Update app.py

app.py

@@ -112,11 +112,9 @@ def plot_ladder(df: pd.DataFrame):
             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)
+        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)
-        # Convert to millions for plotting
-        pivot = pivot / 1_000_000
         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)
@@ -162,6 +160,17 @@ 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
@@ -181,11 +190,11 @@ def irr_sql(cols: List[str]) -> str:
 # =========================
 # Dashboard callback
 # =========================
-def run_dashboard() -> Tuple[str, str, str, str, str, Any, pd.DataFrame, pd.DataFrame]:
+def run_dashboard() -> Tuple[str, str, str, str, str, Any, pd.DataFrame, pd.DataFrame, str, pd.DataFrame]:
     """
     Returns:
-      status, as_of, assets_t1, sof_t1, net_gap_t1, figure, ladder_df, irr_df
-      (text KPIs to avoid component type errors)
+      status, as_of, a1_text, a2_text, a3_text, figure, ladder_df, irr_df,
+      explain_text, drivers_df
     """
     try:
         conn = connect_md()
@@ -207,24 +216,55 @@ def run_dashboard() -> Tuple[str, str, str, str, str, Any, pd.DataFrame, pd.Data
         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
+        # 4) Ladder, IRR, and Gap Drivers
         ladder = conn.execute(LADDER_SQL).fetchdf()
         irr    = conn.execute(irr_sql(cols)).fetchdf()
+        drivers = conn.execute(GAP_DRIVERS_SQL).fetchdf()
+
+        if "Amount (LKR Mn)" in ladder.columns:
+            ladder["Amount (LKR Mn)"] = ladder["Amount (LKR Mn)"].map('{:,.2f}'.format)
         if "Portfolio Value (LKR Mn)" in irr.columns:
             irr["Portfolio Value (LKR Mn)"] = irr["Portfolio Value (LKR Mn)"].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**."
 
-        status = f"Connected to Database (as of {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')})"
+        # 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."
+
+        status = f"✅ OK (as of {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')})"
         return (
             status,
             as_of,
@@ -234,6 +274,8 @@ def run_dashboard() -> Tuple[str, str, str, str, str, Any, pd.DataFrame, pd.Data
             fig,
             ladder,
             irr,
+            explain_text,
+            drivers_display,
         )
 
     except Exception as e:
@@ -249,6 +291,8 @@ def run_dashboard() -> Tuple[str, str, str, str, str, Any, pd.DataFrame, pd.Data
             fig,
             empty_df,
             empty_df,
+            "Analysis could not be performed.",
+            empty_df,
         )
 
 # =========================
@@ -269,13 +313,21 @@ with gr.Blocks(title=APP_TITLE) as demo:
     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...")
 
-    chart = gr.Plot(label="Maturity Ladder")
-    ladder_df = gr.Dataframe(label="Ladder Detail")
-    irr_df = gr.Dataframe(label="Interest-Rate Risk (approx)")
+    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 (approx)")
+        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,
-        outputs=[status, as_of, a1, a2, a3, chart, ladder_df, irr_df],
+        outputs=[status, as_of, a1, a2, a3, chart, ladder_df, irr_df, explain_text, drivers_df],
     )
 
 if __name__ == "__main__":