Update app.py

app.py

@@ -15,6 +15,10 @@ qa = pipeline(
     tokenizer="google/tapas-base-finetuned-sqa"
 )
 
+# Ensure column names are properly capitalized
+metric_columns = {"profitmargin": "ProfitMargin", "profit": "Profit", 
+                  "revenue": "Revenue", "cost": "Cost"}
+
 def answer(q: str) -> str:
     # 1. Conditional query: negative profit
     if re.search(r"products.*negative.*profit", q, re.IGNORECASE):
@@ -24,13 +28,14 @@ def answer(q: str) -> str:
         results = negative_profits[['Product', 'Region', 'FiscalQuarter', 'FiscalYear', 'Profit']]
         return results.to_string(index=False)
 
-    # 2. Numeric summaries (total/average) – improved regex parsing
+    # 2. Numeric summaries (total/average) – Case insensitive metric matching
     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})",
+        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_type, metric, product_letter, quarter, year = match.groups()
+        agg_type, metric_key, product_letter, quarter, year = match.groups()
+        metric = metric_columns[metric_key.lower()]
         product = f"Product {product_letter.upper()}"
         subset = df[
             (df["Product"] == product) &
@@ -44,24 +49,20 @@ def answer(q: str) -> str:
         else:  # average
             val = subset[metric].mean()
 
-        if metric == "ProfitMargin":
-            formatted_val = f"{val:.3f}"
-        else:
-            formatted_val = f"{val:,.2f}"
-
+        formatted_val = f"{val:.3f}" if metric == "ProfitMargin" else f"{val:,.2f}"
         return f"📊 {agg_type.title()} {metric} for {product} in {quarter} {year}: {formatted_val}"
 
-    # 3. TAPAS fallback
+    # 3. TAPAS fallback (with error handling)
     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)}"
+        return f"❗ Error from TAPAS: {str(e)}"
 
 # Launch Gradio
 demo = gr.Interface(
     fn=answer,
-    inputs=gr.Textbox(lines=2, placeholder="e.g. 'total Cost for Product B in Q1 2024?' or 'List products with negative profit.'"),
+    inputs=gr.Textbox(lines=2, placeholder="e.g. 'total Revenue 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."