Update app.py

app.py

@@ -1,6 +1,5 @@
 import os
 import sys
-from datetime import datetime
 from pathlib import Path
 from typing import Tuple, Any, List
 
@@ -9,7 +8,7 @@ import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import gradio as gr
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 from reportlab.lib.pagesizes import A4
 from reportlab.lib.units import mm
 from reportlab.pdfgen import canvas
@@ -18,8 +17,8 @@ from reportlab.pdfgen import canvas
 # Basic configuration
 # -------------------------------------------------------------------
 APP_TITLE = "ALCO Liquidity & Interest-Rate Risk Dashboard"
-TABLE_FQN = "my_db.main.masterdataset_v"
-VIEW_FQN = "my_db.main.positions_v"
+TABLE_FQN = "my_db.main.masterdataset_v"   # your 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)
 
@@ -42,10 +41,12 @@ def connect_md() -> duckdb.DuckDBPyConnection:
 # Column discovery & dynamic SQL
 # -------------------------------------------------------------------
 PRODUCT_ASSETS = [
-    "assets"
+    "loan", "overdraft", "advances", "bills", "bill",
+    "tbond", "t-bond", "tbill", "t-bill", "repo_asset"
 ]
 PRODUCT_SOF = [
-    "fd"
+    "fd", "term_deposit", "td", "savings", "current",
+    "call", "repo_liab"
 ]
 
 def discover_columns(conn: duckdb.DuckDBPyConnection, table_fqn: str) -> List[str]:
@@ -58,7 +59,6 @@ def discover_columns(conn: duckdb.DuckDBPyConnection, table_fqn: str) -> List[st
     df = conn.execute(q).fetchdf()
     return df["col"].tolist()
 
-
 def build_view_sql(existing_cols: List[str]) -> str:
     wanted = [
         "as_of_date", "product", "months", "segments",
@@ -70,6 +70,7 @@ def build_view_sql(existing_cols: List[str]) -> str:
         if c.lower() in existing_cols:
             select_list.append(c)
         else:
+            # fill missing columns with NULLs (typed)
             if c in ("Portfolio_value", "Interest_rate", "days_to_maturity", "months"):
                 select_list.append(f"CAST(NULL AS DOUBLE) AS {c}")
             else:
@@ -95,40 +96,46 @@ def build_view_sql(existing_cols: List[str]) -> str:
 
 
 # -------------------------------------------------------------------
-# Data model
+# Data model (allow pandas types)
 # -------------------------------------------------------------------
 class DashboardResult(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
     as_of_date: str
     assets_t1: float
     sof_t1: float
     net_gap_t1: float
-    ladder: pd.DataFrame
-    irr: pd.DataFrame
+    ladder: Any          # pandas.DataFrame
+    irr: Any             # pandas.DataFrame
 
 
 # -------------------------------------------------------------------
 # Core logic
 # -------------------------------------------------------------------
 def ensure_view(conn: duckdb.DuckDBPyConnection, existing_cols: List[str]):
-    if not {"product", "portfolio_value", "days_to_maturity"}.issubset(set(existing_cols)):
-        raise RuntimeError("Missing required columns in source table.")
-    sql = build_view_sql(existing_cols)
-    conn.execute(sql)
-
+    # Mandatory columns in source:
+    required = {"product", "portfolio_value", "days_to_maturity"}
+    if not required.issubset(set(existing_cols)):
+        raise RuntimeError(
+            f"Source table {TABLE_FQN} must contain {sorted(required)}; "
+            f"found: {sorted(existing_cols)}"
+        )
+    conn.execute(build_view_sql(existing_cols))
 
 def fetch_all(conn: duckdb.DuckDBPyConnection) -> DashboardResult:
     cols = discover_columns(conn, TABLE_FQN)
     ensure_view(conn, cols)
+
     has_asof = "as_of_date" in cols
     has_ir = "interest_rate" in cols
     has_months = "months" in cols
 
-    # As-of date
-    as_of_date = "N/A"
+    # As-of date or N/A
     if has_asof:
         asof_df = conn.execute(f"SELECT max(as_of_date) AS d FROM {VIEW_FQN}").fetchdf()
-        if not asof_df.empty and not pd.isna(asof_df["d"].iloc[0]):
-            as_of_date = str(asof_df["d"].iloc[0])[:10]
+        as_of_date = "N/A" if asof_df.empty or pd.isna(asof_df["d"].iloc[0]) else str(asof_df["d"].iloc[0])[:10]
+    else:
+        as_of_date = "N/A"
 
     # KPIs
     kpi_sql = f"""
@@ -158,19 +165,19 @@ def fetch_all(conn: duckdb.DuckDBPyConnection) -> DashboardResult:
     """
     ladder = conn.execute(ladder_sql).fetchdf()
 
-    # IRR simplified
+    # IRR (approx) — works with or without months/interest_rate
     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 "NULL"
+    y_expr = "(Interest_rate/100.0)" if has_ir else "0.0"
 
     irr_sql = 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+COALESCE({y_expr},0))) / NULLIF(SUM(Portfolio_value),0) AS dur_mod
+      SUM(Portfolio_value * ({t_expr})/(1+({y_expr}))) / NULLIF(SUM(Portfolio_value),0) AS dur_mod
     FROM {VIEW_FQN}
     GROUP BY bucket;
     """
@@ -189,13 +196,20 @@ def fetch_all(conn: duckdb.DuckDBPyConnection) -> DashboardResult:
 # -------------------------------------------------------------------
 # Visualization
 # -------------------------------------------------------------------
+def _zeros_like_index(index) -> pd.Series:
+    return pd.Series([0] * len(index), index=index)
+
 def plot_ladder(df: pd.DataFrame):
     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))
-    ax.bar(pivot.index, pivot.get("Assets", 0), label="Assets")
-    ax.bar(pivot.index, -pivot.get("SoF", 0), label="SoF")
+
+    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)")
@@ -228,7 +242,7 @@ def run_dashboard():
     conn = connect_md()
     res = fetch_all(conn)
     fig = plot_ladder(res.ladder)
-    excel_path = export_excel(res)
+    xlsx = export_excel(res)
     return (
         res.as_of_date,
         res.assets_t1,
@@ -237,10 +251,9 @@ def run_dashboard():
         fig,
         res.ladder,
         res.irr,
-        str(excel_path),
+        str(xlsx),
     )
 
-
 with gr.Blocks(title=APP_TITLE) as demo:
     gr.Markdown(f"# {APP_TITLE}\n_Source:_ `{TABLE_FQN}` → `{VIEW_FQN}`")