app.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "app.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNwemXUBh1hEo6AHHaAdp+7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Gail529/Taylor/blob/main/app.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9JJ4rpCYfBY"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import seaborn as sns\r\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXspWPGiN3my",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "61371f97-08a8-4d26-8330-250159068443"
      },
      "source": [
        "!pip install streamlit\r\n",
        "!pip install pyngrok==4.1.1\r\n",
        "!pip install ngrok"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting streamlit\n",
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            "Collecting blinker\n",
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            "Collecting gitpython\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d7/cb/ec98155c501b68dcb11314c7992cd3df6dce193fd763084338a117967d53/GitPython-3.1.12-py3-none-any.whl (159kB)\n",
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            "Collecting watchdog; platform_system != \"Darwin\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/83/d9/3d1f46b428fd7b646725896b58d2eddb84f79fd76912773e6193cf74263d/watchdog-1.0.2-py3-none-manylinux2014_x86_64.whl (72kB)\n",
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            "Collecting ipykernel>=5.1.2; python_version >= \"3.4\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/cc/e8908bbb2921732f6851ebbbe4b77b925aab62e644ab9402f21c84fa6107/ipykernel-5.4.3-py3-none-any.whl (120kB)\n",
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            "Collecting gitdb<5,>=4.0.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/48/11/d1800bca0a3bae820b84b7d813ad1eff15a48a64caea9c823fc8c1b119e8/gitdb-4.0.5-py3-none-any.whl (63kB)\n",
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            "Collecting smmap<4,>=3.0.1\n",
            "  Downloading https://files.pythonhosted.org/packages/b0/9a/4d409a6234eb940e6a78dfdfc66156e7522262f5f2fecca07dc55915952d/smmap-3.0.4-py2.py3-none-any.whl\n",
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            "Requirement already satisfied: pygments in /usr/local/lib/python3.6/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (2.6.1)\n",
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            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (0.7.5)\n",
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            "Requirement already satisfied: bleach in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (3.2.1)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (0.5.1)\n",
            "Building wheels for collected packages: blinker\n",
            "  Building wheel for blinker (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for blinker: filename=blinker-1.4-cp36-none-any.whl size=13450 sha256=e811e37809898eb8ddc9c6e50d457de3f094a7b6aef7a8ffef6e510914cebb17\n",
            "  Stored in directory: /root/.cache/pip/wheels/92/a0/00/8690a57883956a301d91cf4ec999cc0b258b01e3f548f86e89\n",
            "Successfully built blinker\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement ipykernel~=4.10, but you'll have ipykernel 5.4.3 which is incompatible.\u001b[0m\n",
            "Installing collected packages: ipykernel, pydeck, base58, blinker, smmap, gitdb, gitpython, watchdog, validators, streamlit\n",
            "  Found existing installation: ipykernel 4.10.1\n",
            "    Uninstalling ipykernel-4.10.1:\n",
            "      Successfully uninstalled ipykernel-4.10.1\n",
            "Successfully installed base58-2.1.0 blinker-1.4 gitdb-4.0.5 gitpython-3.1.12 ipykernel-5.4.3 pydeck-0.5.0 smmap-3.0.4 streamlit-0.74.1 validators-0.18.2 watchdog-1.0.2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "pip_warning": {
                "packages": [
                  "ipykernel"
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          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "Collecting pyngrok==4.1.1\n",
            "  Downloading https://files.pythonhosted.org/packages/e4/a9/de2e15c92eb3aa4a2646ce3a7542317eb69ac47f667578ce8bf916320847/pyngrok-4.1.1.tar.gz\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyngrok==4.1.1) (0.16.0)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.6/dist-packages (from pyngrok==4.1.1) (3.13)\n",
            "Building wheels for collected packages: pyngrok\n",
            "  Building wheel for pyngrok (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyngrok: filename=pyngrok-4.1.1-cp36-none-any.whl size=15971 sha256=9c338a2bca7e78a2f189bb9e4b530be913d72ac00c5bd5d4c26e74d96b96b4ba\n",
            "  Stored in directory: /root/.cache/pip/wheels/97/71/0d/1695f7c8815c0beb3b5d9b35d6eec9243c87e6070fbe3977fa\n",
            "Successfully built pyngrok\n",
            "Installing collected packages: pyngrok\n",
            "Successfully installed pyngrok-4.1.1\n",
            "Collecting ngrok\n",
            "  Downloading https://files.pythonhosted.org/packages/67/50/c64abd519f50537df4726bc2572862d1925e377d4a80a9c06e70db048682/ngrok-0.1.6.tar.gz\n",
            "Collecting ping\n",
            "  Downloading https://files.pythonhosted.org/packages/03/ac/9a3f332f8d7d27cd8929922b2e225842c1623760364b37fc79dc0f65e77d/ping-0.2.tar.gz\n",
            "\u001b[31mERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "hrxOJWe6YLmI",
        "outputId": "c233d349-4101-41f1-9bde-4025235cc91b"
      },
      "source": [
        "df=pd.read_csv('/content/song_df (1).csv')\r\n",
        "df.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>song</th>\n",
              "      <th>Positive</th>\n",
              "      <th>Negative</th>\n",
              "      <th>Anger</th>\n",
              "      <th>Anticipation</th>\n",
              "      <th>Disgust</th>\n",
              "      <th>Fear</th>\n",
              "      <th>Joy</th>\n",
              "      <th>Sadness</th>\n",
              "      <th>Surprise</th>\n",
              "      <th>Trust</th>\n",
              "      <th>album</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>valence</th>\n",
              "      <th>popularity</th>\n",
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              "  </thead>\n",
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              "      <td>0.647</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Treacherous</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>Red</td>\n",
              "      <td>0.1010</td>\n",
              "      <td>0.704</td>\n",
              "      <td>0.621</td>\n",
              "      <td>0.1400</td>\n",
              "      <td>-8.086</td>\n",
              "      <td>0.0334</td>\n",
              "      <td>109.995</td>\n",
              "      <td>0.395</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>All Too Well</td>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>Red</td>\n",
              "      <td>0.0448</td>\n",
              "      <td>0.602</td>\n",
              "      <td>0.605</td>\n",
              "      <td>0.1280</td>\n",
              "      <td>-7.803</td>\n",
              "      <td>0.0244</td>\n",
              "      <td>93.061</td>\n",
              "      <td>0.316</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Begin Again</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Red</td>\n",
              "      <td>0.1990</td>\n",
              "      <td>0.530</td>\n",
              "      <td>0.526</td>\n",
              "      <td>0.1400</td>\n",
              "      <td>-8.349</td>\n",
              "      <td>0.0263</td>\n",
              "      <td>79.025</td>\n",
              "      <td>0.323</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Welcome To New York</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1989</td>\n",
              "      <td>0.0380</td>\n",
              "      <td>0.793</td>\n",
              "      <td>0.634</td>\n",
              "      <td>0.3040</td>\n",
              "      <td>-4.807</td>\n",
              "      <td>0.0324</td>\n",
              "      <td>117.023</td>\n",
              "      <td>0.615</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                 song  Positive  ...    tempo  valence  popularity\n",
              "0           0                  Red        14  ...  124.985    0.647          46\n",
              "1           1          Treacherous        11  ...  109.995    0.395          39\n",
              "2           2         All Too Well         8  ...   93.061    0.316          48\n",
              "3           3          Begin Again         9  ...   79.025    0.323          40\n",
              "4           4  Welcome To New York         8  ...  117.023    0.615          62\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6PE59IOzKTe"
      },
      "source": [
        "df.sort_values(by='popularity', ascending=False,inplace=True )\r\n",
        "df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RYnRVs3oNc1M",
        "outputId": "535d3182-5368-401f-cdcd-a007fa59ce68"
      },
      "source": [
        "%%writefile app.py\r\n",
        "import streamlit as st\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import seaborn as sns\r\n",
        "from PIL import Image\r\n",
        "from sklearn.metrics.pairwise import cosine_similarity\r\n",
        "PAGE_CONFIG = {\"page_title\":\"Taylor\",\"page_icon\":\":smiley:\",\"layout\":\"centered\"}\r\n",
        "st.set_page_config(**PAGE_CONFIG)\r\n",
        "\r\n",
        "\r\n",
        "image = Image.open('tay_img.jpg')\r\n",
        "\r\n",
        "df=pd.read_csv('/content/song_df (1).csv')\r\n",
        "song_df=df.filter(['song'])\r\n",
        "\r\n",
        "#calculating the similarities scores\r\n",
        "cols=song_df.columns\r\n",
        "scores_df=df[['Positive','Negative','Anger','Anticipation','Disgust','Fear','Joy','Sadness','Surprise','Trust','acousticness','danceability','energy','liveness','loudness','speechiness','tempo','valence','popularity']]\r\n",
        "cosine_sim=cosine_similarity(scores_df,scores_df)\r\n",
        "indices=pd.Series(song_df.index, song_df['song']).drop_duplicates()\r\n",
        "\r\n",
        "#function that will generate the recommendations\r\n",
        "def get_recommendations(title,cosine_sim=cosine_sim):   \r\n",
        "    index=indices[title]\r\n",
        "    sim_scores = list(enumerate(cosine_sim[index]))\r\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\r\n",
        "    sim_scores = sim_scores[1:11]\r\n",
        "    song_indices = [i[0] for i in sim_scores]\r\n",
        "    return df[['song','album']].iloc[song_indices]\r\n",
        "\r\n",
        "\r\n",
        "choice = st.sidebar.text_input('Key in a Song')\r\n",
        "st.sidebar.write(\"Select a song\")\r\n",
        "st.sidebar.dataframe(song_df,500,400)\r\n",
        "\r\n",
        "\r\n",
        "col1,col2=st.beta_columns(2)\r\n",
        "\r\n",
        "with col1:  \r\n",
        "    st.header(\"Music Recommendation Engine\")\r\n",
        "    st.write(\"Get Recommendations for your favourite Taylor Swift songs based on your current emotional state.\")\r\n",
        "with col2:    \r\n",
        "    st.image(image,width=300,height=200)\r\n",
        "\r\n",
        "\r\n",
        "if choice:\r\n",
        "    st.subheader(\"More songs like \"+choice )\r\n",
        "    recommendations=get_recommendations(choice)\r\n",
        "    st.table(recommendations)\r\n",
        "\r\n",
        "if not choice:\r\n",
        "    st.header('Pick a song')\r\n",
        "    st.subheader('recommendations will appear here :smiley:')\r\n",
        "\r\n",
        "\r\n",
        "    \r\n",
        "\t"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overwriting app.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8zsxwraaSQ5p",
        "outputId": "baa7cd5e-e9f4-47a7-d36f-8bec8efd284b"
      },
      "source": [
        "! ngrok authtoken 1n3n9zfRI524JaNlhCuS3GHARgM_7Z465eCAfxd5tsaavs7Tj\r\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Authtoken saved to configuration file: /root/.ngrok2/ngrok.yml\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m5z-9lW1S73b",
        "outputId": "7bef5bf9-0f01-43f4-a318-3cae70918349"
      },
      "source": [
        "! ngrok"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NAME:\n",
            "   ngrok - tunnel local ports to public URLs and inspect traffic\n",
            "\n",
            "DESCRIPTION:\n",
            "    ngrok exposes local networked services behinds NATs and firewalls to the\n",
            "    public internet over a secure tunnel. Share local websites, build/test\n",
            "    webhook consumers and self-host personal services.\n",
            "    Detailed help for each command is available with 'ngrok help <command>'.\n",
            "    Open http://localhost:4040 for ngrok's web interface to inspect traffic.\n",
            "\n",
            "EXAMPLES:\n",
            "    ngrok http 80                    # secure public URL for port 80 web server\n",
            "    ngrok http -subdomain=baz 8080   # port 8080 available at baz.ngrok.io\n",
            "    ngrok http foo.dev:80            # tunnel to host:port instead of localhost\n",
            "    ngrok http https://localhost     # expose a local https server\n",
            "    ngrok tcp 22                     # tunnel arbitrary TCP traffic to port 22\n",
            "    ngrok tls -hostname=foo.com 443  # TLS traffic for foo.com to port 443\n",
            "    ngrok start foo bar baz          # start tunnels from the configuration file\n",
            "\n",
            "VERSION:\n",
            "   2.3.35\n",
            "\n",
            "AUTHOR:\n",
            "  inconshreveable - <alan@ngrok.com>\n",
            "\n",
            "COMMANDS:\n",
            "   authtoken\tsave authtoken to configuration file\n",
            "   credits\tprints author and licensing information\n",
            "   http\t\tstart an HTTP tunnel\n",
            "   start\tstart tunnels by name from the configuration file\n",
            "   tcp\t\tstart a TCP tunnel\n",
            "   tls\t\tstart a TLS tunnel\n",
            "   update\tupdate ngrok to the latest version\n",
            "   version\tprint the version string\n",
            "   help\t\tShows a list of commands or help for one command\n",
            "\n",
            "PYNGROK VERSION:\n",
            "   4.1.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACFpoGB0O0sc"
      },
      "source": [
        "!streamlit run app.py &>/dev/null&"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "v9A07GWaPGRQ",
        "outputId": "d49c8230-afee-4763-9171-afcc3239fed8"
      },
      "source": [
        "from pyngrok import ngrok\r\n",
        "# Setup a tunnel to the streamlit port 8501\r\n",
        "public_url = ngrok.connect(port='8501')\r\n",
        "public_url\r\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'http://23253f2dd1dc.ngrok.io'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkATZxpaSPuR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4abdab0-90a7-4e7d-f3fe-07d2d2f9978a"
      },
      "source": [
        "!pgrep streamlit\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "334\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBMrajDqvsPx"
      },
      "source": [
        "!kill 334\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFHys110v7Em",
        "outputId": "76a68087-04a9-4d0f-87ed-0f8d2c9b87c8"
      },
      "source": [
        "!ngrok.kill()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/bin/bash: -c: line 1: syntax error: unexpected end of file\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}