Update app.py

app.py

@@ -84,22 +84,24 @@ def batch_predict(app_choice, file_obj):
             except UnicodeDecodeError:
                 df = pd.read_csv(filename, encoding='latin1', on_bad_lines='skip')
         elif filename.endswith(('.xls', '.xlsx')):
-            df = pd.read_excel(filename)
+            # Added engine='openpyxl' for better compatibility
+            df = pd.read_excel(filename, engine='openpyxl')
         else:
             err_path = tempfile.NamedTemporaryFile(delete=False, suffix=".txt").name
-            with open(err_path, "w") as f: f.write("Unsupported file format. Please upload a .csv or .xlsx file.")
+            with open(err_path, "w") as f: f.write("Unsupported format. Please upload .csv or .xlsx")
             return None, err_path
 
-        # Find the column containing SMILES (case-insensitive)
-        s_col = next((c for c in df.columns if 'smiles' in c.lower()), None)
+        # MODIFIED: Look for 'smile' OR 'smiles' (handles your SMILES.xlsx)
+        s_col = next((c for c in df.columns if 'smile' in c.lower()), None)
+        
         if not s_col:
             err_path = tempfile.NamedTemporaryFile(delete=False, suffix=".txt").name
-            with open(err_path, "w") as f: f.write("Error: File must have a 'SMILES' column.")
+            with open(err_path, "w") as f: 
+                f.write(f"Error: Could not find SMILES column. Found: {df.columns.tolist()}")
             return None, err_path
         
         results = []
         for s in df[s_col]:
-            # This utility handles both *CC* (p-SMILES) and CCC (SMILES)
             feats = get_mff_features(str(s))
             if feats is not None:
                 if app_choice == "Structural (Strength)":