Backend_Article370.ipynb

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      "name": "Backend_Article370.ipynb",
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    "kernelspec": {
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      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba7e9780-f632-4113-d040-39a99147bee4"
      },
      "source": [
        "pip install youtube-data-api"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: youtube-data-api in /usr/local/lib/python3.7/dist-packages (0.0.20)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from youtube-data-api) (2.23.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from youtube-data-api) (1.1.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->youtube-data-api) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->youtube-data-api) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->youtube-data-api) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->youtube-data-api) (3.0.4)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->youtube-data-api) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->youtube-data-api) (2.8.1)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas->youtube-data-api) (1.19.5)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->youtube-data-api) (1.15.0)\n"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "pL38sCdkGGOJ"
      },
      "source": [
        "import joblib\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.decomposition import PCA\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "palette = sns.color_palette(\"bright\", 3)\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.svm import SVC\n",
        "import random\n",
        "from matplotlib import pyplot as plt\n",
        "from youtube_api import YouTubeDataAPI"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "03hh2IUFaaPM",
        "outputId": "8374f808-3f0f-4b47-a050-dfdddad34eee"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNop5U-NGegq"
      },
      "source": [
        "data = joblib.load(\"google_universal_encoder.pkl\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aI0TVe5jNVhQ"
      },
      "source": [
        "url_data = joblib.load(\"total578_video_urls.pkl\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "k3ERGKzbdTzf",
        "outputId": "99f14731-88f5-482c-c9d5-55709fa71f45"
      },
      "source": [
        "out= pd.DataFrame(url_data,columns=['Video url'])\n",
        "out"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th>Video url</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>https://www.youtube.com/watch?v=motq95iWsHw</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>https://www.youtube.com/watch?v=mBKcfJAqI1Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>https://www.youtube.com/watch?v=sPELkkD6SWk</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>https://www.youtube.com/watch?v=1OMIORV7OH0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>https://www.youtube.com/watch?v=oojdt-tVIhE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>573</th>\n",
              "      <td>https://www.youtube.com/watch?v=0z3stf6nqIY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>574</th>\n",
              "      <td>https://www.youtube.com/watch?v=ivZ8LmC6LxM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>575</th>\n",
              "      <td>https://www.youtube.com/watch?v=wleiXoRllrU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>576</th>\n",
              "      <td>https://www.youtube.com/watch?v=zt0NkL9J8jk</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577</th>\n",
              "      <td>https://www.youtube.com/watch?v=oYziuMU2Ubs</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>578 rows × 1 columns</p>\n",
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            "text/plain": [
              "                                       Video url\n",
              "0    https://www.youtube.com/watch?v=motq95iWsHw\n",
              "1    https://www.youtube.com/watch?v=mBKcfJAqI1Y\n",
              "2    https://www.youtube.com/watch?v=sPELkkD6SWk\n",
              "3    https://www.youtube.com/watch?v=1OMIORV7OH0\n",
              "4    https://www.youtube.com/watch?v=oojdt-tVIhE\n",
              "..                                           ...\n",
              "573  https://www.youtube.com/watch?v=0z3stf6nqIY\n",
              "574  https://www.youtube.com/watch?v=ivZ8LmC6LxM\n",
              "575  https://www.youtube.com/watch?v=wleiXoRllrU\n",
              "576  https://www.youtube.com/watch?v=zt0NkL9J8jk\n",
              "577  https://www.youtube.com/watch?v=oYziuMU2Ubs\n",
              "\n",
              "[578 rows x 1 columns]"
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
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        "index = [472,\n",
        " 517,\n",
        " 519,\n",
        " 527,\n",
        " 528,\n",
        " 533,\n",
        " 534,\n",
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        " 542,\n",
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        " 555,\n",
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        " 564,\n",
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        " 566,\n",
        " 568,\n",
        " 570,\n",
        " 571,\n",
        " 572]"
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      "execution_count": null,
      "outputs": []
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          "height": 388
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        "id": "p-sKVvSzdqH7",
        "outputId": "0bb1ef31-0e88-4258-a8b8-0253476130f4"
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      "source": [
        "out.drop(index,inplace=True)\n",
        "out\n"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Video url</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>https://www.youtube.com/watch?v=motq95iWsHw</td>\n",
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              "      <td>https://www.youtube.com/watch?v=sPELkkD6SWk</td>\n",
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              "      <th>3</th>\n",
              "      <td>https://www.youtube.com/watch?v=1OMIORV7OH0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>https://www.youtube.com/watch?v=oojdt-tVIhE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>573</th>\n",
              "      <td>https://www.youtube.com/watch?v=0z3stf6nqIY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>574</th>\n",
              "      <td>https://www.youtube.com/watch?v=ivZ8LmC6LxM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>575</th>\n",
              "      <td>https://www.youtube.com/watch?v=wleiXoRllrU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>576</th>\n",
              "      <td>https://www.youtube.com/watch?v=zt0NkL9J8jk</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577</th>\n",
              "      <td>https://www.youtube.com/watch?v=oYziuMU2Ubs</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>548 rows × 1 columns</p>\n",
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            "text/plain": [
              "                                       Video url\n",
              "0    https://www.youtube.com/watch?v=motq95iWsHw\n",
              "1    https://www.youtube.com/watch?v=mBKcfJAqI1Y\n",
              "2    https://www.youtube.com/watch?v=sPELkkD6SWk\n",
              "3    https://www.youtube.com/watch?v=1OMIORV7OH0\n",
              "4    https://www.youtube.com/watch?v=oojdt-tVIhE\n",
              "..                                           ...\n",
              "573  https://www.youtube.com/watch?v=0z3stf6nqIY\n",
              "574  https://www.youtube.com/watch?v=ivZ8LmC6LxM\n",
              "575  https://www.youtube.com/watch?v=wleiXoRllrU\n",
              "576  https://www.youtube.com/watch?v=zt0NkL9J8jk\n",
              "577  https://www.youtube.com/watch?v=oYziuMU2Ubs\n",
              "\n",
              "[548 rows x 1 columns]"
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          },
          "metadata": {
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          "execution_count": 20
        }
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        },
        "id": "14oRcChnbKN6",
        "outputId": "d53a1248-45e4-4560-f8c9-c503fb466f92"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.02640763,  0.03316175, -0.01264823, ...,  0.00305158,\n",
              "        -0.03889298, -0.06036416],\n",
              "       [ 0.00455121, -0.0416862 , -0.01352912, ...,  0.03719347,\n",
              "        -0.02848883, -0.0308826 ],\n",
              "       [ 0.0413438 , -0.04144462, -0.00339706, ...,  0.00640473,\n",
              "        -0.01921365, -0.07293434],\n",
              "       ...,\n",
              "       [-0.05378248,  0.00832757,  0.04142925, ..., -0.00314851,\n",
              "        -0.00721204, -0.02045638],\n",
              "       [-0.04512906, -0.03092816,  0.10086783, ..., -0.01660114,\n",
              "        -0.00106492, -0.06558236],\n",
              "       [-0.00261104,  0.04090556, -0.02637166, ..., -0.04333348,\n",
              "         0.01902526,  0.02021845]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_UKylk1sHPw5",
        "outputId": "4f78e343-76e1-42b4-df1b-9d7cbd77662f"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(578, 512)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THkMJxJ2bM4A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ccbd9d51-1602-4555-8cb6-28954f329161"
      },
      "source": [
        "len(url_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "578"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fTmJx6cHXPp",
        "outputId": "cc51387b-1ae0-429d-f7be-186e2095431c"
      },
      "source": [
        "red_data = PCA(0.99)\n",
        "new_data = red_data.fit_transform(data)\n",
        "new_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(578, 288)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGHUIH_QIuS8"
      },
      "source": [
        "kmeans = KMeans(n_clusters=3, random_state=0).fit(new_data)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "gRiS-vA1I-R6",
        "outputId": "1c2c2052-6bb8-4541-8c07-b891372d4bfc"
      },
      "source": [
        "X_embedded = PCA(n_components=2).fit_transform(new_data)\n",
        "sns.scatterplot(X_embedded[:,0], X_embedded[:,1], legend='full',palette=palette )\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Wb8M4ekZC4v",
        "outputId": "6ada28e1-0988-47b5-a340-9388eaa686fb"
      },
      "source": [
        "new_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.2564531 , -0.09158049, -0.00979162, ..., -0.00348169,\n",
              "         0.00960145,  0.00206068],\n",
              "       [ 0.10882258,  0.297767  ,  0.24752295, ...,  0.01144364,\n",
              "         0.00943761, -0.00554724],\n",
              "       [-0.22905944,  0.14848195,  0.17434812, ...,  0.00434789,\n",
              "         0.01364791,  0.02068027],\n",
              "       ...,\n",
              "       [-0.09116068,  0.13273384, -0.13546912, ..., -0.00488913,\n",
              "         0.01254378,  0.00148054],\n",
              "       [ 0.35893965,  0.32294407, -0.33302727, ..., -0.01020102,\n",
              "        -0.00404081, -0.00463767],\n",
              "       [ 0.49616858,  0.04288401, -0.00590315, ..., -0.00193084,\n",
              "        -0.00262474,  0.00236416]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "0u_I69p7Jhy2",
        "outputId": "f31921c6-b09c-4f8b-bb16-665d38b26d46"
      },
      "source": [
        "kmeans = KMeans(n_clusters= 3,random_state= 0 , n_init=20)\n",
        " \n",
        "#predict the labels of clusters.\n",
        "label = kmeans.fit_predict(new_data)\n",
        " \n",
        "#Getting unique labels\n",
        "u_labels = np.unique(label)\n",
        " \n",
        "#plotting the results:\n",
        "for i in u_labels:\n",
        "    plt.scatter(new_data[label == i , 0] , new_data[label == i , 1] , label = i)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PhvNPbUcS2TT",
        "outputId": "7b5d757f-2dd4-4a84-f8bd-ba5e1014456b"
      },
      "source": [
        "pred_labels=kmeans.labels_\n",
        "pred_labels2=pd.DataFrame(pred_labels)\n",
        "print(pred_labels2.value_counts())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    288\n",
            "1    181\n",
            "2    109\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8SusR5PMKRR"
      },
      "source": [
        "url_data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxspW3TiNiOp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "60a85335-8d3d-435f-e953-6601d5b91d26"
      },
      "source": [
        "out= pd.DataFrame(label,columns=['Video label'])\n",
        "out"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Video label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <th>3</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>573</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>574</th>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>575</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>576</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>578 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Video label\n",
              "0              0\n",
              "1              2\n",
              "2              0\n",
              "3              0\n",
              "4              1\n",
              "..           ...\n",
              "573            2\n",
              "574            2\n",
              "575            0\n",
              "576            2\n",
              "577            2\n",
              "\n",
              "[578 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3Va9gD_Puge"
      },
      "source": [
        "\n",
        "\n",
        "0=Neutral\n",
        "2= Article 370 Stays\n",
        "1= Article 370 Ends "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lGqeDVnct5OV",
        "outputId": "5fbaf132-fe98-43c9-ef56-c2c01515af30"
      },
      "source": [
        "Artcile_370 = pd.read_csv('/content/Artcile_370.csv')\n",
        "Artcile_370"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Video Url</th>\n",
              "      <th>Title</th>\n",
              "      <th>Vidoe id</th>\n",
              "      <th>Labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>https://www.youtube.com/watch?v=motq95iWsHw</td>\n",
              "      <td>Kashmir Special Status (Article 370) Ends \"At ...</td>\n",
              "      <td>motq95iWsHw</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>https://www.youtube.com/watch?v=mBKcfJAqI1Y</td>\n",
              "      <td>Article 370 | What are Article 370 and Article...</td>\n",
              "      <td>mBKcfJAqI1Y</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>https://www.youtube.com/watch?v=sPELkkD6SWk</td>\n",
              "      <td>Article 370 &amp; Jammu and Kashmir | Article 35 A</td>\n",
              "      <td>sPELkkD6SWk</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>https://www.youtube.com/watch?v=1OMIORV7OH0</td>\n",
              "      <td>Article 370 Removal: Right or Wrong? | Explain...</td>\n",
              "      <td>1OMIORV7OH0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>https://www.youtube.com/watch?v=oojdt-tVIhE</td>\n",
              "      <td>Inside story of scrapping of Article 370</td>\n",
              "      <td>oojdt-tVIhE</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>529</th>\n",
              "      <td>538</td>\n",
              "      <td>https://www.youtube.com/watch?v=7fLn4_XH2xU</td>\n",
              "      <td>चुनाव के लिए पोलिंग बूथ का हुआ निरीक्षण|Ep-679|</td>\n",
              "      <td>XDzyfJ5avaw</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>530</th>\n",
              "      <td>539</td>\n",
              "      <td>https://www.youtube.com/watch?v=dlTHj8KUYKY</td>\n",
              "      <td>After removing Article 370, PM Modi will addre...</td>\n",
              "      <td>7lJ4sEeVypg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>531</th>\n",
              "      <td>540</td>\n",
              "      <td>https://www.youtube.com/watch?v=ts9cWuPniYo</td>\n",
              "      <td>#study_powerful#assam_police_gk preparation of...</td>\n",
              "      <td>d6CooJOIhyw</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>532</th>\n",
              "      <td>541</td>\n",
              "      <td>https://www.youtube.com/watch?v=z-aZOxn_FHI</td>\n",
              "      <td>Reorganization of J&amp;K II Part 4 II Article 35-...</td>\n",
              "      <td>0EP-rCa3qRM</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>533</th>\n",
              "      <td>546</td>\n",
              "      <td>https://www.youtube.com/watch?v=zt0NkL9J8jk</td>\n",
              "      <td>Sunil Dimple \" PHE, PWD, PDD Daily wagers, SPO...</td>\n",
              "      <td>1j8GkrUL3Ac</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>534 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Unnamed: 0  ... Labels\n",
              "0             0  ...      2\n",
              "1             1  ...      0\n",
              "2             2  ...      2\n",
              "3             3  ...      2\n",
              "4             4  ...      1\n",
              "..          ...  ...    ...\n",
              "529         538  ...      0\n",
              "530         539  ...      0\n",
              "531         540  ...      0\n",
              "532         541  ...      0\n",
              "533         546  ...      0\n",
              "\n",
              "[534 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6IvugIXua49"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UsmcMsD0NyT_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "71faeefe-9748-406e-eeef-4b2cec370c0a"
      },
      "source": [
        "out['Video Url'] = url_data\n",
        "out"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Video label</th>\n",
              "      <th>Video Url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>https://www.youtube.com/watch?v=motq95iWsHw</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>https://www.youtube.com/watch?v=mBKcfJAqI1Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>https://www.youtube.com/watch?v=sPELkkD6SWk</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>https://www.youtube.com/watch?v=1OMIORV7OH0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>https://www.youtube.com/watch?v=oojdt-tVIhE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>573</th>\n",
              "      <td>2</td>\n",
              "      <td>https://www.youtube.com/watch?v=0z3stf6nqIY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>574</th>\n",
              "      <td>2</td>\n",
              "      <td>https://www.youtube.com/watch?v=ivZ8LmC6LxM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>575</th>\n",
              "      <td>0</td>\n",
              "      <td>https://www.youtube.com/watch?v=wleiXoRllrU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>576</th>\n",
              "      <td>2</td>\n",
              "      <td>https://www.youtube.com/watch?v=zt0NkL9J8jk</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577</th>\n",
              "      <td>2</td>\n",
              "      <td>https://www.youtube.com/watch?v=oYziuMU2Ubs</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>578 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Video label                                    Video Url\n",
              "0              0  https://www.youtube.com/watch?v=motq95iWsHw\n",
              "1              2  https://www.youtube.com/watch?v=mBKcfJAqI1Y\n",
              "2              0  https://www.youtube.com/watch?v=sPELkkD6SWk\n",
              "3              0  https://www.youtube.com/watch?v=1OMIORV7OH0\n",
              "4              1  https://www.youtube.com/watch?v=oojdt-tVIhE\n",
              "..           ...                                          ...\n",
              "573            2  https://www.youtube.com/watch?v=0z3stf6nqIY\n",
              "574            2  https://www.youtube.com/watch?v=ivZ8LmC6LxM\n",
              "575            0  https://www.youtube.com/watch?v=wleiXoRllrU\n",
              "576            2  https://www.youtube.com/watch?v=zt0NkL9J8jk\n",
              "577            2  https://www.youtube.com/watch?v=oYziuMU2Ubs\n",
              "\n",
              "[578 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5gsemQQ7oIm"
      },
      "source": [
        "For_dataset=[] #For Removal of Article 370 i.e 1\n",
        "Against_dataset=[] #Against Removal of Article 370 i.e 2\n",
        "Neutral_dataset=[] #Neutral Videos i.e 0\n",
        "for i in range(len(out)):\n",
        "  if out['Video label'][i]==1:\n",
        "    For_dataset.append(out['Video Url'][i])\n",
        "  elif out['Video label'][i]==2:\n",
        "    Against_dataset.append(out['Video Url'][i])\n",
        "  elif out['Video label'][i]==0:\n",
        "    Neutral_dataset.append(out['Video Url'][i])\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B1gtvNVSg84L",
        "outputId": "41fd8ffa-cea2-4eb1-d196-daec08286c8a"
      },
      "source": [
        "print(\"Enter a URL\")\n",
        "url=input()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter a URL\n",
            "https://www.youtube.com/watch?v=sPELkkD6SWk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onuXet9a-TDA"
      },
      "source": [
        "def Rand(start, end, num,res,arr,op_type):\n",
        "    count=0\n",
        "    j=0\n",
        "    while (j<num):\n",
        "        id=random.randint(start, end)\n",
        "        if arr[id] not in res:\n",
        "          res.append(op_type+\": \"+arr[id])\n",
        "          j=j+1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lvOPtBy-hDdm",
        "outputId": "37904d9a-77c0-4b33-8f08-5d43237ba279"
      },
      "source": [
        "arr_url=list(out['Video Url'])\n",
        "url_arr=[]\n",
        "if url in arr_url:\n",
        "  ind=arr_url.index(url)\n",
        "  predicted_label=label[ind]\n",
        "  predicted_label\n",
        "  if predicted_label==0:\n",
        "    print(\"You are watching a Neutral Video on the Topic: Article 370\")\n",
        "    print(\"------------------------------------------------------------------\")\n",
        "    print(\"Recommemded Videos:\")\n",
        "    recom=[]\n",
        "    Rand(0,len(For_dataset)-1,3,recom,For_dataset,\"For\")\n",
        "    Rand(0,len(Against_dataset)-1,3,recom,Against_dataset,\"Against\")\n",
        "    Rand(0,len(Neutral_dataset)-1,4,recom,Neutral_dataset,\"Neutral\")\n",
        "    for i in range(len(recom)):\n",
        "      print(recom[i])\n",
        "      url_arr.append(recom[i])\n",
        "  elif predicted_label==1:\n",
        "    print(\"You are watching a Video which indicates the Advantages of Removing Article 370\")\n",
        "    print(\"------------------------------------------------------------------\")\n",
        "    print(\"Recommemded Videos:\")\n",
        "    recom=[]\n",
        "    Rand(0,len(For_dataset)-1,3,recom,For_dataset,\"For\")\n",
        "    Rand(0,len(Against_dataset)-1,2,recom,Against_dataset,\"Against\")\n",
        "    Rand(0,len(Neutral_dataset)-1,5,recom,Neutral_dataset,\"Neutral\")\n",
        "    for i in range(len(recom)):\n",
        "      print(recom[i])\n",
        "      url_arr.append(recom[i])\n",
        "  elif predicted_label==2:\n",
        "    print(\"You are watching a Video which indicates the Disadvantages of Removing Article 370\")\n",
        "    print(\"------------------------------------------------------------------\")\n",
        "    print(\"Recommemded Videos:\")\n",
        "    recom=[]\n",
        "    Rand(0,len(For_dataset)-1,2,recom,For_dataset,\"For\")\n",
        "    Rand(0,len(Against_dataset)-1,3,recom,Against_dataset,\"Against\")\n",
        "    Rand(0,len(Neutral_dataset)-1,5,recom,Neutral_dataset,\"Neutral\")\n",
        "    for i in range(len(recom)):\n",
        "      print(recom[i])\n",
        "      url_arr.append(recom[i])\n",
        "else:\n",
        "  print(\"You are not watching a Video on Article 370 so currently our Algorithm can't provide you Unbiased Recommendations\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "You are watching a Neutral Video on the Topic: Article 370\n",
            "------------------------------------------------------------------\n",
            "Recommemded Videos:\n",
            "For: https://www.youtube.com/watch?v=473G5rQTEbQ\n",
            "For: https://www.youtube.com/watch?v=gYkDhs6fYWY\n",
            "For: https://www.youtube.com/watch?v=5DoT-5vrmFY\n",
            "Against: https://www.youtube.com/watch?v=_4JB8J2gnB8\n",
            "Against: https://www.youtube.com/watch?v=pn_imgLwo38\n",
            "Against: https://www.youtube.com/watch?v=7RAi9jVGZas\n",
            "Neutral: https://www.youtube.com/watch?v=nblNym5aBU4\n",
            "Neutral: https://www.youtube.com/watch?v=7fYLLN-Hc0I\n",
            "Neutral: https://www.youtube.com/watch?v=Jei0mRMi1Ro\n",
            "Neutral: https://www.youtube.com/watch?v=IkjUXiOJyJc\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rDCKgQMiByNb"
      },
      "source": [
        "#Analysing Video History"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzkIDD9SB0uL",
        "outputId": "78b02473-680f-498b-f117-4eff97c7a3a3"
      },
      "source": [
        "past_url_arr=[]\n",
        "for i in range(5):\n",
        "  print(\"Enter a URL\")\n",
        "  val=input()\n",
        "  past_url_arr.append(val)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter a URL\n",
            "https://www.youtube.com/watch?v=473G5rQTEbQ\n",
            "Enter a URL\n",
            "https://www.youtube.com/watch?v=_4JB8J2gnB8\n",
            "Enter a URL\n",
            "https://www.youtube.com/watch?v=nblNym5aBU4\n",
            "Enter a URL\n",
            "https://www.youtube.com/watch?v=IkjUXiOJyJc\n",
            "Enter a URL\n",
            "https://www.youtube.com/watch?v=Jei0mRMi1Ro\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "mdL8P6FBC_oW",
        "outputId": "c3b8253c-d7a3-4784-bdf2-068e62da7c26"
      },
      "source": [
        "predicted_labels=[]\n",
        "for i in range(len(past_url_arr)):\n",
        "  ind=arr_url.index(past_url_arr[i])\n",
        "  pred=label[ind]\n",
        "  if pred==1:\n",
        "    predicted_labels.append(\"For Removal of Article 370\")\n",
        "  elif pred==0:\n",
        "    predicted_labels.append(\"Neutral opinion\")\n",
        "  elif pred==2:\n",
        "    predicted_labels.append(\"Against Removal of Article 370\")\n",
        "#print(predicted_labels)\n",
        "dicti_freq={}\n",
        "for i in predicted_labels:\n",
        "  if dicti_freq.get(i)==None:\n",
        "    dicti_freq[i]=1\n",
        "  else:\n",
        "    dicti_freq[i]+=1\n",
        "dicti_freq\n",
        "fig = plt.figure(figsize =(10, 7))\n",
        "plt.pie(dicti_freq.values(), labels = dicti_freq.keys())\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7I3fNGQ5MBHO"
      },
      "source": [
        "base_url1 = 'https://www.googleapis.com/youtube/v3/videos?id='\n",
        "base_url2=\"&part=contentDetails&key=\"\n",
        "api_key = 'AIzaSyAFl1zpS0pHSXtDTWC3TQVkKBSlVJh0Qbw'\n",
        "import json, requests\n",
        "def func2(vid):\n",
        "    url = base_url1+vid+base_url2 + api_key + '&videos_id=' + vid\n",
        "    response = requests.get(url).text\n",
        "    response_info = json.loads(response)\n",
        "    #print(response_info)\n",
        "    return response_info['items']\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dF2lEVukPbnx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6cd6a884-3253-4640-8c31-4ed33845c8dd"
      },
      "source": [
        "vids=[]\n",
        "for i in range(len(past_url_arr)):\n",
        "  indi=past_url_arr[i].rindex(\"=\")\n",
        "  id=past_url_arr[i][indi+1:]\n",
        "  vids.append(id)\n",
        "vids"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['473G5rQTEbQ', '_4JB8J2gnB8', 'nblNym5aBU4', 'IkjUXiOJyJc', 'Jei0mRMi1Ro']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKEZxrGwPbue",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce81f2b6-39fb-4832-da02-8d34176c2ff8"
      },
      "source": [
        "total_duration=0\n",
        "for i in range(len(vids)):\n",
        "  val=func2(vids[i])\n",
        "  dur=val[0]['contentDetails']['duration']\n",
        "  time=dur[2:]\n",
        "  indi_H=time.find(\"H\")\n",
        "  indi_M=time.find(\"M\")\n",
        "  indi_S=time.find(\"S\")\n",
        "  if indi_H!=-1:\n",
        "    total_duration+=int(time[indi_H-1])*60\n",
        "  if indi_M!=-1:\n",
        "    total_duration+=int(time[indi_M-1])\n",
        "  if indi_S!=-1:\n",
        "    total_duration+=int(int(time[indi_S-1])/60)\n",
        "print(\"YOU ARE WATCHING VIDEOS ON A SENSTIVIE TOPIC:ARTICLE 370 FOR PAST \"+str(total_duration)+\" MINUTES\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "YOU ARE WATCHING VIDEOS ON A SENSTIVIE TOPIC:ARTICLE 370 FOR PAST 18 MINUTES\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}