application.py

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

from sklearn.preprocessing import StandardScaler

application = Flask(__name__)
app = application

# Route for home page
@app.route('/')
def index():
    return render_template("index.html")


@app.route('/predict', 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"),
            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()
        results = predict_pipeline.predict(pred_df)
        
        return render_template("home.html", results=results[0])
    

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