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 PredictPipeline, CustomData

application = Flask(__name__)

app = application

## ROute for a home page

@app.route('/')
def index():
    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=float(request.form.get('reading_score')),
            writing_score=float(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])

@app.route('/eda')
def eda():
    return render_template('eda.html')


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