Update app.py

app.py

@@ -105,6 +105,7 @@ model = AutoModel.from_pretrained("sentence-transformers/all-mpnet-base-v2")
 #     return outputs.last_hidden_state.mean(dim=1).squeeze().numpy()
 
 
+
 import re
 import nltk
 from nltk.corpus import stopwords
@@ -112,6 +113,10 @@ from nltk.tokenize import word_tokenize
 # Download necessary NLTK data
 nltk.download('punkt')
 nltk.download('stopwords')
+nltk.download('averaged_perceptron_tagger')
+
+
+
 
 # def combined_text_processing(text):
 #     # Remove punctuation, numbers, URLs, and special characters
@@ -307,23 +312,28 @@ def process_excel(file):
         # Process the DataFrame
         result_df = nlp_pipeline(df)
 
-        output_file = "Output_ProjectProposals.xlsx"
+        # output_file = "Output_ProjectProposals.xlsx"
+        output_file = "Output_Proposals.xlsx"
         result_df.to_excel(output_file, index=False)
         
         return output_file  # Return the processed DataFrame as Excel file
         
     except Exception as e:
-        return str(e)  # Return the error message
+        # return str(e)  # Return the error message
+        return f"Error: {str(e)}"
+
 
+        
 
 
 
 
+# example_files = ['#TaxDirection (Responses)_BasicExample.xlsx',
+#                  '#TaxDirection (Responses)_IntermediateExample.xlsx',
+#                  '#TaxDirection (Responses)_UltimateExample.xlsx'
+#                 ]
 
-example_files = ['#TaxDirection (Responses)_BasicExample.xlsx',
-                 '#TaxDirection (Responses)_IntermediateExample.xlsx',
-                 '#TaxDirection (Responses)_UltimateExample.xlsx'
-                ]
+example_files = ['a.xlsx',]
 
 
 import random