{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1F01gIADqs8d"
   },
   "source": [
    "# Cattle disease prediction using Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pto645uRu8v2"
   },
   "source": [
    "**Project:**Cattle-disease-prediction-using-Machine-Learning.\n",
    "**Author:** Thyagarajan K\n",
    "**Date:** 19/02/20\n",
    "**Description:** \n",
    "Cattle disease prediction using Machine Learning. This project is used to predict the disease based on the symptoms. It predicts using 4 different machine learning algorithms. So, the output is accurate.\n",
    "**Github:**https://github.com/thyagarajank/Cattle-disease-prediction-using-Machine-Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "qyY0gFHkqs8h"
   },
   "outputs": [],
   "source": [
    "#Importing libraries and packages\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "from tkinter import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "ZaS2ONt6qs8j"
   },
   "outputs": [],
   "source": [
    "#List of the all symptoms is listed here in list l1.\n",
    "\n",
    "l1=['anorexia','abdominal_pain','anaemia','abortions','acetone','aggression','arthrogyposis',\n",
    "    'ankylosis','anxiety','bellowing','blood_loss','blood_poisoning','blisters','colic','Condemnation_of_livers',\n",
    "    'coughing','depression','discomfort','dyspnea','dysentery','diarrhoea','dehydration','drooling',\n",
    "    'dull','decreased_fertility','diffculty_breath','emaciation','encephalitis','fever','facial_paralysis','frothing_of_mouth',\n",
    "    'frothing','gaseous_stomach','highly_diarrhoea','high_pulse_rate','high_temp','high_proportion','hyperaemia','hydrocephalus',\n",
    "    'isolation_from_herd','infertility','intermittent_fever','jaundice','ketosis','loss_of_appetite','lameness',\n",
    "    'lack_of-coordination','lethargy','lacrimation','milk_flakes','milk_watery','milk_clots',\n",
    "    'mild_diarrhoea','moaning','mucosal_lesions','milk_fever','nausea','nasel_discharges','oedema',\n",
    "    'pain','painful_tongue','pneumonia','photo_sensitization','quivering_lips','reduction_milk_vields','rapid_breathing',\n",
    "    'rumenstasis','reduced_rumination','reduced_fertility','reduced_fat','reduces_feed_intake','raised_breathing','stomach_pain',\n",
    "    'salivation','stillbirths','shallow_breathing','swollen_pharyngeal','swelling','saliva','swollen_tongue',\n",
    "    'tachycardia','torticollis','udder_swelling','udder_heat','udder_hardeness','udder_redness','udder_pain','unwillingness_to_move',\n",
    "    'ulcers','vomiting','weight_loss','weakness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "J4qbgQcuqs8k"
   },
   "outputs": [],
   "source": [
    "#List of Diseases (26 Cattle Diseases Mention) is listed in list disease.\n",
    "\n",
    "disease=['mastitis','blackleg','bloat','coccidiosis','cryptosporidiosis',\n",
    "        'displaced_abomasum','gut_worms','listeriosis','liver_fluke','necrotic_enteritis','peri_weaning_diarrhoea',\n",
    "        ' rift_valley_fever','rumen_acidosis',\n",
    "        'traumatic_reticulitis','calf_diphtheria','foot_rot','foot_and_mouth','ragwort_poisoning','wooden_tongue','infectious_bovine_rhinotracheitis',\n",
    "'acetonaemia','fatty_liver_syndrome','calf_pneumonia','schmallen_berg_virus','trypanosomosis','fog_fever']\n",
    "\n",
    "#disease = [df['prognosis'].unique()]\n",
    "#print(disease)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "7VfB0kyzqs8l",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "l2=[]\n",
    "for i in range(0,len(l1)):\n",
    "    l2.append(0)\n",
    "print(l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "xkIc3pGWqs8m"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anorexia</th>\n",
       "      <th>abdominal_pain</th>\n",
       "      <th>anaemia</th>\n",
       "      <th>abortions</th>\n",
       "      <th>acetone</th>\n",
       "      <th>aggression</th>\n",
       "      <th>arthrogyposis</th>\n",
       "      <th>ankylosis</th>\n",
       "      <th>anxiety</th>\n",
       "      <th>bellowing</th>\n",
       "      <th>...</th>\n",
       "      <th>udder_swelling</th>\n",
       "      <th>udder_heat</th>\n",
       "      <th>udder_hardeness</th>\n",
       "      <th>udder_redness</th>\n",
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       "      <th>unwillingness_to_move</th>\n",
       "      <th>ulcers</th>\n",
       "      <th>vomiting</th>\n",
       "      <th>weight_loss</th>\n",
       "      <th>weakness</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prognosis</th>\n",
       "      <th></th>\n",
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      "text/plain": [
       "           anorexia  abdominal_pain  anaemia  abortions  acetone  aggression  \\\n",
       "prognosis                                                                      \n",
       "mastitis          0               0        0          0        0           0   \n",
       "mastitis          0               0        0          0        0           0   \n",
       "mastitis          0               0        0          0        0           0   \n",
       "mastitis          0               0        0          0        0           0   \n",
       "mastitis          0               0        0          0        0           0   \n",
       "\n",
       "           arthrogyposis  ankylosis  anxiety  bellowing  ...  udder_swelling  \\\n",
       "prognosis                                                ...                   \n",
       "mastitis               0          0        0          0  ...               0   \n",
       "mastitis               0          0        0          0  ...               0   \n",
       "mastitis               0          0        0          0  ...               1   \n",
       "mastitis               0          0        0          0  ...               1   \n",
       "mastitis               0          0        0          0  ...               1   \n",
       "\n",
       "           udder_heat  udder_hardeness  udder_redness  udder_pain  \\\n",
       "prognosis                                                           \n",
       "mastitis            0                0              0           0   \n",
       "mastitis            0                0              0           0   \n",
       "mastitis            0                0              0           0   \n",
       "mastitis            1                0              0           0   \n",
       "mastitis            1                1              0           0   \n",
       "\n",
       "           unwillingness_to_move  ulcers  vomiting  weight_loss  weakness  \n",
       "prognosis                                                                  \n",
       "mastitis                       0       0         0            0         0  \n",
       "mastitis                       0       0         0            0         0  \n",
       "mastitis                       0       0         0            0         0  \n",
       "mastitis                       0       0         0            0         0  \n",
       "mastitis                       0       0         0            0         0  \n",
       "\n",
       "[5 rows x 93 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading the Cattle training Dataset .csv file\n",
    "df=pd.read_csv(\"training.csv\")\n",
    "DF= pd.read_csv('training.csv', index_col='prognosis')\n",
    "#Replace the values in the imported file by pandas by the inbuilt function replace in pandas.\n",
    "\n",
    "df.replace({'prognosis':{'mastitis':0,'blackleg':1,'bloat':2,'coccidiosis':3,'cryptosporidiosis':4,\n",
    "'displaced_abomasum':5,'gut_worms':6,'listeriosis':7,'liver_fluke':8,'necrotic_enteritis':9,'peri_weaning_diarrhoea':10,\n",
    "'rift_valley_fever':11,'rumen_acidosis':12,\n",
    "'traumatic_reticulitis':13,'calf_diphtheria':14,'foot_rot':15,'foot_and_mouth':16,'ragwort_poisoning':17,'wooden_tongue':18,'infectious_bovine_rhinotracheitis':19,\n",
    "'acetonaemia':20,'fatty_liver_syndrome':21,'calf_pneumonia':22,'schmallen_berg_virus':23,'trypanosomosis':24,'fog_fever':25}},inplace=True)\n",
    "#df.head()\n",
    "DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "DUe8PEXXqs8n"
   },
   "outputs": [],
   "source": [
    "# Distribution graphs (histogram/bar graph) of column data\n",
    "def plotPerColumnDistribution(df1, nGraphShown, nGraphPerRow):\n",
    "    nunique = df1.nunique()\n",
    "    df1 = df1[[col for col in df if nunique[col] > 1 and nunique[col] < 50]] # For displaying purposes, pick columns that have between 1 and 50 unique values\n",
    "    nRow, nCol = df1.shape\n",
    "    columnNames = list(df1)\n",
    "    nGraphRow = (nCol + nGraphPerRow - 1) / nGraphPerRow\n",
    "    plt.figure(num = None, figsize = (6 * nGraphPerRow, 8 * nGraphRow), dpi = 80, facecolor = 'w', edgecolor = 'k')\n",
    "    for i in range(min(nCol, nGraphShown)):\n",
    "        plt.subplot(nGraphRow, nGraphPerRow, i + 1)\n",
    "        columnDf = df.iloc[:, i]\n",
    "        if (not np.issubdtype(type(columnDf.iloc[0]), np.number)):\n",
    "            valueCounts = columnDf.value_counts()\n",
    "            valueCounts.plot.bar()\n",
    "        else:\n",
    "            columnDf.hist()\n",
    "        plt.ylabel('counts')\n",
    "        plt.xticks(rotation = 90)\n",
    "        plt.title(f'{columnNames[i]} (column {i})')\n",
    "    plt.tight_layout(pad = 1.0, w_pad = 1.0, h_pad = 1.0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "Cz5CsBqEqs8o"
   },
   "outputs": [],
   "source": [
    "# Scatter and density plots\n",
    "def plotScatterMatrix(df1, plotSize, textSize):\n",
    "    df1 = df1.select_dtypes(include =[np.number]) # keep only numerical columns\n",
    "    # Remove rows and columns that would lead to df being singular\n",
    "    df1 = df1.dropna('columns')\n",
    "    df1 = df1[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values\n",
    "    columnNames = list(df)\n",
    "    if len(columnNames) > 10: # reduce the number of columns for matrix inversion of kernel density plots\n",
    "        columnNames = columnNames[:10]\n",
    "    df1 = df1[columnNames]\n",
    "    ax = pd.plotting.scatter_matrix(df1, alpha=0.75, figsize=[plotSize, plotSize], diagonal='kde')\n",
    "    corrs = df1.corr().values\n",
    "    for i, j in zip(*plt.np.triu_indices_from(ax, k = 1)):\n",
    "        ax[i, j].annotate('Corr. coef = %.3f' % corrs[i, j], (0.8, 0.2), xycoords='axes fraction', ha='center', va='center', size=textSize)\n",
    "    plt.suptitle('Scatter and Density Plot')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "rJQX3r5gqs8p"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-3de5d0542a9c>:10: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nGraphRow, nGraphPerRow, i + 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x12544 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPerColumnDistribution(df, 10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Q-ugpINNqs8p"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotScatterMatrix(df, 20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "pd5DQuEuqs8q"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      anorexia  abdominal_pain  anaemia  abortions  acetone  aggression  \\\n",
      "0            0               0        0          0        0           0   \n",
      "1            0               0        0          0        0           0   \n",
      "2            0               0        0          0        0           0   \n",
      "3            0               0        0          0        0           0   \n",
      "4            0               0        0          0        0           0   \n",
      "...        ...             ...      ...        ...      ...         ...   \n",
      "2039         0               0        0          0        0           0   \n",
      "2040         0               0        0          0        0           0   \n",
      "2041         0               0        0          0        0           0   \n",
      "2042         0               0        0          0        0           0   \n",
      "2043         0               0        0          0        0           0   \n",
      "\n",
      "      arthrogyposis  ankylosis  anxiety  bellowing  ...  udder_swelling  \\\n",
      "0                 0          0        0          0  ...               0   \n",
      "1                 0          0        0          0  ...               0   \n",
      "2                 0          0        0          0  ...               1   \n",
      "3                 0          0        0          0  ...               1   \n",
      "4                 0          0        0          0  ...               1   \n",
      "...             ...        ...      ...        ...  ...             ...   \n",
      "2039              0          0        0          0  ...               0   \n",
      "2040              0          0        0          0  ...               0   \n",
      "2041              0          0        0          0  ...               0   \n",
      "2042              0          0        0          0  ...               0   \n",
      "2043              0          0        0          0  ...               0   \n",
      "\n",
      "      udder_heat  udder_hardeness  udder_redness  udder_pain  \\\n",
      "0              0                0              0           0   \n",
      "1              0                0              0           0   \n",
      "2              0                0              0           0   \n",
      "3              1                0              0           0   \n",
      "4              1                1              0           0   \n",
      "...          ...              ...            ...         ...   \n",
      "2039           0                0              0           0   \n",
      "2040           0                0              0           0   \n",
      "2041           0                0              0           0   \n",
      "2042           0                0              0           0   \n",
      "2043           0                0              0           0   \n",
      "\n",
      "      unwillingness_to_move  ulcers  vomiting  weight_loss  weakness  \n",
      "0                         0       0         0            0         0  \n",
      "1                         0       0         0            0         0  \n",
      "2                         0       0         0            0         0  \n",
      "3                         0       0         0            0         0  \n",
      "4                         0       0         0            0         0  \n",
      "...                     ...     ...       ...          ...       ...  \n",
      "2039                      0       0         0            1         0  \n",
      "2040                      0       0         0            1         0  \n",
      "2041                      0       0         0            1         0  \n",
      "2042                      0       0         0            0         0  \n",
      "2043                      0       0         0            0         0  \n",
      "\n",
      "[2044 rows x 92 columns]\n"
     ]
    }
   ],
   "source": [
    "X= df[l1]\n",
    "y = df[[\"prognosis\"]]\n",
    "np.ravel(y)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "gM_0UgWWqs8q"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      prognosis\n",
      "0             0\n",
      "1             0\n",
      "2             0\n",
      "3             0\n",
      "4             0\n",
      "...         ...\n",
      "2039         12\n",
      "2040         12\n",
      "2041         12\n",
      "2042         12\n",
      "2043         13\n",
      "\n",
      "[2044 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "EresQp-Iqs8r"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>abdominal_pain</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   anorexia  abdominal_pain  anaemia  abortions  acetone  aggression  \\\n",
       "0         0               0        0          0        0           0   \n",
       "1         1               0        0          0        0           0   \n",
       "2         0               0        0          0        0           0   \n",
       "3         0               0        0          0        0           0   \n",
       "4         0               0        0          0        0           0   \n",
       "\n",
       "   arthrogyposis  ankylosis  anxiety  bellowing  ...  udder_heat  \\\n",
       "0              0          0        0          0  ...           1   \n",
       "1              0          0        0          0  ...           0   \n",
       "2              0          0        0          1  ...           0   \n",
       "3              0          0        0          0  ...           0   \n",
       "4              0          0        0          0  ...           0   \n",
       "\n",
       "   udder_hardeness  udder_redness  udder_pain  unwillingness_to_move  ulcers  \\\n",
       "0                1              1           1                      0       0   \n",
       "1                0              0           0                      1       0   \n",
       "2                0              0           0                      0       0   \n",
       "3                0              0           0                      0       0   \n",
       "4                0              0           0                      0       0   \n",
       "\n",
       "   vomiting  weight_loss  weakness  prognosis  \n",
       "0         0            0         0          0  \n",
       "1         0            0         0          1  \n",
       "2         0            0         0          2  \n",
       "3         0            1         0          3  \n",
       "4         0            1         0          4  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading the Cattle testing Dataset .csv file\n",
    "tr=pd.read_csv(\"testing.csv\")\n",
    "\n",
    "#Using inbuilt function replace in pandas for replacing the values\n",
    "\n",
    "tr.replace({'prognosis':{'mastitis':0,'blackleg':1,'bloat':2,'coccidiosis':3,'cryptosporidiosis':4,\n",
    "'displaced_abomasum':5,'gut_worms':6,'listeriosis':7,'liver_fluke':8,'necrotic_enteritis':9,'peri_weaning_diarrhoea':10,\n",
    "'rift_valley_fever':11,'rumen_acidosis':12,\n",
    "'traumatic_reticulitis':13,'calf_diphtheria':14,'foot_rot':15,'foot_and_mouth':16,'ragwort_poisoning':17,'wooden_tongue':18,'infectious_bovine_rhinotracheitis':19,\n",
    "'acetonaemia':20,'fatty_liver_syndrome':21,'calf_pneumonia':22,'schmallen_berg_virus':23,'trypanosomosis':24,'fog_fever':25}},inplace=True)\n",
    "tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "flD7u4M9qs8r"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-3de5d0542a9c>:10: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nGraphRow, nGraphPerRow, i + 1)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2400x12544 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPerColumnDistribution(tr, 10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "DmsqJe1sqs8r"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotScatterMatrix(tr, 20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "Bc-_RXfjqs8s"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    anorexia  abdominal_pain  anaemia  abortions  acetone  aggression  \\\n",
      "0          0               0        0          0        0           0   \n",
      "1          1               0        0          0        0           0   \n",
      "2          0               0        0          0        0           0   \n",
      "3          0               0        0          0        0           0   \n",
      "4          0               0        0          0        0           0   \n",
      "5          0               0        0          0        0           0   \n",
      "6          0               1        0          0        0           0   \n",
      "7          0               0        0          1        0           0   \n",
      "8          0               0        1          0        0           0   \n",
      "9          0               0        0          0        0           0   \n",
      "10         0               0        0          0        0           0   \n",
      "11         1               0        0          1        0           0   \n",
      "12         0               0        0          0        0           0   \n",
      "13         0               1        0          0        0           0   \n",
      "14         0               0        0          0        0           0   \n",
      "15         1               0        0          0        0           0   \n",
      "16         0               0        0          0        0           0   \n",
      "17         0               1        0          0        0           0   \n",
      "18         0               0        0          0        0           0   \n",
      "19         0               0        0          1        0           0   \n",
      "20         0               0        0          0        1           1   \n",
      "21         0               0        0          0        0           0   \n",
      "22         0               0        0          0        0           0   \n",
      "23         1               0        0          0        0           0   \n",
      "24         0               0        1          1        0           0   \n",
      "25         0               0        0          0        0           0   \n",
      "\n",
      "    arthrogyposis  ankylosis  anxiety  bellowing  ...  udder_swelling  \\\n",
      "0               0          0        0          0  ...               1   \n",
      "1               0          0        0          0  ...               0   \n",
      "2               0          0        0          1  ...               0   \n",
      "3               0          0        0          0  ...               0   \n",
      "4               0          0        0          0  ...               0   \n",
      "5               0          0        0          0  ...               0   \n",
      "6               0          0        0          0  ...               0   \n",
      "7               0          0        0          0  ...               0   \n",
      "8               0          0        0          0  ...               0   \n",
      "9               0          0        0          0  ...               0   \n",
      "10              0          0        0          0  ...               0   \n",
      "11              0          0        0          0  ...               0   \n",
      "12              0          0        0          0  ...               0   \n",
      "13              0          0        0          0  ...               0   \n",
      "14              0          0        0          0  ...               0   \n",
      "15              0          0        0          0  ...               0   \n",
      "16              0          0        0          0  ...               0   \n",
      "17              0          0        0          0  ...               0   \n",
      "18              0          0        0          0  ...               0   \n",
      "19              0          0        0          0  ...               0   \n",
      "20              0          0        0          0  ...               0   \n",
      "21              0          0        0          0  ...               0   \n",
      "22              0          0        0          0  ...               0   \n",
      "23              1          1        0          0  ...               0   \n",
      "24              0          0        0          0  ...               0   \n",
      "25              0          0        1          0  ...               0   \n",
      "\n",
      "    udder_heat  udder_hardeness  udder_redness  udder_pain  \\\n",
      "0            1                1              1           1   \n",
      "1            0                0              0           0   \n",
      "2            0                0              0           0   \n",
      "3            0                0              0           0   \n",
      "4            0                0              0           0   \n",
      "5            0                0              0           0   \n",
      "6            0                0              0           0   \n",
      "7            0                0              0           0   \n",
      "8            0                0              0           0   \n",
      "9            0                0              0           0   \n",
      "10           0                0              0           0   \n",
      "11           0                0              0           0   \n",
      "12           0                0              0           0   \n",
      "13           0                0              0           0   \n",
      "14           0                0              0           0   \n",
      "15           0                0              0           0   \n",
      "16           0                0              0           0   \n",
      "17           0                0              0           0   \n",
      "18           0                0              0           0   \n",
      "19           0                0              0           0   \n",
      "20           0                0              0           0   \n",
      "21           0                0              0           0   \n",
      "22           0                0              0           0   \n",
      "23           0                0              0           0   \n",
      "24           0                0              0           0   \n",
      "25           0                0              0           0   \n",
      "\n",
      "    unwillingness_to_move  ulcers  vomiting  weight_loss  weakness  \n",
      "0                       0       0         0            0         0  \n",
      "1                       1       0         0            0         0  \n",
      "2                       0       0         0            0         0  \n",
      "3                       0       0         0            1         0  \n",
      "4                       0       0         0            1         0  \n",
      "5                       0       0         0            0         0  \n",
      "6                       0       0         0            1         0  \n",
      "7                       0       0         0            0         0  \n",
      "8                       0       0         0            1         0  \n",
      "9                       0       1         0            0         0  \n",
      "10                      0       0         0            1         0  \n",
      "11                      0       0         1            0         1  \n",
      "12                      0       0         0            1         0  \n",
      "13                      0       0         0            0         0  \n",
      "14                      0       1         0            0         0  \n",
      "15                      0       0         0            0         0  \n",
      "16                      0       0         0            1         0  \n",
      "17                      0       0         0            0         0  \n",
      "18                      0       1         0            1         0  \n",
      "19                      0       0         0            0         0  \n",
      "20                      0       0         0            1         0  \n",
      "21                      0       0         0            0         0  \n",
      "22                      0       0         0            0         0  \n",
      "23                      0       0         0            0         0  \n",
      "24                      0       0         0            1         0  \n",
      "25                      0       0         0            0         0  \n",
      "\n",
      "[26 rows x 92 columns]\n"
     ]
    }
   ],
   "source": [
    "X_test= tr[l1]\n",
    "y_test = tr[[\"prognosis\"]]\n",
    "np.ravel(y_test)\n",
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "HxeX0ytiqs8s"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    prognosis\n",
      "0           0\n",
      "1           1\n",
      "2           2\n",
      "3           3\n",
      "4           4\n",
      "5           5\n",
      "6           6\n",
      "7           7\n",
      "8           8\n",
      "9           9\n",
      "10         10\n",
      "11         11\n",
      "12         12\n",
      "13         13\n",
      "14         14\n",
      "15         15\n",
      "16         16\n",
      "17         17\n",
      "18         18\n",
      "19         19\n",
      "20         20\n",
      "21         21\n",
      "22         22\n",
      "23         23\n",
      "24         24\n",
      "25         25\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pcVPt9dSqs8s"
   },
   "source": [
    "**To build the precision of the model, we utilized three distinctive algorithms which are as per the following**\n",
    "* Decision Tree algorithm\n",
    "* Random Forest algorithm\n",
    "* Naive bayes algorithm algorithm\n",
    "* K-Nearest neighbour algorithm.\n",
    "\n",
    "Project LINK:- **Github:**https://github.com/thyagarajank/Cattle-disease-prediction-using-Machine-Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "l1vaUnsOqs8t"
   },
   "outputs": [],
   "source": [
    "#list1 = DF['prognosis'].unique()\n",
    "def scatterplt(disea):\n",
    "    x = ((DF.loc[disea]).sum())#total sum of symptom reported for given disease\n",
    "    x.drop(x[x==0].index,inplace=True)#droping symptoms with values 0\n",
    "    print(x.values)\n",
    "    y = x.keys()#storing nameof symptoms in y\n",
    "    print(len(x))\n",
    "    print(len(y))\n",
    "    plt.title(disea)\n",
    "    plt.scatter(y,x.values)\n",
    "    plt.show()\n",
    "\n",
    "def scatterinp(sym1,sym2,sym3,sym4,sym5):\n",
    "    x = [sym1,sym2,sym3,sym4,sym5]#storing input symptoms in y\n",
    "    y = [0,0,0,0,0]#creating and giving values to the input symptoms\n",
    "    if(sym1!='Select Here'):\n",
    "        y[0]=1\n",
    "    if(sym2!='Select Here'):\n",
    "        y[1]=1\n",
    "    if(sym3!='Select Here'):\n",
    "        y[2]=1\n",
    "    if(sym4!='Select Here'):\n",
    "        y[3]=1\n",
    "    if(sym5!='Select Here'):\n",
    "        y[4]=1\n",
    "    print(x)\n",
    "    print(y)\n",
    "    plt.scatter(x,y)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lvcbHzLQqs8t"
   },
   "source": [
    "# Decision Tree Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "vOcgO_EZqs8t"
   },
   "outputs": [],
   "source": [
    "root = Tk()\n",
    "pred1=StringVar()\n",
    "def DecisionTree():\n",
    "    if len(NameEn.get()) == 0:\n",
    "        pred1.set(\" \")\n",
    "        comp=messagebox.askokcancel(\"System\",\"Kindly Fill the Name\")\n",
    "        if comp:\n",
    "            root.mainloop()\n",
    "    elif((Symptom1.get()==\"Select Here\") or (Symptom2.get()==\"Select Here\")):\n",
    "        pred1.set(\" \")\n",
    "        sym=messagebox.askokcancel(\"System\",\"Kindly Fill atleast first two Symptoms\")\n",
    "        if sym:\n",
    "            root.mainloop()\n",
    "    else:\n",
    "        from sklearn import tree\n",
    "\n",
    "        clf3 = tree.DecisionTreeClassifier() \n",
    "        clf3 = clf3.fit(X,y)\n",
    "\n",
    "        from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "        y_pred=clf3.predict(X_test)\n",
    "        print(\"Decision Tree\")\n",
    "        print(\"Accuracy\")\n",
    "        print(accuracy_score(y_test, y_pred))\n",
    "        print(accuracy_score(y_test, y_pred,normalize=False))\n",
    "        print(\"Confusion matrix\")\n",
    "        conf_matrix=confusion_matrix(y_test,y_pred)\n",
    "        print(conf_matrix)\n",
    "\n",
    "        psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()]\n",
    "\n",
    "        for k in range(0,len(l1)):\n",
    "            for z in psymptoms:\n",
    "                if(z==l1[k]):\n",
    "                    l2[k]=1\n",
    "\n",
    "        inputtest = [l2]\n",
    "        predict = clf3.predict(inputtest)\n",
    "        predicted=predict[0]\n",
    "\n",
    "        h='no'\n",
    "        for a in range(0,len(disease)):\n",
    "            if(predicted == a):\n",
    "                h='yes'\n",
    "                break\n",
    "\n",
    "    \n",
    "        if (h=='yes'):\n",
    "            pred1.set(\" \")\n",
    "            pred1.set(disease[a])\n",
    "        else:\n",
    "            pred1.set(\" \")\n",
    "            pred1.set(\"Not Found\")\n",
    "        #Creating the database if not exists named as database.db and creating table if not exists named as DecisionTree using sqlite3 \n",
    "        import sqlite3 \n",
    "        conn = sqlite3.connect('database.db') \n",
    "        c = conn.cursor() \n",
    "        c.execute(\"CREATE TABLE IF NOT EXISTS DecisionTree(Name StringVar,Symtom1 StringVar,Symtom2 StringVar,Symtom3 StringVar,Symtom4 TEXT,Symtom5 TEXT,Disease StringVar)\")\n",
    "        c.execute(\"INSERT INTO DecisionTree(Name,Symtom1,Symtom2,Symtom3,Symtom4,Symtom5,Disease) VALUES(?,?,?,?,?,?,?)\",(NameEn.get(),Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get(),pred1.get()))\n",
    "        conn.commit()  \n",
    "        c.close() \n",
    "        conn.close()\n",
    "        \n",
    "        #printing scatter plot of input symptoms\n",
    "        #printing scatter plot of disease predicted vs its symptoms\n",
    "        scatterinp(Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get())\n",
    "        scatterplt(pred1.get())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "frQgnUAOqs8u"
   },
   "source": [
    "# Random Forest Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PFnr00l4qs8u"
   },
   "outputs": [],
   "source": [
    "pred2=StringVar()\n",
    "def randomforest():\n",
    "    if len(NameEn.get()) == 0:\n",
    "        pred1.set(\" \")\n",
    "        comp=messagebox.askokcancel(\"System\",\"Kindly Fill the Name\")\n",
    "        if comp:\n",
    "            root.mainloop()\n",
    "    elif((Symptom1.get()==\"Select Here\") or (Symptom2.get()==\"Select Here\")):\n",
    "        pred1.set(\" \")\n",
    "        sym=messagebox.askokcancel(\"System\",\"Kindly Fill atleast first two Symptoms\")\n",
    "        if sym:\n",
    "            root.mainloop()\n",
    "    else:\n",
    "        from sklearn.ensemble import RandomForestClassifier\n",
    "        clf4 = RandomForestClassifier(n_estimators=100)\n",
    "        clf4 = clf4.fit(X,np.ravel(y))\n",
    "\n",
    "        # calculating accuracy \n",
    "        from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "        y_pred=clf4.predict(X_test)\n",
    "        print(\"Random Forest\")\n",
    "        print(\"Accuracy\")\n",
    "        print(accuracy_score(y_test, y_pred))\n",
    "        print(accuracy_score(y_test, y_pred,normalize=False))\n",
    "        print(\"Confusion matrix\")\n",
    "        conf_matrix=confusion_matrix(y_test,y_pred)\n",
    "        print(conf_matrix)\n",
    "    \n",
    "        psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()]\n",
    "\n",
    "        for k in range(0,len(l1)):\n",
    "            for z in psymptoms:\n",
    "                if(z==l1[k]):\n",
    "                    l2[k]=1\n",
    "\n",
    "        inputtest = [l2]\n",
    "        predict = clf4.predict(inputtest)\n",
    "        predicted=predict[0]\n",
    "\n",
    "        h='no'\n",
    "        for a in range(0,len(disease)):\n",
    "            if(predicted == a):\n",
    "                h='yes'\n",
    "                break\n",
    "        if (h=='yes'):\n",
    "            pred2.set(\" \")\n",
    "            pred2.set(disease[a])\n",
    "        else:\n",
    "            pred2.set(\" \")\n",
    "            pred2.set(\"Not Found\")\n",
    "         #Creating the database if not exists named as database.db and creating table if not exists named as RandomForest using sqlite3\n",
    "        import sqlite3 \n",
    "        conn = sqlite3.connect('database.db') \n",
    "        c = conn.cursor() \n",
    "        c.execute(\"CREATE TABLE IF NOT EXISTS RandomForest(Name StringVar,Symtom1 StringVar,Symtom2 StringVar,Symtom3 StringVar,Symtom4 TEXT,Symtom5 TEXT,Disease StringVar)\")\n",
    "        c.execute(\"INSERT INTO RandomForest(Name,Symtom1,Symtom2,Symtom3,Symtom4,Symtom5,Disease) VALUES(?,?,?,?,?,?,?)\",(NameEn.get(),Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get(),pred2.get()))\n",
    "        conn.commit()  \n",
    "        c.close() \n",
    "        conn.close()\n",
    "        #printing scatter plot of disease predicted vs its symptoms\n",
    "        scatterplt(pred2.get())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ng-3_ewsqs8u"
   },
   "source": [
    "# K-NearestNeighbour Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "kbPo9QKsqs8v"
   },
   "outputs": [],
   "source": [
    "pred4=StringVar()\n",
    "def KNN():\n",
    "    if len(NameEn.get()) == 0:\n",
    "        pred1.set(\" \")\n",
    "        comp=messagebox.askokcancel(\"System\",\"Kindly Fill the Name\")\n",
    "        if comp:\n",
    "            root.mainloop()\n",
    "    elif((Symptom1.get()==\"Select Here\") or (Symptom2.get()==\"Select Here\")):\n",
    "        pred1.set(\" \")\n",
    "        sym=messagebox.askokcancel(\"System\",\"Kindly Fill atleast first two Symptoms\")\n",
    "        if sym:\n",
    "            root.mainloop()\n",
    "    else:\n",
    "        from sklearn.neighbors import KNeighborsClassifier\n",
    "        knn=KNeighborsClassifier(n_neighbors=5,metric='minkowski',p=2)\n",
    "        knn=knn.fit(X,np.ravel(y))\n",
    "    \n",
    "        from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "        y_pred=knn.predict(X_test)\n",
    "        print(\"kNearest Neighbour\")\n",
    "        print(\"Accuracy\")\n",
    "        print(accuracy_score(y_test, y_pred))\n",
    "        print(accuracy_score(y_test, y_pred,normalize=False))\n",
    "        print(\"Confusion matrix\")\n",
    "        conf_matrix=confusion_matrix(y_test,y_pred)\n",
    "        print(conf_matrix)\n",
    "\n",
    "        psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()]\n",
    "\n",
    "        for k in range(0,len(l1)):\n",
    "            for z in psymptoms:\n",
    "                if(z==l1[k]):\n",
    "                    l2[k]=1\n",
    "\n",
    "        inputtest = [l2]\n",
    "        predict = knn.predict(inputtest)\n",
    "        predicted=predict[0]\n",
    "\n",
    "        h='no'\n",
    "        for a in range(0,len(disease)):\n",
    "            if(predicted == a):\n",
    "                h='yes'\n",
    "                break\n",
    "\n",
    "\n",
    "        if (h=='yes'):\n",
    "            pred4.set(\" \")\n",
    "            pred4.set(disease[a])\n",
    "        else:\n",
    "            pred4.set(\" \")\n",
    "            pred4.set(\"Not Found\")\n",
    "         #Creating the database if not exists named as database.db and creating table if not exists named as KNearestNeighbour using sqlite3   \n",
    "        import sqlite3 \n",
    "        conn = sqlite3.connect('database.db') \n",
    "        c = conn.cursor() \n",
    "        c.execute(\"CREATE TABLE IF NOT EXISTS KNearestNeighbour(Name StringVar,Symtom1 StringVar,Symtom2 StringVar,Symtom3 StringVar,Symtom4 TEXT,Symtom5 TEXT,Disease StringVar)\")\n",
    "        c.execute(\"INSERT INTO KNearestNeighbour(Name,Symtom1,Symtom2,Symtom3,Symtom4,Symtom5,Disease) VALUES(?,?,?,?,?,?,?)\",(NameEn.get(),Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get(),pred4.get()))\n",
    "        conn.commit()  \n",
    "        c.close() \n",
    "        conn.close()\n",
    "        #printing scatter plot of disease predicted vs its symptoms\n",
    "        \n",
    "        scatterplt(pred4.get())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XkoWdHUoqs8v"
   },
   "source": [
    "# Naive Bayes Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "IZTUlhXOqs8v"
   },
   "outputs": [],
   "source": [
    "pred3=StringVar()\n",
    "def NaiveBayes():\n",
    "    if len(NameEn.get()) == 0:\n",
    "        pred1.set(\" \")\n",
    "        comp=messagebox.askokcancel(\"System\",\"Kindly Fill the Name\")\n",
    "        if comp:\n",
    "            root.mainloop()\n",
    "    elif((Symptom1.get()==\"Select Here\") or (Symptom2.get()==\"Select Here\")):\n",
    "        pred1.set(\" \")\n",
    "        sym=messagebox.askokcancel(\"System\",\"Kindly Fill atleast first two Symptoms\")\n",
    "        if sym:\n",
    "            root.mainloop()\n",
    "    else:\n",
    "        from sklearn.naive_bayes import GaussianNB\n",
    "        gnb = GaussianNB()\n",
    "        gnb=gnb.fit(X,np.ravel(y))\n",
    "\n",
    "        from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "        y_pred=gnb.predict(X_test)\n",
    "        print(\"Naive Bayes\")\n",
    "        print(\"Accuracy\")\n",
    "        print(accuracy_score(y_test, y_pred))\n",
    "        print(accuracy_score(y_test, y_pred,normalize=False))\n",
    "        print(\"Confusion matrix\")\n",
    "        conf_matrix=confusion_matrix(y_test,y_pred)\n",
    "        print(conf_matrix)\n",
    "\n",
    "        psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()]\n",
    "        for k in range(0,len(l1)):\n",
    "            for z in psymptoms:\n",
    "                if(z==l1[k]):\n",
    "                    l2[k]=1\n",
    "\n",
    "        inputtest = [l2]\n",
    "        predict = gnb.predict(inputtest)\n",
    "        predicted=predict[0]\n",
    "\n",
    "        h='no'\n",
    "        for a in range(0,len(disease)):\n",
    "            if(predicted == a):\n",
    "                h='yes'\n",
    "                break\n",
    "        if (h=='yes'):\n",
    "            pred3.set(\" \")\n",
    "            pred3.set(disease[a])\n",
    "        else:\n",
    "            pred3.set(\" \")\n",
    "            pred3.set(\"Not Found\")\n",
    "         #Creating the database if not exists named as database.db and creating table if not exists named as NaiveBayes using sqlite3\n",
    "        import sqlite3 \n",
    "        conn = sqlite3.connect('database.db') \n",
    "        c = conn.cursor() \n",
    "        c.execute(\"CREATE TABLE IF NOT EXISTS NaiveBayes(Name StringVar,Symtom1 StringVar,Symtom2 StringVar,Symtom3 StringVar,Symtom4 TEXT,Symtom5 TEXT,Disease StringVar)\")\n",
    "        c.execute(\"INSERT INTO NaiveBayes(Name,Symtom1,Symtom2,Symtom3,Symtom4,Symtom5,Disease) VALUES(?,?,?,?,?,?,?)\",(NameEn.get(),Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get(),pred3.get()))\n",
    "        conn.commit()  \n",
    "        c.close() \n",
    "        conn.close()\n",
    "        #printing scatter plot of disease predicted vs its symptoms\n",
    "        scatterplt(pred3.get())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3GpvATGxqs8v"
   },
   "source": [
    "# Building Graphical User Interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "pH80wkbSqs8w"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a root window with a different background and title\n",
    "root.configure(background='lightblue')  # Changed the background to light blue\n",
    "root.title('CattleCare AI: Cattle Health Diagnostistics')\n",
    "root.resizable(True, True)  # Prevent window resizing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "xi6PuVbPqs8w"
   },
   "outputs": [],
   "source": [
    "Symptom1 = StringVar()\n",
    "Symptom1.set(\"Select Here\")\n",
    "\n",
    "Symptom2 = StringVar()\n",
    "Symptom2.set(\"Select Here\")\n",
    "\n",
    "Symptom3 = StringVar()\n",
    "Symptom3.set(\"Select Here\")\n",
    "\n",
    "Symptom4 = StringVar()\n",
    "Symptom4.set(\"Select Here\")\n",
    "\n",
    "Symptom5 = StringVar()\n",
    "Symptom5.set(\"Select Here\")\n",
    "Name = StringVar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "dKseVQ_yqs8w"
   },
   "outputs": [],
   "source": [
    "prev_win=None\n",
    "def Reset():\n",
    "    global prev_win\n",
    "\n",
    "    Symptom1.set(\"Select Here\")\n",
    "    Symptom2.set(\"Select Here\")\n",
    "    Symptom3.set(\"Select Here\")\n",
    "    Symptom4.set(\"Select Here\")\n",
    "    Symptom5.set(\"Select Here\")\n",
    "    NameEn.delete(first=0,last=100)\n",
    "    pred1.set(\" \")\n",
    "    pred2.set(\" \")\n",
    "    pred3.set(\" \")\n",
    "    pred4.set(\" \")\n",
    "    try:\n",
    "        prev_win.destroy()\n",
    "        prev_win=None\n",
    "    except AttributeError:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "ymZcr5zwqs8w"
   },
   "outputs": [],
   "source": [
    "from tkinter import messagebox\n",
    "def Exit():\n",
    "    qExit=messagebox.askyesno(\"System\",\"Do you want to exit the system\")\n",
    "    \n",
    "    if qExit:\n",
    "        root.destroy()\n",
    "        exit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "w1ALHIuYqs8w"
   },
   "outputs": [],
   "source": [
    "# Heading with more stylistic changes\n",
    "w2 = Label(root, text=\"CattleCare AI: Cattle Health Diagnostics\", fg=\"#2C3E50\", bg=\"#ECF0F1\")\n",
    "w2.config(font=(\"Arial\", 35, \"bold\"), relief=\"raised\", bd=3)\n",
    "w2.grid(row=1, column=0, columnspan=2, pady=20, padx=50)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "jhVtBxX0qs8x"
   },
   "outputs": [],
   "source": [
    "#Label for the name\n",
    "NameLb = Label(root, text=\"Cattle ID/Name *\", fg=\"Black\", bg=\"White\")\n",
    "NameLb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "NameLb.grid(row=6, column=0, pady=15, sticky=W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "i3R3w-jlqs8x"
   },
   "outputs": [],
   "source": [
    "# Creating Labels for the symptoms\n",
    "label_style = {\"fg\": \"Black\", \"bg\": \"White\", \"font\": (\"Times\", 15, \"bold italic\")}\n",
    "\n",
    "S1Lb = Label(root, text=\"Symptom 1 *\", **label_style)\n",
    "S1Lb.grid(row=7, column=0, pady=10, sticky=W)\n",
    "\n",
    "S2Lb = Label(root, text=\"Symptom 2 *\", **label_style)\n",
    "S2Lb.grid(row=8, column=0, pady=10, sticky=W)\n",
    "\n",
    "S3Lb = Label(root, text=\"Symptom 3 *\", **label_style)\n",
    "S3Lb.grid(row=9, column=0, pady=10, sticky=W)\n",
    "\n",
    "S4Lb = Label(root, text=\"Symptom 4 *\", **label_style)\n",
    "S4Lb.grid(row=10, column=0, pady=10, sticky=W)\n",
    "\n",
    "S5Lb = Label(root, text=\"Symptom 5\", **label_style)\n",
    "S5Lb.grid(row=11, column=0, pady=10, sticky=W)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "EtLghXjRqs8x"
   },
   "outputs": [],
   "source": [
    "#Labels for the different algorithms\n",
    "lrLb = Label(root, text=\"DecisionTree\", fg=\"white\", bg=\"deepskyblue\", width = 20)\n",
    "lrLb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "lrLb.grid(row=15, column=0, pady=10,sticky=W)\n",
    "\n",
    "destreeLb = Label(root, text=\"RandomForest\", fg=\"White\", bg=\"deepskyblue\", width = 20)\n",
    "destreeLb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "destreeLb.grid(row=17, column=0, pady=10, sticky=W)\n",
    "\n",
    "\n",
    "knnLb = Label(root, text=\"Naive Bayes\", fg=\"White\", bg=\"deepskyblue\", width = 20)\n",
    "knnLb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "knnLb.grid(row=19, column=0, pady=10, sticky=W)\n",
    "OPTIONS = sorted(l1)\n",
    "\n",
    "ranfLb = Label(root, text=\"K-Nearest Neighbour\", fg=\"White\", bg=\"deepskyblue\", width = 20)\n",
    "ranfLb.config(font=(\"Times\",15,\"bold italic\"))\n",
    "ranfLb.grid(row=21, column=0, pady=10, sticky=W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "gRr-N4HMqs8x"
   },
   "outputs": [],
   "source": [
    "# Taking name as input from user\n",
    "NameEn = Entry(root, textvariable=Name)\n",
    "NameEn.grid(row=6, column=1)\n",
    "\n",
    "# Taking Symptoms as input from the user (text fields instead of dropdowns)\n",
    "Symptom1En = Entry(root, textvariable=Symptom1)\n",
    "Symptom1En.grid(row=7, column=1)\n",
    "\n",
    "Symptom2En = Entry(root, textvariable=Symptom2)\n",
    "Symptom2En.grid(row=8, column=1)\n",
    "\n",
    "Symptom3En = Entry(root, textvariable=Symptom3)\n",
    "Symptom3En.grid(row=9, column=1)\n",
    "\n",
    "Symptom4En = Entry(root, textvariable=Symptom4)\n",
    "Symptom4En.grid(row=10, column=1)\n",
    "\n",
    "Symptom5En = Entry(root, textvariable=Symptom5)\n",
    "Symptom5En.grid(row=11, column=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "AR0MYCzHqs8x"
   },
   "outputs": [],
   "source": [
    "from tkinter import Button\n",
    "\n",
    "# Define common button properties for a uniform, modern look\n",
    "button_properties = {\n",
    "    \"font\": (\"Arial\", 12, \"bold\"),\n",
    "    \"fg\": \"white\",\n",
    "    \"relief\": \"flat\",  # No border for a clean look\n",
    "    \"bd\": 0,  # Remove borders\n",
    "    \"width\": 20,\n",
    "    \"height\": 2,\n",
    "    \"activeforeground\": \"white\",  # Text color when button is active\n",
    "    \"activebackground\": \"#34495E\",  # Dark background when hovered\n",
    "    \"highlightthickness\": 0,  # Remove any highlight around the button\n",
    "}\n",
    "\n",
    "# Define background colors with a subtle gradient effect for each button\n",
    "button_colors = {\n",
    "    \"Prediction 1\": (\"#2980B9\", \"#3498DB\"),  # Blue gradient\n",
    "    \"Prediction 2\": (\"#9B59B6\", \"#8E44AD\"),  # Purple gradient\n",
    "    \"Prediction 3\": (\"#2ECC71\", \"#27AE60\"),  # Green gradient\n",
    "    \"Prediction 4\": (\"#F39C12\", \"#E67E22\"),  # Yellow gradient\n",
    "    \"Reset Inputs\": (\"#E74C3C\", \"#C0392B\"),   # Red gradient\n",
    "    \"Exit System\": (\"#C0392B\", \"#D35400\")     # Exit red gradient\n",
    "}\n",
    "\n",
    "# Function to set button color (gradient effect) and smooth transition on hover\n",
    "def set_button_gradient(button, start_color, end_color):\n",
    "    # Set the initial button color\n",
    "    button.config(bg=start_color)\n",
    "    \n",
    "    # Change button color on hover\n",
    "    button.bind(\"<Enter>\", lambda event: button.config(bg=end_color))\n",
    "    button.bind(\"<Leave>\", lambda event: button.config(bg=start_color))\n",
    "\n",
    "# Create the buttons with a more refined design\n",
    "dst = Button(root, text=\"Prediction 1\", command=DecisionTree, **button_properties)\n",
    "set_button_gradient(dst, *button_colors[\"Prediction 1\"])\n",
    "dst.grid(row=6, column=3, padx=20, pady=10)\n",
    "\n",
    "rnf = Button(root, text=\"Prediction 2\", command=randomforest, **button_properties)\n",
    "set_button_gradient(rnf, *button_colors[\"Prediction 2\"])\n",
    "rnf.grid(row=7, column=3, padx=20, pady=10)\n",
    "\n",
    "lr = Button(root, text=\"Prediction 4\", command=NaiveBayes, **button_properties)\n",
    "set_button_gradient(lr, *button_colors[\"Prediction 4\"])\n",
    "lr.grid(row=9, column=3, padx=20, pady=10)\n",
    "\n",
    "kn = Button(root, text=\"Prediction 3\", command=KNN, **button_properties)\n",
    "set_button_gradient(kn, *button_colors[\"Prediction 3\"])\n",
    "kn.grid(row=8, column=3, padx=20, pady=10)\n",
    "\n",
    "rs = Button(root, text=\"Reset Inputs\", command=Reset, **button_properties)\n",
    "set_button_gradient(rs, *button_colors[\"Reset Inputs\"])\n",
    "rs.grid(row=10, column=3, padx=20, pady=10)\n",
    "\n",
    "ex = Button(root, text=\"Exit System\", command=Exit, **button_properties)\n",
    "set_button_gradient(ex, *button_colors[\"Exit System\"])\n",
    "ex.grid(row=11, column=3, padx=20, pady=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "IIfTIdZSqs8x"
   },
   "outputs": [],
   "source": [
    "# Define common label properties in a dictionary to avoid repetition\n",
    "label_properties = {\n",
    "    \"font\": (\"Times\", 15, \"bold italic\"),\n",
    "    \"height\": 1,\n",
    "    \"width\": 40,\n",
    "    \"fg\": \"white\",\n",
    "    \"relief\": \"sunken\"\n",
    "}\n",
    "\n",
    "# Create the labels with unique background colors and text values for different algorithms\n",
    "t1 = Label(root, **label_properties, text=\"Decision Tree\", bg=\"deepskyblue\", textvariable=pred1)\n",
    "t1.grid(row=15, column=1, padx=10)\n",
    "\n",
    "t2 = Label(root, **label_properties, text=\"Random Forest\", bg=\"Purple\", textvariable=pred2)\n",
    "t2.grid(row=17, column=1, padx=10)\n",
    "\n",
    "t3 = Label(root, **label_properties, text=\"Naive Bayes\", bg=\"royalblue\", textvariable=pred3)\n",
    "t3.grid(row=21, column=1, padx=10)\n",
    "\n",
    "t4 = Label(root, **label_properties, text=\"K-Nearest Neighbour\", bg=\"lightseagreen\", textvariable=pred4)\n",
    "t4.grid(row=19, column=1, padx=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "3Cyu-bzHyMrQ"
   },
   "outputs": [],
   "source": [
    "#Mainloop the application is ready to run\n",
    "root.mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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