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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/wenqinglim/hum_me_a_melody/blob/main/hum_me_a_melody_gradio_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Could you please hum me a melody? 🥺 🎤  \n",
        "\n"
      ],
      "metadata": {
        "id": "c6DiU0liqTnb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# These 2 packages are for avoiding this bug in transformers on colab: https://stackoverflow.com/questions/76902752/importerror-using-low-cpu-mem-usage-true-or-a-device-map-requires-accelerat\n",
        "# Note: They require restarting the session\n",
        "!pip install --upgrade --quiet pip\n",
        "!pip install --quiet accelerate\n",
        "!pip install -U --quiet peft\n",
        "\n",
        "# Force-restart session\n",
        "exit()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ufOoppTvwfTI",
        "outputId": "8f479f6d-1c94-401b-b26a-903dcc8a10ea"
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            "\u001b[?25h\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[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.1/199.1 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install ffmpeg for processing audio and video\n",
        "!pip install --quiet ffmpeg-python\n",
        "\n",
        "# Essentia for tagging the music\n",
        "\n",
        "!pip install --quiet essentia-tensorflow\n",
        "!wget https://essentia.upf.edu/models/music-style-classification/discogs-effnet/discogs-effnet-bs64-1.pb\n",
        "!wget https://essentia.upf.edu/models/classification-heads/mtg_jamendo_moodtheme/mtg_jamendo_moodtheme-discogs-effnet-1.pb\n",
        "\n",
        "\n",
        "# Install kerykeion, to get today's astrology information\n",
        "!pip install --quiet kerykeion\n",
        "\n",
        "# Install audiocraft, which requires torch 2.1.0\n",
        "!pip install --quiet torch==2.1.0+cu121 torchvision torchtext==0.16.0 torchaudio==2.1.0+cu121 -f https://download.pytorch.org/whl/torch_stable.html\n",
        "!pip install --quiet git+https://github.com/facebookresearch/audiocraft.git\n",
        "\n",
        "\n",
        "# Install AniimateDiff-Lightning (video generation)\n",
        "!pip install --quiet diffusers[torch]\n",
        "\n",
        "# Gradio for UI\n",
        "!pip install --quiet gradio"
      ],
      "metadata": {
        "id": "-RgwMvEF0RB0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "263389ba-07ca-4326-915c-98768f25e710"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\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[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m291.4/291.4 MB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\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--2024-04-26 21:01:14--  https://essentia.upf.edu/models/music-style-classification/discogs-effnet/discogs-effnet-bs64-1.pb\n",
            "Resolving essentia.upf.edu (essentia.upf.edu)... 84.89.139.43\n",
            "Connecting to essentia.upf.edu (essentia.upf.edu)|84.89.139.43|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 18366619 (18M) [application/octet-stream]\n",
            "Saving to: ‘discogs-effnet-bs64-1.pb’\n",
            "\n",
            "discogs-effnet-bs64 100%[===================>]  17.52M  1.30MB/s    in 15s     \n",
            "\n",
            "2024-04-26 21:01:29 (1.15 MB/s) - ‘discogs-effnet-bs64-1.pb’ saved [18366619/18366619]\n",
            "\n",
            "--2024-04-26 21:01:30--  https://essentia.upf.edu/models/classification-heads/mtg_jamendo_moodtheme/mtg_jamendo_moodtheme-discogs-effnet-1.pb\n",
            "Resolving essentia.upf.edu (essentia.upf.edu)... 84.89.139.43\n",
            "Connecting to essentia.upf.edu (essentia.upf.edu)|84.89.139.43|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2739668 (2.6M) [application/octet-stream]\n",
            "Saving to: ‘mtg_jamendo_moodtheme-discogs-effnet-1.pb’\n",
            "\n",
            "mtg_jamendo_moodthe 100%[===================>]   2.61M   888KB/s    in 3.0s    \n",
            "\n",
            "2024-04-26 21:01:33 (888 KB/s) - ‘mtg_jamendo_moodtheme-discogs-effnet-1.pb’ saved [2739668/2739668]\n",
            "\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m303.9/303.9 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m499.4/499.4 kB\u001b[0m \u001b[31m30.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.7/48.7 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.5/57.5 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\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[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 GB\u001b[0m \u001b[31m828.2 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m67.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m69.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.7/65.7 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.5/65.5 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m69.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m62.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m49.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.5/65.5 MB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.1/65.1 MB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m79.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.2/6.2 MB\u001b[0m \u001b[31m87.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m52.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.5/65.5 MB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.1/65.1 MB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.2/6.2 MB\u001b[0m \u001b[31m24.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m61.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.5/65.5 MB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.1/65.1 MB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m70.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.2/6.2 MB\u001b[0m \u001b[31m86.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m37.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.1/65.1 MB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.9/63.9 MB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m84.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.1/6.1 MB\u001b[0m \u001b[31m89.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.1/65.1 MB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.9/63.9 MB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m61.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.1/6.1 MB\u001b[0m \u001b[31m80.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m59.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.2/65.2 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.7/4.7 MB\u001b[0m \u001b[31m38.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.2/89.2 MB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\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  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.4/72.4 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.6/59.6 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m22.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.7/3.7 MB\u001b[0m \u001b[31m69.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.1/87.1 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.0/117.0 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Building wheel for audiocraft (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for flashy (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for demucs (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for julius (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for encodec (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for dora-search (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for treetable (setup.py) ... \u001b[?25l\u001b[?25hdone\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",
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            "\u001b[0m\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# A bug in Colab causes this error: https://github.com/googlecolab/colabtools/issues/3409\n",
        "# These 2 lines ensure that the bug does not occur during this notebook.\n",
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ],
      "metadata": {
        "id": "GXfWI1qR2zo7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import ffmpeg"
      ],
      "metadata": {
        "id": "C9AoWsZeqnKD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mood detection on audio\n",
        "#@title Load the MTG Listening Models\n",
        "\n",
        "# Thank you Dr Colton for the MTG model code snippets from lab notebooks.\n",
        "\n",
        "from essentia.standard import AudioLoader, MonoLoader, TensorflowPredictEffnetDiscogs, TensorflowPredict2D\n",
        "import essentia\n",
        "\n",
        "embeddings_model = TensorflowPredictEffnetDiscogs(\n",
        "    graphFilename=\"discogs-effnet-bs64-1.pb\",\n",
        "    output=\"PartitionedCall:1\",\n",
        ")\n",
        "\n",
        "mood_classification_model = TensorflowPredict2D(\n",
        "    graphFilename=\"mtg_jamendo_moodtheme-discogs-effnet-1.pb\",\n",
        "    output='model/Sigmoid',\n",
        ")\n",
        "\n",
        "# Functions for Using the Listening Models\n",
        "\n",
        "all_tags = [\n",
        "  \"action\", \"adventure\", \"advertising\",\n",
        "  \"background\", \"ballad\",\n",
        "  \"calm\", \"children\", \"christmas\", \"commercial\", \"cool\", \"corporate\",\n",
        "  \"dark\", \"deep\", \"documentary\", \"drama\", \"dramatic\", \"dream\",\n",
        "  \"emotional\", \"energetic\", \"epic\",\n",
        "  \"fast\", \"film\", \"fun\", \"funny\",\n",
        "  \"game\", \"groovy\",\n",
        "  \"happy\", \"heavy\", \"holiday\", \"hopeful\",\n",
        "  \"inspiring\",\n",
        "  \"love\",\n",
        "  \"meditative\", \"melancholic\", \"melodic\", \"motivational\", \"movie\",\n",
        "  \"nature\",\n",
        "  \"party\", \"positive\", \"powerful\",\n",
        "  \"relaxing\", \"retro\", \"romantic\",\n",
        "  \"sad\", \"sexy\", \"slow\", \"soft\", \"soundscape\", \"space\", \"sport\", \"summer\",\n",
        "  \"trailer\", \"travel\",\n",
        "  \"upbeat\", \"uplifting\"\n",
        "]\n",
        "\n",
        "# Tags categorised with help from ChatGPT (checked and refined by me)\n",
        "# Moods\n",
        "mood_tags = [\n",
        "    \"calm\", \"cool\",\n",
        "    \"dark\", \"deep\", \"dramatic\",\n",
        "    \"emotional\", \"energetic\", \"epic\",\n",
        "    \"fast\", \"fun\", \"funny\",\n",
        "    \"groovy\",\n",
        "    \"happy\", \"heavy\", \"hopeful\",\n",
        "    \"inspiring\",\n",
        "    \"meditative\", \"melancholic\", \"motivational\",\n",
        "    \"positive\", \"powerful\",\n",
        "    \"relaxing\", \"romantic\", \"sad\", \"sexy\", \"slow\", \"soft\",\n",
        "     \"upbeat\", \"uplifting\"\n",
        "]\n",
        "\n",
        "# Themes\n",
        "theme_tags = [\n",
        "    \"action\", \"adventure\", \"ballad\",\n",
        "    \"children\", \"christmas\",\n",
        "     \"dream\", \"film\", \"game\",\n",
        "    \"holiday\", \"love\", \"movie\",\n",
        "    \"nature\", \"party\", \"retro\", \"space\",\"sport\",\n",
        "    \"summer\", \"travel\"\n",
        "]\n",
        "\n",
        "# Functions\n",
        "function_tags = [\n",
        "    \"advertising\", \"background\", \"commercial\",\n",
        "    \"corporate\", \"documentary\", \"drama\",\n",
        "    \"soundscape\", \"trailer\"\n",
        "]\n",
        "\n",
        "def get_mood_activations_dict(audio):\n",
        "  embeddings = embeddings_model(audio)\n",
        "\n",
        "  activations = mood_classification_model(embeddings)\n",
        "  activation_avs = []\n",
        "  for i in range(0, len(activations[0])):\n",
        "    vals = [activations[j][i] for j in range(0, len(activations))]\n",
        "    # Note - this does the averaging bit\n",
        "    activation_avs.append(sum(vals)/len(vals))\n",
        "  activations_dict = {}\n",
        "  for ind, tag in enumerate(all_tags):\n",
        "    activations_dict[tag] = activation_avs[ind]\n",
        "\n",
        "  return activations_dict\n",
        "\n",
        "\n",
        "def get_moods(audio):\n",
        "\n",
        "  # Get mood activations\n",
        "  mood_activations_dict = get_mood_activations_dict(essentia.array(audio))\n",
        "\n",
        "  # Calculate upper threshold for outliers\n",
        "  q1 = np.quantile(list(mood_activations_dict.values()), 0.25)\n",
        "  q3 = np.quantile(list(mood_activations_dict.values()), 0.75)\n",
        "  # Outliers defined as Q3 + 1.5*IQR\n",
        "  outlier_threshold = q3 + (1.5*(q3-q1))\n",
        "\n",
        "  # Select moods that are above outlier threshold (i.e. most prominent moods)\n",
        "  tags = [tag for tag, score in mood_activations_dict.items() if (score >= outlier_threshold) & (tag != 'melodic')]\n",
        "  moods = [tag for tag in tags if tag in mood_tags]\n",
        "  themes = [tag for tag in tags if tag in theme_tags]\n",
        "  functions = [tag for tag in tags if tag in function_tags]\n",
        "\n",
        "  return moods, themes, functions"
      ],
      "metadata": {
        "id": "7os2kauo_Js8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title functions for Astrology information and instrumentation\n",
        "from kerykeion import AstrologicalSubject\n",
        "from datetime import datetime\n",
        "\n",
        "import random\n",
        "\n",
        "# Define Holst's planets instrumentation and sound descriptions\n",
        "# Definition generated by ChatGPT (checked by me)\n",
        "planets_instruments = {\n",
        "    'Mars': {\n",
        "        'instruments': ['trombone', 'trumpet'],\n",
        "        'sound': 'Aggressive, martial rhythms and powerful brass fanfares'\n",
        "    },\n",
        "    'Venus': {\n",
        "        'instruments': ['french horn'],\n",
        "        'sound': 'Warm, mellow tones symbolizing serenity and tranquility'\n",
        "    },\n",
        "    'Mercury': {\n",
        "        'instruments': ['flute', 'clarinet'],\n",
        "        'sound': 'Light and nimble melodies evoking swift movements'\n",
        "    },\n",
        "    'Jupiter': {\n",
        "        'instruments': ['french horn', 'violin'],\n",
        "        'sound': 'Majestic brass fanfares and lush string melodies'\n",
        "    },\n",
        "    'Saturn': {\n",
        "        'instruments': ['organ'],\n",
        "        'sound': 'Deep, resonant tones adding a sense of gravity and solemnity'\n",
        "    },\n",
        "    'Uranus': {\n",
        "        'instruments': ['timpani', 'brass'],\n",
        "        'sound': 'Bold rhythms and unconventional brass and percussion usage'\n",
        "    },\n",
        "    'Neptune': {\n",
        "        'instruments': ['celesta', 'harp'],\n",
        "        'sound': 'Ethereal, shimmering tones creating an otherworldly atmosphere'\n",
        "    }\n",
        "}\n",
        "\n",
        "holst_planets = list(planets_instruments.keys())\n",
        "\n",
        "# Helper function to get today's planets, based on an Astrological sign\n",
        "# Note: We default the Astrological sign to Aries, since this idea was conceived in April\n",
        "# Alternatively, the sign could be set to the audience's sign, but our AI system wants its own sign too\n",
        "\n",
        "def get_planets(filtered_sign='Ari', valid_planets=holst_planets):\n",
        "  \"\"\"\n",
        "  Returns list of planets that are in the filtered_sign today.\n",
        "\n",
        "  \"\"\"\n",
        "  today = datetime.now()\n",
        "\n",
        "  # Create object for astrological subject\n",
        "  astro_subj = AstrologicalSubject(\"GenAI\", today.year, today.month, today.day, today.hour, today.minute, \"London\", \"GB\")\n",
        "\n",
        "\n",
        "  today_planets = []\n",
        "  for planet in astro_subj.planets_list:\n",
        "    if (planet['sign'] == filtered_sign) & (planet['name'] in valid_planets):\n",
        "      today_planets.append(planet['name'])\n",
        "\n",
        "  if len(today_planets) == 0:\n",
        "    # If no planets are in filtered_sign today, pick a random planet\n",
        "    today_planets.append(random.choice(holst_planets))\n",
        "\n",
        "  return today_planets"
      ],
      "metadata": {
        "id": "Vi361Tab73DT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Setup MusicGen\n",
        "\n",
        "# Import music generation model\n",
        "import torch\n",
        "import torchaudio\n",
        "\n",
        "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "from audiocraft.models import MusicGen\n",
        "from audiocraft.utils.notebook import display_audio\n",
        "from audiocraft.data.audio import audio_write\n",
        "\n",
        "def construct_musicgen_prompt(moods, themes, functions, planets, planets_instruments):\n",
        "  moods_str = \", \".join(moods)\n",
        "\n",
        "  prompt = f\"a {moods_str} orchestral piece\"\n",
        "  if themes:\n",
        "    prompt += f\" with {', '.join(themes)} themes\"\n",
        "  for planet in planets:\n",
        "    planet_instr = planets_instruments[planet]['instruments']\n",
        "    planet_sound = planets_instruments[planet]['sound']\n",
        "    planet_str = f\"{', '.join(planet_instr)} with {str.lower(planet_sound)}\"\n",
        "    prompt += f\". {planet_str}\"\n",
        "  if functions:\n",
        "    prompt += f\"perfect for {', '.join(functions)}\"\n",
        "  return prompt\n",
        "\n",
        "\n",
        "def generate_music(musicgen_prompt, audio, sr):\n",
        "  \"\"\"\n",
        "  Generates music using pre-trained MusicGen model\n",
        "  \"\"\"\n",
        "  model = MusicGen.get_pretrained('melody')\n",
        "  model.set_generation_params(duration=8)  # generate 8 seconds.\n",
        "\n",
        "  # generates using the melody from the given audio and the provided descriptions.\n",
        "  wav_gpu = model.generate_with_chroma([musicgen_prompt], torch.tensor(audio).float().reshape(1, 1, -1), sr)[0]\n",
        "  wav = wav_gpu.cpu()\n",
        "  del wav_gpu\n",
        "\n",
        "  # Will save under composition.wav, with loudness normalization at -14 db LUFS.\n",
        "  audio_write(f'composition', wav, model.sample_rate, strategy=\"loudness\")\n",
        "\n",
        "  return wav\n",
        "\n"
      ],
      "metadata": {
        "id": "3QJPF18iuJWU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2db07ad0-a0b3-4742-bf8e-d2c0a2e38f65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: 'libc10_hip.so: cannot open shared object file: No such file or directory'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?\n",
            "  warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Setup AniimateDiff-Lightning (video generation)\n",
        "\n",
        "# Setup video generation pipeline\n",
        "# Note: This takes up ~4GB of GPU RAM\n",
        "from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler\n",
        "from diffusers.utils import export_to_video\n",
        "\n",
        "from huggingface_hub import hf_hub_download\n",
        "from safetensors.torch import load_file\n",
        "import moviepy.editor\n",
        "\n",
        "\n",
        "dtype = torch.float16\n",
        "step = 4  # Options: [1,2,4,8]\n",
        "repo = \"ByteDance/AnimateDiff-Lightning\"\n",
        "ckpt = f\"animatediff_lightning_{step}step_diffusers.safetensors\"\n",
        "base = \"emilianJR/epiCRealism\"\n",
        "\n",
        "# Load Motion Low-Rank Adaptations (LoRAs), which determines the type of motion in the videos\n",
        "adapter = MotionAdapter().to(device, dtype)\n",
        "adapter.load_state_dict(load_file(hf_hub_download(repo ,ckpt), device=device))\n",
        "\n",
        "# Setup pipeline with base model and animateDiff-lightning motion adaptor\n",
        "pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype).to(device)\n",
        "\n",
        "# Set motion type\n",
        "pipe.load_lora_weights(\n",
        "    \"guoyww/animatediff-motion-lora-zoom-out\", adapter_name=\"zoom-out\"\n",
        ")\n",
        "\n",
        "# Setup scheduler\n",
        "pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing=\"trailing\", beta_schedule=\"linear\")"
      ],
      "metadata": {
        "id": "6wI1-LQF87fC",
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          ]
        },
        "outputId": "cf176cf1-bda1-4556-9a73-f117ea04f53b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "text": [
            "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.\n",
            "  warnings.warn(\n",
            "\n",
            "The config attributes {'center_input_sample': False, 'flip_sin_to_cos': True, 'freq_shift': 0, 'mid_block_type': 'UNetMidBlock2DCrossAttn', 'only_cross_attention': False, 'attention_head_dim': 8, 'dual_cross_attention': False, 'class_embed_type': None, 'addition_embed_type': None, 'num_class_embeds': None, 'upcast_attention': False, 'resnet_time_scale_shift': 'default', 'resnet_skip_time_act': False, 'resnet_out_scale_factor': 1.0, 'time_embedding_type': 'positional', 'time_embedding_dim': None, 'time_embedding_act_fn': None, 'timestep_post_act': None, 'conv_in_kernel': 3, 'conv_out_kernel': 3, 'projection_class_embeddings_input_dim': None, 'class_embeddings_concat': False, 'mid_block_only_cross_attention': None, 'cross_attention_norm': None, 'addition_embed_type_num_heads': 64} were passed to UNetMotionModel, but are not expected and will be ignored. Please verify your config.json configuration file.\n"
          ]
        },
        {
          "output_type": "display_data",
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    {
      "cell_type": "code",
      "source": [
        "# @title Define wrapper functions for Gradio\n",
        "def melody_to_composition(audio_file):\n",
        "  \"\"\"\n",
        "  Wrapper function for Gradio\n",
        "\n",
        "  Takes input audio and outputs generated music with an output message detailing its creative process.\n",
        "  \"\"\"\n",
        "  sr, audio = audio_file\n",
        "  moods, themes, functions = get_moods(audio)\n",
        "  message = f\"I'd describe your voice and melody as {', '.join(moods)}.\\\n",
        "    If that's not what you intended to sound like, then maybe practice singing more. \"\n",
        "  if themes:\n",
        "    message += f\"It has some {', '.join(themes)} vibes. \"\n",
        "  if functions:\n",
        "    message += f\"And it also sounds like it can be used as {', '.join(functions)} music. \"\n",
        "\n",
        "  planets = get_planets()\n",
        "  if planets:\n",
        "    message += f\"\\nThese are the planet(s) in my astrology sign today: {planets}.\\\n",
        "    I have composed a piece inspired by them. \"\n",
        "\n",
        "  musicgen_prompt = construct_musicgen_prompt(moods, themes, functions, planets, planets_instruments)\n",
        "\n",
        "  wav = generate_music(musicgen_prompt, audio, sr)\n",
        "\n",
        "  message += f\"\\nBased on your melody, I'm have created {musicgen_prompt}. Take a listen - what does it remind you of? \"\n",
        "\n",
        "  torch.cuda.empty_cache()\n",
        "\n",
        "\n",
        "  return \"composition.wav\", moods, message\n",
        "\n",
        "\n",
        "def text_to_muvi(user_input_txt, moods):\n",
        "  \"\"\"\n",
        "  Wrapper function for Gradio\n",
        "\n",
        "  Takes user input text and generates a video.\n",
        "  Outputs a combined video with audio, and a message detailing its creative process.\n",
        "  \"\"\"\n",
        "  video_prompt = f\"{user_input_txt}, {', '.join(moods)} mood\"\n",
        "  # Generate video (+ 10.6GB of GPU RAM usage)\n",
        "  output = pipe(\n",
        "                prompt=video_prompt,\n",
        "                negative_prompt=\"bad quality, worse quality, watermark\",\n",
        "                guidance_scale=1.0,\n",
        "                num_frames=16,\n",
        "                num_inference_steps=step\n",
        "                )\n",
        "  export_to_video(output.frames[0]*5, \"video_only.mp4\")\n",
        "  torch.cuda.empty_cache()\n",
        "\n",
        "  # Combine audio and video\n",
        "  audio = ffmpeg.input(\"composition.wav\")\n",
        "  video = ffmpeg.input(\"video_only.mp4\")\n",
        "\n",
        "\n",
        "  music_video = ffmpeg.concat(video, audio, v=1, a=1)\n",
        "\n",
        "  music_video.output(\"muvi.mp4\").run(overwrite_output=True)\n",
        "\n",
        "  message = f\"Since the music sounds like '{user_input_txt}' to you,\\\n",
        "  I've created a video that captures that imagery in a {', '.join(moods)} mood.\\\n",
        "  Here's the masterpiece: A music video!\"\n",
        "\n",
        "  return \"muvi.mp4\", message"
      ],
      "metadata": {
        "id": "68J2JQvQ035S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Gradio application\n",
        "import gradio as gr\n",
        "\n",
        "demo = gr.Blocks()\n",
        "\n",
        "with demo:\n",
        "\n",
        "    # 1. Get users to record a melody\n",
        "    gr.Markdown(\n",
        "    \"\"\"\n",
        "    # Hello World!\n",
        "    Could you please hum me a melody?\n",
        "    \"\"\")\n",
        "    moods = gr.State([])\n",
        "    audio_file = gr.Audio(type=\"numpy\")\n",
        "    b1 = gr.Button(\"Submit melody\")\n",
        "    message = gr.Textbox()\n",
        "\n",
        "    # 2. Let users listen to generated music\n",
        "    wav_path = gr.Audio(interactive=False)\n",
        "    user_text = gr.Textbox(placeholder=\"What does the melody remind you of?\")\n",
        "    b2 = gr.Button(\"Submit interpretation\")\n",
        "\n",
        "    # 3. Show final music video\n",
        "    message_final = gr.Textbox()\n",
        "    muvi_path = gr.PlayableVideo()\n",
        "\n",
        "    b1.click(melody_to_composition, inputs=audio_file, outputs=[wav_path, moods, message])\n",
        "    b2.click(text_to_muvi, inputs=[user_text, moods], outputs=[muvi_path, message_final])\n",
        "\n",
        "    demo.unload(lambda: print(\"unloading\"))\n",
        "\n",
        "demo.launch(debug=True, height=1500)"
      ],
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      "execution_count": null,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://421b58129607745b2e.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
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            ]
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            "WARNING:root:No latitude specified, using London as default\n",
            "WARNING:root:No longitude specified, using London as default\n",
            "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/audiocraft/models/musicgen.py:80: UserWarning: MusicGen pretrained model relying on deprecated checkpoint mapping. Please use full pre-trained id instead: facebook/musicgen-melody\n",
            "  warnings.warn(\n",
            "\n"
          ]
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          "text": [
            "Downloading: \"https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/955717e8-8726e21a.th\" to /root/.cache/torch/hub/checkpoints/955717e8-8726e21a.th\n",
            "100%|██████████| 80.2M/80.2M [00:00<00:00, 88.9MB/s]\n"
          ]
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          "text": [
            "WARNING:py.warnings:/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\n",
            "  warnings.warn(\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\")\n",
            "\n"
          ]
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          "text": [
            "Keyboard interruption in main thread... closing server.\n",
            "unloading\n",
            "Killing tunnel 127.0.0.1:7860 <> https://421b58129607745b2e.gradio.live\n"
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    {
      "cell_type": "code",
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      "metadata": {
        "id": "GLvgCfLSH1EP"
      },
      "execution_count": null,
      "outputs": []
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  ]
}