src/inverse_model_forward.py

import streamlit as st
import pandas as pd
import numpy as np
import joblib
import os

# --- KONFIGURASI PATH ---
BASE_MODEL_FOLDER = r"C:\Dokumen\One To Many_17_10_2025\MMBTU\DASHBOARD\One To Many\MODEL CHECKPOINT FOR INVERSE MODEL"
AVAILABLE_PRODUCTS = ["BMR BASE", "CKP BASE", "CKR BASE", "CMR BASE", "MORIGRO BASE"]
INPUT_FEATURES = [
    "D101330TT", "D102260TIC_CV", "D102265TIC_PV",
    "D102265TIC_CV", "D102266TIC", "D101264FTSCL"
]
@st.cache_resource(show_spinner="Memuat model...")
def load_model_artifacts(product_name, base_folder):
    file_name = f"model_checkpoint_xgb_{product_name}.joblib"
    model_path = os.path.join(base_folder, file_name)
    if not os.path.exists(model_path):
        st.error(f"File model tidak ditemukan untuk **{product_name}** di: **{model_path}**")
        return None, None, None, None

    try:
        deployment_bundle = joblib.load(model_path)
        
        # Mengambil artefak sesuai dengan struktur kode awal Anda
        model = deployment_bundle.get('model')
        poly_transformer = deployment_bundle.get('poly_transformer')
        input_features = deployment_bundle.get('input_features')
        poly_feature_names = deployment_bundle.get('poly_feature_names')
        
        # Validasi sederhana
        if model is None or poly_transformer is None or input_features is None or poly_feature_names is None:
             st.error(f"Salah satu artefak (model, poly_transformer, input_features, poly_feature_names) hilang dalam file joblib **{product_name}**.")
             return None, None, None, None
             
        return model, poly_transformer, input_features, poly_feature_names
    
    except Exception as e:
        st.error(f"Gagal memuat atau membaca file joblib. Error: {e}")
        return None, None, None, None

# --- TAMPILAN (UI) STREAMLIT ---
st.set_page_config(page_title="Dashboard Prediksi GAS MMBTU", layout="wide")
with st.sidebar:
    st.header("⚙️ Konfigurasi Model")
    # Dropdown untuk memilih produk
    selected_product = st.selectbox(
        "Pilih Produk Target:", 
        AVAILABLE_PRODUCTS, 
        index=AVAILABLE_PRODUCTS.index("CKR BASE") if "CKR BASE" in AVAILABLE_PRODUCTS else 0
    )
    st.markdown(f"**Folder Model:** `{BASE_MODEL_FOLDER}`")
st.title(f"🔥 Dashboard Prediksi GAS MMBTU: **{selected_product}**")
st.markdown("Dashboard ini menggunakan model **XGBoost** dan transformasi **Polinomial**.")
st.markdown("---")
model, poly_transformer, input_features_loaded, poly_feature_names = load_model_artifacts(
    selected_product, BASE_MODEL_FOLDER
)

if model is None:
    st.stop() # Berhenti jika model gagal dimuat
    
# Cek apakah fitur input konsisten
if set(input_features_loaded) != set(INPUT_FEATURES):
    st.warning("Fitur Input yang dimuat dari joblib berbeda dengan daftar fitur default. Menggunakan fitur dari joblib.")
    INPUT_FEATURES = input_features_loaded
st.subheader("🧪 Masukkan Nilai Input Mentah")
st.markdown("Harap masukkan nilai numerik untuk 6 fitur di bawah:")

# Membuat kolom input untuk 6 fitur
cols = st.columns(len(INPUT_FEATURES))
user_raw_data = {}
for i, feature in enumerate(INPUT_FEATURES):
    # Menggunakan nilai default untuk contoh
    default_value = 0.0 
    if feature == "D101330TT": default_value = 95.0
    elif feature == "D102260TIC_CV": default_value = 45.0
    elif feature == "D102265TIC_PV": default_value = 185.0
    elif feature == "D102265TIC_CV": default_value = 17.0
    elif feature == "D102266TIC": default_value = 16.0
    elif feature == "D101264FTSCL": default_value = 3800.0
    
    # Input nilai per fitur
    user_raw_data[feature] = cols[i].number_input(
        feature, 
        value=default_value, 
        format="%.4f", 
        key=f"input_{feature}"
    )

st.markdown("---")

# Tombol prediksi
if st.button("🔮 Prediksi GAS MMBTU Sekarang", type="primary", use_container_width=True):
    
    # Membuat DataFrame dari input mentah (sesuai urutan fitur input)
    sim_input_df = pd.DataFrame([user_raw_data])[INPUT_FEATURES]

    st.subheader("⚙️ Proses Transformasi & Prediksi")

    # 1. Transformasi Polinomial
    with st.spinner("1. Menerapkan transformasi polinomial..."):
        transformed_input_np = poly_transformer.transform(sim_input_df)
        transformed_input_df = pd.DataFrame(
            transformed_input_np, 
            columns=poly_feature_names,
            index=sim_input_df.index
        )
    # 2. Prediksi
    with st.spinner("2. Melakukan prediksi dengan model..."):
        predictions = model.predict(transformed_input_df)
        prediksi_final = predictions[0]
    
    st.markdown("### ✅ Hasil Prediksi")
    st.metric(
        f"Prediksi Kebutuhan **GAS MMBTU** untuk {selected_product}", 
        f"{prediksi_final:.6f}MMBTU"
    )
st.markdown("---")