app.py

from flask import Flask, request, render_template, make_response
from flask_cors import CORS
import numpy as np
import pandas as pd

from sklearn.preprocessing import StandardScaler
from src.pipeline.predict_pipeline import CustomData, PredictPipeline

application = Flask(__name__)
CORS(application)

app = application

# Route for a home page
@app.route('/')
def index():
    response = make_response(render_template('index.html'))
    response.headers['Set-Cookie'] = 'my_cookie=value; SameSite=None; Secure'
    return response  # redirects to the html file in templates

@app.route('/predictdata', methods=['GET', 'POST'])
def predict_datapoint():
    if request.method == 'GET':
        return render_template("home.html")
    else:
        data = CustomData(
            gender=request.form.get('gender'),
            race_ethnicity=request.form.get('ethnicity'),
            parental_level_of_education=request.form.get('parental_level_of_education'),
            lunch=request.form.get('lunch'),
            test_preparation_course=request.form.get('test_preparation_course'),
            reading_score=float(request.form.get('reading_score')),
            writing_score=float(request.form.get('writing_score'))
        )

        pred_df = data.get_data_as_data_frame()
        print(pred_df)

        predict_pipeline = PredictPipeline()
        result = predict_pipeline.predict(pred_df)
        return render_template('home.html', result=result[0])

if __name__ == "__main__":
    app.run(host="0.0.0.0", debug=True)