Update app.py

app.py

@@ -24,12 +24,24 @@ class HybridExcelQuerySystem:
         self.sheet_names = []
 
     def _pivot_numerical_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Pivots the special 'Numerical Data' sheet into a clean, usable format."""
         header_row_index = 0
         df = df.T
-        df.columns = df.iloc[header_row_index]
         
-        # --- THIS IS THE FIXED LINE ---
-        df = df.iloc[1:] # Keep all rows from the second row onwards
+        # Get potential new headers and de-duplicate them
+        new_headers = df.iloc[header_row_index].fillna('Unnamed')
+        seen = {}
+        final_headers = []
+        for col in new_headers:
+            if col in seen:
+                seen[col] += 1
+                final_headers.append(f"{col}_{seen[col]}")
+            else:
+                seen[col] = 0
+                final_headers.append(col)
+        df.columns = final_headers
+        
+        df = df.iloc[1:]
         
         df = df.reset_index().rename(columns={'index': 'Month'})
         df.columns = df.columns.str.strip().str.replace(r'[^a-zA-Z0-9_%]', '_', regex=True).str.replace('__', '_')
@@ -37,7 +49,6 @@ class HybridExcelQuerySystem:
         # Identify and convert numeric columns
         for col in df.columns:
             if col != 'Month':
-                # Remove '%' and convert to numeric, coercing errors
                 df[col] = df[col].astype(str).str.replace('%', '', regex=False)
                 df[col] = pd.to_numeric(df[col], errors='coerce')