Update app.py

app.py

@@ -45,7 +45,6 @@ with st.sidebar:
 # Filter the DataFrame based on selected values
 filtered_df = df[(df['Instituição'] == selected_instituicao) & (df['Conta'] == selected_conta)]
 
-
 # Set custom width for columns
 col1_width = 400
 col2_width = 400
@@ -99,3 +98,36 @@ if not filtered_df.empty:
 
 else:
     col2.warning('No data available for the selected filters.')
+
+# Create a table in the third column
+col3 = st.column()
+col3.header('Tabela com previsões')
+
+if not filtered_df.empty:
+    data_string = filtered_df['Forecasts'].iloc[0]
+
+    # Create a list to store data for each period
+    data = []
+
+    # Split the string into lines
+    lines = data_string.split('\n')
+
+    # 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})
+
+    # Create a DataFrame from the list
+    table_data = pd.DataFrame(data)
+
+    # Add a row for the sum
+    total_sum = table_data['Valor Monetário'].str.replace('R$ ', '').str.replace(',', '').astype(float).sum()
+    table_data = table_data.append({'Período': 'Total', 'Valor Monetário': f'R$ {total_sum:,.2f}'}, ignore_index=True)
+
+    # Display the table
+    col3.table(table_data)
+
+else:
+    col3.warning('No data available for the selected filters.')