Update app.py

app.py

@@ -18,32 +18,32 @@ def data_pre_processing(file_responses):
         for col in columns:
             file_responses[col] = pd.to_numeric(file_responses[col], errors='coerce').fillna(0)
         
-        # Calculate the Total Allocation
-        file_responses['Total Allocation'] = file_responses[columns].sum(axis=1)
+        # # Calculate the Total Allocation
+        # file_responses['Total Allocation'] = file_responses[columns].sum(axis=1)
         
-        # Convert the Tax Payment column to numeric
-        tax_payment_col = '''How much was your latest Tax payment (in U$D) ?
+        # # Convert the Tax Payment column to numeric
+        # tax_payment_col = '''How much was your latest Tax payment (in U$D) ?
         
-        Please try to be as accurate as possible:
-        Eg.: If your last tax amount was INR 25,785/-; then convert it in U$D and enter only the amount as: 310.
+        # Please try to be as accurate as possible:
+        # Eg.: If your last tax amount was INR 25,785/-; then convert it in U$D and enter only the amount as: 310.
         
-        If you have never paid tax, consider putting in a realistic donation amount which wish to contribute towards helping yourself obtain the desired relief.'''
+        # If you have never paid tax, consider putting in a realistic donation amount which wish to contribute towards helping yourself obtain the desired relief.'''
         
-        file_responses[tax_payment_col] = pd.to_numeric(file_responses[tax_payment_col], errors='coerce').fillna(0)
+        # file_responses[tax_payment_col] = pd.to_numeric(file_responses[tax_payment_col], errors='coerce').fillna(0)
         
-        # Calculate Financial Token Weights
-        for i, col in enumerate(columns, start=1):
-            file_responses[f'Financial Token Weight for Problem {i}'] = (
-                file_responses[tax_payment_col] * file_responses[col] / file_responses['Total Allocation']
-            ).fillna(0)
+        # # Calculate Financial Token Weights
+        # for i, col in enumerate(columns, start=1):
+        #     file_responses[f'Financial Token Weight for Problem {i}'] = (
+        #         file_responses[tax_payment_col] * file_responses[col] / file_responses['Total Allocation']
+        #     ).fillna(0)
         
         return file_responses
     except Exception as e:
         return str(e)
 
 def nlp_pipeline(original_df):
-    #processed_df = data_pre_processing(original_df)
-    return original_df
+    processed_df = data_pre_processing(original_df)
+    return processed_df
 
 def process_excel(file):
     try: