src/streamlit_app.py

import os
import json
import time
from datetime import datetime
import numpy as np
import pandas as pd
import streamlit as st
import matplotlib.pyplot as plt
import seaborn as sns
import joblib
import zipfile
import io
import gc

# ML imports
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.metrics import mean_squared_error, r2_score

# SHAP
import shap

# Optuna (used later)
import optuna
from sklearn.model_selection import cross_val_score, KFold
from sklearn.neural_network import MLPRegressor

# --- Safe defaults for Streamlit session state ---
defaults = {
    "llm_result": None,
    "automl_summary": {},
    "shap_recommendations": [],
    "hf_clicked": False,
    "hf_ran_once": False,
    "run_automl_clicked": False,
}
for k, v in defaults.items():
    st.session_state.setdefault(k, v)

if "llm_result" not in st.session_state:
    st.session_state["llm_result"] = None
if "automl_summary" not in st.session_state:
    st.session_state["automl_summary"] = {}
if "shap_recommendations" not in st.session_state:
    st.session_state["shap_recommendations"] = []
if "hf_clicked" not in st.session_state:
    st.session_state["hf_clicked"] = False

# -------------------------
# Config & paths
# -------------------------
st.set_page_config(page_title="Steel Authority of India Limited (MODEX)", layout="wide")
plt.style.use("seaborn-v0_8-muted")
sns.set_palette("muted")
sns.set_style("whitegrid")

LOG_DIR = "./logs"
os.makedirs(LOG_DIR, exist_ok=True)

# Permanent artifact filenames (never change)
CSV_PATH = os.path.join(LOG_DIR, "flatfile_universe_advanced.csv")
META_PATH = os.path.join(LOG_DIR, "feature_metadata_advanced.json")
ENSEMBLE_PATH = os.path.join(LOG_DIR, "ensemble_models.joblib")
LOG_PATH = os.path.join(LOG_DIR, "run_master.log")

# Simple logger that time-stamps inside one file
SESSION_STARTED = False

def log(msg: str):
    global SESSION_STARTED
    stamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    with open(LOG_PATH, "a", encoding="utf-8") as f:
        if not SESSION_STARTED:
            f.write("\n\n===== New Session Started at {} =====\n".format(stamp))
            SESSION_STARTED = True
        f.write(f"[{stamp}] {msg}\n")
    print(msg)

log("=== Streamlit session started ===")

if os.path.exists("/data"):
    st.sidebar.success(f" Using persistent storage | Logs directory: {LOG_DIR}")
else:
    st.sidebar.warning(f" Using ephemeral storage | Logs directory: {LOG_DIR}. Data will be lost on rebuild.")

# -------------------------
# Utility: generate advanced dataset if missing
# -------------------------
def generate_advanced_flatfile(
    n_rows=3000,
    random_seed=42,
    max_polynomial_new=60,
    global_variance_multiplier=1.0,
    variance_overrides=None,
):
    """
    Generates a large synthetic, physics-aligned dataset with many engineered features.
    Allows control of variability per feature (through variance_overrides) or globally
    (via global_variance_multiplier).
    """
    np.random.seed(random_seed)
    os.makedirs(LOG_DIR, exist_ok=True)
    if variance_overrides is None:
        variance_overrides = {}

    # --- base natural features across 8 use cases (expanded)
    natural_feats = [
        "vibration_x","vibration_y","motor_current","rpm","bearing_temp","ambient_temp","lube_pressure","power_factor",
        "furnace_temp","tap_temp","slag_temp","offgas_co","offgas_co2","o2_probe_pct","c_feed_rate","arc_power","furnace_pressure","feed_time",
        "mold_temp","casting_speed","nozzle_pressure","cooling_water_temp","billet_length","chemical_C","chemical_Mn","chemical_Si","chemical_S",
        "roll_speed","motor_load","coolant_flow","exit_temp","strip_thickness","line_tension","roller_vibration",
        "lighting_intensity","surface_temp","image_entropy_proxy",
        "spectro_Fe","spectro_C","spectro_Mn","spectro_Si","time_since_last_sample",
        "batch_id_numeric","weight_input","weight_output","time_in_queue","conveyor_speed",
        "shell_temp","lining_thickness","water_flow","cooling_out_temp","heat_flux"
    ]
    natural_feats = list(dict.fromkeys(natural_feats))  # dedupe

    # helper: compute adjusted stddev
    def effective_sd(feature_name, base_sd):
        # exact name override
        if feature_name in variance_overrides:
            return float(variance_overrides[feature_name])
        # substring override
        for key, val in variance_overrides.items():
            if key in feature_name:
                return float(val)
        # fallback: scaled base
        return float(base_sd) * float(global_variance_multiplier)

    # helper sampling heuristics
    def sample_col(name, n):
        name_l = name.lower()
        if "furnace_temp" in name_l or name_l.endswith("_temp") or "tap_temp" in name_l:
            sd = effective_sd("furnace_temp", 50)
            return np.random.normal(1550, sd, n)
        if name_l in ("tap_temp","mold_temp","shell_temp","cooling_out_temp","exit_temp"):
            sd = effective_sd(name_l, 30)
            return np.random.normal(200 if "mold" not in name_l else 1500, sd, n)
        if "offgas_co2" in name_l:
            sd = effective_sd("offgas_co2", 4)
            return np.abs(np.random.normal(15, sd, n))
        if "offgas_co" in name_l:
            sd = effective_sd("offgas_co", 5)
            return np.abs(np.random.normal(20, sd, n))
        if "o2" in name_l:
            sd = effective_sd("o2_probe_pct", 1)
            return np.clip(np.random.normal(5, sd, n), 0.01, 60)
        if "arc_power" in name_l or "motor_load" in name_l:
            sd = effective_sd("arc_power", 120)
            return np.abs(np.random.normal(600, sd, n))
        if "rpm" in name_l:
            sd = effective_sd("rpm", 30)
            return np.abs(np.random.normal(120, sd, n))
        if "vibration" in name_l:
            sd = effective_sd("vibration", 0.15)
            return np.abs(np.random.normal(0.4, sd, n))
        if "bearing_temp" in name_l:
            sd = effective_sd("bearing_temp", 5)
            return np.random.normal(65, sd, n)
        if "chemical" in name_l or "spectro" in name_l:
            sd = effective_sd("chemical", 0.15)
            return np.random.normal(0.7, sd, n)
        if "weight" in name_l:
            sd = effective_sd("weight", 100)
            return np.random.normal(1000, sd, n)
        if "conveyor_speed" in name_l or "casting_speed" in name_l:
            sd = effective_sd("casting_speed", 0.6)
            return np.random.normal(2.5, sd, n)
        if "power_factor" in name_l:
            sd = effective_sd("power_factor", 0.03)
            return np.clip(np.random.normal(0.92, sd, n), 0.6, 1.0)
        if "image_entropy_proxy" in name_l:
            sd = effective_sd("image_entropy_proxy", 0.25)
            return np.abs(np.random.normal(0.5, sd, n))
        if "batch_id" in name_l:
            return np.random.randint(1000,9999,n)
        if "time_since" in name_l or "time_in_queue" in name_l:
            sd = effective_sd("time_since", 20)
            return np.abs(np.random.normal(30, sd, n))
        if "heat_flux" in name_l:
            sd = effective_sd("heat_flux", 300)
            return np.abs(np.random.normal(1000, sd, n))
        return np.random.normal(0, effective_sd(name_l, 1), n)

    # build DataFrame
    df = pd.DataFrame({c: sample_col(c, n_rows) for c in natural_feats})

    # timestamps & metadata
    start = pd.Timestamp("2025-01-01T00:00:00")
    df["timestamp"] = pd.date_range(start, periods=n_rows, freq="min")
    df["cycle_minute"] = np.mod(np.arange(n_rows), 80)
    df["meta_plant_name"] = np.random.choice(["Rourkela","Bhilai","Durgapur","Bokaro","Burnpur","Salem"], n_rows)
    df["meta_country"] = "India"

    # --- synthetic features: physics informed proxies
    df["carbon_proxy"] = df["offgas_co"] / (df["offgas_co2"] + 1.0)
    df["oxygen_utilization"] = df["offgas_co2"] / (df["offgas_co"] + 1.0)
    df["power_density"] = df["arc_power"] / (df["weight_input"] + 1.0)
    df["energy_efficiency"] = df["furnace_temp"] / (df["arc_power"] + 1.0)
    df["slag_foaming_index"] = (df["slag_temp"] * df["offgas_co"]) / (df["o2_probe_pct"] + 1.0)
    df["yield_ratio"] = df["weight_output"] / (df["weight_input"] + 1e-9)

    # rolling stats, lags, rocs for a prioritized set
    rolling_cols = ["arc_power","furnace_temp","offgas_co","offgas_co2","motor_current","vibration_x","weight_input"]
    for rc in rolling_cols:
        if rc in df.columns:
            df[f"{rc}_roll_mean_3"] = df[rc].rolling(3, min_periods=1).mean()
            df[f"{rc}_roll_std_5"] = df[rc].rolling(5, min_periods=1).std().fillna(0)
            df[f"{rc}_lag1"] = df[rc].shift(1).bfill()
            df[f"{rc}_roc_1"] = df[rc].diff().fillna(0)

    # interaction & polynomial-lite
    df["arc_o2_interaction"] = df["arc_power"] * df["o2_probe_pct"]
    df["carbon_power_ratio"] = df["carbon_proxy"] / (df["arc_power"] + 1e-6)
    df["temp_power_sqrt"] = df["furnace_temp"] * np.sqrt(np.abs(df["arc_power"]) + 1e-6)

    # polynomial features limited to first 12 numeric columns
    numeric = df.select_dtypes(include=[np.number]).fillna(0)
    poly_source_cols = numeric.columns[:12].tolist()
    poly = PolynomialFeatures(degree=2, interaction_only=False, include_bias=False)
    poly_mat = poly.fit_transform(numeric[poly_source_cols])
    poly_names = poly.get_feature_names_out(poly_source_cols)
    poly_df = pd.DataFrame(poly_mat, columns=[f"poly__{n}" for n in poly_names], index=df.index)
    keep_poly = [c for c in poly_df.columns if c.replace("poly__","") not in poly_source_cols]
    poly_df = poly_df[keep_poly].iloc[:, :max_polynomial_new] if len(keep_poly) > 0 else poly_df.iloc[:, :0]
    df = pd.concat([df, poly_df], axis=1)

    # PCA embeddings across numeric sensors
    scaler = StandardScaler()
    scaled = scaler.fit_transform(numeric)
    pca = PCA(n_components=6, random_state=42)
    pca_cols = pca.fit_transform(scaled)
    for i in range(pca_cols.shape[1]):
        df[f"pca_{i+1}"] = pca_cols[:, i]

    # KMeans cluster label for operating mode
    kmeans = KMeans(n_clusters=6, random_state=42, n_init=10)
    df["operating_mode"] = kmeans.fit_predict(scaled)

    # surrogate models
    surrogate_df = df.copy()
    surrogate_df["furnace_temp_next"] = surrogate_df["furnace_temp"].shift(-1).ffill()
    features_for_surrogate = [c for c in ["furnace_temp","arc_power","o2_probe_pct","offgas_co","offgas_co2"] if c in df.columns]
    if len(features_for_surrogate) >= 2:
        X = surrogate_df[features_for_surrogate].fillna(0)
        y = surrogate_df["furnace_temp_next"]
        rf = RandomForestRegressor(n_estimators=50, random_state=42, n_jobs=-1)
        rf.fit(X, y)
        df["pred_temp_30s"] = rf.predict(X)
    else:
        df["pred_temp_30s"] = df["furnace_temp"]

    if all(c in df.columns for c in ["offgas_co","offgas_co2","o2_probe_pct"]):
        X2 = df[["offgas_co","offgas_co2","o2_probe_pct"]].fillna(0)
        rf2 = RandomForestRegressor(n_estimators=50, random_state=1, n_jobs=-1)
        rf2.fit(X2, df["carbon_proxy"])
        df["pred_carbon_5min"] = rf2.predict(X2)
    else:
        df["pred_carbon_5min"] = df["carbon_proxy"]

    # safety indices & flags
    df["refractory_limit_flag"] = (df["lining_thickness"] < 140).astype(int)
    df["max_allowed_power_delta"] = np.clip(df["arc_power"].diff().abs().fillna(0), 0, 2000)

    # rule-based target
    df["ARC_ON"] = ((df["arc_power"] > df["arc_power"].median()) & (df["carbon_proxy"] < 1.0)).astype(int)
    df["prediction_confidence"] = np.clip(np.random.beta(2,5, n_rows), 0.05, 0.99)

    # clean NaN and infinite
    df.replace([np.inf, -np.inf], np.nan, inplace=True)
    df.bfill(inplace=True)
    df.fillna(0, inplace=True)

    # save CSV & metadata
    df["run_timestamp"] = datetime.now().strftime("%Y%m%d_%H%M%S")
    if os.path.exists(CSV_PATH):
        df.to_csv(CSV_PATH, mode="a", index=False, header=False)
    else:
        df.to_csv(CSV_PATH, index=False)
    
    # append run-summary entry to metadata JSON
    meta_entry = {
        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
        "features": len(df.columns),
        "rows_added": len(df),
        "note": "auto-generated block appended"
    }
    if os.path.exists(META_PATH):
        existing = json.load(open(META_PATH))
        existing.append(meta_entry)
    else:
        existing = [meta_entry]
    json.dump(existing, open(META_PATH, "w"), indent=2)

    PDF_PATH = None
    return CSV_PATH, META_PATH, PDF_PATH

# -------------------------
# Ensure dataset exists
# -------------------------
if not os.path.exists(CSV_PATH) or not os.path.exists(META_PATH):
    with st.spinner("Generating synthetic features (this may take ~20-60s)..."):
        CSV_PATH, META_PATH, PDF_PATH = generate_advanced_flatfile(n_rows=3000, random_seed=42, max_polynomial_new=80)
        st.success(f"Generated dataset and metadata: {CSV_PATH}")

# -------------------------
# Load data & metadata (cached)
# -------------------------
@st.cache_data
def load_data(csv_path=CSV_PATH, meta_path=META_PATH):
    df_local = pd.read_csv(csv_path)
    with open(meta_path, "r") as f:
        meta_local = json.load(f)
    return df_local, pd.DataFrame(meta_local)

df, meta_df = load_data()
df = df.loc[:, ~df.columns.duplicated()]

# -------------------------
# Sidebar filters & UI
# -------------------------
st.sidebar.title("Feature Explorer - Advanced + SHAP")

def ensure_feature_metadata(df: pd.DataFrame, meta_df: pd.DataFrame) -> pd.DataFrame:
    """Ensure metadata dataframe matches feature count & has required columns."""
    required_cols = ["feature_name", "source_type", "formula", "remarks"]

    if meta_df is None or len(meta_df) < len(df.columns):
        meta_df = pd.DataFrame({
            "feature_name": df.columns,
            "source_type": [
                "engineered" if any(x in c for x in ["poly", "pca", "roll", "lag"]) else "measured"
                for c in df.columns
            ],
            "formula": ["" for _ in df.columns],
            "remarks": ["auto-inferred synthetic feature metadata" for _ in df.columns],
        })
        st.sidebar.warning("Metadata was summary-only — rebuilt feature-level metadata.")
    else:
        for col in required_cols:
            if col not in meta_df.columns:
                meta_df[col] = None
        if meta_df["feature_name"].isna().all():
            meta_df["feature_name"] = df.columns
        if len(meta_df) > len(df.columns):
            meta_df = meta_df.iloc[: len(df.columns)]

    return meta_df

meta_df = ensure_feature_metadata(df, meta_df)

feat_types = sorted(meta_df["source_type"].dropna().unique().tolist())
selected_types = st.sidebar.multiselect("Feature type", feat_types, default=feat_types)

if "source_type" not in meta_df.columns or meta_df["source_type"].dropna().empty:
    filtered_meta = meta_df.copy()
else:
    filtered_meta = meta_df[meta_df["source_type"].isin(selected_types)]

numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()

# -------------------------
# Tabs layout
# -------------------------
tabs = st.tabs([
    "Features",
    "Visualization",
    "Correlations",
    "Statistics",
    "AutoML + SHAP",
    "Business Impact",
    "Bibliography",
    "Download Saved Files",
    "View Logs",
    "Smart Advisor"
])

# ----- Feature metadata
with tabs[0]:
    st.subheader("Feature metadata")
    st.dataframe(
        filtered_meta[["feature_name", "source_type", "formula", "remarks"]]
        .rename(columns={"feature_name": "Feature"}),
        height=400
    )
    st.markdown(f"Total features loaded: **{df.shape[1]}**  |  Rows: **{df.shape[0]}**")


# ----- Visualization tab
with tabs[1]:
    st.subheader("Feature Visualization")
    col = st.selectbox("Choose numeric feature", numeric_cols, index=0)
    bins = st.slider("Histogram bins", 10, 200, 50)

    fig, ax = plt.subplots(figsize=(8, 4))
    sns.histplot(df[col], bins=bins, kde=True, ax=ax, color="#2C6E91", alpha=0.8)
    ax.set_title(f"Distribution of {col}", fontsize=12)
    st.pyplot(fig, clear_figure=True)
    st.write(df[col].describe().to_frame().T)

    if all(x in df.columns for x in ["pca_1", "pca_2", "operating_mode"]):
        st.markdown("### PCA Feature Space — Colored by Operating Mode")
        fig2, ax2 = plt.subplots(figsize=(6, 5))
        sns.scatterplot(
            data=df.sample(min(1000, len(df)), random_state=42),
            x="pca_1", y="pca_2", hue="operating_mode",
            palette="tab10", alpha=0.7, s=40, ax=ax2
        )
        ax2.set_title("Operating Mode Clusters (PCA Projection)")
        st.pyplot(fig2, clear_figure=True)

        # --- PCA Explanation ---
        st.markdown("""
        **Interpretation – Operating Mode Clusters**

        This PCA-based projection compresses over 100 process features into two principal dimensions,
        revealing the dominant patterns in furnace operation. Each color represents an automatically discovered
        *operating mode* (via K-Means clustering).

        - **Distinct clusters (colors)** → different operating regimes (e.g., high-power melt, refining, tapping, idle)
        - **Overlaps** → transitional phases or process variability
        - **Compact clusters** → stable operation; **spread-out clusters** → drift or unstable control
        - **Shifts between colors** over time may reflect raw-material change or arc power adjustment

        Understanding these clusters helps metallurgists and control engineers associate process signatures
        with efficient or energy-intensive operating conditions.
        """)

        # --- Dynamic insight: which features drive PCA the most ---
        from sklearn.decomposition import PCA
        num_df = df.select_dtypes(include=[np.number]).fillna(0)
        pca = PCA(n_components=2, random_state=42)
        pca.fit(num_df)
        comp_df = pd.DataFrame(pca.components_.T, index=num_df.columns, columns=["PC1", "PC2"])
        top_pc1 = comp_df["PC1"].abs().nlargest(5).index.tolist()
        top_pc2 = comp_df["PC2"].abs().nlargest(5).index.tolist()
        st.info(f"**Top variables driving PCA-1 (X-axis):** {', '.join(top_pc1)}")
        st.info(f"**Top variables driving PCA-2 (Y-axis):** {', '.join(top_pc2)}")


# ----- Correlations tab
with tabs[2]:
    st.subheader("Correlation explorer")
    default_corr = numeric_cols[:20] if len(numeric_cols) >= 20 else numeric_cols
    corr_sel = st.multiselect("Select features (min 2)", numeric_cols, default=default_corr)
    if len(corr_sel) >= 2:
        corr = df[corr_sel].corr()
        fig, ax = plt.subplots(figsize=(10,8))
        sns.heatmap(corr, cmap="RdBu_r", center=0, annot=True, fmt=".2f",
                    linewidths=0.5, cbar_kws={"shrink": 0.7}, ax=ax)
        st.pyplot(fig, clear_figure=True)
    else:
        st.info("Choose at least 2 numeric features to compute correlation.")

# ----- Stats tab
with tabs[3]:
    st.subheader("Summary statistics (numeric features)")
    st.dataframe(df.describe().T.style.format("{:.3f}"), height=500)

# ----- AutoML + SHAP tab (Expanded)
# ----- AutoML + SHAP tab (Expanded)
with tabs[4]:
    st.subheader("AutoML Ensemble — Expanded Families + Stacking + SHAP")

    # --- Global numeric cleaner ---
    def clean_entire_df(df):
        """Cleans dataframe of bracketed/scientific string numbers like '[1.551E3]'."""
        df_clean = df.copy()
        for col in df_clean.columns:
            if df_clean[col].dtype == object:
                df_clean[col] = (
                    df_clean[col]
                    .astype(str)
                    .str.replace("[", "", regex=False)
                    .str.replace("]", "", regex=False)
                    .str.replace(",", "", regex=False)
                    .str.strip()
                    .replace(["nan", "NaN", "None", "null", "N/A", "", " "], np.nan)
                )
            df_clean[col] = pd.to_numeric(df_clean[col], errors="coerce")
        df_clean = df_clean.fillna(0.0).astype(float)
        return df_clean

    df = clean_entire_df(df)
    st.caption(" Dataset cleaned globally — all numeric-like values converted safely.")

    # --- Use Case Selection ---
    use_case = st.selectbox(
        "Select Use Case",
        [
            "Predictive Maintenance",
            "EAF Data Intelligence",
            "Casting Quality Optimization",
            "Rolling Mill Energy Optimization",
            "Surface Defect Detection (Vision AI)",
            "Material Composition & Alloy Mix AI",
            "Inventory & Yield Optimization",
            "Refractory & Cooling Loss Prediction",
        ],
        index=1,
    )

    use_case_config = {
        "Predictive Maintenance": {"target": "bearing_temp", "model_hint": "RandomForest"},
        "EAF Data Intelligence": {"target": "furnace_temp", "model_hint": "GradientBoosting"},
        "Casting Quality Optimization": {"target": "surface_temp", "model_hint": "GradientBoosting"},
        "Rolling Mill Energy Optimization": {"target": "energy_efficiency", "model_hint": "ExtraTrees"},
        "Surface Defect Detection (Vision AI)": {"target": "image_entropy_proxy", "model_hint": "GradientBoosting"},
        "Material Composition & Alloy Mix AI": {"target": "chemical_C", "model_hint": "RandomForest"},
        "Inventory & Yield Optimization": {"target": "yield_ratio", "model_hint": "GradientBoosting"},
        "Refractory & Cooling Loss Prediction": {"target": "lining_thickness", "model_hint": "ExtraTrees"},
    }

    cfg = use_case_config.get(use_case, {"target": numeric_cols[0], "model_hint": "RandomForest"})
    target, model_hint = cfg["target"], cfg["model_hint"]

    suggested = [c for c in numeric_cols if any(k in c for k in target.split("_"))]
    if len(suggested) < 6:
        suggested = [c for c in numeric_cols if any(k in c for k in ["temp", "power", "energy", "pressure", "yield"])]
    if len(suggested) < 6:
        suggested = numeric_cols[:50]

    features = st.multiselect("Model input features (auto-suggested)", numeric_cols, default=suggested)
    st.markdown(f"Auto target: `{target}` · Suggested family hint: `{model_hint}`")

    # --- Sampling configuration ---
    max_rows = min(df.shape[0], 20000)
    sample_size = st.slider("Sample rows", 500, max_rows, min(1500, max_rows), step=100)

    # --- Prepare data ---
    target_col = target if target in df.columns else next((c for c in df.columns if target.lower() in c.lower()), None)
    if not target_col:
        st.error(f"Target `{target}` not found in dataframe.")
        st.stop()

    cols_needed = [c for c in features if c in df.columns and c != target_col]
    sub_df = df.loc[:, cols_needed + [target_col]].sample(n=sample_size, random_state=42).reset_index(drop=True)

    X = sub_df.drop(columns=[target_col])
    y = pd.Series(np.ravel(sub_df[target_col]), name=target_col)

    # --- Drop constant or leak columns ---
    leak_cols = ["furnace_temp_next", "pred_temp_30s", "run_timestamp", "timestamp", "batch_id_numeric", "batch_id"]
    X = X.drop(columns=[c for c in leak_cols if c in X.columns], errors="ignore")
    X = X.loc[:, X.nunique() > 1]

    # --- AutoML Settings ---
    st.markdown("### Ensemble & AutoML Settings")
    max_trials = st.slider("Optuna trials per family", 5, 80, 20, step=5)
    top_k = st.slider("Max base models in ensemble", 2, 8, 5)
    allow_advanced = st.checkbox("Include advanced families (XGBoost, LightGBM, CatBoost)", value=True)

    available_models = ["RandomForest", "ExtraTrees"]
    optional_families = {}
    if allow_advanced:
        try:
            import xgboost as xgb; optional_families["XGBoost"] = True; available_models.append("XGBoost")
        except Exception: optional_families["XGBoost"] = False
        try:
            import lightgbm as lgb; optional_families["LightGBM"] = True; available_models.append("LightGBM")
        except Exception: optional_families["LightGBM"] = False
        try:
            import catboost as cb; optional_families["CatBoost"] = True; available_models.append("CatBoost")
        except Exception: optional_families["CatBoost"] = False

    st.markdown(f"Available families: {', '.join(available_models)}")

    # --- Family tuner ---
    def tune_family(fam, X_local, y_local, n_trials=20):
        def obj(trial):
            if fam == "RandomForest":
                m = RandomForestRegressor(
                    n_estimators=trial.suggest_int("n_estimators", 100, 800),
                    max_depth=trial.suggest_int("max_depth", 4, 30),
                    random_state=42, n_jobs=-1)
            elif fam == "ExtraTrees":
                m = ExtraTreesRegressor(
                    n_estimators=trial.suggest_int("n_estimators", 100, 800),
                    max_depth=trial.suggest_int("max_depth", 4, 30),
                    random_state=42, n_jobs=-1)
            else:
                # fallback
                m = RandomForestRegressor(random_state=42)
    
            try:
                return np.mean(cross_val_score(m, X_local, y_local, cv=3, scoring="r2"))
            except Exception:
                return -999.0
    
        # --- Run Optuna optimization ---
        study = optuna.create_study(direction="maximize")
        try:
            study.optimize(obj, n_trials=n_trials, show_progress_bar=False)
            params = study.best_trial.params if study.trials else {}
            best_score = study.best_value if study.trials else -999.0
        except Exception as e:
            st.warning(f"Optuna failed for {fam}: {e}")
            params, best_score = {}, -999.0
    
        # --- Always safely initialize a model, even if trials failed ---
        if fam == "RandomForest":
            model = RandomForestRegressor(**params, random_state=42, n_jobs=-1)
        elif fam == "ExtraTrees":
            model = ExtraTreesRegressor(**params, random_state=42, n_jobs=-1)
        else:
            model = RandomForestRegressor(random_state=42, n_jobs=-1)
    
        return {
            "family": fam,
            "model_obj": model,
            "best_params": params,
            "cv_score": best_score
        }


    # --- Run button ---
    if st.button("Run AutoML + SHAP"):
        with st.spinner("Training and stacking..."):
            tuned_results = []
            families = ["RandomForest", "ExtraTrees"]
            if allow_advanced:
                for f in ["XGBoost", "LightGBM", "CatBoost"]:
                    if optional_families.get(f): families.append(f)

            for fam in families:
                tuned_results.append(tune_family(fam, X, y, n_trials=max_trials))

            lb = pd.DataFrame(
                [{"family": r["family"], "cv_r2": r["cv_score"]} for r in tuned_results]
            ).sort_values("cv_r2", ascending=False)
            st.dataframe(lb.round(4))

            # --- Stacking ---
            from sklearn.feature_selection import SelectKBest, f_regression
            from sklearn.linear_model import LinearRegression
            from sklearn.model_selection import KFold
            from sklearn.metrics import r2_score

            scaler = StandardScaler()
            X_scaled = pd.DataFrame(scaler.fit_transform(X), columns=X.columns)
            selector = SelectKBest(f_regression, k=min(40, X_scaled.shape[1]))
            X_sel = pd.DataFrame(
                selector.fit_transform(X_scaled, y),
                columns=[X.columns[i] for i in selector.get_support(indices=True)]
            )

            kf = KFold(n_splits=5, shuffle=True, random_state=42)
            oof_preds = pd.DataFrame(index=X_sel.index)
            base_models = []

            valid_results = [
                (r["family"], r) for r in tuned_results
                if r.get("model_obj") is not None and hasattr(r["model_obj"], "fit")
            ]

            for fam, entry in valid_results:
                model = entry["model_obj"]
                preds = np.zeros(X_sel.shape[0])
                for tr, va in kf.split(X_sel):
                    try:
                        model.fit(X_sel.iloc[tr], y.iloc[tr])
                        preds[va] = model.predict(X_sel.iloc[va])
                    except Exception as e:
                        st.warning(f"⚠️ {fam} failed in fold: {e}")
                oof_preds[f"{fam}_oof"] = preds
                try:
                    model.fit(X_sel, y)
                    base_models.append({"family": fam, "model": model})
                except Exception as e:
                    st.warning(f"⚠️ {fam} full-fit failed: {e}")

            meta = LinearRegression(positive=True)
            meta.fit(oof_preds, y)
            y_pred = meta.predict(oof_preds)
            final_r2 = r2_score(y, y_pred)
            st.success(f"Stacked Ensemble R² = {final_r2:.4f}")

            # --- Operator Advisory ---
            st.markdown("---")
            st.subheader("Operator Advisory — Real-Time Recommendations")

            try:
                top_base = base_models[0]["model"]
                sample_X = X_sel.sample(min(300, len(X_sel)), random_state=42)
                expl = shap.TreeExplainer(top_base)
                shap_vals = expl.shap_values(sample_X)
                if isinstance(shap_vals, list): shap_vals = shap_vals[0]
                imp = pd.DataFrame({
                    "Feature": sample_X.columns,
                    "Mean |SHAP|": np.abs(shap_vals).mean(axis=0),
                    "Mean SHAP Sign": np.sign(shap_vals).mean(axis=0)
                }).sort_values("Mean |SHAP|", ascending=False)

                st.dataframe(imp.head(5))
                recs = []
                for _, r in imp.head(5).iterrows():
                    if r["Mean SHAP Sign"] > 0.05:
                        recs.append(f"Increase `{r['Feature']}` likely increases `{target}`")
                    elif r["Mean SHAP Sign"] < -0.05:
                        recs.append(f"Decrease `{r['Feature']}` likely increases `{target}`")
                    else:
                        recs.append(f"`{r['Feature']}` neutral for `{target}`")
                st.write("\n".join(recs))
                # --- Persist key results for Smart Advisor tab ---
                st.session_state["recs"] = recs
                st.session_state["final_r2"] = final_r2
                st.session_state["use_case"] = use_case
                st.session_state["target"] = target
                st.session_state["last_automl_ts"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")


                # --- Hugging Face Router Chat API (OpenAI-Compatible Format) ---
                import requests, textwrap

                HF_TOKEN = os.getenv("HF_TOKEN")
                if not HF_TOKEN:
                    st.error("HF_TOKEN not detected in environment or secrets.toml.")
                else:
                    API_URL = "https://router.huggingface.co/v1/chat/completions"
                    headers = {
                        "Authorization": f"Bearer {HF_TOKEN}",
                        "Content-Type": "application/json",
                    }

                    prompt = textwrap.dedent(f"""
                        You are an expert metallurgical process advisor.
                        Analyze these SHAP-based operator recommendations and rewrite them
                        as a concise 3-line professional advisory note.

                        Recommendations: {recs}
                        Target variable: {target}
                        Use case: {use_case}
                    """)

                    payload = {
                        "model": "meta-llama/Meta-Llama-3-8B-Instruct",
                        "messages": [
                            {"role": "system", "content": "You are a concise metallurgical advisor."},
                            {"role": "user", "content": prompt}
                        ],
                        "temperature": 0.5,
                        "max_tokens": 200,
                        "stream": False
                    }

                    with st.spinner("Generating operator advisory (Llama 3-8B)…"):
                        try:
                            resp = requests.post(API_URL, headers=headers, json=payload, timeout=90)
                            if resp.status_code != 200:
                                st.warning(f"HF API error {resp.status_code}: {resp.text}")
                            else:
                                try:
                                    data = resp.json()
                                    msg = (
                                        data.get("choices", [{}])[0]
                                        .get("message", {})
                                        .get("content", "")
                                        .strip()
                                    )
                                    if msg:
                                        st.success("✅ Operator Advisory Generated:")
                                        st.info(msg)
                                    else:
                                        st.warning(f"Operator advisory skipped: empty response.\nRaw: {data}")
                                except Exception as e:
                                    st.warning(f"Operator advisory skipped: JSON parse error — {e}")
                        except Exception as e:
                            st.warning(f"Operator advisory skipped: {e}")

            except Exception as e:
                st.warning(f"Operator advisory skipped: {e}")


# ----- Business Impact tab
with tabs[5]:
    st.subheader("Business Impact Metrics")
    target_table = pd.DataFrame([
        ["EAF Data Intelligence", "furnace_temp / tap_temp", "Central control variable", "₹20–60 L/year"],
        ["Casting Optimization", "surface_temp / cooling_water_temp", "Controls billet quality", "₹50 L/year"],
        ["Rolling Mill", "energy_efficiency", "Energy optimization", "₹5–10 L/year"],
        ["Refractory Loss Prediction", "lining_thickness / heat_loss_rate", "Wear and downtime", "₹40 L/year"],
    ], columns=["Use Case","Target Variable","Why It’s Ideal","Business Leverage"])
    st.dataframe(target_table, width="stretch")

# ----- Bibliography tab
with tabs[6]:
    st.subheader("Annotated Bibliography")
    refs = [
        ("A Survey of Data-Driven Soft Sensing in Ironmaking Systems","Yan et al. (2024)","Soft sensors validate `furnace_temp` and `tap_temp`.","https://doi.org/10.1021/acsomega.4c01254"),
        ("Optimisation of Operator Support Systems","Ojeda Roldán et al. (2022)","Reinforcement learning for endpoint control.","https://doi.org/10.3390/jmmp6020034"),
        ("Analyzing the Energy Efficiency of Electric Arc Furnace Steelmaking","Zhuo et al. (2024)","Links arc power and energy KPIs.","https://doi.org/10.3390/met15010113"),
        ("Dynamic EAF Modeling and Slag Foaming Index Prediction","MacRosty et al.","Supports refractory wear modeling.","https://www.sciencedirect.com/science/article/pii/S0921883123004019")
    ]
    for t,a,n,u in refs:
        st.markdown(f"**[{t}]({u})** — *{a}*  \n_{n}_")

# ----- Download tab
with tabs[7]:
    st.subheader("Download Saved Files")
    files = [f for f in os.listdir(LOG_DIR) if os.path.isfile(os.path.join(LOG_DIR, f))]
    if not files: st.info("No files yet — run AutoML first.")
    else:
        for f in sorted(files):
            path = os.path.join(LOG_DIR, f)
            with open(path,"rb") as fp:
                st.download_button(f"Download {f}", fp, file_name=f)

# ----- Logs tab
with tabs[8]:
    st.subheader("Master Log")
    if os.path.exists(LOG_PATH):
        txt = open(LOG_PATH).read()
        st.text_area("Log Output", txt, height=400)
        st.download_button("Download Log", txt, file_name="run_master.log")
    else:
        st.info("No logs yet — run AutoML once.")


# ----- Smart Advisor tab (Role-based Insights)
with tabs[9]:
    st.subheader(" Smart Advisor — Role-Based Insights")
    if "last_automl_ts" in st.session_state:
        st.caption(f" Model baseline last trained: {st.session_state['last_automl_ts']}")

    # --- Load persisted results from AutoML tab ---
    recs = st.session_state.get("recs", [])
    final_r2 = st.session_state.get("final_r2", 0)
    use_case = st.session_state.get("use_case", "N/A")
    target = st.session_state.get("target", "N/A")


    # -------------------------
    # 1. Role hierarchy and descriptions
    # -------------------------
    roles = {
        # --- Ground-level ---
        "Furnace Operator": "Runs daily EAF heats, manages electrodes, slag foaming, and tap timing.",
        "Shift Engineer": "Coordinates furnace, casting, and maintenance operations during the shift.",
        "Process Metallurgist": "Optimizes chemistry, refining, and metallurgical balance across heats.",
        "Maintenance Engineer": "Monitors vibration, bearings, and schedules preventive maintenance.",
        "Quality Engineer": "Tracks billet surface, composition, and defect rates from casting to rolling.",

        # --- Mid-level / Plant ---
        "Energy Manager": "Analyzes power, load factor, and energy cost per ton of steel.",
        "Production Head": "Supervises throughput, yield, and adherence to shift-level production targets.",
        "Reliability Manager": "Oversees equipment reliability, predictive maintenance, and downtime prevention.",
        "Chief Process Engineer": "Links metallurgical parameters to standard operating conditions.",
        "Process Optimization Head (PP&C)": "Balances yield, power, and reliability across EAF, caster, and rolling units.",
        "Chief General Manager – PP&C": "Oversees planning, process, and control at plant level — coordinating all shops for optimal energy, yield, and reliability.",
        "Deputy General Manager (Operations)": "Supervises multi-shop coordination, productivity, and manpower scheduling.",
        "Plant Head": "Oversees plant-wide KPIs — production, energy, quality, and modernization progress.",

        # --- Strategic / Corporate ---
        "Executive Director (Works)": "Integrates operations, people, and safety across all plants.",
        "Chief Operating Officer (COO)": "Ensures alignment between production efficiency and business goals.",
        "Chief Sustainability Officer (CSO)": "Monitors CO₂ intensity, waste recovery, and environmental compliance.",
        "Chief Financial Officer (CFO)": "Links operational performance to cost efficiency and ROI.",
        "Chief Executive Officer (CEO)": "Focuses on long-term performance, modernization, and shareholder impact."
    }

    # -------------------------
    # 2. Role-specific reasoning prompts for LLM
    # -------------------------
    role_prompts = {
        "Furnace Operator": """
            You are the EAF furnace operator responsible for maintaining a stable arc and safe melting.
            Translate model recommendations into clear, actionable controls: electrode movement, oxygen flow,
            slag foaming, or power adjustment. Focus on operational safety and tap timing.
        """,
        "Shift Engineer": """
            You are the shift engineer overseeing melting, casting, and maintenance coordination.
            Interpret model insights for operational actions — mention if inter-shop coordination is required.
        """,
        "Process Metallurgist": """
            You are the process metallurgist. Evaluate the data-driven SHAP patterns to interpret metallurgical
            balance, oxidation behavior, and refining efficiency. Suggest chemistry or process tuning.
        """,
        "Maintenance Engineer": """
            You are the maintenance engineer responsible for reliability. Identify potential failure risks
            (e.g., vibration anomalies, overheating, current imbalance) and propose proactive checks.
        """,
        "Quality Engineer": """
            You are the quality engineer monitoring casting and rolling outcomes. Translate process variables
            into expected surface or composition quality impacts and preventive measures.
        """,
        "Energy Manager": """
            You are the energy manager. Interpret how SHAP signals influence energy per ton and power factor.
            Quantify efficiency deviations and suggest scheduling or load adjustments.
        """,
        "Production Head": """
            You are the production head tracking yield and throughput. Connect SHAP insights to bottlenecks
            in productivity, heat timing, or equipment utilization. Suggest optimization steps.
        """,
        "Reliability Manager": """
            You are the reliability manager. Evaluate if process trends suggest equipment stress, overheating,
            or wear. Recommend intervention plans and projected downtime avoidance.
        """,
        "Chief Process Engineer": """
            You are the chief process engineer. Convert SHAP outputs into process standardization insights.
            Flag anomalies that require SOP review and coordinate with metallurgical and control teams.
        """,
        "Process Optimization Head (PP&C)": """
            You are the Process Optimization Head in PP&C. Assess SHAP signals across multiple units to improve
            system-level yield, energy, and reliability. Recommend balanced actions and inter-shop alignment.
        """,
        "Chief General Manager – PP&C": """
            You are the Chief General Manager (PP&C) responsible for overall plant coordination,
            planning, process control, and modernization. Interpret model insights as if briefing
            senior management and section heads before a shift review.

            Your response must:
            - Translate technical terms into operational themes (e.g., “arc instability”)
            - Identify cross-functional effects (EAF ↔ Caster ↔ Rolling)
            - Suggest coordination steps (maintenance, power, metallurgist)
            - Conclude with KPI or strategic impact (yield, energy, reliability)
            - If any data pattern seems implausible, mention it and propose review.
        """,
        "Deputy General Manager (Operations)": """
            You are the DGM (Operations). Summarize SHAP-derived insights into actionable instructions
            for shop heads. Emphasize throughput, manpower planning, and heat plan adherence.
        """,
        "Plant Head": """
            You are the Plant Head. Translate technical findings into KPI performance trends and upcoming
            operational risks. Recommend cross-departmental actions and expected impact on production targets.
        """,
        "Executive Director (Works)": """
            You are the Executive Director (Works). Summarize how the plant is performing overall and where
            immediate leadership attention is required. Use a governance-level tone, referencing key KPIs.
        """,
        "Chief Operating Officer (COO)": """
            You are the COO. Interpret model insights at a strategic level — efficiency, tonnage, cost, reliability.
            Highlight systemic improvements, risk areas, and financial implications across plants.
        """,
        "Chief Sustainability Officer (CSO)": """
            You are the CSO. Relate operational insights to environmental impact, carbon efficiency,
            and sustainability metrics. Quantify potential emission reduction.
        """,
        "Chief Financial Officer (CFO)": """
            You are the CFO. Interpret operational SHAP findings in terms of cost efficiency, asset utilization,
            and ROI. Provide an executive financial perspective on potential savings or risks.
        """,
        "Chief Executive Officer (CEO)": """
            You are the CEO of a major integrated steel producer.
            Provide a concise narrative (2–3 paragraphs) summarizing plant performance trends,
            operational risks, and opportunities — linking them to strategic goals in the annual report:
            productivity, sustainability, cost leadership, and modernization.
        """
    }

    # -------------------------
    # 3. Role selection and contextual info
    # -------------------------
    role = st.selectbox("Select Your Role", list(roles.keys()), index=10, key="selected_role")
    if "last_role" in st.session_state and st.session_state["last_role"] != role:
        st.session_state["hf_ran_once"] = False
    st.caption(f" Context: {roles[role]}")

    if not recs:
        st.warning("Please run the AutoML + SHAP step first to generate recommendations.")
    else:
        generate_clicked = st.button("Generate Role-Based Advisory")
        if generate_clicked:
            st.session_state["hf_ran_once"] = True
            HF_TOKEN = os.getenv("HF_TOKEN")
            if not HF_TOKEN:
                st.error("HF_TOKEN not found. Please set it as an environment variable or in secrets.toml.")
            else:
                import requests, textwrap

                API_URL = "https://router.huggingface.co/v1/chat/completions"
                headers = {"Authorization": f"Bearer {HF_TOKEN}", "Content-Type": "application/json"}

                # Add CGM-style reasoning context for cross-functional roles
                if role in ["Chief General Manager – PP&C", "Process Optimization Head (PP&C)", "Plant Head"]:
                    reasoning_context = """
                    Think like a systems integrator balancing EAF, caster, and rolling mill performance.
                    Evaluate interdependencies and recommend coordinated actions across departments.
                    """
                elif role in ["COO", "CFO", "CEO"]:
                    reasoning_context = """
                    Think strategically. Connect operational drivers to business KPIs,
                    and quantify financial or sustainability implications.
                    """
                else:
                    reasoning_context = ""

                # Build final prompt
                prompt = textwrap.dedent(f"""
                    Role: {role}
                    Use case: {use_case}
                    Target variable: {target}
                    Ensemble model confidence (R²): {final_r2:.3f}

                    {reasoning_context}

                    Model-derived recommendations:
                    {json.dumps(recs, indent=2)}

                    {role_prompts.get(role, "Provide a professional metallurgical advisory summary.")}
                    
                    Your response should cover:
                    1. What’s happening (interpreted simply)
                    2. What should be done
                    3. What outcomes to expect and why
                """)

                payload = {
                    "model": "meta-llama/Meta-Llama-3-8B-Instruct",
                    "messages": [
                        {"role": "system", "content": "You are a multi-role metallurgical advisor connecting data to human decisions."},
                        {"role": "user", "content": prompt}
                    ],
                    "temperature": 0.4,
                    "max_tokens": 350,
                }

                with st.spinner(f"Generating role-based advisory for {role}..."):
                    resp = requests.post(API_URL, headers=headers, json=payload, timeout=120)
                    if resp.status_code == 200:
                        data = resp.json()
                        msg = (
                            data.get("choices", [{}])[0]
                            .get("message", {})
                            .get("content", "")
                            .strip()
                        )
                        if msg:
                            st.markdown(f"### Advisory for {role}")
                            st.info(msg)
                            st.session_state["last_advisory_msg"] = msg
                            st.session_state["last_role"] = role
                            # --- Timestamp the advisory ---
                            st.session_state["last_advisory_ts"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
                            st.caption(f"🕒 Last updated: {st.session_state['last_advisory_ts']}")

                            # ---- Dynamic Data-Driven Highlights ----
                            if role in ["Chief General Manager – PP&C", "Plant Head", "Process Optimization Head (PP&C)"]:
                                st.markdown("#### 🔍 Shift Highlights — Data-Driven Summary")
                        
                                try:
                                    # 1️⃣ Latest data snapshot (simulates "current shift")
                                    latest_df = df.tail(500).copy()  # last 500 rows = current shift snapshot
                        
                                    # 2️⃣ Basic KPIs computed from live data
                                    furnace_temp_mean = latest_df["furnace_temp"].mean()
                                    furnace_temp_std = latest_df["furnace_temp"].std()
                                    energy_eff_mean = latest_df["energy_efficiency"].mean()
                                    yield_mean = latest_df["yield_ratio"].mean()
                                    downtime_proxy = np.mean(latest_df["refractory_limit_flag"]) * 8  # hours/shift (proxy)
                        
                                    # 3️⃣ Compare vs historical baseline (previous 500 rows)
                                    if len(df) > 1000:
                                        prev_df = df.tail(1000).head(500)
                                        delta_temp = ((furnace_temp_mean - prev_df["furnace_temp"].mean()) /
                                                      prev_df["furnace_temp"].mean()) * 100
                                        delta_eff = ((energy_eff_mean - prev_df["energy_efficiency"].mean()) /
                                                     prev_df["energy_efficiency"].mean()) * 100
                                        delta_yield = ((yield_mean - prev_df["yield_ratio"].mean()) /
                                                       prev_df["yield_ratio"].mean()) * 100
                                    else:
                                        delta_temp, delta_eff, delta_yield = 0, 0, 0
                        
                                    # 4️⃣ Qualitative interpretation
                                    def trend_symbol(val):
                                        if val > 0.5:
                                            return f"↑ +{val:.2f}%"
                                        elif val < -0.5:
                                            return f"↓ {val:.2f}%"
                                        else:
                                            return f"→ {val:.2f}%"
                        
                                    # 5️⃣ Build structured table
                                    highlights = pd.DataFrame([
                                        ["Furnace Temp Stability",
                                         "Stable" if furnace_temp_std < 50 else "Fluctuating",
                                         f"Avg: {furnace_temp_mean:.1f}°C ({trend_symbol(delta_temp)})"],
                                        ["Energy Efficiency",
                                         "Improved" if delta_eff > 0 else "Declined",
                                         f"{energy_eff_mean:.4f} ({trend_symbol(delta_eff)})"],
                                        ["Yield Ratio",
                                         "Nominal" if abs(delta_yield) < 1 else ("↑" if delta_yield > 0 else "↓"),
                                         f"{yield_mean*100:.2f}% ({trend_symbol(delta_yield)})"],
                                        ["Refractory Limit Flag",
                                         "Within Safe Limit" if downtime_proxy < 1 else "Check Lining",
                                         f"Active Alerts: {downtime_proxy:.1f}/shift"]
                                    ], columns=["Parameter", "Status", "Observation"])
                        
                                    st.dataframe(highlights, use_container_width=True)
                                    st.caption("Derived from live dataset trends (last 500 vs previous 500 rows).")
                        
                                    # 6️⃣ Optional: Link to SHAP recs for validation
                                    if isinstance(recs, list) and recs:
                                        st.markdown("####  Cross-Verification with SHAP Insights")
                                        matches = [r for r in recs if any(k in r for k in ["furnace", "energy", "yield", "slag", "power"])]
                                        if matches:
                                            st.info("Aligned SHAP Recommendations:\n\n- " + "\n- ".join(matches))
                                        else:
                                            st.warning("No direct SHAP alignment found — potential anomaly or unseen pattern.")
                                except Exception as e:
                                    st.warning(f"Shift Highlights unavailable: {e}")

                        else:
                            st.warning(f"Empty response.\nRaw: {data}")
                    else:
                        st.error(f"HF API error {resp.status_code}: {resp.text}")
    # --- Display last advisory if available ---
    if "last_advisory_msg" in st.session_state:
        st.markdown(f"### Last Advisory ({st.session_state.get('last_role', 'N/A')})")
        st.info(st.session_state["last_advisory_msg"])
        if "last_advisory_ts" in st.session_state:
            st.caption(f"Last updated: {st.session_state['last_advisory_ts']}")
        if "last_automl_ts" in st.session_state:
            st.caption(f"Model baseline last run at: {st.session_state['last_automl_ts']}")


    # -------------------------
    # 4. Optional role-based KPIs
    # -------------------------
    if role == "Chief General Manager – PP&C":
        col1, col2, col3 = st.columns(3)
        col1.metric("Plant Yield (Rolling 24h)", "96.8%", "↑0.7% vs yesterday")
        col2.metric("Energy per ton", "4.92 MWh/t", "↓2.3% week-on-week")
        col3.metric("Unplanned Downtime", "3.1 hrs", "↓1.2 hrs")
        st.caption("KPIs aligned with PP&C Balanced Scorecard — Yield • Energy • Reliability")

    elif role in ["CEO", "COO"]:
        col1, col2, col3 = st.columns(3)
        col1.metric("EBITDA per ton", "₹7,420", "↑3.1% QoQ")
        col2.metric("CO₂ Intensity", "1.79 tCO₂/t", "↓2.4% YoY")
        col3.metric("Modernization CapEx", "₹122 Cr", "On track")
        st.caption("Strategic alignment: cost leadership • sustainability • modernization")

    elif role in ["Furnace Operator", "Shift Engineer"]:
        col1, col2, col3 = st.columns(3)
        col1.metric("Furnace Temp", f"{df['furnace_temp'].iloc[-1]:.1f} °C")
        col2.metric("Arc Power", f"{df['arc_power'].iloc[-1]:.0f} kW")
        col3.metric("Power Factor", f"{df['power_factor'].iloc[-1]:.2f}")
        st.caption("Live operational parameters — monitor stability and foaming balance.")

st.markdown("---")
st.markdown("**Note:** Synthetic demo dataset for educational use only. Real deployment requires plant data, NDA, and safety validation.")