import streamlit as st
import pandas as pd
import pandas_datareader.data as web
import yfinance as yf
import datetime
import plotly.graph_objs as go
import numpy as np

# ---------- Page config (must be the first Streamlit call) ----------
st.set_page_config(layout="wide")

# ---------- Stable CSS for wider sidebar (avoid fragile class names) ----------
st.markdown(
    """
    <style>
    /* Make the sidebar wider in a stable way */
    [data-testid="stSidebar"] {
        width: 350px;
        min-width: 350px;
    }
    </style>
    """,
    unsafe_allow_html=True,
)

# ---------- Session state for persistent "Run Analysis" ----------
if "run_analysis" not in st.session_state:
    st.session_state.run_analysis = False

# ---------- App title and description ----------
st.title("Key Economic Recession Indicators")
st.markdown("""
This tool allows you to visualize and analyze various recession indicators over time.
- The shaded areas in the charts represent historical recession periods.
- Use the checkboxes in the sidebar to choose the indicators you'd like to explore.
""")

# ---------- Sidebar controls ----------
with st.sidebar.expander("How to Use", expanded=False):
    st.write("""
    **How to use this app:**
    1. Select the indicators you want to visualize from the sidebar.
    2. Click "Run Analysis" to generate the plots.
    3. The plots will show historical data for the selected indicators, with recession periods shaded in gray.
    4. Hover over the charts to see detailed information for each data point.
    """)

st.sidebar.header("Select Indicators")
with st.sidebar.expander("Indicators", expanded=True):
    # Removed "Federal Funds Rate" per your request
    indicators = {
        'Sahm Recession Indicator': 'SAHMREALTIME',
        'U.S. Recession Probabilities': 'RECPROUSM156N',
        'Yield Spread (10Y - 2Y)': 'Yield_Spread',  # Calculated, not fetched
        'Stock Market (S&P 500)': 'SP500',          # Fetched from yfinance
        'VIX': 'VIX',                               # Fetched from yfinance
        'Treasury Rates': ('GS10', 'DGS2', 'DGS1MO', 'TB3MS'),
        # 'Federal Funds Rate': 'FEDFUNDS',         # <-- removed
        'Unemployment Rate': 'UNRATE',
        'Nonfarm Payrolls': 'PAYEMS',
        'Jobless Claims': 'ICSA',
        'Retail Sales': 'RSXFS',
        'Industrial Production': ('INDPRO', 'INDPRO_PCT'),
        'Housing Starts': 'HOUST',
        'Consumer Confidence': 'UMCSENT',
        'Inflation (CPI)': ('CPIAUCSL', 'CPIAUCSL_PCT')
    }
    selected_indicators = {key: st.checkbox(key, value=True) for key in indicators.keys()}

# Single Run button (no explicit "clear" — re-running implies clearing)
if st.sidebar.button("Run Analysis"):
    st.session_state.run_analysis = True

# ---------- Dates ----------
start_date = datetime.datetime(1920, 1, 1)
end_date = datetime.datetime.today()

# ---------- Recession periods ----------
crash_periods = {
    '1929-08-01': '1933-03-01',
    '1937-05-01': '1938-06-01',
    '1945-02-01': '1945-10-01',
    '1948-11-01': '1949-10-01',
    '1953-07-01': '1954-05-01',
    '1957-08-01': '1958-04-01',
    '1960-04-01': '1961-02-01',
    '1969-12-01': '1970-11-01',
    '1973-11-01': '1975-03-01',
    '1980-01-01': '1980-07-01',
    '1981-07-01': '1982-11-01',
    '1990-07-01': '1991-03-01',
    '2001-03-01': '2001-11-01',
    '2007-12-01': '2009-06-01',
    '2020-02-01': '2020-04-01'
}

# ---------- Helpers ----------
def pct_rank(series: pd.Series, value: float) -> float:
    s = pd.to_numeric(series, errors="coerce").dropna()
    if s.empty or not np.isfinite(value):
        return np.nan
    return float((s < value).mean() * 100.0)

def fmt_pct(x, decimals=1):
    return "n/a" if pd.isna(x) else f"{x*100:.{decimals}f}%"

def fmt_val(x, decimals=2):
    return "n/a" if pd.isna(x) else f"{x:.{decimals}f}"

def series_change(s: pd.Series, periods: int = 1, pct: bool = True):
    if len(s) <= periods:
        return np.nan
    if pct:
        return float((s.iloc[-1] / s.iloc[-(periods+1)] - 1.0))
    else:
        return float(s.iloc[-1] - s.iloc[-(periods+1)])

def current_and_date(s: pd.Series):
    if s is None or s.empty:
        return np.nan, "n/a"
    return float(s.iloc[-1]), s.index[-1].date().isoformat()

def inversion_streak(series: pd.Series):
    """Consecutive periods the series has been < 0 at the end of series."""
    if series is None or series.dropna().empty:
        return 0
    v = (series < 0).astype(int).to_numpy()
    streak = 0
    for x in v[::-1]:
        if x == 1:
            streak += 1
        else:
            break
    return streak

# ---------- Cached data fetchers ----------
@st.cache_data(ttl=6 * 60 * 60, show_spinner=False)
def fetch_fred_series(series_code: str, start: datetime.datetime, end: datetime.datetime) -> pd.Series:
    """Fetch a single FRED series as a named Series (empty Series if fails)."""
    try:
        df = web.DataReader(series_code, 'fred', start, end)
        if isinstance(df, pd.DataFrame):
            s = df.squeeze("columns")
        else:
            s = df
        s = s.rename(series_code)
        return s
    except Exception as e:
        st.warning(f"Failed to fetch {series_code} from FRED: {e}")
        return pd.Series(name=series_code, dtype="float64")

@st.cache_data(ttl=6 * 60 * 60, show_spinner=False)
def fetch_yf_series(ticker: str, label: str, start: datetime.datetime, end: datetime.datetime) -> pd.Series:
    """Fetch Adj Close from Yahoo Finance as a named Series."""
    try:
        df = yf.download(ticker, start=start, end=end, auto_adjust=False, progress=False, threads=False)
        if isinstance(df.columns, pd.MultiIndex):
            df.columns = df.columns.get_level_values(0)
        s = df.get('Adj Close', pd.Series(dtype="float64")).rename(label)
        return s
    except Exception as e:
        st.warning(f"Failed to fetch {label} ({ticker}) from Yahoo Finance: {e}")
        return pd.Series(name=label, dtype="float64")

# ---------- Build dataset ----------
def build_dataset(selected: dict) -> pd.DataFrame:
    series_list = []

    # FRED (skip derived)
    for key, col in indicators.items():
        if not selected.get(key, False):
            continue
        if isinstance(col, tuple):
            for c in col:
                if c in ["INDPRO_PCT", "CPIAUCSL_PCT"]:
                    continue  # derived later
                s = fetch_fred_series(c, start_date, end_date)
                if not s.empty:
                    series_list.append(s)
        else:
            if col in ["Yield_Spread", "SP500", "VIX"]:
                continue  # handled separately / derived
            s = fetch_fred_series(col, start_date, end_date)
            if not s.empty:
                series_list.append(s)

    # YFinance
    if selected.get('Stock Market (S&P 500)', False):
        s = fetch_yf_series('^GSPC', 'SP500', start_date, end_date)
        if not s.empty:
            series_list.append(s)
    if selected.get('VIX', False):
        s = fetch_yf_series('^VIX', 'VIX', start_date, end_date)
        if not s.empty:
            series_list.append(s)

    if not series_list:
        return pd.DataFrame()

    combined = pd.concat(series_list, axis=1).sort_index()

    # Derived columns
    if selected.get('Industrial Production', False) and 'INDPRO' in combined.columns:
        combined['INDPRO_PCT'] = combined['INDPRO'].pct_change() * 100
    if selected.get('Inflation (CPI)', False) and 'CPIAUCSL' in combined.columns:
        combined['CPIAUCSL_PCT'] = combined['CPIAUCSL'].pct_change() * 100

    # Interpolate (time index required)
    combined = combined.interpolate(method='time')

    # Yield spread
    if selected.get('Yield Spread (10Y - 2Y)', False) and {'GS10', 'DGS2'}.issubset(combined.columns):
        combined['Yield_Spread'] = combined['GS10'] - combined['DGS2']

    return combined

# ---------- Plotting helpers ----------
def add_recession_shading(fig: go.Figure):
    for peak, trough in crash_periods.items():
        fig.add_shape(
            type="rect",
            xref="x",
            yref="paper",
            x0=peak,
            y0=0,
            x1=trough,
            y1=1,
            fillcolor="gray",
            opacity=0.3,
            layer="below",
            line_width=0,
        )

def finalize_layout(fig: go.Figure, title: str, ytitle: str):
    fig.update_layout(
        title=title,
        xaxis_title='Date',
        yaxis_title=ytitle,
        template='plotly_dark',                 # dark-friendly defaults
        paper_bgcolor='rgba(0,0,0,0)',          # transparent to match theme background
        plot_bgcolor='rgba(0,0,0,0)',           # transparent to match theme background
        font=dict(color="white"),
        xaxis=dict(
            tickformat="%Y",
            tickmode="linear",
            dtick="M36",
            showspikes=True,
            spikemode='across',
            spikesnap='cursor',
            spikethickness=1
        ),
        hovermode="x unified",
        hoverlabel=dict(
            bgcolor="rgba(14,17,23,0.95)",      # blends with backgroundColor "#0e1117"
            font_size=12,
            font_family="Rockwell",
            font_color="white"
        ),
        legend=dict(
            x=0.02,
            y=0.95,
            traceorder='normal',
            bgcolor='rgba(0,0,0,0)',            # transparent legend
            bordercolor='rgba(0,0,0,0)',
            font=dict(color="white"),
            title_font=dict(color="white")
        ),
        margin=dict(l=60, r=20, t=40, b=40)
    )
    fig.update_xaxes(
        showgrid=True,
        gridwidth=1,
        gridcolor='rgba(255,255,255,0.12)',     # subtle grid for dark
        tickangle=45,
        tickformatstops=[
            dict(dtickrange=[None, "M1"], value="%b %d, %Y"),
            dict(dtickrange=["M1", None], value="%Y")
        ]
    )
    fig.update_yaxes(
        showgrid=True,
        gridwidth=1,
        gridcolor='rgba(255,255,255,0.12)'
    )
    fig.update_traces(hovertemplate='%{x|%b %d, %Y}<br>%{y}<extra></extra>')

# ---------- Interpretation blocks ----------
def show_interpretation_for(key: str, column, data: pd.DataFrame):
    with st.expander("Interpretation", expanded=False):
        # Helper to write a bullet line
        def blt(text): st.write(f"- {text}")

        if key == 'Sahm Recession Indicator' and 'SAHMREALTIME' in data.columns:
            s = data['SAHMREALTIME'].dropna()
            cur, d = current_and_date(s)
            pr = pct_rank(s, cur)
            ch_3 = series_change(s, 3, pct=False)
            ma3 = s.rolling(3, min_periods=2).mean().iloc[-1] if len(s) else np.nan
            blt(f"Latest reading ({d}): **{fmt_val(cur, 2)}**; historical percentile: **{fmt_val(pr,1)}**.")
            blt("Rule-of-thumb threshold is **0.5** (dashed line in the chart). Values above this often coincide with recessions.")
            if not pd.isna(ch_3):
                blt(f"3-period change (approx. 3 months for monthly data): **{fmt_val(ch_3, 2)}** points.")
            if not pd.isna(ma3):
                blt(f"Trend check: the indicator is {'above' if cur>ma3 else 'below' if cur<ma3 else 'near'} its 3-period average.")
            st.write("**How to read**: A sharp rise above 0.5 historically flags ongoing recessions; falling values suggest recovery.")

        elif key == 'U.S. Recession Probabilities' and 'RECPROUSM156N' in data.columns:
            s = data['RECPROUSM156N'].dropna()
            cur, d = current_and_date(s)
            pr = pct_rank(s, cur)
            ch_3 = series_change(s, 3, pct=False)
            blt(f"Latest probability ({d}): **{fmt_val(cur, 1)}%**; historical percentile: **{fmt_val(pr,1)}**.")
            if not pd.isna(ch_3):
                blt(f"3-period change: **{fmt_val(ch_3,1)}** percentage points.")
            blt("Sustained moves to elevated probabilities (e.g., >50%) tend to align with recession periods, but short spikes can be false alarms.")

        elif key == 'Yield Spread (10Y - 2Y)' and 'Yield_Spread' in data.columns:
            s = data['Yield_Spread'].dropna()
            cur, d = current_and_date(s)
            pr = pct_rank(s, cur)
            inv_streak = inversion_streak(s)
            ch_3 = series_change(s, 3, pct=False)
            blt(f"Latest spread ({d}): **{fmt_val(cur,2)} pp**; historical percentile: **{fmt_val(pr,1)}**.")
            if cur < 0:
                blt(f"**Inversion** is active (10Y < 2Y). Current inversion streak: **{inv_streak}** observations.")
            else:
                blt("Curve is **not inverted** currently.")
            if not pd.isna(ch_3):
                blt(f"3-period change: **{fmt_val(ch_3,2)}** pp.")
            st.write("**How to read**: Deep or persistent inversion often precedes recessions by several months; steepening from very negative levels can signal normalization.")

        elif key == 'Stock Market (S&P 500)' and 'SP500' in data.columns:
            s = data['SP500'].dropna()
            cur, d = current_and_date(s)
            pr = pct_rank(s, cur)
            r_21 = series_change(s, 21, pct=True)
            r_63 = series_change(s, 63, pct=True)
            r_252 = series_change(s, 252, pct=True)
            rolling_max = s.cummax()
            drawdown = float(s.iloc[-1] / rolling_max.iloc[-1] - 1.0) if len(s) else np.nan
            vol20 = float(s.pct_change().rolling(20).std(ddof=0).iloc[-1] * np.sqrt(252)) if len(s) >= 20 else np.nan
            blt(f"Last close ({d}): **{fmt_val(cur,2)}**; percentile vs history: **{fmt_val(pr,1)}**.")
            blt(f"Returns — 1m: **{fmt_pct(r_21)}**, 3m: **{fmt_pct(r_63)}**, 12m: **{fmt_pct(r_252)}**.")
            if not pd.isna(drawdown):
                blt(f"Drawdown from peak: **{fmt_pct(drawdown)}**.")
            if not pd.isna(vol20):
                blt(f"Realized vol (20d, annualized): **{fmt_pct(vol20)}**.")
            st.write("**How to read**: Equity weakness often leads or coincides with recessions; watch for persistent downtrends and elevated volatility near shaded bands.")

        elif key == 'VIX' and 'VIX' in data.columns:
            s = data['VIX'].dropna()
            cur, d = current_and_date(s)
            pr = pct_rank(s, cur)
            m20 = float(s.rolling(20).mean().iloc[-1]) if len(s) >= 20 else np.nan
            m60 = float(s.rolling(60).mean().iloc[-1]) if len(s) >= 60 else np.nan
            blt(f"Latest VIX ({d}): **{fmt_val(cur,2)}**; percentile vs history: **{fmt_val(pr,1)}**.")
            if not pd.isna(m20):
                blt(f"Position vs 20-day avg: **{('above' if cur>m20 else 'below' if cur<m20 else 'near')}** ({fmt_val(m20,2)}).")
            if not pd.isna(m60):
                blt(f"Position vs 60-day avg: **{('above' if cur>m60 else 'below' if cur<m60 else 'near')}** ({fmt_val(m60,2)}).")
            st.write("**How to read**: High percentiles indicate stress; falling VIX from high levels can mark stabilization, while spikes from low levels often accompany drawdowns.")

        elif key == 'Treasury Rates':
            cols = [c for c in ['GS10', 'DGS2', 'TB3MS', 'DGS1MO'] if c in data.columns]
            if cols:
                latests = {c: current_and_date(data[c].dropna())[0] for c in cols}
                # Spread diagnostics if available
                s_10_2 = data['GS10'] - data['DGS2'] if {'GS10', 'DGS2'}.issubset(data.columns) else None
                s_10_3m = data['GS10'] - data['TB3MS'] if {'GS10', 'TB3MS'}.issubset(data.columns) else None
                blt("Latest yields (percent): " + ", ".join([f"**{k}={fmt_val(v,2)}**" for k, v in latests.items() if not pd.isna(v)]))
                if s_10_2 is not None:
                    cur = float(s_10_2.dropna().iloc[-1])
                    blt(f"10Y−2Y spread: **{fmt_val(cur,2)} pp** ({'inverted' if cur<0 else 'normal'}).")
                if s_10_3m is not None:
                    cur = float(s_10_3m.dropna().iloc[-1])
                    blt(f"10Y−3M spread: **{fmt_val(cur,2)} pp** ({'inverted' if cur<0 else 'normal'}).")
                st.write("**How to read**: Rising short rates vs long rates flatten/invert the curve. Inversions often precede recessions; re-steepening from very negative levels can precede recoveries.")

        elif key == 'Unemployment Rate' and 'UNRATE' in data.columns:
            s = data['UNRATE'].dropna()
            cur, d = current_and_date(s)
            pr = pct_rank(s, cur)
            ch_3 = series_change(s, 3, pct=False)
            ch_12 = series_change(s, 12, pct=False)
            blt(f"Latest unemployment ({d}): **{fmt_val(cur,2)}%**; percentile vs history: **{fmt_val(pr,1)}**.")
            if not pd.isna(ch_3): blt(f"Change over 3 periods: **{fmt_val(ch_3,2)} pp**.")
            if not pd.isna(ch_12): blt(f"Change over 12 periods: **{fmt_val(ch_12,2)} pp**.")
            st.write("**How to read**: Rapid rises often occur around recessions; peaks typically lag recession start dates.")

        elif key == 'Nonfarm Payrolls' and 'PAYEMS' in data.columns:
            s = data['PAYEMS'].dropna()
            cur, d = current_and_date(s)
            ch_1 = series_change(s, 1, pct=False)
            ch_3 = series_change(s, 3, pct=False)
            yoy = series_change(s, 12, pct=True)
            blt(f"Latest payrolls ({d}): **{fmt_val(cur,0)}k jobs**.")
            if not pd.isna(ch_1): blt(f"1-period change: **{fmt_val(ch_1,0)}k**.")
            if not pd.isna(ch_3): blt(f"3-period change: **{fmt_val(ch_3,0)}k**.")
            if not pd.isna(yoy):  blt(f"12-period growth: **{fmt_pct(yoy)}**.")
            st.write("**How to read**: Payroll growth slows before and during recessions; contractions are strong signals of broad weakness.")

        elif key == 'Jobless Claims' and 'ICSA' in data.columns:
            s = data['ICSA'].dropna()
            cur, d = current_and_date(s)
            ma4 = float(s.rolling(4).mean().iloc[-1]) if len(s) >= 4 else np.nan
            yoy = series_change(s, 52, pct=True)  # weekly series (approx.)
            blt(f"Latest claims ({d}): **{fmt_val(cur,0)}**; 4-wk avg: **{fmt_val(ma4,0)}**.")
            if not pd.isna(yoy): blt(f"YoY change (approx.): **{fmt_pct(yoy)}**.")
            st.write("**How to read**: Persistent uptrends in the 4-week average often precede rising unemployment and recessions.")

        elif key == 'Retail Sales' and 'RSXFS' in data.columns:
            s = data['RSXFS'].dropna()
            cur, d = current_and_date(s)
            mom = series_change(s, 1, pct=True)
            qoq = series_change(s, 3, pct=True)
            yoy = series_change(s, 12, pct=True)
            blt(f"Latest ({d}): **{fmt_val(cur,2)}** (index). MoM: **{fmt_pct(mom)}**, QoQ: **{fmt_pct(qoq)}**, YoY: **{fmt_pct(yoy)}**.")
            st.write("**How to read**: Retail sales proxy consumption strength; broad slowdowns or contractions often align with late-cycle and recessionary phases.")

        elif key == 'Industrial Production' and 'INDPRO' in data.columns:
            s = data['INDPRO'].dropna()
            pct = data.get('INDPRO_PCT', pd.Series(dtype='float64')).dropna()
            cur, d = current_and_date(s)
            yoy = series_change(s, 12, pct=True)
            blt(f"Level ({d}): **{fmt_val(cur,2)}** (index). YoY: **{fmt_pct(yoy)}**.")
            if not pct.empty:
                blt(f"Latest monthly change: **{fmt_val(pct.iloc[-1],2)}%**; average over last 6m: **{fmt_val(pct.tail(6).mean(),2)}%**.")
            st.write("**How to read**: Production falls and negative monthly prints tend to cluster near recessions; rebounds suggest early recovery.")

        elif key == 'Housing Starts' and 'HOUST' in data.columns:
            s = data['HOUST'].dropna()
            cur, d = current_and_date(s)
            mom = series_change(s, 1, pct=True)
            yoy = series_change(s, 12, pct=True)
            blt(f"Latest starts ({d}): **{fmt_val(cur,0)}k** (annualized). MoM: **{fmt_pct(mom)}**, YoY: **{fmt_pct(yoy)}**.")
            st.write("**How to read**: Housing is interest-rate sensitive and typically weakens well before recessions; stabilization often leads broader upturns.")

        elif key == 'Consumer Confidence' and 'UMCSENT' in data.columns:
            s = data['UMCSENT'].dropna()
            cur, d = current_and_date(s)
            pr = pct_rank(s, cur)
            mom = series_change(s, 1, pct=False)
            yoy = series_change(s, 12, pct=False)
            blt(f"Latest sentiment ({d}): **{fmt_val(cur,1)}**; percentile vs history: **{fmt_val(pr,1)}**.")
            if not pd.isna(mom): blt(f"1-period change: **{fmt_val(mom,1)}** points.")
            if not pd.isna(yoy): blt(f"12-period change: **{fmt_val(yoy,1)}** points.")
            st.write("**How to read**: Collapses in sentiment often occur around recessions; recovering sentiment can confirm early-cycle improvement.")

        elif key == 'Inflation (CPI)' and 'CPIAUCSL' in data.columns:
            s = data['CPIAUCSL'].dropna()
            mom = data.get('CPIAUCSL_PCT', pd.Series(dtype='float64')).dropna()
            cur, d = current_and_date(s)
            yoy = series_change(s, 12, pct=True)
            blt(f"CPI level ({d}): **{fmt_val(cur,1)}** (index). YoY: **{fmt_pct(yoy)}**.")
            if not mom.empty:
                blt(f"Latest month-over-month change: **{fmt_val(mom.iloc[-1],2)}%**; 3-month average: **{fmt_val(mom.tail(3).mean(),2)}%**.")
            st.write("**How to read**: Cooling inflation eases pressure on policy and supports soft-landing scenarios; re-acceleration risks tighter financial conditions.")

        else:
            st.write("No interpretation available for this selection (insufficient data).")

# ---------- Main render ----------
if st.session_state.run_analysis:
    with st.spinner("Fetching data and building charts..."):
        combined_data = build_dataset(selected_indicators)

    if combined_data.empty:
        st.error("No data was successfully fetched for the selected indicators.")
    else:
        # Loop through selections and plot
        for key, column in indicators.items():
            if not selected_indicators.get(key, False):
                continue

            fig = go.Figure()
            add_recession_shading(fig)

            if isinstance(column, tuple):
                # Industrial Production: level + % change on y2
                if column == ('INDPRO', 'INDPRO_PCT') and 'INDPRO' in combined_data.columns:
                    fig.add_trace(go.Scatter(
                        x=combined_data.index, y=combined_data['INDPRO'],
                        mode='lines', name='Industrial Production'
                    ))
                    if 'INDPRO_PCT' in combined_data.columns:
                        fig.add_trace(go.Scatter(
                            x=combined_data.index, y=combined_data['INDPRO_PCT'],
                            mode='lines', name='Industrial Production % Change', yaxis='y2'
                        ))
                        fig.update_layout(yaxis2=dict(
                            title="Industrial Production % Change",
                            overlaying='y', side='right'
                        ))
                    finalize_layout(fig, key, key)

                # Inflation: CPI + % change on y2
                elif column == ('CPIAUCSL', 'CPIAUCSL_PCT') and 'CPIAUCSL' in combined_data.columns:
                    fig.add_trace(go.Scatter(
                        x=combined_data.index, y=combined_data['CPIAUCSL'],
                        mode='lines', name='Inflation (CPI)'
                    ))
                    if 'CPIAUCSL_PCT' in combined_data.columns:
                        fig.add_trace(go.Scatter(
                            x=combined_data.index, y=combined_data['CPIAUCSL_PCT'],
                            mode='lines', name='Inflation % Change', yaxis='y2'
                        ))
                        fig.update_layout(yaxis2=dict(
                            title="Inflation % Change",
                            overlaying='y', side='right'
                        ))
                    finalize_layout(fig, key, key)

                # Treasury rates: plot each available
                elif column == ('GS10', 'DGS2', 'DGS1MO', 'TB3MS'):
                    any_added = False
                    for col in column:
                        if col in combined_data.columns:
                            any_added = True
                            fig.add_trace(go.Scatter(
                                x=combined_data.index, y=combined_data[col], mode='lines', name=col
                            ))
                    if any_added:
                        finalize_layout(fig, key, key)

                else:
                    # Generic multi-series if needed
                    for col in column:
                        if col in combined_data.columns:
                            fig.add_trace(go.Scatter(
                                x=combined_data.index, y=combined_data[col], mode='lines', name=col
                            ))
                    finalize_layout(fig, key, key)

            else:
                # Single series or derived
                if column in combined_data.columns:
                    fig.add_trace(go.Scatter(
                        x=combined_data.index, y=combined_data[column], mode='lines', name=key
                    ))
                    if key == 'Sahm Recession Indicator':
                        fig.add_hline(
                            y=0.5, line=dict(color="#ff6b6b", dash="dash"),
                            annotation_text="Recession Threshold",
                            annotation_position="bottom right"
                        )
                    finalize_layout(fig, key, key)

                elif column == 'Yield_Spread' and {'GS10', 'DGS2'}.issubset(combined_data.columns):
                    fig.add_trace(go.Scatter(
                        x=combined_data.index, y=combined_data['Yield_Spread'],
                        mode='lines', name='Yield Spread (10Y - 2Y)'
                    ))
                    finalize_layout(fig, key, key)

            # Only render if we actually added something beyond the shading
            if fig.data:
                st.plotly_chart(fig, use_container_width=True)
                # --- Interpretation for this panel ---
                show_interpretation_for(key, column, combined_data)

# ---------- Hide default Streamlit branding ----------
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)