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


@app.route('/')
def index():
    return render_template('index.html')


@app.route('/predictions', 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=5001)