src/streamlit_app.py

from pathlib import Path
import pandas as pd
import numpy as np
import streamlit as st

st.set_page_config(
    page_title="Bad Actor Simulation",
    page_icon="⚠️",
    layout="wide",
    initial_sidebar_state="expanded",
)

st.markdown("""
<style>
/* ── Section card headers ─────────────────────────────────────────────────── */
.section-card {
    background: #f8f9fa;
    border-left: 4px solid #e63946;
    border-radius: 6px;
    padding: 10px 16px;
    margin: 16px 0 8px 0;
}
.section-card h3 { margin: 0; font-size: 1.05rem; font-weight: 700; color: #1d3557; }
.section-card .sub { font-size: 0.78rem; color: #6c757d; margin-top: 3px; }

/* ── KPI metric cards ─────────────────────────────────────────────────────── */
[data-testid="metric-container"] {
    background: #ffffff;
    border: 1px solid #e9ecef;
    border-radius: 10px;
    padding: 14px 18px !important;
    box-shadow: 0 1px 3px rgba(0,0,0,0.06);
}
[data-testid="stMetricValue"] { color: #1d3557; font-weight: 700; }
[data-testid="stMetricLabel"] { color: #6c757d; font-size: 0.82rem; }

/* ── Welcome card ─────────────────────────────────────────────────────────── */
.welcome-card {
    background: linear-gradient(135deg, #1d3557 0%, #457b9d 100%);
    border-radius: 12px;
    padding: 32px 36px;
    color: white;
    margin-bottom: 24px;
}
.welcome-card h2 { margin: 0 0 8px 0; font-size: 1.4rem; color: white; }
.welcome-card p  { margin: 0 0 20px 0; color: rgba(255,255,255,0.8); font-size: 0.9rem; }
.step-list { list-style: none; padding: 0; margin: 0; }
.step-list li {
    display: flex; align-items: center; gap: 10px;
    padding: 7px 0; border-bottom: 1px solid rgba(255,255,255,0.15);
    color: rgba(255,255,255,0.9); font-size: 0.88rem;
}
.step-list li:last-child { border-bottom: none; }
.step-num {
    background: #e63946; color: white; font-weight: 700;
    border-radius: 50%; width: 22px; height: 22px;
    display: flex; align-items: center; justify-content: center;
    font-size: 0.75rem; flex-shrink: 0;
}

/* ── App header ───────────────────────────────────────────────────────────── */
.app-header { margin-bottom: 4px; }
.app-header h1 { margin: 0; font-size: 1.7rem; color: #1d3557; font-weight: 800; }
.app-header .tagline { color: #6c757d; font-size: 0.85rem; margin-top: 2px; }
.dataset-badge {
    display: inline-block;
    background: #e9ecef; color: #495057;
    border-radius: 20px; padding: 4px 12px;
    font-size: 0.78rem; margin-top: 4px;
}

/* ── Admin banner ─────────────────────────────────────────────────────────── */
.admin-banner {
    background: linear-gradient(135deg, #212529 0%, #343a40 100%);
    border-radius: 10px;
    padding: 16px 20px;
    color: white;
    margin-bottom: 20px;
    border-left: 4px solid #ffc107;
}
.admin-banner h3 { margin: 0 0 4px 0; font-size: 1rem; color: #ffc107; }
.admin-banner p  { margin: 0; font-size: 0.82rem; color: rgba(255,255,255,0.75); }

/* ── Sidebar ──────────────────────────────────────────────────────────────── */
[data-testid="stSidebar"] { background: #f8f9fa; }
[data-testid="stSidebar"] .stButton > button {
    background: #e63946 !important; color: white !important;
    border: none !important; font-weight: 600 !important;
    letter-spacing: 0.3px;
}
[data-testid="stSidebar"] .stButton > button:hover {
    background: #c1121f !important;
}
</style>
""", unsafe_allow_html=True)


# ── Admin mode detection ───────────────────────────────────────────────────────
is_admin = "admin" in st.query_params

# ── Session state init ─────────────────────────────────────────────────────────
if "custom_df" not in st.session_state:
    st.session_state.custom_df = None
    st.session_state.custom_source = None


def _section(icon, title, subtitle=""):
    sub_html = f'<div class="sub">{subtitle}</div>' if subtitle else ""
    st.markdown(
        f'<div class="section-card"><h3>{icon}&nbsp; {title}</h3>{sub_html}</div>',
        unsafe_allow_html=True,
    )

REQUIRED_COLS = {"year", "month", "site_id", "section", "model", "eqm_no", "MA", "MTBF"}

@st.cache_data
def load_default():
    df = pd.read_excel(Path(__file__).parent / "bad_actor_simulation.xlsx", sheet_name="data_badactor")
    df.columns = df.columns.str.strip()
    df["model"] = df["model"].astype(str)
    return df

@st.cache_data
def load_csv(data: bytes) -> pd.DataFrame:
    import io
    df = pd.read_csv(io.BytesIO(data))
    df.columns = df.columns.str.strip()
    df["model"] = df["model"].astype(str)
    return df

@st.cache_data
def load_xlsx(data: bytes, sheet: str) -> pd.DataFrame:
    import io
    df = pd.read_excel(io.BytesIO(data), sheet_name=sheet)
    df.columns = df.columns.str.strip()
    df["model"] = df["model"].astype(str)
    return df

def get_xlsx_sheets(data: bytes) -> list:
    import io
    xf = pd.ExcelFile(io.BytesIO(data))
    return xf.sheet_names

# ── Helpers ────────────────────────────────────────────────────────────────────
def minmax_norm(s):
    lo, hi = s.min(), s.max()
    if hi == lo:
        return pd.Series(0.5, index=s.index)
    return (s - lo) / (hi - lo)


def flag_consecutive(group, col_below, col_period, threshold):
    """Mark rows belonging to a consecutive-True run of length >= threshold."""
    g       = group.sort_values(col_period)
    below   = g[col_below].values
    periods = g[col_period].values
    result  = np.zeros(len(g), dtype=bool)
    run_start = None
    for i in range(len(g)):
        if not below[i]:
            run_start = None
            continue
        if run_start is None:
            run_start = i
        elif periods[i] != periods[i - 1] + 1:
            run_start = i
        if (i - run_start + 1) >= threshold:
            result[run_start : i + 1] = True
    return pd.Series(result, index=g.index)


# ── Default filter values (edit here to change the initial widget state) ───────
DEFAULT_YEARS         = [2026]
DEFAULT_SITES         = [2009]
DEFAULT_SECTIONS      = ["OB LOADER"]
DEFAULT_MODELS        = ["6015B", "6020B", "EX2500-5", "EX3600-6", "PC1250SP-7", "PC1250SP-8", "PC2000-8", "PC4000-6"]
DEFAULT_CONSECUTIVE_N = 2
DEFAULT_OBS_MONTH     = 2

def run_simulation(years, sites, sections, models, consecutive_n, obs_month):
    # 1. Filter  (obs_month = observation cutoff: only months 1..obs_month)
    mask = (
        df["year"].isin(years) &
        df["site_id"].isin(sites) &
        df["section"].isin(sections) &
        (df["month"] <= obs_month)
    )
    if "ALL" not in models:
        mask &= df["model"].isin(models)
    filt = df[mask].copy()

    if filt.empty:
        st.warning("No data found for the selected filters.")
        return

    # basis_key: used for per-model detail stats and Q1
    # agg_key:   used for normalization min/max (matches the aggregated Normalisation Reference table)
    basis_key = ["month", "site_id", "section", "model"]
    agg_key   = ["month", "site_id", "section"]

    # 2. Acuan Basis 1 — min-max normalise using section-level min/max (agg_key, no model)
    filt["norm_MA"]   = filt.groupby(agg_key)["MA"]  .transform(minmax_norm)
    filt["norm_MTBF"] = filt.groupby(agg_key)["MTBF"].transform(minmax_norm)

    # Normalisation reference stats (min, max, avg) for display — detail per model
    norm_stats = filt.groupby(basis_key).agg(
        MA_min=("MA", "min"), MA_max=("MA", "max"), MA_avg=("MA", "mean"),
        MTBF_min=("MTBF", "min"), MTBF_max=("MTBF", "max"), MTBF_avg=("MTBF", "mean"),
    ).round(4).reset_index()

    # 3. Bad actor score
    filt["bad_actor_score"] = filt["norm_MA"] * filt["norm_MTBF"]

    # 4. Acuan Basis 2 — Q1 threshold using the same basis_key
    q1_df = (
        filt.groupby(basis_key)["bad_actor_score"]
        .quantile(0.25)
        .reset_index()
        .rename(columns={"bad_actor_score": "q1_threshold"})
    )
    filt = filt.merge(q1_df, on=basis_key, how="left")

    # 5. Below-Q1 flag
    filt["below_q1"] = filt["bad_actor_score"] < filt["q1_threshold"]

    # 6. Consecutive detection (period = year*12 + month for cross-year safety)
    filt["period"] = filt["year"] * 12 + filt["month"]
    filt["is_bad_actor"] = (
        filt.groupby("eqm_no", group_keys=False)
        .apply(flag_consecutive,
               col_below="below_q1",
               col_period="period",
               threshold=consecutive_n)
    )

    # ── Build bad actor summary ────────────────────────────────────────────────
    bad_ids = filt.loc[filt["is_bad_actor"], "eqm_no"].unique()

    def _streak(g):
        periods = sorted(g.loc[g["below_q1"], "period"].tolist())
        if not periods:
            return 0
        mx = cur = 1
        for i in range(1, len(periods)):
            cur = cur + 1 if periods[i] == periods[i - 1] + 1 else 1
            mx = max(mx, cur)
        return mx

    rows = []
    for eid, grp in filt[filt["eqm_no"].isin(bad_ids)].groupby("eqm_no"):
        rows.append({
            "eqm_no"          : eid,
            "site_id"         : grp["site_id"].iloc[0],
            "section"         : grp["section"].iloc[0],
            "model"           : grp["model"].iloc[0],
            "flagged_months"  : int(grp["below_q1"].sum()),
            "max_streak"      : _streak(grp),
            "bad_actor_months": ", ".join(
                str(int(m)) for m in sorted(
                    grp.loc[grp["is_bad_actor"], "month"].unique())),
        })

    summary = (
        pd.DataFrame(rows)
        .sort_values(["section", "max_streak"], ascending=[True, False])
        .reset_index(drop=True)
    )
    summary["last_bad_actor_month"] = summary["bad_actor_months"].apply(
        lambda s: int(s.split(", ")[-1]) if s else None
    )
    summary = summary[summary["last_bad_actor_month"] == obs_month].reset_index(drop=True)

    # ── KPI row ────────────────────────────────────────────────────────────────
    total_eqm  = filt["eqm_no"].nunique()
    n_bad      = len(summary)
    rate       = n_bad / total_eqm * 100 if total_eqm else 0
    k1, k2, k3 = st.columns(3)
    k1.metric("Equipment Evaluated", f"{total_eqm:,}")
    k2.metric("Bad Actors Detected", f"{n_bad:,}")
    k3.metric("Bad Actor Rate", f"{rate:.1f}%")
    st.divider()

    # ── Tabs ───────────────────────────────────────────────────────────────────
    tab1, tab2, tab3 = st.tabs([
        "⚠️  Bad Actors",
        "📊  Reference Basis",
        "🔢  Scored Data",
    ])

    # ── Tab 1: Bad actor list ──────────────────────────────────────────────────
    with tab1:
        _section("⚠️", "Bad Actor List",
                 f"Min {consecutive_n} consecutive month(s)  ·  last flagged = Month {obs_month}")
        if summary.empty:
            st.success(f"No bad actors with last flagged month = Month {obs_month}.")
        else:
            st.markdown(
                f'<p style="color:#e63946;font-weight:600;margin:4px 0 12px">'
                f'{n_bad} equipment flagged</p>',
                unsafe_allow_html=True,
            )
            st.dataframe(summary, use_container_width=True)

            # Bad actor rate per section
            _section("📈", "Bad Actor Rate by Section")
            for sec, grp in summary.groupby("section"):
                sec_total = filt.loc[filt["section"] == sec, "eqm_no"].nunique()
                sec_rate  = len(grp) / sec_total if sec_total else 0
                st.caption(f"{sec}  —  {len(grp)} / {sec_total} ({sec_rate*100:.1f}%)")
                st.progress(sec_rate)

    # ── Tab 2: Reference basis ─────────────────────────────────────────────────
    with tab2:
        norm_agg = (
            filt.groupby(agg_key).agg(
                MA_min=("MA", "min"), MA_max=("MA", "max"), MA_avg=("MA", "mean"),
                MTBF_min=("MTBF", "min"), MTBF_max=("MTBF", "max"), MTBF_avg=("MTBF", "mean"),
            )
            .round(4)
            .reset_index()
        )
        _section("📐", "Normalisation Reference",
                 "min / max / avg of MA & MTBF used for normalization — aggregated across models")
        st.dataframe(
            norm_agg.style.format({
                "MA_min": "{:.4f}", "MA_max": "{:.4f}", "MA_avg": "{:.4f}",
                "MTBF_min": "{:.4f}", "MTBF_max": "{:.4f}", "MTBF_avg": "{:.4f}",
            }),
            use_container_width=True,
        )
        with st.expander("Detail per model"):
            st.dataframe(
                norm_stats.style.format({
                    "MA_min": "{:.4f}", "MA_max": "{:.4f}", "MA_avg": "{:.4f}",
                    "MTBF_min": "{:.4f}", "MTBF_max": "{:.4f}", "MTBF_avg": "{:.4f}",
                }),
                use_container_width=True,
            )

        pivot_idx = [k for k in basis_key if k not in ("model", "month")]
        q1_pivot = q1_df.pivot_table(
            index=pivot_idx, columns="month", values="q1_threshold", aggfunc="mean"
        ).round(4)
        q1_pivot.columns = [f"Month {int(c)}" for c in q1_pivot.columns]

        _section("📉", "Q1 Threshold Table",
                 "25th percentile of bad actor score — aggregated across models")
        st.dataframe(q1_pivot, use_container_width=True)
        with st.expander("Detail per model"):
            q1_pivot_detail = q1_df.pivot_table(
                index=[k for k in basis_key if k != "month"],
                columns="month", values="q1_threshold"
            ).round(4)
            q1_pivot_detail.columns = [f"Month {int(c)}" for c in q1_pivot_detail.columns]
            st.dataframe(q1_pivot_detail, use_container_width=True)

    # ── Tab 3: Scored data ─────────────────────────────────────────────────────
    with tab3:
        _section("🔢", "Scored Data",
                 "norm_MA × norm_MTBF = bad_actor_score  ·  rows in red = bad actor")
        show_cols = [
            "year", "month", "site_id", "section", "model", "eqm_no",
            "MA", "MTBF", "norm_MA", "norm_MTBF",
            "bad_actor_score", "q1_threshold", "below_q1", "is_bad_actor",
        ]
        scored = (
            filt[show_cols]
            .sort_values(["site_id", "section", "month", "eqm_no"])
            .reset_index(drop=True)
        )

        def _highlight(row):
            color = "background-color: #ffe0e0" if row["is_bad_actor"] else ""
            return [color] * len(row)

        st.dataframe(
            scored.style
            .format({
                "norm_MA": "{:.4f}", "norm_MTBF": "{:.4f}",
                "bad_actor_score": "{:.4f}", "q1_threshold": "{:.4f}",
            })
            .apply(_highlight, axis=1),
            use_container_width=True,
            height=420,
        )


# ── Sidebar controls ───────────────────────────────────────────────────────────
with st.sidebar:
    st.markdown(
        '<p style="font-size:1.15rem;font-weight:800;color:#1d3557;'
        'border-left:4px solid #e63946;padding-left:10px;margin-bottom:12px">'
        'Simulation Controls</p>',
        unsafe_allow_html=True,
    )

    # ── Dataset management (admin only) ───────────────────────────────────────
    if is_admin:
        with st.expander("🗂️ Dataset", expanded=True):
            # Status aktif saat ini
            if st.session_state.custom_df is not None:
                st.success(
                    f"**Custom dataset aktif**\n\n"
                    f"{st.session_state.custom_source}\n\n"
                    f"{len(st.session_state.custom_df):,} rows"
                )
                if st.button("🗑️ Hapus & kembali ke default", use_container_width=True):
                    st.session_state.custom_df = None
                    st.session_state.custom_source = None
                    st.rerun()
            else:
                st.info("Menggunakan **default** dataset\n\n`bad_actor_simulation.xlsx`")

            st.markdown("---")
            st.caption("Upload file untuk mengganti dataset aktif:")
            uploaded = st.file_uploader(
                "CSV atau XLSX",
                type=["csv", "xlsx"],
                label_visibility="collapsed",
            )

            if uploaded is not None:
                raw = uploaded.read()
                ext = uploaded.name.rsplit(".", 1)[-1].lower()

                if ext == "csv":
                    candidate_df = load_csv(raw)
                    candidate_source = uploaded.name
                else:
                    sheets = get_xlsx_sheets(raw)
                    sheet = sheets[0] if len(sheets) == 1 else st.selectbox("Pilih sheet", sheets)
                    candidate_df = load_xlsx(raw, sheet)
                    candidate_source = f"{uploaded.name}  [sheet: {sheet}]"

                missing = REQUIRED_COLS - set(candidate_df.columns)
                if missing:
                    st.error(f"Kolom tidak ditemukan: {', '.join(sorted(missing))}")
                else:
                    st.caption(f"Preview — {len(candidate_df):,} rows, {len(candidate_df.columns)} cols")
                    st.dataframe(candidate_df.head(3), use_container_width=True)
                    if st.button("✅ Terapkan dataset ini", use_container_width=True):
                        st.session_state.custom_df = candidate_df
                        st.session_state.custom_source = candidate_source
                        st.rerun()

        st.divider()

    # ── Determine active dataframe ─────────────────────────────────────────────
    df = st.session_state.custom_df if st.session_state.custom_df is not None else load_default()
    data_source = st.session_state.custom_source or "bad_actor_simulation.xlsx (default)"

    # ── Filters ────────────────────────────────────────────────────────────────
    with st.expander("🎛️  Filters", expanded=True):
        all_years    = sorted(df["year"].dropna().unique().tolist())
        all_sites    = sorted(df["site_id"].dropna().unique().tolist())
        all_sections = sorted(df["section"].dropna().unique().tolist())
        all_models   = ["ALL"] + sorted(df["model"].dropna().astype(str).unique().tolist())

        def _default(lst, vals):
            r = [v for v in vals if v in lst]
            return r if r else lst[:1]

        sel_years = st.multiselect(
            "🗓️  Year(s)", all_years, default=_default(all_years, DEFAULT_YEARS))

        obs_month = st.slider("📅  Observation Month (cutoff)", 1, 12, DEFAULT_OBS_MONTH,
                              help="Only data up to this month is included in the evaluation.")

        sel_sites = st.multiselect(
            "🏭  Site(s)", all_sites, default=_default(all_sites, DEFAULT_SITES))

        sel_sections = st.multiselect(
            "🔧  Section(s)", all_sections,
            default=_default(all_sections, DEFAULT_SECTIONS))

        sel_models = st.multiselect(
            "🚜  Model(s)  (ALL = no model filter)",
            all_models, default=_default(all_models, DEFAULT_MODELS))

        consecutive_n = st.slider("🔁  Min Consecutive Months", 1, 3, DEFAULT_CONSECUTIVE_N)

    st.markdown("<br>", unsafe_allow_html=True)
    run = st.button("▶  Run Simulation", type="primary", use_container_width=True)
    st.caption("Adjust filters above, then click Run.")

# ── Main area ──────────────────────────────────────────────────────────────────

# Admin banner (hanya muncul di mode admin)
if is_admin:
    st.markdown(
        '<div class="admin-banner">'
        '<h3>⚙️ Admin Mode</h3>'
        '<p>Dataset management aktif. Gunakan panel <strong>Dataset</strong> di sidebar '
        'untuk upload, replace, atau hapus dataset. '
        'File default (<code>bad_actor_simulation.xlsx</code>) tidak akan pernah diubah.</p>'
        '</div>',
        unsafe_allow_html=True,
    )

hcol1, hcol2 = st.columns([3, 1])
with hcol1:
    st.markdown(
        '<div class="app-header">'
        '<h1>⚠️ Bad Actor Simulation</h1>'
        '<div class="tagline">Equipment reliability scoring based on normalised MA × MTBF</div>'
        '</div>',
        unsafe_allow_html=True,
    )
with hcol2:
    st.markdown(
        f'<div style="text-align:right;padding-top:8px">'
        f'<span class="dataset-badge">📁 {data_source}</span><br>'
        f'<span class="dataset-badge" style="margin-top:4px;display:inline-block">'
        f'📅 Month 1 – {obs_month}</span>'
        f'</div>',
        unsafe_allow_html=True,
    )

st.divider()

if run:
    if not sel_years or not sel_sites or not sel_sections or not sel_models:
        st.warning("Please select at least one value for each filter.")
    else:
        run_simulation(sel_years, sel_sites, sel_sections, sel_models,
                       consecutive_n, obs_month)
else:
    st.markdown("""
    <div class="welcome-card">
      <h2>Welcome to the Simulation Console</h2>
      <p>Identify equipment that consistently underperforms relative to its peers.</p>
      <ul class="step-list">
        <li><span class="step-num">1</span> Expand <strong>Filters</strong> to set year, site, section, model, and observation month.</li>
        <li><span class="step-num">2</span> Click <strong>Run Simulation</strong> to compute scores and flag bad actors.</li>
      </ul>
    </div>
    """, unsafe_allow_html=True)