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app.py
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app.py
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import pickle
import json
import os
from flask import Flask, request, app, jsonify, url_for, render_template
import numpy as np
import pandas as pd
app = Flask(__name__)
#Load the models for both regression and scaling
scalar = pickle.load(open('scaling.pkl', 'rb'))
regmodel = pickle.load(open('regmodel.pkl', 'rb'))
@app.route('/')
def home():
return render_template('home.html')
@app.route('/predict_api', methods=['POST'])
def predict_api():
data = request.json['data']
print(data)
print(np.array(list(data.values())).reshape(1,-1))
new_data = scalar.transform(np.array(list(data.values())).reshape(1,-1))
output = regmodel.predict(new_data)
print(output[0])
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)