File size: 1,622 Bytes
d1fb0cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | 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
## import gridsearchcv regressor and standard scaler pickle
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") |