setup_gradio.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gqt11Y_RYxU"
      },
      "source": [
        "# Setting up the environment. PLEASE WAIT 🙃"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RHmGnnTZL6os"
      },
      "outputs": [],
      "source": [
        "!pip install --upgrade --no-cache-dir gdown\n",
        "!pip install rembg[gpu] gradio==3.45.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uX3LsFFPKSwo"
      },
      "outputs": [],
      "source": [
        "! wget -c \"https://github.com/Kitware/CMake/releases/download/v3.19.6/cmake-3.19.6.tar.gz\"\n",
        "! tar xf cmake-3.19.6.tar.gz\n",
        "! cd cmake-3.19.6 && ./configure && make && sudo make install"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "51QJAhPOK9cK"
      },
      "outputs": [],
      "source": [
        "# Install library\n",
        "! sudo apt-get --assume-yes update\n",
        "! sudo apt-get --assume-yes install build-essential\n",
        "# OpenCV\n",
        "! sudo apt-get --assume-yes install libopencv-dev\n",
        "# General dependencies\n",
        "! sudo apt-get --assume-yes install libatlas-base-dev libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler\n",
        "! sudo apt-get --assume-yes install --no-install-recommends libboost-all-dev\n",
        "# Remaining dependencies, 14.04\n",
        "! sudo apt-get --assume-yes install libgflags-dev libgoogle-glog-dev liblmdb-dev\n",
        "# Python3 libs\n",
        "! sudo apt-get --assume-yes install python3-setuptools python3-dev build-essential\n",
        "! sudo apt-get --assume-yes install python3-pip\n",
        "! sudo -H pip3 install --upgrade numpy protobuf opencv-python\n",
        "# OpenCL Generic\n",
        "! sudo apt-get --assume-yes install opencl-headers ocl-icd-opencl-dev\n",
        "! sudo apt-get --assume-yes install libviennacl-dev"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x7tqqDLHLNDr"
      },
      "outputs": [],
      "source": [
        "ver_openpose = \"v1.7.0\"\n",
        "! echo $ver_openpose"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b11_hkSgLDl5"
      },
      "outputs": [],
      "source": [
        "! git clone  --depth 1 -b \"$ver_openpose\" https://github.com/CMU-Perceptual-Computing-Lab/openpose.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s-XyxfV8Q-DE"
      },
      "outputs": [],
      "source": [
        "# manually downloading openpose models\n",
        "%%bash\n",
        "gdown 1I4UWTXN5RjtmcmUy6oKUzdUAfD1haNYC\n",
        "unzip models.zip\n",
        "mv /content/models/face/pose_iter_116000.caffemodel /content/openpose/models/face/pose_iter_116000.caffemodel\n",
        "mv /content/models/hand/pose_iter_102000.caffemodel /content/openpose/models/hand/pose_iter_102000.caffemodel\n",
        "mv /content/models/pose/body_25/pose_iter_584000.caffemodel /content/openpose/models/pose/body_25/pose_iter_584000.caffemodel\n",
        "mv /content/models/pose/coco/pose_iter_440000.caffemodel /content/openpose/models/pose/coco/pose_iter_440000.caffemodel\n",
        "mv /content/models/pose/mpi/pose_iter_160000.caffemodel /content/openpose/models/pose/mpi/pose_iter_160000.caffemodel\n",
        "rm -rf models\n",
        "rm models.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bs-zIObzQLYj"
      },
      "outputs": [],
      "source": [
        "! cd openpose && mkdir build && cd build"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7i7oHh2vQqHv"
      },
      "outputs": [],
      "source": [
        "! cd openpose/build && cmake -DUSE_CUDNN=OFF -DBUILD_PYTHON=ON .."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "iBvxsDM-EYJk"
      },
      "outputs": [],
      "source": [
        "# ! cd openpose/build && cmake .."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XEAY8VW0RzD0"
      },
      "outputs": [],
      "source": [
        "! cd openpose/build && make -j`nproc`\n",
        "! cd openpose && mkdir output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fo-q2Q-XMFen",
        "outputId": "a50d4013-41e8-43af-d34a-4bae39389557"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "Cloning into 'clothes-virtual-try-on'...\n",
            "remote: Enumerating objects: 182, done.\u001b[K\n",
            "remote: Counting objects: 100% (50/50), done.\u001b[K\n",
            "remote: Compressing objects: 100% (35/35), done.\u001b[K\n",
            "remote: Total 182 (delta 28), reused 19 (delta 15), pack-reused 132\u001b[K\n",
            "Receiving objects: 100% (182/182), 20.49 MiB | 24.25 MiB/s, done.\n",
            "Resolving deltas: 100% (66/66), done.\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "%cd /content/\n",
        "!rm -rf clothes-virtual-try-on\n",
        "!git clone https://github.com/practice404/clothes-virtual-try-on.git\n",
        "os.makedirs(\"/content/clothes-virtual-try-on/checkpoints\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tnud6ptA9ZwL",
        "outputId": "49b22f90-2423-4d45-9d24-e7564973d4ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:126: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From (uriginal): https://drive.google.com/uc?id=18q4lS7cNt1_X8ewCgya1fq0dSk93jTL6\n",
            "From (redirected): https://drive.google.com/uc?id=18q4lS7cNt1_X8ewCgya1fq0dSk93jTL6&confirm=t&uuid=234ac255-f864-4cbd-aa9b-75885520ad1c\n",
            "To: /content/clothes-virtual-try-on/checkpoints/alias_final.pth\n",
            "100% 402M/402M [00:04<00:00, 89.6MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:126: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1uDRPY8gh9sHb3UDonq6ZrINqDOd7pmTz\n",
            "To: /content/clothes-virtual-try-on/checkpoints/gmm_final.pth\n",
            "100% 76.2M/76.2M [00:01<00:00, 69.3MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:126: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From (uriginal): https://drive.google.com/uc?id=1d7lZNLh51Qt5Mi1lXqyi6Asb2ncLrEdC\n",
            "From (redirected): https://drive.google.com/uc?id=1d7lZNLh51Qt5Mi1lXqyi6Asb2ncLrEdC&confirm=t&uuid=76a523c2-5ad8-46d0-a2c4-130e34d1d59d\n",
            "To: /content/clothes-virtual-try-on/checkpoints/seg_final.pth\n",
            "100% 138M/138M [00:01<00:00, 79.0MB/s]\n"
          ]
        }
      ],
      "source": [
        "!gdown --id 18q4lS7cNt1_X8ewCgya1fq0dSk93jTL6 --output /content/clothes-virtual-try-on/checkpoints/alias_final.pth\n",
        "!gdown --id 1uDRPY8gh9sHb3UDonq6ZrINqDOd7pmTz --output /content/clothes-virtual-try-on/checkpoints/gmm_final.pth\n",
        "!gdown --id 1d7lZNLh51Qt5Mi1lXqyi6Asb2ncLrEdC --output /content/clothes-virtual-try-on/checkpoints/seg_final.pth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "qWPkjShFMK82",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad8c55f5-916f-42a0-adfa-5fee56061077"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:126: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From (uriginal): https://drive.google.com/uc?id=1ysEoAJNxou7RNuT9iKOxRhjVRNY5RLjx\n",
            "From (redirected): https://drive.google.com/uc?id=1ysEoAJNxou7RNuT9iKOxRhjVRNY5RLjx&confirm=t&uuid=7134c8ce-9873-434d-b999-07f913690fb0\n",
            "To: /content/clothes-virtual-try-on/cloth_segm_u2net_latest.pth\n",
            "100% 177M/177M [00:02<00:00, 69.3MB/s]\n"
          ]
        }
      ],
      "source": [
        "!gdown --id 1ysEoAJNxou7RNuT9iKOxRhjVRNY5RLjx --output /content/clothes-virtual-try-on/cloth_segm_u2net_latest.pth --no-cookies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "I9MhYntvMP84",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1748e52a-f671-4084-8dba-003cd7ccabf7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "Collecting ninja\n",
            "  Downloading ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl (307 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.2/307.2 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: ninja\n",
            "Successfully installed ninja-1.11.1.1\n"
          ]
        }
      ],
      "source": [
        "%cd /content/\n",
        "!pip install ninja"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Rz9LOnvyMWEJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7af47da8-d833-49ff-cf4a-f46159b3b982"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Self-Correction-Human-Parsing'...\n",
            "remote: Enumerating objects: 719, done.\u001b[K\n",
            "remote: Counting objects: 100% (719/719), done.\u001b[K\n",
            "remote: Compressing objects: 100% (568/568), done.\u001b[K\n",
            "remote: Total 719 (delta 149), reused 610 (delta 140), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (719/719), 3.88 MiB | 9.53 MiB/s, done.\n",
            "Resolving deltas: 100% (149/149), done.\n",
            "/content/Self-Correction-Human-Parsing\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/PeikeLi/Self-Correction-Human-Parsing\n",
        "%cd Self-Correction-Human-Parsing\n",
        "!mkdir checkpoints"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "b2k0DLCsMaG0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ffcc9d7-837e-48fc-95ed-23cca2811421"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:126: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From (uriginal): https://drive.google.com/uc?id=1k4dllHpu0bdx38J7H28rVVLpU-kOHmnH\n",
            "From (redirected): https://drive.google.com/uc?id=1k4dllHpu0bdx38J7H28rVVLpU-kOHmnH&confirm=t&uuid=6f0914d6-1ee1-487f-b50e-baec49d08aee\n",
            "To: /content/Self-Correction-Human-Parsing/exp-schp-201908261155-lip.pth\n",
            "100% 267M/267M [00:03<00:00, 72.8MB/s]\n"
          ]
        }
      ],
      "source": [
        "# downloading LIP dataset model\n",
        "!gdown --id 1k4dllHpu0bdx38J7H28rVVLpU-kOHmnH\n",
        "!mv /content/Self-Correction-Human-Parsing/exp-schp-201908261155-lip.pth /content/Self-Correction-Human-Parsing/checkpoints/final.pth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Y4f3VRyMd9Z",
        "outputId": "0c6451b3-2667-4480-dde6-b0884e94c453"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "%cd /content/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1k2dVj4vMhwA"
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "MINICONDA_INSTALLER_SCRIPT=Miniconda3-4.5.4-Linux-x86_64.sh\n",
        "MINICONDA_PREFIX=/usr/local\n",
        "wget https://repo.continuum.io/miniconda/$MINICONDA_INSTALLER_SCRIPT\n",
        "chmod +x $MINICONDA_INSTALLER_SCRIPT\n",
        "./$MINICONDA_INSTALLER_SCRIPT -b -f -p $MINICONDA_PREFIX\n",
        "conda install --channel defaults conda python=3.8 --yes\n",
        "conda update --channel defaults --all --yes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "I6entwp3MliV"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "_ = (sys.path\n",
        "        .append(\"/usr/local/lib/python3.6/site-packages\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "cseosViyMtYx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d05b470d-a3fb-4991-ae44-c03927fef3d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \n",
            "The environment is inconsistent, please check the package plan carefully\n",
            "The following packages are causing the inconsistency:\n",
            "\n",
            "  - defaults/linux-64::chardet==3.0.4=py36h0f667ec_1\n",
            "  - defaults/linux-64::ruamel_yaml==0.15.37=py36h14c3975_2\n",
            "  - defaults/linux-64::yaml==0.1.7=had09818_2\n",
            "  - defaults/linux-64::six==1.11.0=py36h372c433_1\n",
            "  - defaults/linux-64::asn1crypto==0.24.0=py36_0\n",
            "\b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "\n",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "  current version: 23.9.0\n",
            "  latest version: 23.11.0\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base -c defaults conda\n",
            "\n",
            "Or to minimize the number of packages updated during conda update use\n",
            "\n",
            "     conda install conda=23.11.0\n",
            "\n",
            "\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - featuretools\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    asn1crypto-1.5.1           |     pyhd8ed1ab_0          79 KB  conda-forge\n",
            "    blas-1.0                   |              mkl           6 KB\n",
            "    bottleneck-1.3.5           |   py38h7deecbd_0         115 KB\n",
            "    ca-certificates-2023.11.17 |       hbcca054_0         151 KB  conda-forge\n",
            "    certifi-2023.11.17         |     pyhd8ed1ab_0         155 KB  conda-forge\n",
            "    chardet-3.0.4              |        py38_1003         170 KB  conda-forge\n",
            "    cloudpickle-3.0.0          |     pyhd8ed1ab_0          24 KB  conda-forge\n",
            "    featuretools-1.28.0        |     pyhd8ed1ab_0         646 KB  conda-forge\n",
            "    holidays-0.32              |     pyhd8ed1ab_0         276 KB  conda-forge\n",
            "    importlib-resources-6.1.1  |     pyhd8ed1ab_0           9 KB  conda-forge\n",
            "    importlib_resources-6.1.1  |     pyhd8ed1ab_0          29 KB  conda-forge\n",
            "    intel-openmp-2023.1.0      |   hdb19cb5_46306        17.2 MB\n",
            "    joblib-1.3.2               |     pyhd8ed1ab_0         216 KB  conda-forge\n",
            "    libgfortran-ng-13.2.0      |       h69a702a_0          23 KB  conda-forge\n",
            "    libgfortran5-13.2.0        |       ha4646dd_0         1.4 MB  conda-forge\n",
            "    mkl-2023.1.0               |   h213fc3f_46344       171.5 MB\n",
            "    mkl-service-2.4.0          |   py38h5eee18b_1          54 KB\n",
            "    mkl_fft-1.3.8              |   py38h5eee18b_0         221 KB\n",
            "    mkl_random-1.2.4           |   py38hdb19cb5_0         327 KB\n",
            "    numexpr-2.8.4              |   py38hc78ab66_1         136 KB\n",
            "    numpy-1.24.3               |   py38hf6e8229_1          10 KB\n",
            "    numpy-base-1.24.3          |   py38h060ed82_1         6.2 MB\n",
            "    pandas-2.0.3               |   py38h1128e8f_0        12.4 MB\n",
            "    platformdirs-4.1.0         |     pyhd8ed1ab_0          20 KB  conda-forge\n",
            "    pooch-1.8.0                |     pyhd8ed1ab_0          51 KB  conda-forge\n",
            "    psutil-5.9.0               |   py38h5eee18b_0         330 KB\n",
            "    python-dateutil-2.8.2      |     pyhd8ed1ab_0         240 KB  conda-forge\n",
            "    python-tzdata-2023.3       |     pyhd8ed1ab_0         140 KB  conda-forge\n",
            "    pytz-2023.3.post1          |     pyhd8ed1ab_0         183 KB  conda-forge\n",
            "    ruamel_yaml-0.15.80        |py38h516909a_1000         274 KB  conda-forge\n",
            "    scikit-learn-1.3.0         |   py38h1128e8f_0         8.5 MB\n",
            "    scipy-1.10.1               |   py38hf6e8229_1        22.4 MB\n",
            "    six-1.16.0                 |     pyh6c4a22f_0          14 KB  conda-forge\n",
            "    tbb-2021.8.0               |       hdb19cb5_0         1.6 MB\n",
            "    threadpoolctl-3.2.0        |     pyha21a80b_0          20 KB  conda-forge\n",
            "    woodwork-0.26.0            |     pyhd8ed1ab_0         233 KB  conda-forge\n",
            "    yaml-0.2.5                 |       h7f98852_2          87 KB  conda-forge\n",
            "    zipp-3.17.0                |     pyhd8ed1ab_0          19 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:       245.4 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  blas               pkgs/main/linux-64::blas-1.0-mkl \n",
            "  bottleneck         pkgs/main/linux-64::bottleneck-1.3.5-py38h7deecbd_0 \n",
            "  cloudpickle        conda-forge/noarch::cloudpickle-3.0.0-pyhd8ed1ab_0 \n",
            "  featuretools       conda-forge/noarch::featuretools-1.28.0-pyhd8ed1ab_0 \n",
            "  holidays           conda-forge/noarch::holidays-0.32-pyhd8ed1ab_0 \n",
            "  importlib-resourc~ conda-forge/noarch::importlib-resources-6.1.1-pyhd8ed1ab_0 \n",
            "  importlib_resourc~ conda-forge/noarch::importlib_resources-6.1.1-pyhd8ed1ab_0 \n",
            "  intel-openmp       pkgs/main/linux-64::intel-openmp-2023.1.0-hdb19cb5_46306 \n",
            "  joblib             conda-forge/noarch::joblib-1.3.2-pyhd8ed1ab_0 \n",
            "  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-13.2.0-h69a702a_0 \n",
            "  libgfortran5       conda-forge/linux-64::libgfortran5-13.2.0-ha4646dd_0 \n",
            "  mkl                pkgs/main/linux-64::mkl-2023.1.0-h213fc3f_46344 \n",
            "  mkl-service        pkgs/main/linux-64::mkl-service-2.4.0-py38h5eee18b_1 \n",
            "  mkl_fft            pkgs/main/linux-64::mkl_fft-1.3.8-py38h5eee18b_0 \n",
            "  mkl_random         pkgs/main/linux-64::mkl_random-1.2.4-py38hdb19cb5_0 \n",
            "  numexpr            pkgs/main/linux-64::numexpr-2.8.4-py38hc78ab66_1 \n",
            "  numpy              pkgs/main/linux-64::numpy-1.24.3-py38hf6e8229_1 \n",
            "  numpy-base         pkgs/main/linux-64::numpy-base-1.24.3-py38h060ed82_1 \n",
            "  pandas             pkgs/main/linux-64::pandas-2.0.3-py38h1128e8f_0 \n",
            "  platformdirs       conda-forge/noarch::platformdirs-4.1.0-pyhd8ed1ab_0 \n",
            "  pooch              conda-forge/noarch::pooch-1.8.0-pyhd8ed1ab_0 \n",
            "  psutil             pkgs/main/linux-64::psutil-5.9.0-py38h5eee18b_0 \n",
            "  python-dateutil    conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0 \n",
            "  python-tzdata      conda-forge/noarch::python-tzdata-2023.3-pyhd8ed1ab_0 \n",
            "  pytz               conda-forge/noarch::pytz-2023.3.post1-pyhd8ed1ab_0 \n",
            "  scikit-learn       pkgs/main/linux-64::scikit-learn-1.3.0-py38h1128e8f_0 \n",
            "  scipy              pkgs/main/linux-64::scipy-1.10.1-py38hf6e8229_1 \n",
            "  tbb                pkgs/main/linux-64::tbb-2021.8.0-hdb19cb5_0 \n",
            "  threadpoolctl      conda-forge/noarch::threadpoolctl-3.2.0-pyha21a80b_0 \n",
            "  woodwork           conda-forge/noarch::woodwork-0.26.0-pyhd8ed1ab_0 \n",
            "  zipp               conda-forge/noarch::zipp-3.17.0-pyhd8ed1ab_0 \n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  asn1crypto         pkgs/main/linux-64::asn1crypto-0.24.0~ --> conda-forge/noarch::asn1crypto-1.5.1-pyhd8ed1ab_0 \n",
            "  chardet            pkgs/main::chardet-3.0.4-py36h0f667ec~ --> conda-forge::chardet-3.0.4-py38_1003 \n",
            "  ruamel_yaml        pkgs/main::ruamel_yaml-0.15.37-py36h1~ --> conda-forge::ruamel_yaml-0.15.80-py38h516909a_1000 \n",
            "  six                pkgs/main/linux-64::six-1.11.0-py36h3~ --> conda-forge/noarch::six-1.16.0-pyh6c4a22f_0 \n",
            "  yaml                     pkgs/main::yaml-0.1.7-had09818_2 --> conda-forge::yaml-0.2.5-h7f98852_2 \n",
            "\n",
            "The following packages will be SUPERSEDED by a higher-priority channel:\n",
            "\n",
            "  ca-certificates    pkgs/main::ca-certificates-2023.12.12~ --> conda-forge::ca-certificates-2023.11.17-hbcca054_0 \n",
            "  certifi            pkgs/main/linux-64::certifi-2023.11.1~ --> conda-forge/noarch::certifi-2023.11.17-pyhd8ed1ab_0 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages:\n",
            "libgfortran5-13.2.0  | 1.4 MB    | :   0% 0/1 [00:00<?, ?it/s]\n",
            "intel-openmp-2023.1. | 17.2 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
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            "\n",
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            "woodwork-0.26.0      | 233 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "pytz-2023.3.post1    | 183 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "bottleneck-1.3.5     | 115 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "numexpr-2.8.4        | 136 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "platformdirs-4.1.0   | 20 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "python-tzdata-2023.3 | 140 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "\n",
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            "\n",
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            "tbb-2021.8.0         | 1.6 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "chardet-3.0.4        | 170 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "numpy-base-1.24.3    | 6.2 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "pooch-1.8.0          | 51 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "asn1crypto-1.5.1     | 79 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "mkl-service-2.4.0    | 54 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "psutil-5.9.0         | 330 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "pandas-2.0.3         | 12.4 MB   | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "libgfortran5-13.2.0  | 1.4 MB    | :   1% 0.011365157717229271/1 [00:00<00:12, 13.12s/it]\n",
            "\n",
            "\n",
            "woodwork-0.26.0      | 233 KB    | :   7% 0.0685543091220245/1 [00:00<00:02,  2.72s/it]\u001b[A\u001b[A\u001b[A\n",
            "intel-openmp-2023.1. | 17.2 MB   | :   0% 0.0009096082871621815/1 [00:00<03:45, 226.06s/it]\u001b[A\n",
            "\n",
            "scikit-learn-1.3.0   | 8.5 MB    | :   0% 0.0018434473906398239/1 [00:00<01:51, 111.50s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "pytz-2023.3.post1    | 183 KB    | :   9% 0.08740277614774825/1 [00:00<00:02,  2.43s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "platformdirs-4.1.0   | 20 KB     | :  82% 0.8168311895503041/1 [00:00<00:00,  2.91it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "scikit-learn-1.3.0   | 8.5 MB    | :  19% 0.18803163384526203/1 [00:00<00:01,  1.31s/it]   \u001b[A\u001b[A\n",
            "intel-openmp-2023.1. | 17.2 MB   | :   8% 0.07822631269594761/1 [00:00<00:02,  3.23s/it]   \u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "python-tzdata-2023.3 | 140 KB    | :  11% 0.11446856376326582/1 [00:00<00:02,  2.94s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
            "importlib_resources- | 29 KB     | :  55% 0.5470268104570799/1 [00:00<00:00,  1.39it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "bottleneck-1.3.5     | 115 KB    | :  14% 0.1392770920466524/1 [00:00<00:02,  2.86s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "scikit-learn-1.3.0   | 8.5 MB    | :  48% 0.47560942678507456/1 [00:00<00:00,  1.54it/s]\u001b[A\u001b[A\n",
            "intel-openmp-2023.1. | 17.2 MB   | :  19% 0.19465617345270683/1 [00:00<00:01,  1.63s/it]\u001b[A\n",
            "\n",
            "\n",
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            "\n",
            "\n",
            "numexpr-2.8.4        | 136 KB    | :  12% 0.11776375371964981/1 [00:00<00:03,  3.63s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "importlib-resources- | 9 KB      | : 100% 1.0/1 [00:00<00:00,  2.30it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "libgfortran5-13.2.0  | 1.4 MB    | : 100% 1.0/1 [00:00<00:00,  2.35it/s]\n",
            "\n",
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            "\n",
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            "\n",
            "\n",
            "chardet-3.0.4        | 170 KB    | :   9% 0.09427524181622542/1 [00:00<00:04,  5.09s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "numpy-base-1.24.3    | 6.2 MB    | :   0% 0.002510203062312911/1 [00:00<03:10, 191.06s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "scikit-learn-1.3.0   | 8.5 MB    | :  68% 0.6765451923648154/1 [00:00<00:00,  1.69it/s] \u001b[A\u001b[A\n",
            "intel-openmp-2023.1. | 17.2 MB   | :  32% 0.31745329221960134/1 [00:00<00:00,  1.22s/it]\u001b[A\n",
            "\n",
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            "\n",
            "pooch-1.8.0          | 51 KB     | :  31% 0.3128030852648058/1 [00:00<00:01,  1.68s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "asn1crypto-1.5.1     | 79 KB     | :  20% 0.20207950466840163/1 [00:00<00:02,  2.77s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "numpy-base-1.24.3    | 6.2 MB    | :  28% 0.2786325399167331/1 [00:00<00:01,  1.62s/it]   \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "scikit-learn-1.3.0   | 8.5 MB    | :  87% 0.8701071683819969/1 [00:00<00:00,  1.75it/s]\u001b[A\u001b[A\n",
            "intel-openmp-2023.1. | 17.2 MB   | :  42% 0.4193294203817657/1 [00:00<00:00,  1.14s/it] \u001b[A\n",
            "\n",
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            "psutil-5.9.0         | 330 KB    | :   5% 0.04846161582101384/1 [00:00<00:11, 12.56s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "pandas-2.0.3         | 12.4 MB   | :   0% 0.0012602621289845674/1 [00:00<08:33, 514.51s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "mkl-service-2.4.0    | 54 KB     | :  30% 0.2962320098358284/1 [00:00<00:01,  2.25s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "numpy-base-1.24.3    | 6.2 MB    | :  55% 0.5522446737088403/1 [00:00<00:00,  1.10it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "intel-openmp-2023.1. | 17.2 MB   | :  54% 0.5421265391486602/1 [00:00<00:00,  1.06s/it]\u001b[A\n",
            "\n",
            "\n",
            "woodwork-0.26.0      | 233 KB    | : 100% 1.0/1 [00:00<00:00,  1.44it/s]               \u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "pandas-2.0.3         | 12.4 MB   | :  15% 0.14871093122017895/1 [00:00<00:03,  3.74s/it]   \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "numpy-base-1.24.3    | 6.2 MB    | :  82% 0.8208364013763217/1 [00:00<00:00,  1.50it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "intel-openmp-2023.1. | 17.2 MB   | :  65% 0.651279533608122/1 [00:00<00:00,  1.02s/it] \u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "platformdirs-4.1.0   | 20 KB     | : 100% 1.0/1 [00:00<00:00,  2.91it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "pandas-2.0.3         | 12.4 MB   | :  29% 0.2860795032794968/1 [00:00<00:01,  2.03s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "intel-openmp-2023.1. | 17.2 MB   | :  75% 0.7531556617702863/1 [00:00<00:00,  1.06s/it]\u001b[A\n",
            "\n",
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            "pandas-2.0.3         | 12.4 MB   | :  44% 0.4448725315315523/1 [00:00<00:00,  1.36s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "intel-openmp-2023.1. | 17.2 MB   | :  86% 0.8613990479425858/1 [00:01<00:00,  1.02s/it]\u001b[A\n",
            "\n",
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            "pandas-2.0.3         | 12.4 MB   | :  60% 0.5961039870097005/1 [00:01<00:00,  1.08s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "intel-openmp-2023.1. | 17.2 MB   | :  98% 0.9778289086993451/1 [00:01<00:00,  1.04it/s]\u001b[A\n",
            "\n",
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            "pandas-2.0.3         | 12.4 MB   | :  76% 0.7599380637776942/1 [00:01<00:00,  1.11it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "pandas-2.0.3         | 12.4 MB   | :  93% 0.9262926648036571/1 [00:01<00:00,  1.26it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "pytz-2023.3.post1    | 183 KB    | : 100% 1.0/1 [00:01<00:00,  1.79s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "\n",
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            "\n",
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            "\n",
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            "bottleneck-1.3.5     | 115 KB    | : 100% 1.0/1 [00:02<00:00,  1.99s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "bottleneck-1.3.5     | 115 KB    | : 100% 1.0/1 [00:02<00:00,  1.99s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "python-tzdata-2023.3 | 140 KB    | : 100% 1.0/1 [00:02<00:00,  2.01s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "numexpr-2.8.4        | 136 KB    | : 100% 1.0/1 [00:02<00:00,  2.05s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "numexpr-2.8.4        | 136 KB    | : 100% 1.0/1 [00:02<00:00,  2.05s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "\n",
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            "asn1crypto-1.5.1     | 79 KB     | : 100% 1.0/1 [00:02<00:00,  2.29s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "psutil-5.9.0         | 330 KB    | : 100% 1.0/1 [00:02<00:00,  2.25s/it]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "psutil-5.9.0         | 330 KB    | : 100% 1.0/1 [00:02<00:00,  2.25s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "mkl-service-2.4.0    | 54 KB     | : 100% 1.0/1 [00:02<00:00,  2.45s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " ... (more hidden) ...\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "scikit-learn-1.3.0   | 8.5 MB    | : 100% 1.0/1 [00:04<00:00,  1.75it/s]               \u001b[A\u001b[A\n",
            "\n",
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            "numpy-base-1.24.3    | 6.2 MB    | : 100% 1.0/1 [00:04<00:00,  1.50it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "intel-openmp-2023.1. | 17.2 MB   | : 100% 1.0/1 [00:06<00:00,  1.04it/s]               \u001b[A\n",
            "\n",
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            "pandas-2.0.3         | 12.4 MB   | : 100% 1.0/1 [00:09<00:00,  1.26it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "Preparing transaction: - \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Verifying transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \n",
            "\n",
            "    Installed package of scikit-learn can be accelerated using scikit-learn-intelex.\n",
            "    More details are available here: https://intel.github.io/scikit-learn-intelex\n",
            "\n",
            "    For example:\n",
            "\n",
            "        $ conda install scikit-learn-intelex\n",
            "        $ python -m sklearnex my_application.py\n",
            "\n",
            "    \n",
            "\n",
            "\b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n"
          ]
        }
      ],
      "source": [
        "!conda install --channel conda-forge featuretools --yes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "BEnK6NI6M0cz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a60f4944-eb02-4153-be77-34b6c2dad675"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hRequirement already satisfied: numpy>=1.17.0 in /usr/local/lib/python3.8/site-packages (from opencv-python) (1.24.3)\n",
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            "\u001b[?25hCollecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.0.0->torchgeometry)\n",
            "  Downloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
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            "\u001b[?25hCollecting triton==2.1.0 (from torch>=1.0.0->torchgeometry)\n",
            "  Downloading triton-2.1.0-0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.3 kB)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.0.0->torchgeometry)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.3.101-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting MarkupSafe>=2.0 (from jinja2->torch>=1.0.0->torchgeometry)\n",
            "  Downloading MarkupSafe-2.1.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.0 kB)\n",
            "Collecting mpmath>=0.19 (from sympy->torch>=1.0.0->torchgeometry)\n",
            "  Downloading mpmath-1.3.0-py3-none-any.whl (536 kB)\n",
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            "\u001b[?25hDownloading opencv_python-4.8.1.78-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.7 MB)\n",
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            "\u001b[?25hDownloading torch-2.1.2-cp38-cp38-manylinux1_x86_64.whl (670.2 MB)\n",
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            "\u001b[?25hDownloading nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
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            "\u001b[?25hDownloading triton-2.1.0-0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (89.2 MB)\n",
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            "\u001b[?25hDownloading filelock-3.13.1-py3-none-any.whl (11 kB)\n",
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            "\u001b[?25hDownloading typing_extensions-4.9.0-py3-none-any.whl (32 kB)\n",
            "Downloading MarkupSafe-2.1.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB)\n",
            "Downloading nvidia_nvjitlink_cu12-12.3.101-py3-none-manylinux1_x86_64.whl (20.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.5/20.5 MB\u001b[0m \u001b[31m87.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: mpmath, typing-extensions, sympy, opencv-python, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, networkx, MarkupSafe, fsspec, filelock, triton, nvidia-cusparse-cu12, nvidia-cudnn-cu12, jinja2, nvidia-cusolver-cu12, torch, torchgeometry\n",
            "Successfully installed MarkupSafe-2.1.3 filelock-3.13.1 fsspec-2023.12.2 jinja2-3.1.2 mpmath-1.3.0 networkx-3.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.18.1 nvidia-nvjitlink-cu12-12.3.101 nvidia-nvtx-cu12-12.1.105 opencv-python-4.8.1.78 sympy-1.12 torch-2.1.2 torchgeometry-0.1.2 triton-2.1.0 typing-extensions-4.9.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install opencv-python torchgeometry"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "HkySNWttHdW2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25bc22ca-2397-4f28-c3c8-acfb293e4e6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting torchvision\n",
            "  Downloading torchvision-0.16.2-cp38-cp38-manylinux1_x86_64.whl.metadata (6.6 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/site-packages (from torchvision) (1.24.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/site-packages (from torchvision) (2.31.0)\n",
            "Requirement already satisfied: torch==2.1.2 in /usr/local/lib/python3.8/site-packages (from torchvision) (2.1.2)\n",
            "Collecting pillow!=8.3.*,>=5.3.0 (from torchvision)\n",
            "  Downloading Pillow-10.1.0-cp38-cp38-manylinux_2_28_x86_64.whl.metadata (9.5 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (4.9.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (2023.12.2)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.18.1 in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (2.18.1)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (12.1.105)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.8/site-packages (from torch==2.1.2->torchvision) (2.1.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.8/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.1.2->torchvision) (12.3.101)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.8/site-packages (from requests->torchvision) (2.0.4)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.8/site-packages (from requests->torchvision) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.8/site-packages (from requests->torchvision) (1.26.18)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/site-packages (from requests->torchvision) (2023.11.17)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.8/site-packages (from sympy->torch==2.1.2->torchvision) (1.3.0)\n",
            "Downloading torchvision-0.16.2-cp38-cp38-manylinux1_x86_64.whl (6.8 MB)\n",
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            "\u001b[?25hDownloading Pillow-10.1.0-cp38-cp38-manylinux_2_28_x86_64.whl (3.6 MB)\n",
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            "\u001b[?25hInstalling collected packages: pillow, torchvision\n",
            "Successfully installed pillow-10.1.0 torchvision-0.16.2\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install torchvision"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-wvtaXujRhNp"
      },
      "source": [
        "# Welcome to Virtual-Cloth-Assistant\n",
        "\n",
        "> It'll take some extra time in first execution for setting up and downloading of model weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "RwcUm39LM8H0"
      },
      "outputs": [],
      "source": [
        "def make_dir():\n",
        "  os.system(\"cd /content/ && mkdir inputs && cd inputs && mkdir test && cd test && mkdir cloth cloth-mask image image-parse openpose-img openpose-json\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "\n",
        "def run(cloth, model):\n",
        "  make_dir()\n",
        "  cloth.save(\"/content/inputs/test/cloth/cloth.jpg\")\n",
        "  model.save(\"/content/inputs/test/image/model.jpg\")\n",
        "\n",
        "  # running script to compute the predictions\n",
        "  os.system(\"rm -rf /content/output/\")\n",
        "  os.system(\"python /content/clothes-virtual-try-on/run.py\")\n",
        "\n",
        "  # loading output\n",
        "  op = os.listdir(\"/content/output\")[0]\n",
        "  op = Image.open(f\"/content/output/{op}\")\n",
        "  return op"
      ],
      "metadata": {
        "id": "BQi4SxwbWUtQ"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "\n",
        "with gr.Blocks() as app:\n",
        "    with gr.Row():\n",
        "        gr.Markdown(\"<center><h1> Clothes Virtual Try ON </h1><center>\")\n",
        "    with gr.Row():\n",
        "        with gr.Column():\n",
        "            cloth_input = gr.Image(sources=['upload'], type=\"pil\", label=\"Upload the Cloth Image\")\n",
        "        with gr.Column():\n",
        "            model_input = gr.Image(sources=['upload'], type=\"pil\", label=\"Upload the Human Image\")\n",
        "    with gr.Row():\n",
        "        final_output = gr.Image(sources=['upload'], type=\"pil\", label=\"Final Prediction\")\n",
        "    with gr.Row():\n",
        "        submit_button = gr.Button(\"Submit\")\n",
        "\n",
        "    submit_button.click(fn=run, inputs=[cloth_input, model_input],\n",
        "                        outputs=[final_output])\n",
        "app.launch(debug=True, share=True)"
      ],
      "metadata": {
        "id": "JUfwPsfcVEUD"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "8gqt11Y_RYxU"
      ],
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}