Model

model/.ipynb_checkpoints/prediction-checkpoint.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b95bcb5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "def predict(colour,select_person,react,select_image,sleeping_hours):    \n",
+    "    load_model=pickle.load(open(\"best_model.sav\", 'rb'))\n",
+    "    in_X=pd.read_csv('in_X.csv')\n",
+    "    col_list=['Purple', 'Dark Blue', 'Light Blue', 'Brown', 'Black', 'Green','Yellow', 'Orange', 'Grey']\n",
+    "    react_list=['happy','mid happy','un happy','low sad','angry','sad']\n",
+    "    selimg_list=['angry image','happy image','proud image','sad image','suicide image']\n",
+    "    in_X['perfercol_'+col_list[colour]]=1\n",
+    "    in_X['react_'+react_list[react]]=1\n",
+    "    in_X['sel_image_'+selimg_list[select_image]]=1\n",
+    "    in_X['sleeping hours']=sleeping_hours/12\n",
+    "    for i in range(3):\n",
+    "        in_X['person_category'+str(select_person[i]+1)]=in_X['person_category'+str(select_person[i]+1)]+1\n",
+    "    y_pred=load_model.predict(in_X)\n",
+    "    return(y_pred[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9c468731",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(colour=4,select_person=(0,1,1),react=1,select_image=2,sleeping_hours=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a6842280",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(colour=4,select_person=(3,3,3),react=0,select_image=1,sleeping_hours=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4d5f185a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(colour=5,select_person=(0,0,0),react=0,select_image=2,sleeping_hours=6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6862609",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

model/.ipynb_checkpoints/prediction1-checkpoint.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b95bcb5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "def predict(colour,select_person1,select_person2,select_person3,react,select_image,sleeping_hours):    \n",
+    "    load_model=pickle.load(open(\"best_model.sav\", 'rb'))\n",
+    "    in_X=pd.read_csv('in_X.csv')\n",
+    "    select_person=(select_person1,select_person2,select_person3)\n",
+    "    col_list=['Purple', 'Dark Blue', 'Light Blue', 'Brown', 'Black', 'Green','Yellow', 'Orange', 'Grey']\n",
+    "    react_list=['happy','mid happy','un happy','low sad','angry','sad']\n",
+    "    selimg_list=['angry image','happy image','proud image','sad image','suicide image']\n",
+    "    in_X['perfercol_'+col_list[colour]]=1\n",
+    "    in_X['react_'+react_list[react]]=1\n",
+    "    in_X['sel_image_'+selimg_list[select_image]]=1\n",
+    "    in_X['sleeping hours']=sleeping_hours/12\n",
+    "    for i in range(3):\n",
+    "        in_X['person_category'+str(select_person[i]+1)]=in_X['person_category'+str(select_person[i]+1)]+1\n",
+    "    y_pred=load_model.predict(in_X)\n",
+    "    return(y_pred[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9c468731",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(4,0,1,1,1,2,10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a6842280",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(4,3,3,3,0,1,10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4d5f185a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(5,0,0,0,0,2,6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a6862609",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(7,0,0,0,0,2,6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "1e3c521a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(5,3,3,3,5,4,6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "42974f85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: flask-ngrok in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (0.0.25)\n",
+      "Requirement already satisfied: requests in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from flask-ngrok) (2.26.0)\n",
+      "Requirement already satisfied: Flask>=0.8 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from flask-ngrok) (2.0.2)\n",
+      "Requirement already satisfied: itsdangerous>=2.0 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from Flask>=0.8->flask-ngrok) (2.0.1)\n",
+      "Requirement already satisfied: Werkzeug>=2.0 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from Flask>=0.8->flask-ngrok) (2.0.2)\n",
+      "Requirement already satisfied: Jinja2>=3.0 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from Flask>=0.8->flask-ngrok) (3.0.2)\n",
+      "Requirement already satisfied: click>=7.1.2 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from Flask>=0.8->flask-ngrok) (8.0.3)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->flask-ngrok) (2021.10.8)\n",
+      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->flask-ngrok) (2.0.7)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->flask-ngrok) (1.26.7)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->flask-ngrok) (3.2)\n",
+      "Requirement already satisfied: colorama in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from click>=7.1.2->Flask>=0.8->flask-ngrok) (0.4.4)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from Jinja2>=3.0->Flask>=0.8->flask-ngrok) (2.0.1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install flask-ngrok"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7c0caa04",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " * Serving Flask app '__main__' (lazy loading)\n",
+      " * Environment: production\n",
+      "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
+      "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
+      " * Debug mode: off\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n",
+      "Exception in thread Thread-8:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Pasindu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\threading.py\", line 973, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"C:\\Users\\Pasindu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\threading.py\", line 1286, in run\n",
+      "    self.function(*self.args, **self.kwargs)\n",
+      "  File \"C:\\Users\\Pasindu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\flask_ngrok.py\", line 70, in start_ngrok\n",
+      "    ngrok_address = _run_ngrok()\n",
+      "  File \"C:\\Users\\Pasindu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\flask_ngrok.py\", line 31, in _run_ngrok\n",
+      "    ngrok = subprocess.Popen([executable, 'http', '5000'])\n",
+      "  File \"C:\\Users\\Pasindu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\subprocess.py\", line 951, in __init__\n",
+      "    self._execute_child(args, executable, preexec_fn, close_fds,\n",
+      "  File \"C:\\Users\\Pasindu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\subprocess.py\", line 1420, in _execute_child\n",
+      "    hp, ht, pid, tid = _winapi.CreateProcess(executable, args,\n",
+      "OSError: [WinError 193] %1 is not a valid Win32 application\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from flask_ngrok import run_with_ngrok\n",
+    "\n",
+    "from flask import Flask,jsonify\n",
+    "app=Flask(__name__)\n",
+    "run_with_ngrok(app) #starts ngrok when the app is running\n",
+    "@app.route(\"/<int:color>/<int:person1>/<int:person2>/<int:person3>/<int:react>/<int:image>/<int:sleepingH>\")\n",
+    "def home(color,person1,person2,person3,react,image,sleepingH):\n",
+    "  p = []\n",
+    "  p +=[color,person1,person2,person3,react,image,sleepingH]\n",
+    "\n",
+    "  \n",
+    "                                      \n",
+    "  arr = np.array([p])\n",
+    "  predict =predict(arr)\n",
+    "  if predict == [0]:\n",
+    "    result = \"normal\"\n",
+    "  elif predict == [1]:\n",
+    "    result = \"mid mod\" \n",
+    "  elif predict == [2]:\n",
+    "    result = \"Severe\"\n",
+    "  else:\n",
+    "    result = \"extreame\"\n",
+    "\n",
+    "  return jsonify(result)\n",
+    "app.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d4524e2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

model/in_X.csv

+sleeping hours,perfercol_Black,perfercol_Brown,perfercol_Dark Blue,perfercol_Green,perfercol_Grey,perfercol_Light Blue,perfercol_Orange,perfercol_Purple,perfercol_Yellow,react_angry,react_happy,react_low sad,react_mid happy,react_sad,react_un happy,sel_image_angry image,sel_image_happy image,sel_image_proud image,sel_image_sad image,sel_image_suicide image,person_category1,person_category2,person_category3,person_category4
+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

model/info.txt

+color=['Purple'-0, 'Dark Blue'-1, 'Light Blue'-2, 'Brown'-3, 'Black'-4, 'Green'-5,'Yellow'-6, 'Orange'-7, 'Grey'-8]
+person=[["Mark Zuckerberg","Jack Ma", "Sharuk Khan","Malala Yousafzai","Mahathma Gandhi", "Nelson Mandela"]-0
+["Kusal Mendis", "Gotabaya Rajapaksha" , "Sakvithi Ranasinghe"]-1
+["Osama-bin-Laden", "Idi Amin", "Adolf Hitler"]-2
+["Sushant Singh Rajput", "Dasun Nishan", "Marilyn Monroe"]-3]
+react=['happy'-0,'mid happy'-1,'un happy'-2,'low sad'-3,'angry'-4,'sad'-5]
+select image=['angry image'-0,'happy image'-1,'proud image'-2,'sad image'-3,'suicide image'-4]
+sleep hours=number between 1-12
\ No newline at end of file

model/prediction.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b95bcb5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "def predict(colour,select_person,react,select_image,sleeping_hours):    \n",
+    "    load_model=pickle.load(open(\"best_model.sav\", 'rb'))\n",
+    "    in_X=pd.read_csv('in_X.csv')\n",
+    "    col_list=['Purple', 'Dark Blue', 'Light Blue', 'Brown', 'Black', 'Green','Yellow', 'Orange', 'Grey']\n",
+    "    react_list=['happy','mid happy','un happy','low sad','angry','sad']\n",
+    "    selimg_list=['angry image','happy image','proud image','sad image','suicide image']\n",
+    "    in_X['perfercol_'+col_list[colour]]=1\n",
+    "    in_X['react_'+react_list[react]]=1\n",
+    "    in_X['sel_image_'+selimg_list[select_image]]=1\n",
+    "    in_X['sleeping hours']=sleeping_hours/12\n",
+    "    for i in range(3):\n",
+    "        in_X['person_category'+str(select_person[i]+1)]=in_X['person_category'+str(select_person[i]+1)]+1\n",
+    "    y_pred=load_model.predict(in_X)\n",
+    "    return(y_pred[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9c468731",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(colour=4,select_person=(0,1,1),react=1,select_image=2,sleeping_hours=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a6842280",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(colour=4,select_person=(3,3,3),react=0,select_image=1,sleeping_hours=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4d5f185a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(colour=5,select_person=(0,0,0),react=0,select_image=2,sleeping_hours=6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6862609",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

model/prediction1.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b95bcb5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "def predict(colour,select_person1,select_person2,select_person3,react,select_image,sleeping_hours):    \n",
+    "    load_model=pickle.load(open(\"best_model.sav\", 'rb'))\n",
+    "    in_X=pd.read_csv('in_X.csv')\n",
+    "    select_person=(select_person1,select_person2,select_person3)\n",
+    "    col_list=['Purple', 'Dark Blue', 'Light Blue', 'Brown', 'Black', 'Green','Yellow', 'Orange', 'Grey']\n",
+    "    react_list=['happy','mid happy','un happy','low sad','angry','sad']\n",
+    "    selimg_list=['angry image','happy image','proud image','sad image','suicide image']\n",
+    "    in_X['perfercol_'+col_list[colour]]=1\n",
+    "    in_X['react_'+react_list[react]]=1\n",
+    "    in_X['sel_image_'+selimg_list[select_image]]=1\n",
+    "    in_X['sleeping hours']=sleeping_hours/12\n",
+    "    for i in range(3):\n",
+    "        in_X['person_category'+str(select_person[i]+1)]=in_X['person_category'+str(select_person[i]+1)]+1\n",
+    "    y_pred=load_model.predict(in_X)\n",
+    "    return(y_pred[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9c468731",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(4,0,1,1,1,2,10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a6842280",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(4,3,3,3,0,1,10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4d5f185a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(5,0,0,0,0,2,6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a6862609",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(7,0,0,0,0,2,6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1e3c521a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(5,3,3,3,5,4,6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "42974f85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: flask-ngrok in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (0.0.25)\n",
+      "Requirement already satisfied: Flask>=0.8 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from flask-ngrok) (2.0.2)\n",
+      "Requirement already satisfied: requests in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from flask-ngrok) (2.26.0)\n",
+      "Requirement already satisfied: click>=7.1.2 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from Flask>=0.8->flask-ngrok) (8.0.3)\n",
+      "Requirement already satisfied: Jinja2>=3.0 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from Flask>=0.8->flask-ngrok) (3.0.2)\n",
+      "Requirement already satisfied: Werkzeug>=2.0 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from Flask>=0.8->flask-ngrok) (2.0.2)\n",
+      "Requirement already satisfied: itsdangerous>=2.0 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from Flask>=0.8->flask-ngrok) (2.0.1)\n",
+      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->flask-ngrok) (2.0.7)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->flask-ngrok) (3.2)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->flask-ngrok) (2021.10.8)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->flask-ngrok) (1.26.7)\n",
+      "Requirement already satisfied: colorama in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from click>=7.1.2->Flask>=0.8->flask-ngrok) (0.4.4)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\pasindu\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from Jinja2>=3.0->Flask>=0.8->flask-ngrok) (2.0.1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install flask-ngrok"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c0caa04",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " * Serving Flask app '__main__' (lazy loading)\n",
+      " * Environment: production\n",
+      "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
+      "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
+      " * Debug mode: off\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n",
+      "Exception in thread Thread-13:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\Pasindu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\threading.py\", line 973, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"C:\\Users\\Pasindu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\threading.py\", line 1286, in run\n",
+      "    self.function(*self.args, **self.kwargs)\n",
+      "  File \"C:\\Users\\Pasindu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\flask_ngrok.py\", line 70, in start_ngrok\n",
+      "    ngrok_address = _run_ngrok()\n",
+      "  File \"C:\\Users\\Pasindu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\flask_ngrok.py\", line 31, in _run_ngrok\n",
+      "    ngrok = subprocess.Popen([executable, 'http', '5000'])\n",
+      "  File \"C:\\Users\\Pasindu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\subprocess.py\", line 951, in __init__\n",
+      "    self._execute_child(args, executable, preexec_fn, close_fds,\n",
+      "  File \"C:\\Users\\Pasindu\\AppData\\Local\\Programs\\Python\\Python39\\lib\\subprocess.py\", line 1420, in _execute_child\n",
+      "    hp, ht, pid, tid = _winapi.CreateProcess(executable, args,\n",
+      "OSError: [WinError 193] %1 is not a valid Win32 application\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from flask_ngrok import run_with_ngrok\n",
+    "\n",
+    "from flask import Flask,jsonify\n",
+    "app=Flask(__name__)\n",
+    "run_with_ngrok(app) #starts ngrok when the app is running\n",
+    "@app.route(\"/<int:color>/<int:person1>/<int:person2>/<int:person3>/<int:react>/<int:image>/<int:sleepingH>\")\n",
+    "def home(color,person1,person2,person3,react,image,sleepingH):\n",
+    "  p = []\n",
+    "  p +=[color,person1,person2,person3,react,image,sleepingH]\n",
+    "\n",
+    "  \n",
+    "                                      \n",
+    "  arr = p\n",
+    "  predict =predict(arr)\n",
+    "  if predict == [0]:\n",
+    "    result = \"normal\"\n",
+    "  elif predict == [1]:\n",
+    "    result = \"mid mod\" \n",
+    "  elif predict == [2]:\n",
+    "    result = \"Severe\"\n",
+    "  else:\n",
+    "    result = \"extreame\"\n",
+    "\n",
+    "  return jsonify(result)\n",
+    "app.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d4524e2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}