import pickle
from flask import Flask, request, app, jsonify, url_for, render_template
import numpy as np
import pandas as pd
import json

app = Flask(__name__)
## Load the 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():
    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))).astype(float)
    output = regmodel.predict(new_data)
    print(output.tolist()[0])
    return jsonify(output.tolist()[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=f"The house price prediction is {output}")


if __name__ == "__main__":
    app.run(debug=True)
    





# ######