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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))
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