Update app.py

app.py

@@ -206,6 +206,7 @@ def process_and_output_files(input_files):
     data = []
 
     total_amount = 0
+    item_no = 1
     
     for file in input_files:
         # Extract and categorize text for each file
@@ -225,29 +226,49 @@ def process_and_output_files(input_files):
         # )
 
         response_dict = json.loads(chats[1]["value"][0][1])
-        if category == "RANDOM":
-            category = response_dict.get("summary")
+        if response_dict.get("summary") : 
         # Extract the relevant data
-        extracted_data = {
-            "Nature of Expenditure": response_dict.get("summary",category),
-            "Bill/Invoice No.": response_dict.get("uids"),
-            "Amount(Rs.)": response_dict.get("total")
-        }
+        
+            extracted_data = {
+                "S.No.": item_no,
+                "Nature of Expenditure": response_dict.get("summary"),
+                "Bill/Invoice No.": response_dict.get("uids"),
+                "Amount(Rs.)": response_dict.get("total")
+            }
+        else:
+            extracted_data = {
+                "S.No.": item_no,
+                "Nature of Expenditure": category,
+                "Bill/Invoice No.": response_dict.get("uids"),
+                "Amount(Rs.)": response_dict.get("total")
+            }
+            
 
         total_amount+=response_dict.get("total")
 
         # Append the relevant data for this file to the data list
         data.append(extracted_data)
-        
 
+        item_no=item_no+1
+        
+    total_data = {
+        "S.No.": ""
+        "Nature of Expenditure": "Total Amount",
+        "Bill/Invoice No.": "",
+        "Amount(Rs.)": total_amount
+    }
+    data.append(total_data)
+    
     string_data = []
     for item in data:
         string_item = {key: str(value) for key, value in item.items()}
         string_data.append(string_item)
+
         
     df = pd.DataFrame(string_data)
-    table_html = df.to_html(classes="table table-bordered", index=True)
-
+    
+    table_html = df.to_html(classes="table table-bordered", index=False)
+    
     scrollable_table = f'<div style="overflow-x: auto;">{table_html}</div>'
     return scrollable_table