| import pickle
|
| from flask import Flask,request,jsonify,render_template
|
| import numpy as np
|
| import pandas as pd
|
| from sklearn.preprocessing import StandardScaler
|
|
|
| application = Flask(__name__)
|
| app=application
|
|
|
|
|
| grid_search_cv=pickle.load(open('Models/gridsearchcv.pkl','rb'))
|
| standard_scaler=pickle.load(open('Models/scaler.pkl','rb'))
|
|
|
| @app.route("/")
|
| def index():
|
| return render_template('index.html')
|
|
|
| @app.route('/predictdata',methods=['GET','POST'])
|
| def predict_datapoint():
|
| if request.method=="POST":
|
| Age=float(request.form.get('Age'))
|
| sex = float(request.form.get('sex'))
|
| cp = float(request.form.get('cp'))
|
| trestbps = float(request.form.get('trestbps'))
|
| chol = float(request.form.get('chol'))
|
| fbs = float(request.form.get('fbs'))
|
| restecg = float(request.form.get('restecg'))
|
| thalach = float(request.form.get('thalach'))
|
| exang = float(request.form.get('exang'))
|
| oldpeak = float(request.form.get('oldpeak'))
|
| slope = float(request.form.get('slope'))
|
| ca = float(request.form.get('ca'))
|
| thal = float(request.form.get('thal'))
|
|
|
|
|
|
|
| new_data_scaled=standard_scaler.transform([[Age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal]])
|
| result=grid_search_cv.predict(new_data_scaled)
|
|
|
| return render_template('home.html',results=result[0])
|
|
|
|
|
| else:
|
| return render_template('home.html')
|
|
|
| if __name__=="__main__":
|
| app.run(host="0.0.0.0") |
