PROJFINAL.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oYTzFT1lW1-R",
        "outputId": "46b37232-a779-4f4b-b324-94afa0c11833"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VRWQQNf1Zr9o"
      },
      "source": [
        "## Installing required libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tuNwaR2uVy7N",
        "outputId": "69f0143b-cb77-4eda-e30c-0651ed422129"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pytesseract in /Users/architgarg/opt/anaconda3/lib/python3.8/site-packages (0.3.10)\n",
            "Requirement already satisfied: Pillow>=8.0.0 in /Users/architgarg/opt/anaconda3/lib/python3.8/site-packages (from pytesseract) (8.0.1)\n",
            "Requirement already satisfied: packaging>=21.3 in /Users/architgarg/opt/anaconda3/lib/python3.8/site-packages (from pytesseract) (23.0)\n",
            "Password:\n",
            "\n",
            "Password:"
          ]
        }
      ],
      "source": [
        "!pip install pytesseract\n",
        "!sudo apt install tesseract-ocr\n",
        "!sudo apt-get install tesseract-ocr-ind\n",
        "!pip install datetime\n",
        "!pip install pytz\n",
        "!pip install pathlib\n",
        "!pip install icalendar"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "q8yczm7WsQgt"
      },
      "outputs": [],
      "source": [
        "  def display(img,cmap='gray'):\n",
        "    fig = plt.figure(figsize=(12,10))\n",
        "    ax = fig.add_subplot(111)\n",
        "    ax.imshow(img,cmap='gray')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8WgKJ0BJZxMu"
      },
      "source": [
        "## Importing the libraries:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "4VpJiAJxXR2V"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "import pytesseract\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "import datetime\n",
        "import os.path\n",
        "import datetime\n",
        "from datetime import datetime , timedelta ,date\n",
        "from icalendar import Calendar, Event, vCalAddress, vText\n",
        "import pytz\n",
        "from datetime import datetime\n",
        "import os\n",
        "from pathlib import Path"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUqZPiYYZ1mf"
      },
      "source": [
        "## Reading the Image:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        },
        "id": "p23vC9t4qkyQ",
        "outputId": "17cbf75b-39ef-4173-ca94-7653a890fb3d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f53735527f0>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "img = cv2.imread(\"/content/drive/MyDrive/PROJECT/TT2.png\")\n",
        "plt.imshow(img)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mr-odPlPaAhN"
      },
      "source": [
        "## Converting Image to grey scale for better text detection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        },
        "id": "gFCmzNrlSMUz",
        "outputId": "5f47c2cd-9ff7-4ed5-cc9a-a684b0bbabfe"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f53732ab220>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "plt.imshow(gray,cmap='gray')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ewXzUxQtZ5Nf"
      },
      "source": [
        "## Finding the Image's threshold to better scan text and extrating data from the Image :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M6gs8HAqGm62",
        "outputId": "72925a35-b72f-4f88-bf64-deca0e1ad79c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "level\tpage_num\tblock_num\tpar_num\tline_num\tword_num\tleft\ttop\twidth\theight\tconf\ttext\n",
            "1\t1\t0\t0\t0\t0\t0\t0\t2418\t558\t-1\t\n",
            "2\t1\t1\t0\t0\t0\t90\t57\t78\t23\t-1\t\n",
            "3\t1\t1\t1\t0\t0\t90\t57\t78\t23\t-1\t\n",
            "4\t1\t1\t1\t1\t0\t90\t57\t78\t23\t-1\t\n",
            "5\t1\t1\t1\t1\t1\t90\t57\t78\t23\t96\tTheory\n",
            "2\t1\t2\t0\t0\t0\t102\t147\t54\t17\t-1\t\n",
            "3\t1\t2\t1\t0\t0\t102\t147\t54\t17\t-1\t\n",
            "4\t1\t2\t1\t1\t0\t102\t147\t54\t17\t-1\t\n",
            "5\t1\t2\t1\t1\t1\t102\t147\t54\t17\t92\tMON\n",
            "2\t1\t3\t0\t0\t0\t107\t229\t43\t17\t-1\t\n",
            "3\t1\t3\t1\t0\t0\t107\t229\t43\t17\t-1\t\n",
            "4\t1\t3\t1\t1\t0\t107\t229\t43\t17\t-1\t\n",
            "5\t1\t3\t1\t1\t1\t107\t229\t43\t17\t89\tTUE\n",
            "2\t1\t4\t0\t0\t0\t103\t311\t51\t17\t-1\t\n",
            "3\t1\t4\t1\t0\t0\t103\t311\t51\t17\t-1\t\n",
            "4\t1\t4\t1\t1\t0\t103\t311\t51\t17\t-1\t\n",
            "5\t1\t4\t1\t1\t1\t103\t311\t51\t17\t92\tWED\n",
            "2\t1\t5\t0\t0\t0\t105\t393\t46\t17\t-1\t\n",
            "3\t1\t5\t1\t0\t0\t105\t393\t46\t17\t-1\t\n",
            "4\t1\t5\t1\t1\t0\t105\t393\t46\t17\t-1\t\n",
            "5\t1\t5\t1\t1\t1\t105\t393\t46\t17\t90\tTHU\n",
            "2\t1\t6\t0\t0\t0\t114\t475\t30\t17\t-1\t\n",
            "3\t1\t6\t1\t0\t0\t114\t475\t30\t17\t-1\t\n",
            "4\t1\t6\t1\t1\t0\t114\t475\t30\t17\t-1\t\n",
            "5\t1\t6\t1\t1\t1\t114\t475\t30\t17\t90\tFRI\n",
            "2\t1\t7\t0\t0\t0\t334\t34\t52\t65\t-1\t\n",
            "3\t1\t7\t1\t0\t0\t334\t34\t52\t65\t-1\t\n",
            "4\t1\t7\t1\t1\t0\t334\t34\t52\t17\t-1\t\n",
            "5\t1\t7\t1\t1\t1\t334\t34\t52\t17\t96\tStart\n",
            "4\t1\t7\t1\t2\t0\t341\t81\t38\t18\t-1\t\n",
            "5\t1\t7\t1\t2\t1\t341\t81\t38\t18\t96\tEnd\n",
            "2\t1\t8\t0\t0\t0\t321\t146\t77\t23\t-1\t\n",
            "3\t1\t8\t1\t0\t0\t321\t146\t77\t23\t-1\t\n",
            "4\t1\t8\t1\t1\t0\t321\t146\t77\t23\t-1\t\n",
            "5\t1\t8\t1\t1\t1\t321\t146\t77\t23\t96\tTheory\n",
            "2\t1\t9\t0\t0\t0\t321\t228\t77\t23\t-1\t\n",
            "3\t1\t9\t1\t0\t0\t321\t228\t77\t23\t-1\t\n",
            "4\t1\t9\t1\t1\t0\t321\t228\t77\t23\t-1\t\n",
            "5\t1\t9\t1\t1\t1\t321\t228\t77\t23\t96\tTheory\n",
            "2\t1\t10\t0\t0\t0\t321\t310\t77\t23\t-1\t\n",
            "3\t1\t10\t1\t0\t0\t321\t310\t77\t23\t-1\t\n",
            "4\t1\t10\t1\t1\t0\t321\t310\t77\t23\t-1\t\n",
            "5\t1\t10\t1\t1\t1\t321\t310\t77\t23\t96\tTheory\n",
            "2\t1\t11\t0\t0\t0\t321\t392\t77\t23\t-1\t\n",
            "3\t1\t11\t1\t0\t0\t321\t392\t77\t23\t-1\t\n",
            "4\t1\t11\t1\t1\t0\t321\t392\t77\t23\t-1\t\n",
            "5\t1\t11\t1\t1\t1\t321\t392\t77\t23\t96\tTheory\n",
            "2\t1\t12\t0\t0\t0\t321\t474\t77\t23\t-1\t\n",
            "3\t1\t12\t1\t0\t0\t321\t474\t77\t23\t-1\t\n",
            "4\t1\t12\t1\t1\t0\t321\t474\t77\t23\t-1\t\n",
            "5\t1\t12\t1\t1\t1\t321\t474\t77\t23\t96\tTheory\n",
            "2\t1\t13\t0\t0\t0\t588\t34\t58\t65\t-1\t\n",
            "3\t1\t13\t1\t0\t0\t588\t34\t58\t65\t-1\t\n",
            "4\t1\t13\t1\t1\t0\t588\t34\t58\t17\t-1\t\n",
            "5\t1\t13\t1\t1\t1\t588\t34\t58\t17\t96\t08:30\n",
            "4\t1\t13\t1\t2\t0\t589\t82\t57\t17\t-1\t\n",
            "5\t1\t13\t1\t2\t1\t589\t82\t57\t17\t96\t10:00\n",
            "2\t1\t14\t0\t0\t0\t596\t147\t37\t17\t-1\t\n",
            "3\t1\t14\t1\t0\t0\t596\t147\t37\t17\t-1\t\n",
            "4\t1\t14\t1\t1\t0\t596\t147\t37\t17\t-1\t\n",
            "5\t1\t14\t1\t1\t1\t596\t147\t37\t17\t69\tA1\n",
            "2\t1\t15\t0\t0\t0\t515\t212\t206\t51\t-1\t\n",
            "3\t1\t15\t1\t0\t0\t515\t212\t206\t51\t-1\t\n",
            "4\t1\t15\t1\t1\t0\t515\t212\t206\t17\t-1\t\n",
            "5\t1\t15\t1\t1\t1\t515\t212\t206\t17\t88\tD11-CHY1001-LTP-\n",
            "4\t1\t15\t1\t2\t0\t585\t246\t65\t17\t-1\t\n",
            "5\t1\t15\t1\t2\t1\t585\t246\t65\t17\t85\tLC036\n",
            "2\t1\t16\t0\t0\t0\t596\t311\t41\t17\t-1\t\n",
            "3\t1\t16\t1\t0\t0\t596\t311\t41\t17\t-1\t\n",
            "4\t1\t16\t1\t1\t0\t596\t311\t41\t17\t-1\t\n",
            "5\t1\t16\t1\t1\t1\t596\t311\t41\t17\t94\tA12\n",
            "2\t1\t17\t0\t0\t0\t515\t376\t206\t51\t-1\t\n",
            "3\t1\t17\t1\t0\t0\t515\t376\t206\t51\t-1\t\n",
            "4\t1\t17\t1\t1\t0\t515\t376\t206\t17\t-1\t\n",
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            "2\t1\t59\t0\t0\t0\t2237\t311\t39\t17\t-1\t\n",
            "3\t1\t59\t1\t0\t0\t2237\t311\t39\t17\t-1\t\n",
            "4\t1\t59\t1\t1\t0\t2237\t311\t39\t17\t-1\t\n",
            "5\t1\t59\t1\t1\t1\t2237\t311\t39\t17\t43\tC22\n",
            "2\t1\t60\t0\t0\t0\t2129\t393\t255\t17\t-1\t\n",
            "3\t1\t60\t1\t0\t0\t2129\t393\t255\t17\t-1\t\n",
            "4\t1\t60\t1\t1\t0\t2129\t393\t255\t17\t-1\t\n",
            "5\t1\t60\t1\t1\t1\t2129\t393\t255\t17\t90\tF22-MAT3002-LT-LC028\n",
            "2\t1\t61\t0\t0\t0\t2237\t475\t40\t17\t-1\t\n",
            "3\t1\t61\t1\t0\t0\t2237\t475\t40\t17\t-1\t\n",
            "4\t1\t61\t1\t1\t0\t2237\t475\t40\t17\t-1\t\n",
            "5\t1\t61\t1\t1\t1\t2237\t475\t40\t17\t90\tD23\n",
            "\n"
          ]
        }
      ],
      "source": [
        "th, threshed = cv2.threshold(gray, 127, 255, cv2.THRESH_TRUNC)\n",
        "data=pytesseract.image_to_data(threshed,lang=\"ind\")\n",
        "print(data)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wLll8wtmaRiT"
      },
      "source": [
        "## Cleaning the above extracted data and getting only text:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lW8IKViIWP7W",
        "outputId": "c2da3d2a-67a9-4414-d27d-0306d47669c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Theory', 'MON', 'TUE', 'WED', 'THU', 'FRI', 'Start', 'End', 'Theory', 'Theory', 'Theory', 'Theory', 'Theory', '08:30', '10:00', 'A1', 'D11-CHY1001-LTP-', 'LC036', 'A12', 'D12-CHY1001-LTP-', 'LC036', 'A13', '10:05', '11:35', 'B11-CSE2001-LTP-', 'LC002', 'E11-EEE1001-LTP-', 'AB506', 'B12-CSE2001-LTP-', 'LC002', 'E12-EEE1001-LTP-', 'AB506', 'B13-CSE2001-LTP-', 'LC002', '11:40', '13:10', 'C1', 'F11', '12', 'F12', 'C1', '3-ENG2005-LT-LC-S-', '202', '1315', '14:45', 'A21', 'D21', 'A22', 'D22', 'A23', '14:50', '16:20', 'E13-EEE1001-LTP-', 'AB506', '(23', 'F13', 'B23', 'D13-CHY1001-LTP-', 'LC036', '16:25', '17:55', 'B21', 'E21-CSE2002-LTP-', 'LC009', 'B22', 'E22-CSE2002-LTP-', 'LC009', 'E23-CSE2002-LTP-', 'LC009', '18:00', '19:30', 'C21', 'F21-MAT3002-LT-LC028', 'C22', 'F22-MAT3002-LT-LC028', 'D23']\n",
            "[13, 14, 22, 23, 34, 35, 44, 50, 51, 59, 60, 69, 70]\n"
          ]
        }
      ],
      "source": [
        "c=[]\n",
        "d=[]\n",
        "for x,b in enumerate(data.splitlines()):\n",
        "  if x!=0:\n",
        "    b=b.split()\n",
        "    if(len(b)>11):\n",
        "      c.append(b[-1])\n",
        "print(c)\n",
        "for j in c :\n",
        "  if(':' in j):\n",
        "    d.append(c.index(j))\n",
        "print(d)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cjMgpAFEaaQI"
      },
      "source": [
        "## Sorting the data in a linked list 'e' which has all the detected data column wise including time slots classes etc:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y_SwzMKedr-h",
        "outputId": "0a91ef96-8ca9-4d15-e0b3-baf9b0bbd291"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['08:30', '10:00', 'A1', 'D11-CHY1001-LTP-', 'LC036', 'A12', 'D12-CHY1001-LTP-', 'LC036', 'A13']\n",
            "['10:05', '11:35', 'B11-CSE2001-LTP-', 'LC002', 'E11-EEE1001-LTP-', 'AB506', 'B12-CSE2001-LTP-', 'LC002', 'E12-EEE1001-LTP-', 'AB506', 'B13-CSE2001-LTP-', 'LC002']\n",
            "['11:40', '13:10', 'C1', 'F11', '12', 'F12', 'C1', '3-ENG2005-LT-LC-S-', '202', '1315']\n",
            "['14:45', 'A21', 'D21', 'A22', 'D22', 'A23']\n",
            "['14:50', '16:20', 'E13-EEE1001-LTP-', 'AB506', '(23', 'F13', 'B23', 'D13-CHY1001-LTP-', 'LC036']\n",
            "['16:25', '17:55', 'B21', 'E21-CSE2002-LTP-', 'LC009', 'B22', 'E22-CSE2002-LTP-', 'LC009', 'E23-CSE2002-LTP-', 'LC009']\n",
            "['18:00', '19:30', 'C21', 'F21-MAT3002-LT-LC028', 'C22', 'F22-MAT3002-LT-LC028', 'D23']\n",
            "[['08:30', '10:00', 'A1', 'D11-CHY1001-LTP-', 'LC036', 'A12', 'D12-CHY1001-LTP-', 'LC036', 'A13'], ['10:05', '11:35', 'B11-CSE2001-LTP-', 'LC002', 'E11-EEE1001-LTP-', 'AB506', 'B12-CSE2001-LTP-', 'LC002', 'E12-EEE1001-LTP-', 'AB506', 'B13-CSE2001-LTP-', 'LC002'], ['11:40', '13:10', 'C1', 'F11', '12', 'F12', 'C1', '3-ENG2005-LT-LC-S-', '202', '1315'], ['14:45', 'A21', 'D21', 'A22', 'D22', 'A23'], ['14:50', '16:20', 'E13-EEE1001-LTP-', 'AB506', '(23', 'F13', 'B23', 'D13-CHY1001-LTP-', 'LC036'], ['16:25', '17:55', 'B21', 'E21-CSE2002-LTP-', 'LC009', 'B22', 'E22-CSE2002-LTP-', 'LC009', 'E23-CSE2002-LTP-', 'LC009'], ['18:00', '19:30', 'C21', 'F21-MAT3002-LT-LC028', 'C22', 'F22-MAT3002-LT-LC028', 'D23']]\n"
          ]
        }
      ],
      "source": [
        "e=[]\n",
        "for k in range(len(d)):\n",
        "  if((d[k]+1) in d and k<len(d)-2):\n",
        "    indexxx=k+2\n",
        "    list1=c[d[k]:d[indexxx]]\n",
        "    print(list1)\n",
        "    e.append(list1)\n",
        "  elif(k==len(d)-2):\n",
        "    list1=c[d[k]:]\n",
        "    print(list1)\n",
        "    e.append(list1)\n",
        "  elif((d[k]+1) not in d and  (d[k]-1) not in d ):\n",
        "    indexxx=k+1\n",
        "    list1=c[d[k]:d[indexxx]]\n",
        "    print(list1)\n",
        "    e.append(list1)\n",
        "print(e)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YrK496OUam98"
      },
      "source": [
        "## Finding dates for every day for next 4 months and then sorting the slots and pushing events in a .ics file and saving it to drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T8lbj_Bitjd3",
        "outputId": "1ade3644-66ad-47c5-efcd-68b749158e42"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ics file will be generated at  /content/drive/MyDrive/Colab_Notebooks/\n"
          ]
        }
      ],
      "source": [
        "timezone='Asia/Kolkata'\n",
        "today =  date.today()\n",
        "name = today.strftime(\"%A\")\n",
        "def generate_dates(days_from_today,date):\n",
        "    dates = []\n",
        "    k=0\n",
        "    for i in range(days_from_today):\n",
        "        d = date + timedelta(k)\n",
        "        k+=7\n",
        "        dates.append(d)\n",
        "    dates = [str(d) for d in dates]\n",
        "    return dates \n",
        "dict={}\n",
        "for i in range(7):\n",
        "  d = today + timedelta(i)\n",
        "  name=d.strftime(\"%A\")\n",
        "  dict[name]=generate_dates(16,d)\n",
        "\n",
        "\n",
        "cal = Calendar()\n",
        "cal.add('prodid', '-//My Product//My Version//EN')\n",
        "cal.add('version', '2.0')\n",
        "cal.add('attendee', 'MAILTO:archit.garg2021@vitbhopal.ac.in')\n",
        "\n",
        "MON=['A11','B11','C11','A21','E13','B21','C21']\n",
        "TUE=['D11','E11','F11','D21','C23','E21','F21']\n",
        "WED=['A12','B12','C12','A22','F13','B22','C22']\n",
        "THUR=['D12','E12','F12','D22','B23','E22','F22']\n",
        "FRI=['D23','E23','D13','A23','C13','B13','A13']\n",
        "\n",
        "for i in e:\n",
        "    for j in i:\n",
        "      if('-' in j):\n",
        "          Z=i.index(j)\n",
        "          timestr=i[0]\n",
        "          roomno=i[Z+1]\n",
        "          slot=j[0:3]\n",
        "\n",
        "          if slot in MON:\n",
        "            val=dict['Monday']\n",
        "            for m in range(len(val)):\n",
        "                dT= val[m] + ' '+ timestr\n",
        "                date_format = '%Y-%m-%d %H:%M'\n",
        "                final = datetime.strptime(dT, date_format)\n",
        "                end=final + timedelta(hours=1)\n",
        "                event = Event()\n",
        "                event.add('summary',j)\n",
        "                event.add('dtstart', final)\n",
        "                event.add('dtend',end)\n",
        "                cal.add_component(event)\n",
        "                \n",
        "          elif slot in TUE:\n",
        "            val=dict['Tuesday']\n",
        "            for m in range(len(val)):\n",
        "                dT= val[m] + ' '+ timestr\n",
        "                date_format = '%Y-%m-%d %H:%M'\n",
        "                final = datetime.strptime(dT, date_format)\n",
        "                end=final + timedelta(hours=1)\n",
        "                event = Event()\n",
        "                event.add('summary',j)\n",
        "                event.add('dtstart', final)\n",
        "                event.add('dtend',end)\n",
        "                cal.add_component(event)\n",
        "\n",
        "          elif slot in WED:\n",
        "            val=dict['Wednesday']\n",
        "            for m in range(len(val)):\n",
        "                dT= val[m] + ' '+ timestr\n",
        "                date_format = '%Y-%m-%d %H:%M'\n",
        "                final = datetime.strptime(dT, date_format)\n",
        "                end=final + timedelta(hours=1)\n",
        "                event = Event()\n",
        "                event.add('summary',j)\n",
        "                event.add('dtstart', final)\n",
        "                event.add('dtend',end)\n",
        "                cal.add_component(event)\n",
        "\n",
        "\n",
        "\n",
        "          elif slot in THUR:\n",
        "            val=dict['Thursday']\n",
        "            for m in range(len(val)):\n",
        "                dT= val[m] + ' '+ timestr\n",
        "                date_format = '%Y-%m-%d %H:%M'\n",
        "                final = datetime.strptime(dT, date_format)\n",
        "                end=final + timedelta(hours=1)\n",
        "                event = Event()\n",
        "                event.add('summary',j)\n",
        "                event.add('dtstart', final)\n",
        "                event.add('dtend',end)\n",
        "                cal.add_component(event)\n",
        "          \n",
        "          elif slot in FRI:\n",
        "            val=dict['Friday']\n",
        "            for m in range(len(val)):\n",
        "                dT= val[m] + ' '+ timestr\n",
        "                date_format = '%Y-%m-%d %H:%M'\n",
        "                final = datetime.strptime(dT, date_format)\n",
        "                end=final + timedelta(hours=1)\n",
        "                event = Event()\n",
        "                event.add('summary',j)\n",
        "                event.add('dtstart', final)\n",
        "                event.add('dtend',end)\n",
        "                cal.add_component(event)\n",
        "\n",
        "directory = '/content/drive/MyDrive/Colab_Notebooks/'\n",
        "print(\"ics file will be generated at \", directory)\n",
        "f = open(os.path.join(directory, 'example.ics'), 'wb')\n",
        "f.write(cal.to_ical())\n",
        "f.close()\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}