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

PRODUCT_ASSETS = [
    "loan", "overdraft", "advances", "bills", "bill",
    "tbond", "t-bond", "tbill", "t-bill", "repo_asset", "assets"
]
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]:
    # Try DESCRIBE first (fast), fall back to information_schema
    try:
        df = conn.execute(f"DESCRIBE {table_fqn};").fetchdf()
        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()]
    except Exception:
        df = conn.execute(
            f"""
            SELECT lower(column_name) AS col
            FROM information_schema.columns
            WHERE table_catalog = split_part('{table_fqn}', '.', 1)
              AND table_schema  = split_part('{table_fqn}', '.', 2)
              AND table_name    = split_part('{table_fqn}', '.', 3)
            """
        ).fetchdf()
        return df["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 "
        f"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 (LKR Mn)").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 (Mn)")
        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) / 1000000.0 AS "Amount (LKR Mn)"
FROM {VIEW_FQN}
GROUP BY 1,2
ORDER BY 1,2;
"""

GAP_DRIVERS_SQL = f"""
SELECT
  product,
  bucket,
  SUM(Portfolio_value) / 1000000.0 AS "Amount (LKR Mn)"
FROM {VIEW_FQN}
WHERE days_to_maturity <= 1
GROUP BY 1, 2
ORDER BY 3 DESC;
"""

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"""
    WITH irr_calcs AS (
        SELECT
            bucket,
            Portfolio_value AS pv,
            -- Modified Duration = Macaulay Duration / (1 + yield)
            -- We approximate Macaulay Duration with time-to-maturity in years (t_expr)
            ({t_expr}) / (1 + {y_expr}) AS mod_dur
        FROM {VIEW_FQN}
    )
    SELECT
        bucket,
        SUM(pv) / 1000000.0 AS "Portfolio Value (LKR Mn)",
        -- BPV (DV01) = SUM(Portfolio Value * Modified Duration * 0.0001)
        SUM(pv * mod_dur * 0.0001) AS "BPV (DV01)"
    FROM irr_calcs
    GROUP BY bucket;
    """

# =========================
# Dashboard callback
# =========================
def run_dashboard(scenario: str) -> Tuple[str, str, str, str, str, Any, pd.DataFrame, pd.DataFrame, str, pd.DataFrame]:
    """
    Returns:
      status, as_of, a1_text, a2_text, a3_text, figure, ladder_df, irr_df,
      explain_text, drivers_df
    """
    try:
        conn = connect_md()

        # --- Scenario Application ---
        # Create a temporary view with scenario adjustments.
        # Subsequent queries will use this stressed view.
        stressed_view_fqn = f"{VIEW_FQN}_stressed"
        runoff_factor = 1.0
        rate_shock_bps = 0.0

        if scenario == "Liquidity Stress: High Deposit Runoff":
            runoff_factor = 0.8 # 20% runoff
        elif scenario == "IRR Stress: Rate Shock (+200bps)":
            rate_shock_bps = 200.0

        scenario_sql = f"""
        CREATE OR REPLACE TEMP VIEW {stressed_view_fqn} AS
        SELECT *,
            CASE WHEN lower(product) IN ('savings', 'fd', 'td', 'term_deposit') THEN Portfolio_value * {runoff_factor} ELSE Portfolio_value END AS stressed_pv
        FROM {VIEW_FQN};
        """
        conn.execute(scenario_sql)

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

        # 2) 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]

        # 3) KPIs
        # Modify queries to use the stressed view and value column
        kpi_sql_stressed = KPI_SQL.replace(f"FROM {VIEW_FQN}", f"FROM {stressed_view_fqn}").replace("Portfolio_value", "stressed_pv")
        kpi = conn.execute(kpi_sql_stressed).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

        # 4) Ladder, IRR, and Gap Drivers
        ladder_sql_stressed = LADDER_SQL.replace(f"FROM {VIEW_FQN}", f"FROM {stressed_view_fqn}").replace("Portfolio_value", "stressed_pv")
        drivers_sql_stressed = GAP_DRIVERS_SQL.replace(f"FROM {VIEW_FQN}", f"FROM {stressed_view_fqn}").replace("Portfolio_value", "stressed_pv")
        irr_sql_stressed = irr_sql(cols).replace(f"FROM {VIEW_FQN}", f"FROM {stressed_view_fqn}").replace("Portfolio_value", "stressed_pv")

        ladder = conn.execute(ladder_sql_stressed).fetchdf()
        irr    = conn.execute(irr_sql_stressed).fetchdf()
        drivers = conn.execute(drivers_sql_stressed).fetchdf()

        # Create display copies of dataframes and format them for the UI
        ladder_display = ladder.copy()
        if "Amount (LKR Mn)" in ladder.columns:
            ladder_display["Amount (LKR Mn)"] = ladder_display["Amount (LKR Mn)"].map('{:,.2f}'.format)
        else:
            ladder_display = pd.DataFrame()

        # Format IRR table
        irr_display = irr.copy()
        if not irr_display.empty:
            irr_display["Portfolio Value (LKR Mn)"] = irr_display["Portfolio Value (LKR Mn)"].map('{:,.2f}'.format)
            irr_display["BPV (DV01)"] = irr_display["BPV (DV01)"].map('{:,.2f}'.format)

        if "Amount (LKR Mn)" in drivers.columns:
            drivers_display = drivers.copy()
            drivers_display["Amount (LKR Mn)"] = drivers_display["Amount (LKR Mn)"].map('{:,.2f}'.format)
        else:
            drivers_display = pd.DataFrame()

        # 5) Chart
        fig = plot_ladder(ladder)

        # 6) Explanations
        assets_t1_mn_str = f"{(assets_t1 / 1_000_000):,.2f}"
        sof_t1_mn_str = f"{(sof_t1 / 1_000_000):,.2f}"
        net_gap_mn_str = f"{(net_gap / 1_000_000):,.2f}"
        gap_sign_str = "positive" if net_gap >= 0 else "negative"

        a1_text = f"The amount of Assets maturing tomorrow (T+1) is **LKR {assets_t1_mn_str} Mn**."
        a2_text = f"The amount of Sources of Funds (SoF) maturing tomorrow (T+1) is **LKR {sof_t1_mn_str} Mn**."
        a3_text = f"The resulting Net Liquidity Gap for tomorrow (T+1) is **LKR {net_gap_mn_str} Mn**."

        # Build "Why" text
        sof_drivers = drivers[drivers["bucket"] == "SoF"]
        asset_drivers = drivers[drivers["bucket"] == "Assets"]
        top_sof_prod = sof_drivers.iloc[0] if not sof_drivers.empty else None
        top_asset_prod = asset_drivers.iloc[0] if not asset_drivers.empty else None

        explain_text = f"### Why is the T+1 Gap {gap_sign_str}?\n\n"
        if top_sof_prod is not None:
            explain_text += f"*   **Largest Liability Maturity:** The largest outflow comes from `{top_sof_prod['product']}`, with **LKR {top_sof_prod['Amount (LKR Mn)']:,.2f} Mn** maturing.\n"
        else:
            explain_text += "*   **Largest Liability Maturity:** No significant liabilities are maturing tomorrow.\n"

        if top_asset_prod is not None:
             explain_text += f"*   **Largest Asset Inflow:** The largest inflow comes from `{top_asset_prod['product']}`, with **LKR {top_asset_prod['Amount (LKR Mn)']:,.2f} Mn** maturing.\n"
        else:
            explain_text += "*   **Largest Asset Inflow:** No significant assets are maturing to provide inflows tomorrow.\n"

        # Note: The data source does not contain features for seasonal analysis (e.g., day_of_week, is_month_end).
        explain_text += "*   **Seasonal Pattern:** Analysis not possible without relevant time-series features in the source data."

        # Add scenario explanation for IRR stress
        if scenario == "IRR Stress: Rate Shock (+200bps)" and not irr.empty:
            net_bpv = irr["BPV (DV01)"].sum()
            eve_impact = net_bpv * rate_shock_bps
            eve_impact_mn = eve_impact / 1_000_000
            explain_text += f"\n\n### IRR Stress Scenario Impact\n*   A **+{rate_shock_bps:.0f} bps** rate shock is projected to change the portfolio's Economic Value by **LKR {eve_impact_mn:,.2f} Mn**."


        status = f"✅ OK (as of {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')})"
        return (
            status,
            as_of,
            a1_text,
            a2_text,
            a3_text,
            fig,
            ladder_display,
            irr_display,
            explain_text,
            drivers_display,
        )

    except Exception as e:
        tb = traceback.format_exc()
        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,
            "Analysis could not be performed.",
            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=8)

    with gr.Row():
        refresh_btn = gr.Button("🔄 Refresh", variant="primary")
        theme_btn = gr.Button("🌗 Toggle Theme")
        theme_btn.click(
            None,
            None,
            _js="() => { document.querySelector('html').classList.toggle('dark'); }"
        )

    scenario_dd = gr.Dropdown(
        label="Select Stress Scenario",
        choices=["Baseline", "Liquidity Stress: High Deposit Runoff", "IRR Stress: Rate Shock (+200bps)"],
        value="Baseline"
    )

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

    a1 = gr.Markdown("The amount of Assets maturing tomorrow (T+1) is...")
    a2 = gr.Markdown("The amount of Sources of Funds (SoF) maturing tomorrow (T+1) is...")
    a3 = gr.Markdown("The resulting Net Liquidity Gap for tomorrow (T+1) is...")

    with gr.Row():
        with gr.Column(scale=2):
            chart = gr.Plot(label="Maturity Ladder")
            ladder_df = gr.Dataframe(label="Ladder Detail")
            irr_df = gr.Dataframe(
                label="Interest-Rate Risk (BPV/DV01)",
                headers=["Bucket", "Portfolio Value (LKR Mn)", "BPV (DV01)"]
            )
        with gr.Column(scale=1):
            explain_text = gr.Markdown("Analysis of the T+1 gap will appear here...")
            drivers_df = gr.Dataframe(
                label="T+1 Gap Drivers (Top Products)",
                headers=["Product", "Bucket", "Amount (LKR Mn)"],
            )

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

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