app.py

import os
from flask import Flask, request, render_template
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from src.pipeline.predict_pipeline import CustomData, PredictPipeline

application = Flask(__name__)
app = application

@app.route('/predictdata', methods=['GET', 'POST'])
def predict_datapoint():
    if request.method == 'POST':
        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'))

        data = CustomData(
            gender=gender,
            race_ethnicity=race_ethnicity,
            parental_level_of_education=parental_level_of_education,
            lunch=lunch,
            test_preparation_course=test_preparation_course,
            reading_score=reading_score,
            writing_score=writing_score
        )
        pred_df = data.get_data_as_data_frame()
        print(pred_df)

        predict_pipeline = PredictPipeline()
        results = f"{predict_pipeline.predict(pred_df)[0]:.2f}"
        print("After Prediction")

        return render_template(
            'index.html',
            results=results,
            gender=gender,
            race_ethnicity=race_ethnicity,
            parental_level_of_education=parental_level_of_education,
            lunch=lunch,
            test_preparation_course=test_preparation_course,
            reading_score=reading_score,
            writing_score=writing_score
        )
    else:
        return render_template('index.html')

if __name__ == '__main__':
    port = int(os.environ.get('PORT', 5000))
    app.run(debug=True, host='0.0.0.0', port=port)