"""
Tugas 3: Contoh Implementasi Berbagai Arsitektur Model Sesuai Karakteristik Data
"""

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Conv2D, Dense, Embedding, Flatten, LSTM, MaxPooling2D
from tensorflow.keras.models import Sequential
from xgboost import XGBClassifier


def build_models():
    print("=" * 60)
    print("TUGAS 3: CONTOH ARSITEKTUR MODEL SELECTION")
    print("=" * 60)

    # (a) Prediksi Stok Barang dari Deret Waktu (LSTM)
    # Input shape: (samples, time_steps=12, features=1)
    model_a = Sequential([
        LSTM(32, input_shape=(12, 1)),
        Dense(1)
    ], name="LSTM_Stock_Forecasting")
    model_a.compile(optimizer='adam', loss='mse')
    print("\n[Case A] Model LSTM untuk Prediksi Stok Barang (Time Series):")
    model_a.summary()

    # (b) Klasifikasi Foto Produk Rusak vs Normal (CNN)
    # Input shape contoh citra RGB 128x128
    model_b = Sequential([
        Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)),
        MaxPooling2D((2, 2)),
        Flatten(),
        Dense(64, activation='relu'),
        Dense(1, activation='sigmoid')  # binary: rusak vs normal
    ], name="CNN_Defect_Classification")
    model_b.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    print("\n[Case B] Model CNN untuk Klasifikasi Foto Produk (Computer Vision):")
    model_b.summary()

    # (c) Analisis Sentimen Ulasan Play Store (LSTM + Embedding)
    vocab_size = 5000
    max_length = 100
    model_c = Sequential([
        Embedding(input_dim=vocab_size, output_dim=64, input_length=max_length),
        LSTM(64),
        Dense(32, activation='relu'),
        Dense(1, activation='sigmoid')  # binary: positif vs negatif
    ], name="LSTM_Sentiment_Analysis")
    model_c.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    print("\n[Case C] Model LSTM + Embedding untuk Analisis Sentimen (NLP):")
    model_c.summary()

    # (d) Prediksi Churn Pelanggan Tabular (ML Klasik - XGBoost)
    # X_train memiliki dimensi (n_samples, 15)
    model_d = XGBClassifier(
        n_estimators=100,
        max_depth=4,
        learning_rate=0.1,
        random_state=42
    )
    print("\n[Case D] Model XGBoost untuk Prediksi Churn (Tabular 15 fitur):")
    print(model_d)


if __name__ == "__main__":
    build_models()