app.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


@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('writing_score')),
            writing_score=float(request.form.get('reading_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',debug=True)
    # port=5000