Update app.py

app.py

@@ -117,26 +117,29 @@ if not filtered_df.empty:
         # Initialize a variable to store the sum for the current 'Conta'
         conta_sum = 0.0
 
+        # Optionally, handle multiple 'Modelos' by aggregating them
+        # For simplicity, here we're just taking the first 'Modelo' found for the 'Conta'
+        modelo = conta_df['Modelo'].iloc[0]
+
         # Iterate over each row in the filtered DataFrame for the current 'Conta'
         for _, row in conta_df.iterrows():
-            # Split the 'Forecasts' column's multiline string into lines
+            # Extract the numeric value and add it to the conta_sum
+            # Assuming 'Forecasts' is already processed correctly above
             lines = row['Forecasts'].split('\n')
-            # For each line, except the last summary line, extract the numeric value and add it to the conta_sum
-            for line in lines[:-1]:  # Skip the last line, which contains the summary
-                if line.strip():  # Ensure the line is not empty
+            for line in lines[:-1]:  # Skip the summary line
+                if line.strip():
                     parts = line.split()
-                    value = parts[-1]  # Take the last element as the value
+                    value = parts[-1]
                     try:
                         conta_sum += float(value)
                     except ValueError:
-                        # Handle unexpected lines that cannot be converted to float
                         print(f"Skipping line unable to convert to float: {line}")
 
         # Format the sum as a monetary value
         monetary_value = f'R$ {conta_sum:,.2f}'
 
-        # Append the sum for the current 'Conta' to the data list
-        data.append({'Conta': conta, 'Valor Monetário': monetary_value})
+        # Append the 'Conta', 'Modelo', and sum to the data list
+        data.append({'Conta': conta, 'Modelo': modelo, 'Valor Monetário': monetary_value})
 
     # Convert the list to a DataFrame
     table_data = pd.DataFrame(data)
@@ -144,8 +147,8 @@ if not filtered_df.empty:
     # Calculate the grand total sum of all 'Conta' values
     total_sum = sum(float(row['Valor Monetário'].replace('R$ ', '').replace(',', '')) for row in data)
 
-    # Create a DataFrame for the "Total" row
-    total_row = pd.DataFrame({'Conta': ['TOTAL'], 'Valor Monetário': [f'R$ {total_sum:,.2f}']})
+    # Create a DataFrame for the "Total" row, without a 'Modelo' value
+    total_row = pd.DataFrame({'Conta': ['TOTAL'], 'Modelo': [''], 'Valor Monetário': [f'R$ {total_sum:,.2f}']})
 
     # Concatenate the "Total" row with the existing table data
     table_data = pd.concat([table_data, total_row], ignore_index=True)