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"
])

# ----- 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)

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

    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 = cfg["target"]
    model_hint = 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)

    # ---------- SAFE target & X preparation ----------
    if isinstance(target, (list, tuple)):
        st.warning(f"Target provided as list/tuple; using first element `{target[0]}` as target.")
        target = target[0]
    
    cols_needed = [c for c in features if c in df.columns]

    if target in df.columns:
        target_col = target
    else:
        matches = [c for c in df.columns if c.lower() == target.lower()]
        if matches:
            target_col = matches[0]
            st.info(f"Auto-corrected to exact match: `{target_col}`")
        else:
            matches = [c for c in df.columns if target.lower() in c.lower()]
            if len(matches) == 1:
                target_col = matches[0]
                st.info(f"Auto-corrected to closest match: `{target_col}`")
            elif len(matches) > 1:
                preferred = [m for m in matches if m.endswith("_temp") or m.endswith("_ratio") or m == target]
                if preferred:
                    target_col = preferred[0]
                    st.warning(f"Multiple matches found {matches}. Using `{target_col}`.")
                else:
                    target_col = matches[0]
                    st.warning(f"Multiple matches found {matches}. Using first: `{target_col}`.")
            else:
                st.error(f"Target `{target}` not found in dataframe columns.")
                st.stop()

    valid_features = [c for c in cols_needed if c in df.columns and c != target_col]
    if not valid_features:
        st.error("No valid feature columns remain after cleaning. Check feature selection.")
        st.stop()

    sub_df = df.loc[:, valid_features + [target_col]].copy()
    sub_df = sub_df.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)

    leak_cols = ["furnace_temp_next", "pred_temp_30s", "run_timestamp", "timestamp", "batch_id_numeric", "batch_id"]
    for lc in leak_cols:
        if lc in X.columns:
            X.drop(columns=[lc], inplace=True)
    
    nunique = X.nunique(dropna=False)
    const_cols = nunique[nunique <= 1].index.tolist()
    if const_cols:
        X.drop(columns=const_cols, inplace=True)
    
    if X.shape[1] == 0:
        st.error("No valid feature columns remain after cleaning. Check feature selection.")
        st.stop()

    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 model families: {', '.join(available_models)}")

    def tune_family(family_name, X_local, y_local, n_trials=20, random_state=42):
        """Tune one model family using Optuna."""
        def obj(trial):
            if family_name == "RandomForest":
                n_estimators = trial.suggest_int("n_estimators", 100, 800)
                max_depth = trial.suggest_int("max_depth", 4, 30)
                m = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, n_jobs=-1, random_state=random_state)
            elif family_name == "ExtraTrees":
                n_estimators = trial.suggest_int("n_estimators", 100, 800)
                max_depth = trial.suggest_int("max_depth", 4, 30)
                m = ExtraTreesRegressor(n_estimators=n_estimators, max_depth=max_depth, n_jobs=-1, random_state=random_state)
            elif family_name == "XGBoost" and optional_families.get("XGBoost"):
                n_estimators = trial.suggest_int("n_estimators", 100, 1000)
                max_depth = trial.suggest_int("max_depth", 3, 12)
                lr = trial.suggest_float("learning_rate", 0.01, 0.3, log=True)
                m = xgb.XGBRegressor(n_estimators=n_estimators, max_depth=max_depth, learning_rate=lr, tree_method="hist", verbosity=0)
            elif family_name == "LightGBM" and optional_families.get("LightGBM"):
                n_estimators = trial.suggest_int("n_estimators", 100, 1000)
                max_depth = trial.suggest_int("max_depth", 3, 16)
                lr = trial.suggest_float("learning_rate", 0.01, 0.3, log=True)
                m = lgb.LGBMRegressor(n_estimators=n_estimators, max_depth=max_depth, learning_rate=lr, n_jobs=1)
            elif family_name == "CatBoost" and optional_families.get("CatBoost"):
                iterations = trial.suggest_int("iterations", 200, 1000)
                depth = trial.suggest_int("depth", 4, 10)
                lr = trial.suggest_float("learning_rate", 0.01, 0.3, log=True)
                m = cb.CatBoostRegressor(iterations=iterations, depth=depth, learning_rate=lr, verbose=0)
            else:
                m = RandomForestRegressor(n_estimators=200, max_depth=8, random_state=random_state)
            try:
                scores = cross_val_score(m, X_local, y_local, scoring="r2", cv=3)
                return float(np.mean(scores))
            except Exception:
                return -999.0

        study = optuna.create_study(direction="maximize")
        study.optimize(obj, n_trials=n_trials, show_progress_bar=False)
        best = study.best_trial.params if study.trials else {}
        try:
            if family_name == "RandomForest":
                model = RandomForestRegressor(**{**{"random_state":42,"n_jobs":-1}, **best})
            elif family_name == "ExtraTrees":
                model = ExtraTreesRegressor(**{**{"random_state":42,"n_jobs":-1}, **best})
            elif family_name == "XGBoost" and optional_families.get("XGBoost"):
                model = xgb.XGBRegressor(**{**{"verbosity":0,"tree_method":"hist"}, **best})
            elif family_name == "LightGBM" and optional_families.get("LightGBM"):
                model = lgb.LGBMRegressor(**{**{"n_jobs":1}, **best})
            elif family_name == "CatBoost" and optional_families.get("CatBoost"):
                model = cb.CatBoostRegressor(**{**{"verbose":0}, **best})
            else:
                model = RandomForestRegressor(random_state=42)
        except Exception:
            model = RandomForestRegressor(random_state=42)

        try:
            score = float(np.mean(cross_val_score(model, X_local, y_local, scoring="r2", cv=3)))
        except Exception:
            score = -999.0
        return {"model_obj": model, "cv_score": score, "best_params": best, "family": family_name}

    if st.button("Run expanded AutoML + Stacking"):
        st.session_state["run_automl_clicked"] = True

    if st.session_state["run_automl_clicked"]:
        log("AutoML + Stacking initiated.")
        with st.spinner("Tuning multiple families..."):
            families_to_try = ["RandomForest", "ExtraTrees", "MLP"]
            if allow_advanced:
                if optional_families.get("XGBoost"): families_to_try.append("XGBoost")
                if optional_families.get("LightGBM"): families_to_try.append("LightGBM")
                if optional_families.get("CatBoost"): families_to_try.append("CatBoost")

            tuned_results = []
            for fam in families_to_try:
                log(f"Tuning family: {fam}")
                st.caption(f"Tuning family: {fam}")
                result = tune_family(fam, X, y, n_trials=max_trials)
                model_obj = result.get("model_obj")
                if hasattr(model_obj, "estimators_"):
                    delattr(model_obj, "estimators_")
                result["model_obj"] = model_obj
                tuned_results.append(result)

            lb = pd.DataFrame([{"family": r["family"], "cv_r2": r["cv_score"], "params": r["best_params"]} for r in tuned_results])
            lb = lb.sort_values("cv_r2", ascending=False).reset_index(drop=True)
            st.markdown("### Tuning Leaderboard (by CV R²)")
            st.dataframe(lb[["family","cv_r2"]].round(4))

            from sklearn.feature_selection import SelectKBest, f_regression
            from sklearn.linear_model import LinearRegression
            from sklearn.model_selection import KFold

            st.markdown("### Building base models & out-of-fold predictions for stacking")

            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 = selector.fit_transform(X_scaled, y)
            selected_feature_names = [X.columns[i] for i in selector.get_support(indices=True)]
            X_sel = pd.DataFrame(X_sel, columns=selected_feature_names)

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

            for r in tuned_results:
                m = r.get("model_obj")
                if m is not None:
                    try:
                        if "__len__" in dir(m) and not hasattr(m, "estimators_"):
                            setattr(m, "__len__", lambda self=m: 0)
                    except Exception:
                        pass

            for fam, entry in [(r["family"], r) for r in tuned_results if r.get("model_obj") is not None]:
                model_obj = entry["model_obj"]
                oof = np.zeros(X_sel.shape[0])
                for tr_idx, val_idx in kf.split(X_sel):
                    X_tr, X_val = X_sel.iloc[tr_idx], X_sel.iloc[val_idx]
                    y_tr = y.iloc[tr_idx]
                    try:
                        model_obj.fit(X_tr, y_tr)
                        preds = model_obj.predict(X_val)
                        oof[val_idx] = preds
                    except Exception:
                        oof[val_idx] = np.mean(y_tr)
                oof_preds[f"{fam}_oof"] = oof
                model_obj.fit(X_sel, y)
                base_models.append({"family": fam, "model": model_obj})

            if oof_preds.empty:
                st.error("No base models built.")
                st.stop()

            corr = oof_preds.corr().abs()
            div = {c: 1 - corr[c].drop(c).mean() for c in corr.columns}
            cv_r2_est = {c: r2_score(y, oof_preds[c]) for c in oof_preds.columns}

            summary_df = pd.DataFrame({
                "family": [c.replace("_oof","") for c in oof_preds.columns],
                "cv_r2": [cv_r2_est[c] for c in oof_preds.columns],
                "diversity": [div[c] for c in oof_preds.columns]
            }).sort_values(["cv_r2","diversity"], ascending=[False,False])

            st.dataframe(summary_df.round(4))
            selected = summary_df.head(top_k)["family"].tolist()
            st.markdown(f"Selected for stacking (top {top_k}): {selected}")

            meta = LinearRegression(positive=True)
            X_stack = oof_preds[[f"{s}_oof" for s in selected]].fillna(0)
            meta.fit(X_stack, y)

            X_tr, X_val, y_tr, y_val = train_test_split(X_sel, y, test_size=0.2, random_state=42)
            meta_inputs = []
            for fam in selected:
                mdl = next((b["model"] for b in base_models if b["family"] == fam), None)
                preds = mdl.predict(X_val) if mdl else np.full(len(X_val), np.mean(y_tr))
                meta_inputs.append(np.ravel(preds))
            X_meta_val = pd.DataFrame(np.column_stack(meta_inputs), columns=X_stack.columns)
            y_meta_pred = meta.predict(X_meta_val)

            final_r2 = r2_score(y_val, y_meta_pred)
            final_rmse = np.sqrt(mean_squared_error(y_val, y_meta_pred))
            st.success(f"Stacked Ensemble — R² = {final_r2:.4f}, RMSE = {final_rmse:.3f}")

            fig, ax = plt.subplots(figsize=(7,4))
            ax.scatter(y_val, y_meta_pred, alpha=0.7)
            ax.plot([y_val.min(), y_val.max()], [y_val.min(), y_val.max()], "r--")
            st.pyplot(fig, clear_figure=True)

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

            try:
                top_base = next((b for b in base_models if b["family"] == selected[0]), None)
                if top_base and hasattr(top_base["model"], "predict"):
                    sample_X = X_val.sample(min(300, len(X_val)), random_state=42).copy()

                    def _clean_to_float(x):
                        if isinstance(x, (int, float, np.floating)):
                            return float(x)
                        try:
                            x_str = str(x).replace("[", "").replace("]", "").replace(",", "").strip()
                            if x_str.lower() in ("nan", "none", "", "null", "na", "n/a"):
                                return 0.0
                            return float(x_str.replace("E", "e"))
                        except Exception:
                            return 0.0

                    for col in sample_X.columns:
                        sample_X[col] = sample_X[col].map(_clean_to_float)
                    sample_X = sample_X.apply(pd.to_numeric, errors="coerce").fillna(0)

                    model = top_base["model"]
                    expl = shap.TreeExplainer(model)
                    shap_vals = expl.shap_values(sample_X)
                    if isinstance(shap_vals, list): shap_vals = shap_vals[0]
                    shap_vals = np.array(shap_vals)
                    importance = 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.markdown("### Top 5 Operational Drivers")
                    st.dataframe(importance.head(5))

                    recommendations = []
                    for _, row in importance.head(5).iterrows():
                        f, s = row["Feature"], row["Mean SHAP Sign"]
                        if s > 0.05:
                            recommendations.append(f"Increase `{f}` likely increases `{target}`")
                        elif s < -0.05:
                            recommendations.append(f"Decrease `{f}` likely increases `{target}`")
                        else:
                            recommendations.append(f"`{f}` neutral for `{target}`")

                    st.markdown("### Suggested Operator Adjustments")
                    st.write("\n".join(recommendations))

                    import requests, json, textwrap
                    HF_TOKEN = os.getenv("HF_TOKEN")
                    if not HF_TOKEN:
                        st.error("HF_TOKEN not detected. Check the Secrets tab.")
                    else:
                        API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-3-8B-Instruct"
                        headers = {"Authorization": f"Bearer {HF_TOKEN}"}
                        prompt = textwrap.dedent(f"""
                            You are an expert metallurgical process advisor.
                            Based on these SHAP-derived recommendations:
                            {recommendations}
                            Target: {target}
                            Use case: {use_case}
                            Summarize in three concise, professional lines what the operator should do this shift.
                        """)
                        payload = {"inputs": prompt, "parameters": {"max_new_tokens": 150, "temperature": 0.6}}
                        with st.spinner("Generating operator note (Llama-3-8B)…"):
                            resp = requests.post(API_URL, headers=headers, json=payload, timeout=90)
                            try:
                                data = resp.json()
                                st.caption("Raw HF response:")
                                st.json(data)
                            except Exception as ex:
                                st.warning(f"HF raw response parse error: {ex}")
                                st.text(resp.text)
                                data = None

                            text = ""
                            if isinstance(data, list) and len(data) > 0 and "generated_text" in data[0]:
                                text = data[0]["generated_text"].strip()
                            elif isinstance(data, dict) and "generated_text" in data:
                                text = data["generated_text"].strip()
                            elif isinstance(data, str):
                                text = data.strip()

                            if text:
                                st.success(" Operator Advisory Generated:")
                                st.info(text)
                            else:
                                st.warning("Operator advisory skipped: no text returned from model.")
            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.")

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