app.py

from fastapi import FastAPI
from asgiref.wsgi import WsgiToAsgi
from flask import Flask, request, render_template
import pickle, os
from fastapi.middleware.wsgi import WSGIMiddleware

# Initialize Flask app
flask_app = Flask(__name__)

def validasi_inputan(form_data):
    errors = {}

    if not form_data.get("XT_1"):
        errors["XT_1"] = "XT_1 tidak boleh kosong."
    else:
        try:
            XT_1 = float(form_data.get("XT_1"))
        except ValueError:
            errors["XT_1"] = "XT_1 harus berupa angka."

    if not form_data.get("XT_2"):
        errors["XT_2"] = "XT_2 tidak boleh kosong."
    else:
        try:
            XT_2 = float(form_data.get("XT_2"))
        except ValueError:
            errors["XT_2"] = "XT_2 harus berupa angka."

    if not form_data.get("XT_3"):
        errors["XT_3"] = "XT_3 tidak boleh kosong."
    else:
        try:
            XT_3 = float(form_data.get("XT_3"))
        except ValueError:
            errors["XT_3"] = "XT_3 harus berupa angka."

    return errors

def validate_data(record):
    errors = {}
    if record["XT_1"] < 5000 or record["XT_1"] > 40000:
        errors["XT_1"] = "XT_1 harus diantara 0 dan 1.0"

    if record["XT_2"] < 5000 or record["XT_2"] > 40000:
        errors["XT_2"] = "XT_2 harus diantara 0.0 dan 1.0"

    if record["XT_3"] < 5000 or record["XT_3"] > 40000:
        errors["XT_3"] = "XT_3 harus diantara 0.0 dan 1.0"

    return errors

# Load models
linear_model_load = pickle.load(open('best_bagging_model.sav', 'rb'))
scaler_load = pickle.load(open('scaler.sav', 'rb'))

# Flask route
@flask_app.route("/", methods=["GET", "POST"])
def index():
    prediction = None
    errors = {}
    if request.method == "POST":
        # Validasi inputan tidak boleh kosong
        errors = validasi_inputan(request.form)
        if not errors:
            record = {
                "XT_1": float(request.form.get("XT_1")),
                "XT_2": float(request.form.get("XT_2")),
                "XT_3": float(request.form.get("XT_3")),
            }

            errors = validate_data(record)
            if not errors:
                # Data input untuk prediksi
                input_data = [
                    record["XT_1"],
                    record["XT_2"],
                    record["XT_3"],
                ]

                # Normalisasi input data
                input_data_normalized = scaler_load.transform([input_data])

                # Membuat prediksi dari model
                predicted_value_normalized = linear_model_load.predict(input_data_normalized)

                # Menyesuaikan bentuk data untuk inverse_transform
                predicted_value_normalized_full = [[predicted_value_normalized[0], 0, 0]]
                predicted_value_full = scaler_load.inverse_transform(predicted_value_normalized_full)

                # Mengambil elemen prediksi pertama sebagai hasil akhir
                prediction = int(predicted_value_full[0][0])

    return render_template('index.html', prediction=prediction, errors=errors, record=request.form)


# FastAPI app to mount Flask app
app = FastAPI()

# Mount Flask app inside FastAPI using WSGIMiddleware
app.mount("/", WSGIMiddleware(flask_app))