application.py

import pickle
from flask import Flask, request, render_template
import numpy as np
import pandas as pd
import os

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

from src.exception import CustomException

application = Flask(__name__)
app = application
#Route for home page
@app.route('/')
def index():
    return render_template('index.html')

@app.route('/predict_datapoint', 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('race_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= request.form.get('reading_score'),
            writing_score= request.form.get('writing_score')
        )
        pred_df = data.get_data_as_dataframe()
        print(pred_df)
        
        predict_pipeline = PredictPipeline()
        results = predict_pipeline.predict(pred_df)
        return render_template('home.html', results=results[0])
    
if __name__ == "__main__":
    port = int(os.environ.get("PORT", 5000))
    app.run(host="0.0.0.0", port=port)