app.py

from flask import Flask, request, render_template
import numpy as np 
import pandas as pd 

from src.mlproject.pipelines.prediction_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=request.form.get('reading_score'),
            writing_score=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",port=8000)