from flask import Flask, render_template, request
import joblib
import pandas as pd

app = Flask(__name__)

model = joblib.load("churn_model.pkl")
model_features = joblib.load("model_features.pkl")

value_map = {
    "Aydan aya": "Month-to-month",
    "1 yıllık": "One year",
    "2 yıllık": "Two year",
    "Elektronik çek": "Electronic check",
    "Posta çeki": "Mailed check",
    "Banka havalesi (otomatik)": "Bank transfer (automatic)",
    "Kredi kartı (otomatik)": "Credit card (automatic)",
    "Hayır": "No",
    "Evet": "Yes",
    "Yok": "No internet service",
    "Telefon hizmeti yok": "No phone service",
    "Fiber optik": "Fiber optic"
}

@app.route("/", methods=["GET", "POST"])
def index():
    result = None

    if request.method == "POST":
        form = request.form
        input_dict = {}

        # Sayısal alanlar
        tenure = float(form.get("tenure"))
        monthly = float(form.get("MonthlyCharges"))
        total = float(form.get("TotalCharges"))

        # Temel sayısal değişkenler
        input_dict["tenure"] = tenure
        input_dict["PhoneService"] = form.get("PhoneService") == "Evet"
        input_dict["avg_charge_per_month"] = total / tenure if tenure > 0 else 0
        input_dict["charge_ratio"] = total / (monthly * tenure) if monthly > 0 and tenure > 0 else 1

        # tenure_bin
        tenure_label = "0-12" if tenure <= 12 else "12-24" if tenure <= 24 else "24+"
        for bin_label in ["0-12", "12-24", "24+"]:
            input_dict[f"tenure_bin_{bin_label}"] = (tenure_label == bin_label)

        # is_long_term_contract
        contract_value = value_map.get(form.get("Contract"), form.get("Contract"))
        input_dict["is_long_term_contract"] = contract_value in ["One year", "Two year"]

        # One-hot kategorik değişkenler
        categorical_fields = [
            "gender", "SeniorCitizen", "Partner", "Dependents", "PaperlessBilling",
            "MultipleLines", "InternetService", "OnlineSecurity", "OnlineBackup",
            "DeviceProtection", "TechSupport", "StreamingTV", "StreamingMovies",
            "Contract", "PaymentMethod"
        ]

        for field in categorical_fields:
            raw_value = form.get(field)
            mapped_value = value_map.get(raw_value, raw_value)
            for col in model_features:
                if col.startswith(f"{field}_"):
                    input_dict[col] = (col == f"{field}_{mapped_value}")

        # Eksik kalan tüm feature'lar tamamlanır
        for col in model_features:
            if col not in input_dict:
                input_dict[col] = 0 if col == "tenure" or "charge" in col or "avg" in col else False

        # DataFrame oluştur ve tahmin yap
        input_df = pd.DataFrame([[input_dict[col] for col in model_features]], columns=model_features)

        print("💬 MODELE GİDEN VERİLER:", flush=True)
        print(input_df.to_dict(orient="records")[0], flush=True)

        prediction = model.predict_proba(input_df)[0][1]
        score = round(prediction * 100, 2)
        if score >= 50:
            comment = "Müşteri Kaybedilebilir."
        else:
            comment = "Müşteri Kayıp Riski Taşımıyor."
        result = f"Churn Riski: %{score} — {comment}"


    return render_template("index.html", result=result)

if __name__ == "__main__":
    app.run(debug=False)