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 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_prepration_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', results=result[0])
    
if __name__=='__main__':
    app.run(host="0.0.0.0")