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app.py
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app.py
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import pickle
import json
from flask import Flask, request,app,jsonify,url_for, render_template
import numpy as np
import pandas as pd
app= Flask(__name__)
#Load model
regmodel= pickle.load(open('regmodel.pkl','rb'))
scalar= pickle.load(open('scaling.pkl','rb'))
@app.route('/')
def home():
return render_template('home.html')
@app.route('/predict_api', methods=['POST'])
def predict_api():
##get data
data= request.json['data']
print(data)
##get list and reshape
new_data=scalar.transform(np.array(list(data.values())).reshape(1,-1))
output= regmodel.predict(new_data)
return jsonify(output[0])
@app.route('/predict',methods=['POST'])
def predict():
data=[float(x) for x in request.form.values()]
final_input=scalar.transform(np.array(data).reshape(1,-1))
print(final_input)
output=regmodel.predict(final_input)[0]
return render_template("home.html",prediction_text="The House price prediction is {}".format(output))
if __name__=="__main__":
app.run(debug= True)