Update app.py

app.py

@@ -88,6 +88,45 @@ col1, col2, col3 = st.columns([col1_width, col2_width, col3_width])
 
 # Display the Forecasts values in the first column
 col1.header('Composição da RLIT')
+
+tab_df = df[df['Instituição'] == selected_instituicao]
+
+data = []
+
+for conta in tab_df['Conta'].unique():
+    # Filter the DataFrame for the current 'Conta'
+    conta_df = tab_df[tab_df['Conta'] == conta]
+
+    if len(conta_df['Modelo'].unique()) > 1 and "Linear Regression" in conta_df['Modelo'].unique():
+        conta_df = conta_df[conta_df['Modelo'] == "Linear Regression"]
+
+    # Initialize a variable to store the sum for the current 'Conta'
+    conta_sum = 0.0
+
+    # Take the first 'Modelo' for simplicity
+    modelo = conta_df['Modelo'].iloc[0]
+
+    # Iterate over each row in the filtered DataFrame for the current 'Conta'
+    for _, row in conta_df.iterrows():
+        lines = row['Forecasts'].split('\n')
+        for line in lines[:-1]:  # Skip the summary line
+            if line.strip():
+                parts = line.split()
+                value = parts[-1]
+                try:
+                    conta_sum += float(value)
+                except ValueError:
+                    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 data to the list
+    data.append({'Conta': conta, 'Modelo': modelo, 'Próximos 12 meses': monetary_value})
+
+# Convert the list to a DataFrame
+table_data = pd.DataFrame(data)
+
 labels = table_data['Conta']
 sizes = [(i / total_sum) * 100 for i in table_data['Próximos 12 meses']]