Update app.py

app.py

@@ -6,8 +6,6 @@ import gradio as gr
 
 # Load numeric data
 df = pd.read_csv("synthetic_profit.csv")
-
-# String DataFrame for TAPAS
 df_str = df.astype(str)
 
 # Initialize TAPAS
@@ -26,32 +24,47 @@ def answer(q: str) -> str:
         results = negative_profits[['Product', 'Region', 'FiscalQuarter', 'FiscalYear', 'Profit']]
         return results.to_string(index=False)
 
-    # 2. Numeric summaries (total/average)
-    match = re.search(r"\b(total|average)\s+(ProfitMargin|Profit|Revenue|Cost)\b.*\bProduct\s*([A-D])\b.*\b(Q[1-4])\s*(\d{4})", q, re.IGNORECASE)
+    # 2. Numeric summaries (total/average) – improved regex parsing
+    match = re.search(
+        r"\b(total|average)\s+(ProfitMargin|Profit|Revenue|Cost)\b.*\bProduct\s*([A-D])\b.*\b(Q[1-4])\s+(\d{4})",
+        q, re.IGNORECASE
+    )
     if match:
-        agg, metric, product, quarter, year = match.groups()
+        agg_type, metric, product_letter, quarter, year = match.groups()
+        product = f"Product {product_letter.upper()}"
         subset = df[
-            (df["Product"] == f"Product {product.upper()}") &
+            (df["Product"] == product) &
             (df["FiscalQuarter"] == quarter) &
             (df["FiscalYear"] == int(year))
         ]
         if subset.empty:
-            return "⚠️ No matching data."
-        value = subset[metric].sum() if agg.lower() == "total" else subset[metric].mean()
-        formatted_val = f"{value:.3f}" if metric == "ProfitMargin" else f"{value:,.2f}"
-        return f"📊 {agg.title()} {metric} for Product {product.upper()} in {quarter} {year}: {formatted_val}"
+            return "⚠️ No matching data found."
+        if agg_type.lower() == "total":
+            val = subset[metric].sum()
+        else:  # average
+            val = subset[metric].mean()
+
+        if metric == "ProfitMargin":
+            formatted_val = f"{val:.3f}"
+        else:
+            formatted_val = f"{val:,.2f}"
+
+        return f"📊 {agg_type.title()} {metric} for {product} in {quarter} {year}: {formatted_val}"
 
-    # 3. TAPAS fallback for everything else
-    res = qa(table=df_str, query=q)
-    return f"🔍 {res['answer']} (agg: {res.get('aggregator','NONE')})"
+    # 3. TAPAS fallback
+    try:
+        res = qa(table=df_str, query=q)
+        return f"🔍 {res['answer']} (agg: {res.get('aggregator','NONE')})"
+    except Exception as e:
+        return f"❗ Error: {str(e)}"
 
 # Launch Gradio
 demo = gr.Interface(
     fn=answer,
-    inputs=gr.Textbox(lines=2, placeholder="e.g. 'total Profit for Product A in Q1 2024?' or 'List products with negative profit.'"),
+    inputs=gr.Textbox(lines=2, placeholder="e.g. 'total Cost for Product B in Q1 2024?' or 'List products with negative profit.'"),
     outputs="text",
     title="🟢 SAP S/4HANA Profitability Chat",
     description="Ask questions on profitability data (synthetic demo). Supports total, average, and conditional queries."
 )
 
-demo.launch()
+demo.launch()