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import numpy as np
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from flask import Flask, request, jsonify, render_template
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import pickle
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from sklearn.preprocessing import normalize
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app = Flask(__name__)
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model = pickle.load(open('model.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'])
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def predict():
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'''
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For rendering results on HTML GUI
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'''
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Age=float(request.form['Age'])
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CigsPerDay=float(request.form['CigsPerDay'])
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Cholestrol=float(request.form['Cholestrol'])
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SysBP=float(request.form['SysBP'])
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DIaBP=float(request.form['DIaBP'])
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BMI=float(request.form['BMI'])
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HeartRate=float(request.form['HeartRate'])
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GlucoseLevel=float(request.form['GlucoseLevel'])
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Gender=float(request.form['Gender'])
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BpMedication=float(request.form['BpMedication'])
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PrevalentStroke=float(request.form['PrevalentStroke'])
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Smoker=float(request.form['Smoker'])
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list_to_be_normalised=np.array([ Age,CigsPerDay, Cholestrol, SysBP,DIaBP, BMI,HeartRate,GlucoseLevel]).reshape(1,-1)
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normalized = normalize(list_to_be_normalised)
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boolean = [Gender,BpMedication,PrevalentStroke,Smoker]
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final_features = np.append(normalized,boolean).reshape(1, -1)
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print(final_features)
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prediction = model.predict(final_features)
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if prediction == 1:
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return render_template('problem.html')
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else:
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return render_template('healthy.html')
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if __name__ == "__main__":
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app.run(debug=True) |
