Update app.py

app.py

@@ -342,31 +342,32 @@ def nlp_pipeline(original_df, console_messages):
         print(f"Optimal clusters: {optimal_clusters}")
         print(result_df.head())
         # console_messages.append(f"Optimal clusters: {optimal_n_clusters}")
+
+        console_messages.append("NLP pipeline completed.")
+        return domain_df, console_messages
     except Exception as e:
-        print(f"Error in extract_problem_domains: {e}")
-        # console_messages.append(f"Error in extract_problem_domains: {str(e)}")
+        # print(f"Error in extract_problem_domains: {e}")
+        console_messages.append(f"Error in extract_problem_domains: {str(e)}")
+        # return processed_df, console_messages
+        return domain_df, console_messages
 
     
     # problem_clusters, problem_model = perform_clustering(processed_df['Problem_Description'], n_clusters=10)
     # location_clusters, location_model = perform_clustering(processed_df['Geographical_Location'], n_clusters=5)
     
     
-    console_messages.append("NLP pipeline completed.")
-    # return processed_df
-    return domain_df, console_messages
+    
     
 
 def process_excel(file):
     console_messages = []
-    console_messages.append("Processing starts...")
-    
+    console_messages.append("Processing starts. Reading the uploaded Excel file...")
+    # Ensure the file path is correct
+    file_path = file.name if hasattr(file, 'name') else file
+    # Read the Excel file
+    df = pd.read_excel(file_path)
+
     try:
-        # Ensure the file path is correct
-        console_messages.append("Reading the uploaded Excel file...")
-        file_path = file.name if hasattr(file, 'name') else file
-        # Read the Excel file
-        df = pd.read_excel(file_path)
-        
         # Process the DataFrame
         console_messages.append("Processing the DataFrame...")
         result_df, console_messages = nlp_pipeline(df, console_messages)