Project_Test.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aliasad20/Chitthi/blob/main/Project_Test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "usE1B6pdQPl3",
        "outputId": "ed061182-c839-4f9f-b1ea-01a8f800108d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to C:\\Users\\Anmol\n",
            "[nltk_data]     Srivastava\\AppData\\Roaming\\nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "945fu9lvQT3A"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from nltk import word_tokenize\n",
        "from nltk.stem.snowball import SnowballStemmer\n",
        "import requests\n",
        "import firebase_admin\n",
        "from firebase_admin import credentials\n",
        "from firebase_admin import firestore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "58j6VQDZPCaD",
        "outputId": "c8ef375d-d2b2-4125-b9e9-be3d9d98041d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting LeXmo\n",
            "  Downloading LeXmo-0.1.4-py3-none-any.whl (6.9 kB)\n",
            "Requirement already satisfied: pandas in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from LeXmo) (1.5.3)\n",
            "Requirement already satisfied: nltk in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from LeXmo) (3.8.1)\n",
            "Requirement already satisfied: requests in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from LeXmo) (2.28.2)\n",
            "Requirement already satisfied: click in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from nltk->LeXmo) (8.1.3)\n",
            "Requirement already satisfied: joblib in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from nltk->LeXmo) (1.2.0)\n",
            "Requirement already satisfied: regex>=2021.8.3 in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from nltk->LeXmo) (2023.3.23)\n",
            "Requirement already satisfied: tqdm in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from nltk->LeXmo) (4.65.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas->LeXmo) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas->LeXmo) (2022.7.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas->LeXmo) (1.24.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->LeXmo) (3.1.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->LeXmo) (3.4)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->LeXmo) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->LeXmo) (2022.12.7)\n",
            "Requirement already satisfied: six>=1.5 in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from python-dateutil>=2.8.1->pandas->LeXmo) (1.16.0)\n",
            "Requirement already satisfied: colorama in c:\\users\\anmol srivastava\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from click->nltk->LeXmo) (0.4.6)\n",
            "Installing collected packages: LeXmo\n",
            "Successfully installed LeXmo-0.1.4\n"
          ]
        }
      ],
      "source": [
        "\n",
        "!pip install -U LeXmo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "4x1gmCT7PFOR"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to C:\\Users\\Anmol\n",
            "[nltk_data]     Srivastava\\AppData\\Roaming\\nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "from LeXmo import LeXmo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "# t= \"\"\"From the beginning, she had sat looking at him fixedly.\n",
        "#   As he now leaned back in his chair, and bent his deep-set eyes upon her in his turn,\n",
        "#   perhaps he might have seen one wavering moment in her, \n",
        "#   when she was impelled to throw herself upon his breast,\n",
        "#   and give him the pent-up confidences of her heart.\n",
        "#   But, to see it, he must have overleaped at a bound the artificial barriers he had for many years been erecting, \n",
        "#   between himself and all those subtle essences of humanity which will elude the utmost cunning of algebra\n",
        "#   until the last trumpet ever to be sounded shall blow even algebra to wreck.\n",
        "#   The barriers were too many and too high for such a leap. With his unbending,\n",
        "#   utilitarian, matter-of-fact face, he hardened her again;\n",
        "#   and the moment shot away into the plumbless depths of the past,\n",
        "#   to mingle with all the lost opportunities that are drowned there.\"\"\"\n",
        "# t=\"Yup bro and will have ice cream and special coffee. Hey. Yes we will surely meet and have fun together. Do you know I am going to visit you tomorrow. Hello. We will visit the park area and Taj Mahal positively and have a vivid view about the places. This will make us have fun and make the day enjoyful.\"\n",
        "# Charles Dickens, Hard Times"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "cred = credentials.Certificate('servicekey.json')\n",
        "app = firebase_admin.initialize_app(cred)\n",
        "db = firestore.client()\n",
        "doc_ref = db.collection(u'chats')\n",
        "# .document(u'72bS8jJYa1Oci3xfYPnxdtCygjP2')\n",
        "# msg_set= set()\n",
        "msg = []\n",
        "doc = doc_ref.get()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "ezCPGImCr8Ul"
      },
      "outputs": [],
      "source": [
        "\n",
        "type(doc)\n",
        "for i in doc:\n",
        "    if i.exists:\n",
        "        doc_data = i.to_dict()\n",
        "        # print(f'Document data: {doc_data}')\n",
        "        data = doc_data.values()\n",
        "        data=list(data)\n",
        "        # print(len(data[0]))\n",
        "        for j in data:\n",
        "            for k in j:\n",
        "                # print(k[\"text\"])\n",
        "                msg.append(k[\"text\"])\n",
        "    else:\n",
        "        print(u'No such document!')\n",
        "uni_msg = set(msg)\n",
        "msg_data =\"\"\n",
        "for i in uni_msg:\n",
        "    msg_data= msg_data+i+\". \"\n",
        "# print(uni_msg)\n",
        "# print(msg_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4oTZBM1h7aV0"
      },
      "source": [
        "# New Section"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "lNBKFLHCDLtw"
      },
      "outputs": [],
      "source": [
        "emo=LeXmo.LeXmo(msg_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "btWc2zliWSza",
        "outputId": "16b08ad0-951f-443c-8c3c-22dbc1b87a1a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'text': 'Yup bro and will have ice cream and special coffee. We will visit the park area and Taj Mahal positively and have a vivid view about the places. Hi. Do you know I am going to visit you tomorrow. This will make us have fun and make the day enjoyful. I am feeling very good today as it is very nice weather. Yes we will surely meet and have fun together. Hey. Hello. GM. ', 'anger': 0.0, 'anticipation': 0.07142857142857142, 'disgust': 0.0, 'fear': 0.0, 'joy': 0.08333333333333333, 'negative': 0.0, 'positive': 0.10714285714285714, 'sadness': 0.0, 'surprise': 0.023809523809523808, 'trust': 0.023809523809523808}\n"
          ]
        }
      ],
      "source": [
        "print(emo)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "1C9UsjeixoX9",
        "outputId": "a1396837-fef5-4993-8eae-aa20c4dc7087"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'Yup bro and will have ice cream and special coffee. We will visit the park area and Taj Mahal positively and have a vivid view about the places. Hi. Do you know I am going to visit you tomorrow. This will make us have fun and make the day enjoyful. I am feeling very good today as it is very nice weather. Yes we will surely meet and have fun together. Hey. Hello. GM. '"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "emo.pop('text', None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ooMbjwYozgyp",
        "outputId": "5f2f6748-0496-4532-d24b-afdfa9bbe43a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'anger': 0.0, 'anticipation': 0.07142857142857142, 'disgust': 0.0, 'fear': 0.0, 'joy': 0.08333333333333333, 'negative': 0.0, 'positive': 0.10714285714285714, 'sadness': 0.0, 'surprise': 0.023809523809523808, 'trust': 0.023809523809523808}\n"
          ]
        }
      ],
      "source": [
        "print(emo)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "cPCNVi-Ts56_"
      },
      "outputs": [],
      "source": [
        "# type(emo)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "jCfxW9pEKlYC",
        "outputId": "ec022b39-b80d-41bc-d6b4-5fd485b96f2d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'anticipation': 0.07142857142857142, 'joy': 0.08333333333333333, 'positive': 0.10714285714285714, 'surprise': 0.023809523809523808, 'trust': 0.023809523809523808}\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "emo_dict = { k: v  for k, v in emo.items() if v >0.00}\n",
        "\n",
        "\n",
        "# Data to plot\n",
        "labels = []\n",
        "values = []\n",
        "\n",
        "for x, y in emo_dict.items():\n",
        "    labels.append(x)\n",
        "    values.append(y)\n",
        "print(emo_dict)\n",
        "\n",
        "# Plot\n",
        "plt.pie(values, labels=labels, autopct=lambda p: '{:.000f}%'.format(p ))\n",
        "\n",
        "# plt.axis('equal')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "RfzWqUD-LS7s",
        "outputId": "05a14d62-45e4-4324-fa41-0bb833a55af9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "emo_dict = {k: v for k, v in emo.items() if v >0.0}\n",
        "\n",
        "plt.bar(range(len(emo_dict)), list(emo_dict.values()), align='center', color=\"deepskyblue\")\n",
        "plt.xticks(range(len(emo_dict)), list(emo_dict.keys()))\n",
        "\n",
        "\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
      "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": "3.11.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}