Update app.py

app.py

@@ -103,30 +103,33 @@ else:
 col3 = st.columns(1)  # You can use st.columns(1) to create a single column layout
 
 if not filtered_df.empty:
-    tab_df = df[(df['Instituição'] == selected_instituicao)]
-    data_string = tab_df['Forecasts'].iloc[0]
+    # Filter the DataFrame for the selected institution
+    tab_df = df[df['Instituição'] == selected_instituicao]
 
-    # Create a list to store data for each period
+    # Create an empty list to store data
     data = []
 
-    # Split the string into lines
-    lines = data_string.split('\n')
+    # Iterate through each unique 'Conta' in the filtered DataFrame
+    for conta in tab_df['Conta'].unique():
+        # Filter the DataFrame for the current 'Conta'
+        conta_df = tab_df[tab_df['Conta'] == conta]
 
-    # Iterate through the lines and extract the values
-    for line in lines[:-2]:
-        period, value = line.split()
-        num_float = float(value)
-        monetary_value = f'R$ {num_float:,.2f}'  # Adding commas for thousands separator
-        data.append({'Período': int(period), 'Valor Monetário': monetary_value})
+        # Assuming 'Forecasts' column contains the monetary values for each 'Conta'
+        # Convert all values in 'Forecasts' to float, sum them, and format as monetary value
+        total_value = conta_df['Forecasts'].astype(float).sum()
+        monetary_value = f'R$ {total_value:,.2f}'
 
-    # Create a DataFrame from the list
+        # Append the sum for the current 'Conta' to the data list
+        data.append({'Conta': conta, 'Valor Monetário': monetary_value})
+
+    # Convert the list to a DataFrame
     table_data = pd.DataFrame(data)
 
-    # Calculate the sum
+    # Calculate the grand total sum of all 'Conta' values
     total_sum = table_data['Valor Monetário'].str.replace('R$ ', '').str.replace(',', '').astype(float).sum()
 
     # Create a DataFrame for the "Total" row
-    total_row = pd.DataFrame({'Período': ['Total'], 'Valor Monetário': [f'R$ {total_sum:,.2f}']})
+    total_row = pd.DataFrame({'Conta': ['TOTAL'], '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)