app.py

import os
import sys
import traceback
from pathlib import Path
from typing import List, Tuple, Any

import duckdb
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use("Agg")  # headless for Spaces
import matplotlib.pyplot as plt
import gradio as gr

# =========================
# Basic configuration
# =========================
APP_TITLE = "ALCO Liquidity & Interest-Rate Risk Dashboard"
TABLE_FQN = "my_db.main.masterdataset_v"   # source table
VIEW_FQN = "my_db.main.positions_v"        # normalized view created by this app
EXPORT_DIR = Path("exports")
EXPORT_DIR.mkdir(exist_ok=True)

PRODUCT_ASSETS = [
    "loan", "overdraft", "advances", "bills", "bill",
    "tbond", "t-bond", "tbill", "t-bill", "repo_asset"
]
PRODUCT_SOF = [
    "fd", "term_deposit", "td", "savings", "current",
    "call", "repo_liab"
]

# =========================
# Helpers
# =========================
def connect_md() -> duckdb.DuckDBPyConnection:
    token = os.environ.get("MOTHERDUCK_TOKEN", "")
    if not token:
        raise RuntimeError("MOTHERDUCK_TOKEN is not set. Add it in Space → Settings → Secrets.")
    return duckdb.connect(f"md:?motherduck_token={token}")

def discover_columns(conn: duckdb.DuckDBPyConnection, table_fqn: str) -> List[str]:
    # More robust than information_schema across DuckDB/MotherDuck
    df = conn.execute(f"DESCRIBE {table_fqn};").fetchdf()
    # DuckDB: columns listed under 'column_name'
    name_col = "column_name" if "column_name" in df.columns else df.columns[0]
    return [str(c).lower() for c in df[name_col].tolist()]

def build_view_sql(existing_cols: List[str]) -> str:
    wanted = [
        "as_of_date", "product", "months", "segments",
        "currency", "Portfolio_value", "Interest_rate",
        "days_to_maturity"
    ]
    sel = []
    for c in wanted:
        if c.lower() in existing_cols:
            sel.append(c)
        else:
            if c in ("Portfolio_value", "Interest_rate", "days_to_maturity", "months"):
                sel.append(f"CAST(NULL AS DOUBLE) AS {c}")
            else:
                sel.append(f"CAST(NULL AS VARCHAR) AS {c}")

    sof_list = ", ".join([f"'{p}'" for p in PRODUCT_SOF])
    asset_list = ", ".join([f"'{p}'" for p in PRODUCT_ASSETS])
    bucket_case = (
        f"CASE WHEN lower(product) IN ({sof_list}) THEN 'SoF' "
        f"WHEN lower(product) IN ({asset_list}) THEN 'Assets' "
        f"ELSE 'Unknown' END AS bucket"
    )
    select_sql = ",\n  ".join(sel + [bucket_case])
    return f"""
    CREATE OR REPLACE VIEW {VIEW_FQN} AS
    SELECT
      {select_sql}
    FROM {TABLE_FQN};
    """

def ensure_view(conn: duckdb.DuckDBPyConnection, cols: List[str]) -> None:
    required = {"product", "portfolio_value", "days_to_maturity"}
    if not required.issubset(set(cols)):
        raise RuntimeError(
            f"Source table {TABLE_FQN} must contain columns {sorted(required)}; found {sorted(cols)}"
        )
    conn.execute(build_view_sql(cols))

def safe_num(x) -> float:
    try:
        return float(0.0 if x is None or (isinstance(x, float) and np.isnan(x)) else x)
    except Exception:
        return 0.0

def zeros_like_index(index) -> pd.Series:
    return pd.Series([0] * len(index), index=index)

def plot_ladder(df: pd.DataFrame):
    try:
        if df is None or df.empty:
            fig, ax = plt.subplots(figsize=(7, 3))
            ax.text(0.5, 0.5, "No data", ha="center", va="center")
            ax.axis("off")
            return fig
        pivot = df.pivot(index="time_bucket", columns="bucket", values="amount").fillna(0)
        order = ["T+1", "T+2..7", "T+8..30", "T+31+"]
        pivot = pivot.reindex(order)
        fig, ax = plt.subplots(figsize=(7, 4))
        assets = pivot["Assets"] if "Assets" in pivot.columns else zeros_like_index(pivot.index)
        sof = pivot["SoF"] if "SoF" in pivot.columns else zeros_like_index(pivot.index)
        ax.bar(pivot.index, assets, label="Assets")
        ax.bar(pivot.index, -sof, label="SoF")
        ax.axhline(0, color="gray", lw=1)
        ax.set_ylabel("LKR")
        ax.set_title("Maturity Ladder (Assets vs SoF)")
        ax.legend()
        fig.tight_layout()
        return fig
    except Exception as e:
        fig, ax = plt.subplots(figsize=(7, 3))
        ax.text(0.01, 0.8, "Chart Error:", fontsize=12, ha="left")
        ax.text(0.01, 0.5, str(e), fontsize=10, ha="left", wrap=True)
        ax.axis("off")
        return fig

# =========================
# Query fragments
# =========================
KPI_SQL = f"""
SELECT
  COALESCE(SUM(CASE WHEN bucket='Assets' AND days_to_maturity<=1 THEN Portfolio_value END),0) AS assets_t1,
  COALESCE(SUM(CASE WHEN bucket='SoF'    AND days_to_maturity<=1 THEN Portfolio_value END),0) AS sof_t1,
  COALESCE(SUM(CASE WHEN bucket='Assets' AND days_to_maturity<=1 THEN Portfolio_value END),0)
  - COALESCE(SUM(CASE WHEN bucket='SoF'  AND days_to_maturity<=1 THEN Portfolio_value END),0) AS net_gap_t1
FROM {VIEW_FQN};
"""

LADDER_SQL = f"""
SELECT
  CASE
    WHEN days_to_maturity <= 1 THEN 'T+1'
    WHEN days_to_maturity BETWEEN 2 AND 7 THEN 'T+2..7'
    WHEN days_to_maturity BETWEEN 8 AND 30 THEN 'T+8..30'
    ELSE 'T+31+'
  END AS time_bucket,
  bucket,
  SUM(Portfolio_value) AS amount
FROM {VIEW_FQN}
GROUP BY 1,2
ORDER BY 1,2;
"""

def irr_sql(cols: List[str]) -> str:
    has_months = "months" in cols
    has_ir = "interest_rate" in cols
    t_expr = "CASE WHEN days_to_maturity IS NOT NULL THEN days_to_maturity/365.0"
    if has_months:
        t_expr += " WHEN months IS NOT NULL THEN months/12.0"
    t_expr += " ELSE NULL END"
    y_expr = "(Interest_rate/100.0)" if has_ir else "0.0"
    return f"""
    SELECT
      bucket,
      SUM(Portfolio_value) AS pv_sum,
      SUM(Portfolio_value * {t_expr}) / NULLIF(SUM(Portfolio_value),0) AS dur_mac,
      SUM(Portfolio_value * ({t_expr})/(1+({y_expr}))) / NULLIF(SUM(Portfolio_value),0) AS dur_mod
    FROM {VIEW_FQN}
    GROUP BY bucket;
    """

def export_excel(as_of: str, assets_t1: float, sof_t1: float, net_gap_t1: float,
                 ladder: pd.DataFrame, irr: pd.DataFrame) -> Path:
    out = EXPORT_DIR / f"alco_report_{as_of or 'NA'}.xlsx"
    with pd.ExcelWriter(out, engine="xlsxwriter") as xw:
        pd.DataFrame({
            "as_of_date": [as_of or "N/A"],
            "assets_t1": [assets_t1],
            "sof_t1": [sof_t1],
            "net_gap_t1": [net_gap_t1],
        }).to_excel(xw, index=False, sheet_name="kpis")
        (ladder if ladder is not None else pd.DataFrame()).to_excel(xw, index=False, sheet_name="ladder")
        (irr if irr is not None else pd.DataFrame()).to_excel(xw, index=False, sheet_name="irr")
    return out

# =========================
# Dashboard callback
# =========================
def run_dashboard() -> Tuple[str, str, str, str, str, Any, pd.DataFrame, pd.DataFrame, str]:
    """
    Returns:
      status, as_of, assets_t1, sof_t1, net_gap_t1, figure, ladder_df, irr_df, excel_path
      (all text values are returned as strings to avoid component type errors)
    """
    try:
        conn = connect_md()

        # Discover columns & build view
        cols = discover_columns(conn, TABLE_FQN)
        ensure_view(conn, cols)

        # as_of (optional)
        as_of = "N/A"
        if "as_of_date" in cols:
            tmp = conn.execute(f"SELECT max(as_of_date) AS d FROM {VIEW_FQN}").fetchdf()
            if not tmp.empty and not pd.isna(tmp["d"].iloc[0]):
                as_of = str(tmp["d"].iloc[0])[:10]

        # KPIs
        kpi = conn.execute(KPI_SQL).fetchdf()
        assets_t1 = safe_num(kpi["assets_t1"].iloc[0]) if not kpi.empty else 0.0
        sof_t1    = safe_num(kpi["sof_t1"].iloc[0]) if not kpi.empty else 0.0
        net_gap   = safe_num(kpi["net_gap_t1"].iloc[0]) if not kpi.empty else 0.0

        # Ladder & IRR
        ladder = conn.execute(LADDER_SQL).fetchdf()
        irr    = conn.execute(irr_sql(cols)).fetchdf()

        fig = plot_ladder(ladder)
        xlsx = export_excel(as_of, assets_t1, sof_t1, net_gap, ladder, irr)
        xlsx_str = str(xlsx if xlsx.exists() else "")

        status = "✅ OK"
        return (
            status,
            as_of,
            f"{assets_t1:,.0f}",
            f"{sof_t1:,.0f}",
            f"{net_gap:,.0f}",
            fig,
            ladder,
            irr,
            xlsx_str,
        )

    except Exception as e:
        tb = traceback.format_exc()
        # Return placeholders + human-readable error in status
        empty_df = pd.DataFrame()
        fig = plot_ladder(empty_df)
        return (
            f"❌ Error: {e}\n\n{tb}",
            "N/A",
            "0",
            "0",
            "0",
            fig,
            empty_df,
            empty_df,
            "",
        )

# =========================
# Build Gradio UI
# =========================
with gr.Blocks(title=APP_TITLE) as demo:
    gr.Markdown(f"# {APP_TITLE}\n_Source:_ `{TABLE_FQN}` → `{VIEW_FQN}`")

    status = gr.Textbox(label="Status", interactive=False, lines=5)

    with gr.Row():
        refresh_btn = gr.Button("🔄 Refresh", variant="primary")

    with gr.Row():
        as_of = gr.Textbox(label="As of date", interactive=False)

    with gr.Row():
        a1 = gr.Textbox(label="Assets T+1 (LKR)", interactive=False)
        a2 = gr.Textbox(label="SoF T+1 (LKR)", interactive=False)
        a3 = gr.Textbox(label="Net Gap T+1 (LKR)", interactive=False)

    chart = gr.Plot(label="Maturity Ladder")
    ladder_df = gr.Dataframe(label="Ladder Detail")
    irr_df = gr.Dataframe(label="Interest-Rate Risk (approx)")
    excel_file = gr.File(label="Excel export", interactive=False)

    refresh_btn.click(
        fn=run_dashboard,
        outputs=[status, as_of, a1, a2, a3, chart, ladder_df, irr_df, excel_file],
    )

if __name__ == "__main__":
    demo.launch()