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‎ClassExamples/ON47/StudentPassFail_Flask/.ipynb_checkpoints/LearningFlask-checkpoint.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Basic setup \n",
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"from flask import Flask\n",
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"\n",
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"#instantiate the object of Flask - app\n",
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"app = Flask(__name__)\n",
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"\n",
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"#the object app will handle incoming request from the web/browser\n",
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"# the method inside app object is route ( parameter is the path)\n",
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"# and define the method attached with that - in our example we have home()\n",
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"@app.route('/')\n",
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"def home() : \n",
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" return \"<h1> Welcome Flask </h1>\" \n",
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"\n",
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"# the default module/program name is __main__\n",
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"if __name__ == '__main__':\n",
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" app.run(debug=False)\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"#adding multiple pages or multiple routes\n",
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"from flask import Flask\n",
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"\n",
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"#instantiate the object of Flask - app\n",
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"app = Flask(__name__)\n",
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"\n",
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"#the object app will handle incoming request from the web/browser\n",
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"# the method inside app object is route ( parameter is the path)\n",
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"# and define the method attached with that - in our example we have home()\n",
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"@app.route('/')\n",
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"def home() : \n",
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" return \"<h1> Welcome Flask </h1><br> Thank you <a href=/page2> Go to Page2 </a> \"\n",
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"\n",
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"@app.route('/page2')\n",
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"def showpage2() : \n",
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" return \"<h1> Welcome to Page2 of Website </h1><br> Deep Learning Champs \"\n",
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"\n",
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"# the default module/program name is __main__\n",
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"if __name__ == '__main__':\n",
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" app.run(debug=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#opening html page in flask\n",
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"\n",
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"from flask import Flask, render_template\n",
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"\n",
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"app = Flask(__name__)\n",
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"\n",
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"@app.route('/')\n",
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"def index():\n",
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" return render_template('first.html')\n",
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"\n",
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"if __name__ == '__main__':\n",
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" app.run()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎ClassExamples/ON47/StudentPassFail_Flask/.ipynb_checkpoints/StudentPassFail_Flask-checkpoint.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Building the ML Model "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"#import packages \n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.linear_model import LogisticRegression\n",
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" \n",
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"import pickle\n",
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"import warnings\n",
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1000\n",
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"[[ 7 27 1]\n",
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" [ 2 43 0]\n",
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" [ 7 26 1]\n",
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" [ 8 29 1]\n",
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" [ 3 42 0]]\n"
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]
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}
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],
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"source": [
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"#https://raw.githubusercontent.com/sarwansingh/Python/master/ClassExamples/data/student-pass-fail-data.csv\n",
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"data = pd.read_csv(\"student-pass-fail-data.csv\")\n",
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"data = np.array(data)\n",
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"print(len(data))\n",
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"print(data[:5])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Rows in X 1000 \n",
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"Rows in y 1000\n",
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"[[ 7 27]\n",
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" [ 2 43]\n",
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" [ 7 26]\n",
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" [ 8 29]\n",
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" [ 3 42]] [1 0 1 1 0]\n"
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]
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}
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],
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"source": [
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"# extract the input matrix from data in X\n",
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"X = data[:, 0:-1]\n",
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"# extract the labels in y \n",
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"y = data[:, -1]\n",
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"#casting the entire data into int\n",
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"y = y.astype('int')\n",
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"X = X.astype('int')\n",
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"\n",
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"print(\"Rows in X \" , len(X) , \" \\nRows in y \", len(y))\n",
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"print(X[:5],y[:5])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"LogisticRegression()"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#build the logisticRegression model\n",
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"log_reg = LogisticRegression()\n",
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"# fit the model with data\n",
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"log_reg.fit(X,y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[7, 27]]\n",
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"[[ 0.12276493 0.87723507]]\n",
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"[1]\n",
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"[0]\n"
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]
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}
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],
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"source": [
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"#testing the build model \n",
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"test=[[ 7,27]]\n",
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"print(test)\n",
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"\n",
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"print(log_reg.predict_proba(test))\n",
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"print(log_reg.predict(test)) # pass\n",
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"print(log_reg.predict([[2,43]])) #fail"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 9.99990993e-01 9.00739994e-06]]\n",
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"[1]\n"
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]
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}
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],
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"source": [
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"# Saving model to disk\n",
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"#Serialization - saving the state (in memory) to file (on disk)\n",
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"pickle.dump(log_reg,open('model.pkl','wb'))\n",
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"\n",
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"# Loading model to compare the results\n",
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"#Deserialization - reteriving the file (on disk) to memory (RAM)\n",
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"model=pickle.load(open('model.pkl','rb'))\n",
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"print(model.predict_proba([[7,43]]))\n",
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"print(model.predict([[7,27]]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## use the model and display in Flask"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"#app.py\n",
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"from flask import Flask,request, url_for, redirect, render_template\n",
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"import pickle\n",
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"import numpy as np\n",
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"\n",
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"app = Flask(__name__)\n",
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"model=pickle.load(open('model.pkl','rb'))\n",
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"\n",
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"@app.route('/')\n",
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"def home():\n",
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" return render_template(\"index.html\")\n",
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"\n",
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"@app.route('/predict',methods=['POST','GET'])\n",
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"def predict():\n",
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" # receive the values send by user in three text boxes thru request object -> requesst.form.values()\n",
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" \n",
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" int_features = [int(x) for x in request.form.values()]\n",
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" final_features = [np.array(int_features)]\n",
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" #print(final_features)\n",
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" #prediction = model.predict(final_features)\n",
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" #output = round(prediction[0], 2)\n",
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" \n",
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" prediction=model.predict_proba(final_features)\n",
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" output='{0:.{1}f}'.format(prediction[0][1], 2)\n",
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" \n",
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" #print(output )\n",
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"\n",
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" return render_template('index.html', pred='Student passing probability is : {}'.format(output))\n",
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"\n",
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"if __name__ == '__main__':\n",
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" app.run(debug=False)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"outputs": [],
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],
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"metadata": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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