Update app.py

app.py

@@ -23,7 +23,13 @@ def process_file(uploaded_file):
         else:
             st.error("Unsupported file format. Please upload Excel, CSV, or PDF.")
             return None
+
+        # Clean column names (remove spaces or fix names)
+        df.columns = df.columns.str.strip()
+        st.write("DataFrame columns after cleaning:", df.columns)
+
         return df
+
     except Exception as e:
         st.error(f"Error processing file: {e}")
         return None
@@ -40,13 +46,21 @@ def main():
         if df is not None:
             st.subheader("Uploaded Data")
             st.write(df.head())
-            
+
+            # Check and debug the column names
+            st.write("Columns in the dataset:", df.columns)
+
+            # Handle missing or incorrectly named columns
+            if 'Marks' not in df.columns:
+                st.error("The 'Marks' column is missing from the uploaded data!")
+                return
+
             # Convert Marks to numeric (if not already numeric)
             df['Marks'] = pd.to_numeric(df['Marks'], errors='coerce')
-            
-            # Add a Total Marks column (sum of all marks if there are multiple subjects)
-            # Assuming "Marks" column represents the total of all subjects for simplicity.
-            df['Total Marks'] = df['Marks'].sum(axis=1)
+
+            # Add a Total Marks column (assuming a single "Marks" column for simplicity)
+            # If you have more subject columns, you could sum across them.
+            df['Total Marks'] = df['Marks']  # Modify this if there are more subject columns
             
             # Show the top 10 students based on total marks
             top_performers = df.sort_values(by="Total Marks", ascending=False).head(10)