Update app.py

app.py

@@ -11,7 +11,7 @@ def clean_df(df):
     print(f"Original columns: {df.columns}")
     
     # Ensure clean URLs from the second column
-    second_col = df.iloc[:, 3].astype(str)  # Pre-packaged Job Solutions column
+    second_col = df.iloc[:, 2].astype(str)  # Pre-packaged Job Solutions column
     
     if second_col.str.contains('http').any() or second_col.str.contains('www').any():
         df["url"] = second_col  # Already has full URLs
@@ -20,18 +20,18 @@ def clean_df(df):
         df["url"] = "https://www.shl.com" + second_col.str.replace(r'^(?!/)', '/', regex=True)
     
     # Map T/F to Yes/No for remote testing and adaptive support
-    df["remote_support"] = df.iloc[:, 4].map(lambda x: "Yes" if x == "T" else "No")
-    df["adaptive_support"] = df.iloc[:, 5].map(lambda x: "Yes" if x == "T" else "No")
+    df["remote_support"] = df.iloc[:, 3].map(lambda x: "Yes" if x == "T" else "No")
+    df["adaptive_support"] = df.iloc[:, 4].map(lambda x: "Yes" if x == "T" else "No")
     
     # Handle test_type properly - convert string representation of list to actual list
-    df["test_type"] = df.iloc[:, 6].apply(lambda x: eval(x) if isinstance(x, str) else x)
+    df["test_type"] = df.iloc[:, 5].apply(lambda x: eval(x) if isinstance(x, str) else x)
     
     # Get description from column 7
-    df["description"] = df.iloc[:, 7]
+    df["description"] = df.iloc[:, 6]
     
     # Extract duration with error handling from column 10
     df["duration"] = pd.to_numeric(
-        df.iloc[:, 10].astype(str).str.extract(r'(\d+)')[0], 
+        df.iloc[:, 9].astype(str).str.extract(r'(\d+)')[0], 
         errors='coerce'
     )