app.py

from flask import Flask, request, render_template
import numpy as np
import pandas as pd
from src.pipeline.predict_pipeline import CustomData, PredictPipeline
from sklearn.preprocessing import StandardScaler

application = Flask(__name__) # entry point

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('race/ethnicity'),
            parental_level_of_education=request.form.get('parental level of education'),
            lunch=request.form.get('lunch'),
            reading_score=request.form.get('reading score'),
            writing_score=request.form.get('writing score'),
            test_preparation_course=request.form.get('test preparation course'),
        )
        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])
    

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