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 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('writing_score')),
            writing_score = float(request.form.get('reading_score'))

        )
        pred_df = data.get_data_as_data_frame()
        print(pred_df)
        print("Before Prediction")

        predict_pipeline = PredictPipeline()
        print("Mid Prediction")
        results = predict_pipeline.predict(pred_df)
        print("after Prediction")
        return render_template('home.html',results=results[0])


if __name__=="__main__":
    app.run(host="0.0.0.0", port=8000)