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‎ClassExamples/HeartDiseaseFlask_ManpreetSakshiJaideep/MakeModel.py‎

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import pandas as pd
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import numpy as np
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import pickle
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from sklearn.neighbors import KNeighborsClassifier
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url = 'https://raw.githubusercontent.com/sarwansingh/Python/master/ClassExamples/data/HeartDisease_cardio_data.csv'
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df = pd.read_csv(url , sep = ";")
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print("reading file " )
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df['age']=df['age']/365
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print("cleaning data ")
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df =df.drop( ['id','cholesterol', 'gluc'] , axis=1)
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#create new column with standard values
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df=df.assign(heartrate='60-200')
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# assigning the values
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df.loc[df['age'] >= 20 ,'heartrate'] = '100-170'
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df.loc[df['age'] >= 30 ,'heartrate'] = '95-165'
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df.loc[df['age'] >= 40 ,'heartrate'] = '90-155'
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df.loc[df['age'] >= 50 ,'heartrate'] = '85-145'
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df.loc[df['age'] >= 60 ,'heartrate'] = '80-140'
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df.loc[df['age'] >= 70 ,'heartrate'] = '75-130'
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#codification of column heartrate
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df['heartrate'] = df['heartrate'].replace(['85-145', '90-155', '80-140', '95-165', '100-170' ],['1', '2', '3', '4', '5'])
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df=df.dropna()
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#convert the columns to int
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df['weight']=df.weight.astype(int)
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df['age']=df.age.astype(int)
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df['heartrate']=df.heartrate.astype(int)
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#realigning the columns
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df = df[['age' , 'gender', 'height','weight', 'ap_hi', 'ap_lo', 'smoke', 'alco', 'active', 'heartrate', 'cardio']]
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print("making model" )
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#creating model
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knnmodel = KNeighborsClassifier()
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X = df.iloc[:,0:10 ]
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y = df.iloc[:, -1]
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#fit the model
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knnmodel.fit(X,y)
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#predict
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print ("using model for prediction" )
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res = knnmodel.predict([[48, 2, 169, 82, 150, 100, 0, 0, 1, 4 ]])
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print( res )
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res= knnmodel.predict([[52 , 2, 168, 76, 120, 80, 1, 0, 1, 4]])
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print( res)
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print("prediction successful " )
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print( " saving model " )
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pickle.dump(knnmodel, open('knnmodel.pkl', 'wb') , protocol=2)
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‎ClassExamples/HeartDiseaseFlask_ManpreetSakshiJaideep/heroku/Procfile‎

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web: gunicorn app:app

‎ClassExamples/HeartDiseaseFlask_ManpreetSakshiJaideep/heroku/app.py‎

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#app.py
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from flask import Flask,request, url_for, redirect, render_template
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import pickle
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import numpy as np
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app = Flask(__name__)
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model=pickle.load(open('knnmodel.pkl','rb'))
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@app.route('/')
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def home():
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return render_template("index.html")
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@app.route('/predict',methods=['POST','GET'])
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def predict():
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# receive the values send by user in three text boxes thru request object -> requesst.form.values()
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int_features = [int(x) for x in request.form.values()]
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final_features = [np.array(int_features)]
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#print(final_features)
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#final_features = [[52 , 2, 168, 76, 120, 80, 1, 0, 1, 4]]
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#[[48, 2, 169, 82, 150, 100, 0, 0, 1, 4 ]]
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prediction=model.predict_proba(final_features)
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output='{0:.{1}f}'.format(prediction[0][1], 2)
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return render_template('index.html', pred='Heart Disease probability is : {}'.format(output))
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#return render_template('index.html', pred= final_features)
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if __name__ == '__main__':
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app.run(debug=False)

‎ClassExamples/HeartDiseaseFlask_ManpreetSakshiJaideep/heroku/knnmodel.pkl‎

12.7 MB
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‎ClassExamples/HeartDiseaseFlask_ManpreetSakshiJaideep/heroku/requirements.txt‎

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Flask==1.1.2
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gunicorn==19.9.0
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itsdangerous==1.1.0
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Jinja2==2.11.2
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MarkupSafe==1.1.1
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Werkzeug==1.0.1
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numpy==1.16.6
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scipy==1.2.3
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scikit-learn>=0.18
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matplotlib>=1.4.3
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pandas>=0.19

‎ClassExamples/HeartDiseaseFlask_ManpreetSakshiJaideep/heroku/templates/index.html‎

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<!DOCTYPE html>
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<html lang="en">
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<head>
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<title> Heart Disease Prediction </title>
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</head>
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<body>
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<h2>Heart Disease Prediction</h2>
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<h3>Predict the probability of Cardio-Vascular Disease </h3>
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<form action='/predict' method="post">
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<table>
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<tr><td> Age
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<td> <input type=number name="age" placeholder="Age (in years) " >
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</tr>
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<tr><td> Gender
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<td> <input name="gender" type=radio value=1> Male <input name="gender" type=radio value=2> Female
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</tr>
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<tr><td> Height
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<td> <input type=number name="height" placeholder="Height (in cms) " >
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</tr>
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<tr><td> Weight
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<td> <input type=number name="weight" placeholder="Weight (in Kgs) " >
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</tr>
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<tr><td> BP (High)
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<td> <input type=number name="ap_hi" placeholder="BP (High) " >
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</tr>
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<tr><td> BP (Low)
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<td> <input type=number name="ap_lo" placeholder="BP (Low) " >
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</tr>
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<tr><td> Smoker
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<td> <input name="smoker" type=radio value=1> Yes <input name="smoker" type=radio value=0> No
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</tr>
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<tr><td> Alcoholic
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<td> <input name="alcohol" type=radio value=1> Yes <input name="alcohol" type=radio value=0> No </tr>
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<tr><td> Exercise Daily
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<td> <input name="active" type=radio value=1> Yes <input name="active" type=radio value=0> No
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</tr><tr><td> Heart Rate
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<td> <select name = "heartrate">
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<option value =1> 85-145
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<option value =2> 90-155
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<option value =3> 80-140
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<option value =4> 95-165
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<option value =5> 100-170
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</select>
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</tr>
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<tr>
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<td colspan=2 align=center><br><button type="submit">Predict Probability</button>
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</tr>
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</table>
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</form>
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<br> {{pred}}<br>
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</body>
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</html>
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‎ClassExamples/HeartDiseaseFlask_ManpreetSakshiJaideep/heroku_v2/Procfile‎

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web: gunicorn app:app

‎ClassExamples/HeartDiseaseFlask_ManpreetSakshiJaideep/heroku_v2/app.py‎

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#app.py
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from flask import Flask,request, url_for, redirect, render_template
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import pickle
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import numpy as np
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app = Flask(__name__)
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model=pickle.load(open('knnmodel.pkl','rb'))
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@app.route('/')
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def home():
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return render_template("index.html")
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@app.route('/predict',methods=['POST','GET'])
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def predict():
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# receive the values send by user in three text boxes thru request object -> requesst.form.values()
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int_features = [int(x) for x in request.form.values()]
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final_features = [np.array(int_features)]
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#print(final_features)
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#final_features = [[52 , 2, 168, 76, 120, 80, 1, 0, 1, 4]]
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#[[48, 2, 169, 82, 150, 100, 0, 0, 1, 4 ]]
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prediction=model.predict_proba(final_features)
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output='{0:.{1}f}'.format(prediction[0][1], 2)
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return render_template('index.html', pred='Heart Disease probability is : {}'.format(output))
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#return render_template('index.html', pred= final_features)
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if __name__ == '__main__':
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app.run(debug=False)

‎ClassExamples/HeartDiseaseFlask_ManpreetSakshiJaideep/heroku_v2/knnmodel.pkl‎

12.7 MB
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‎ClassExamples/HeartDiseaseFlask_ManpreetSakshiJaideep/heroku_v2/requirements.txt‎

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Flask==1.1.2
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gunicorn==19.9.0
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itsdangerous==1.1.0
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Jinja2==2.11.2
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MarkupSafe==1.1.1
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Werkzeug==1.0.1
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numpy==1.16.6
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scipy==1.2.3
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scikit-learn>=0.18
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matplotlib>=1.4.3
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pandas>=0.19

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