application.py

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

# Route for home page
@app.route('/')
def infex():
    return render_template('index.html')

@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=request.form.get('reading_score'),
            writing_score=request.form.get('reading_Score'),
        )
        predict_df = data.get_data_as_data_frame()
        print(predict_df)

        predict_pipeline = PredictPipeline()

        results = predict_pipeline.predict(predict_df)

        return render_template('home.html', results=results[0])

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