",
+            "\n",
+            "power.jpg           100%[===================>]  89.02K  --.-KB/s    in 0.01s   \n",
+            "\n",
+            "2024-03-09 05:03:55 (8.09 MB/s) - ‘power.jpg’ saved [91159/91159]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# ONNX QUANT\n",
+        "To cut down on model size and have it work on mobile devices, quantization is needed (i think).\n",
+        "\n",
+        "First preprocess model for quantization - then quantize.\n",
+        "\n",
+        "The quant model name will be **model.quant.onnx**\n",
+        "\n",
+        "The convnext model went from ~377 MB down to 105 MB!"
+      ],
+      "metadata": {
+        "id": "lgaEjLAo7lMd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python -m onnxruntime.quantization.preprocess --input model.onnx --output model_pre_quant.onnx"
+      ],
+      "metadata": {
+        "id": "sdk95gWw7Imp"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import onnx\n",
+        "from onnxruntime.quantization import quantize_dynamic, QuantType\n",
+        "\n",
+        "model_fp32 = 'model_pre_quant.onnx'\n",
+        "model_quant = 'model.quant.onnx'\n",
+        "# quantized_model = quantize_dynamic(model_fp32, model_quant, nodes_to_exclude=[\"Conv\", \"/core_model/stem/stem.0/Conv\", \"/core_model/stages/stages.0/blocks/blocks.0/conv_dw/Conv\", \"/core_model/stages/stages.0/blocks/blocks.1/conv_dw/Conv\", \"/core_model/stages/stages.0/blocks/blocks.2/conv_dw/Conv\"])\n",
+        "quantized_model = quantize_dynamic(model_fp32, model_quant, op_types_to_quantize=['MatMul', 'Transpose', 'Gemm', 'LayerNormalization'])\n",
+        "\n",
+        "# remove unneeded model\n",
+        "%rm model_pre_quant.onnx"
+      ],
+      "metadata": {
+        "id": "E7M68khX7H93"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Add Preprocessing / Postprocessing\n",
+        "\n",
+        "To make mobile inference easier, we will add preprocessing to the model.\n",
+        "\n",
+        "Instead of resizing, adding padding, converting image to float32 array, and converting to BGR before inferencing - we can add these steps to the model so that only a uint8 tensor is needed.\n",
+        "\n",
+        "The model will be named **model.quant.preproc.onnx**\n",
+        "\n",
+        "**WARNING**  \n",
+        "It's very possible that I could be doing this wrong or that it could have some improvements. I'm not really sure what I'm doing but I found that these settings have given me the closest results to the base quant model."
+      ],
+      "metadata": {
+        "id": "TmCdMPTwb6Mc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import onnx\n",
+        "from onnxruntime_extensions.tools.pre_post_processing import create_named_value, Normalize, Transpose, Debug, ReverseAxis, PixelsToYCbCr, PrePostProcessor, Unsqueeze, LetterBox, ConvertImageToBGR, Resize, CenterCrop, ImageBytesToFloat, ChannelsLastToChannelsFirst\n",
+        "\n",
+        "image_mean = [0.5,0.5,0.5]\n",
+        "image_std = [0.5,0.5,0.5]\n",
+        "\n",
+        "img_size = 448\n",
+        "mean_std = list(zip(image_mean, image_std))\n",
+        "new_input = create_named_value('image', onnx.TensorProto.UINT8, [\"num_bytes\"])\n",
+        "pipeline = PrePostProcessor([new_input], onnx_opset=18)\n",
+        "pipeline.add_pre_processing(\n",
+        "    [\n",
+        "\n",
+        "        ConvertImageToBGR(),\n",
+        "        Resize((img_size, img_size), policy=\"not_larger\"),\n",
+        "        LetterBox(target_shape=(img_size, img_size)), # adds padding\n",
+        "        ImageBytesToFloat((255/2) / 255),  # NO IDEA WHAT IM DOING. all i know is that the default value gives bad results\n",
+        "        Normalize(mean_std, layout='HWC'), # copied values from the config on HF. seems to help results match closer to non-preprocessed model.\n",
+        "        Unsqueeze(axes=[0]),  # add batch dim so shape is {1, 448, 448, channels}.\n",
+        "    ]\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "HzJjPcSrb-DL"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Save Model\n",
+        "model = onnx.load('model.quant.onnx')\n",
+        "new_model = pipeline.run(model)\n",
+        "onnx.save_model(new_model, 'model.quant.preproc.onnx')"
+      ],
+      "metadata": {
+        "id": "_zjoi_AWhIZN"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Test Model\n",
+        "Most of the inference code is directly from SmilingWolf's wd tagger space: https://huggingface.co/spaces/SmilingWolf/wd-tagger/blob/main/app.py"
+      ],
+      "metadata": {
+        "id": "nXk6AZM0kfL4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import onnxruntime as _ort\n",
+        "from onnxruntime_extensions import get_library_path as _lib_path\n",
+        "from PIL import Image\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "# Step 1: setup session options\n",
+        "so = _ort.SessionOptions()\n",
+        "so.register_custom_ops_library(_lib_path())\n",
+        "\n",
+        "# Step 2: create session\n",
+        "sess = _ort.InferenceSession(\"/content/model.quant.preproc.onnx\",so) # Don't forget to add session options (so)\n",
+        "\n",
+        "# Step 3: load image (no preprocessing needed!)\n",
+        "image = np.frombuffer(open('/content/power.jpg', 'rb').read(), dtype=np.uint8)\n",
+        "\n",
+        "# Step 4: run cell!\n",
+        "\n",
+        "\n",
+        "###### Inference Code ######\n",
+        "kaomojis = [\n",
+        "    \"0_0\",\n",
+        "    \"(o)_(o)\",\n",
+        "    \"+_+\",\n",
+        "    \"+_-\",\n",
+        "    \"._.\",\n",
+        "    \"<o>_<o>\",\n",
+        "    \"<|>_<|>\",\n",
+        "    \"=_=\",\n",
+        "    \">_<\",\n",
+        "    \"3_3\",\n",
+        "    \"6_9\",\n",
+        "    \">_o\",\n",
+        "    \"@_@\",\n",
+        "    \"^_^\",\n",
+        "    \"o_o\",\n",
+        "    \"u_u\",\n",
+        "    \"x_x\",\n",
+        "    \"|_|\",\n",
+        "    \"||_||\",\n",
+        "]\n",
+        "\n",
+        "\n",
+        "def load_labels(dataframe) -> list[str]:\n",
+        "    name_series = dataframe[\"name\"]\n",
+        "    name_series = name_series.map(\n",
+        "        lambda x: x.replace(\"_\", \" \") if x not in kaomojis else x\n",
+        "    )\n",
+        "    tag_names = name_series.tolist()\n",
+        "\n",
+        "    rating_indexes = list(np.where(dataframe[\"category\"] == 9)[0])\n",
+        "    general_indexes = list(np.where(dataframe[\"category\"] == 0)[0])\n",
+        "    character_indexes = list(np.where(dataframe[\"category\"] == 4)[0])\n",
+        "    return tag_names, rating_indexes, general_indexes, character_indexes\n",
+        "\n",
+        "csv_path = \"/content/tags.csv\"\n",
+        "\n",
+        "tags_df = pd.read_csv(csv_path)\n",
+        "sep_tags = load_labels(tags_df)\n",
+        "\n",
+        "tag_names = sep_tags[0]\n",
+        "rating_indexes = sep_tags[1]\n",
+        "general_indexes = sep_tags[2]\n",
+        "character_indexes = sep_tags[3]\n",
+        "\n",
+        "input_name = sess.get_inputs()[0].name\n",
+        "label_name = sess.get_outputs()[0].name\n",
+        "\n",
+        "preds = sess.run([label_name], {input_name: image})[0]\n",
+        "\n",
+        "\n",
+        "labels = list(zip(tag_names, preds[0].astype(float)))\n",
+        "ratings_names = [labels[i] for i in rating_indexes]\n",
+        "rating = dict(ratings_names)\n",
+        "\n",
+        "character_names = [labels[i] for i in character_indexes]\n",
+        "\n",
+        "character_res = [x for x in character_names if x[1] > 0.85]\n",
+        "character_res = dict(character_res)\n",
+        "\n",
+        "general_names = [labels[i] for i in general_indexes]\n",
+        "general_res = [x for x in general_names if x[1] > 0.35]\n",
+        "general_res = dict(general_res)\n",
+        "\n",
+        "sorted_general_strings = sorted(\n",
+        "    general_res.items(),\n",
+        "    key=lambda x: x[1],\n",
+        "    reverse=True,\n",
+        ")\n",
+        "sorted_general_strings = [x[0] for x in sorted_general_strings]\n",
+        "sorted_general_strings = (\n",
+        "    \", \".join(sorted_general_strings).replace(\"(\", \"\\(\").replace(\")\", \"\\)\")\n",
+        ")\n",
+        "\n",
+        "print(rating)\n",
+        "print(character_res)\n",
+        "print(general_res)\n",
+        "print(sorted_general_strings)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "m5B0Wj4NkhMt",
+        "outputId": "6b551906-9b9e-4db9-f2bc-5ae5427381a3"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'general': 0.9169240593910217, 'sensitive': 0.0812525749206543, 'questionable': 0.0006865859031677246, 'explicit': 0.0002942383289337158}\n",
+            "{'power (chainsaw man)': 0.9924684762954712}\n",
+            "{'1girl': 0.9980734586715698, 'solo': 0.967477560043335, 'long hair': 0.8743129968643188, 'looking at viewer': 0.8921941518783569, 'smile': 0.7079806327819824, 'open mouth': 0.8572969436645508, 'simple background': 0.6686466336250305, 'shirt': 0.9388805627822876, 'blonde hair': 0.647895336151123, 'white background': 0.5928694009780884, 'red eyes': 0.4210684299468994, 'hair between eyes': 0.8992906212806702, 'jacket': 0.5598545074462891, 'white shirt': 0.8964416980743408, 'upper body': 0.666782557964325, 'horns': 0.9738106727600098, 'teeth': 0.9321538209915161, 'necktie': 0.9494357109069824, 'collared shirt': 0.8381757736206055, 'orange eyes': 0.4594384431838989, 'symbol-shaped pupils': 0.8655499219894409, 'fangs': 0.3685188889503479, 'demon horns': 0.5966249704360962, 'sharp teeth': 0.8942122459411621, 'black necktie': 0.8483953475952148, 'claw pose': 0.5946617722511292, 'red horns': 0.9497503042221069, 'cross-shaped pupils': 0.9292328357696533, 'pillarboxed': 0.766990065574646}\n",
+            "1girl, horns, solo, red horns, necktie, shirt, teeth, cross-shaped pupils, hair between eyes, white shirt, sharp teeth, looking at viewer, long hair, symbol-shaped pupils, open mouth, black necktie, collared shirt, pillarboxed, smile, simple background, upper body, blonde hair, demon horns, claw pose, white background, jacket, orange eyes, red eyes, fangs\n"
+          ]
+        }
+      ]
+    }
+  ]
+}