Update app.py

app.py

@@ -108,132 +108,20 @@ for conta in instituicao_df['Conta'].unique():
 # Combine all adjusted rows back into a single DataFrame
 filtered_df = pd.concat(adjusted_rows)
 
-#col1, col2, col3 = st.columns(3) # This divides the page into three equal parts
-
 # Set custom width for columns
-col1, col2, col3, col4, col5 = st.columns([2,1,1,1,1])
-
-# Display the Forecasts values in the first column
-col1.header('Composição da RLIT')
+tab1, tab2, tab3, tab4 = st.tabs(["Composição RLIT", "Valores Previstos", "Tabela Resumo", "Comparativo - Saúde e Eduacação"])
 
 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}")
-
-    # Append the data to the list
-    data.append({'Conta': conta, 'Modelo': modelo, 'Próximos 12 meses': conta_sum})
-
-# Convert the list to a DataFrame
-table_data = pd.DataFrame(data)
-total_sum = table_data['Próximos 12 meses'].sum()
-
-labels = table_data['Conta']
-sizes = [(i / total_sum) * 100 for i in table_data['Próximos 12 meses']]
-
-fig1, ax1 = plt.subplots()
-ax1.pie(sizes, labels=labels, autopct='%1.1f%%',)
-ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
-
-col1.pyplot(fig1)
-
-
-# Display the Forecasts values in the first column
-col2.header('Valores previstos')
-
-if not filtered_df.empty:
-    data_string = filtered_df['Forecasts'].iloc[0]
-
-    # Split the string into lines
-    lines = data_string.split('\n')
-
-    mes = 0
-
-    # Iterate through the lines and extract the values
-    for line in lines[:-1]:  # Skip the last two lines which might not contain forecast data
-        period, value = line.split()
-        num_float = float(value)
-        monetary_value = f'R$ {num_float:,.2f}'  # Adding commas for thousands separator
-        mes += 1
-        col2.write(f"Mês {mes}: {monetary_value}")
-else:
-    col2.warning('No data available for the selected filters.')
-
-# Display the Forecasts values as line plots in the second column
-col3.header('Gráfico 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')
-
-    mes = 0
-
-    # Iterate through the lines and extract the values
-    for line in lines[:-1]:
-        period, value = line.split()
-        num_float = float(value)
-        monetary_value = f'R$ {num_float:,.2f}'  # Adding commas for thousands separator
-        mes += 1
-        data.append({'Period': int(mes), 'Monetary Value': num_float})
-
-    # Create a DataFrame from the list
-    chart_data = pd.DataFrame(data)
-
-    # Sort the DataFrame by 'Period'
-    chart_data = chart_data.sort_values(by='Period')
-
-    # Display line chart with "period" on X-axis and "Monetary Value" on Y-axis
-    col3.line_chart(chart_data.set_index('Period'))
-
-else:
-    col3.warning('No data available for the selected filters.')
-
-# Display the table in the third column
-#col3 = st.columns(1)  # You can use st.columns(1) to create a single column layout
-
-if not filtered_df.empty:
-    # Filter the DataFrame for the selected institution
-    tab_df = df[df['Instituição'] == selected_instituicao]
-
-    # Create an empty list to store data
-    data = []
-
-    # Iterate through each unique 'Conta' in the filtered DataFrame
+with tab1:
     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"]
+        conta_df = conta_df[conta_df['Modelo'] == "Linear Regression"]
 
         # Initialize a variable to store the sum for the current 'Conta'
         conta_sum = 0.0
@@ -253,79 +141,184 @@ if not filtered_df.empty:
                     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})
+        data.append({'Conta': conta, 'Modelo': modelo, 'Próximos 12 meses': conta_sum})
 
     # Convert the list to a DataFrame
     table_data = pd.DataFrame(data)
+    total_sum = table_data['Próximos 12 meses'].sum()
 
-    last_df = ultimo_ano[ultimo_ano['Instituição'] == selected_instituicao]
-    last_df.drop(['Instituição'], axis=1, inplace=True)
-    print(last_df)
-    last_sum = last_df.iloc[:,-1].sum()
-    def format_currency(x):
-        return "R${:,.2f}".format(x)
-    last_df['Últimos 12 meses'] = last_df['Últimos 12 meses'].apply(format_currency)
-    table_data = pd.merge(table_data, last_df)
-
-    print(table_data)
-
-    try:
-        # Calculate the grand total sum of 'Próximos 12 meses' and 'Últimos 12 meses' values
-        total_sum =  sum(float(row['Próximos 12 meses'].replace('R$ ', '').replace(',', '')) for row in data)
-        total_sum_prev = last_sum
-
-        # Append the "Total" row
-        total_row = pd.DataFrame({
-            'Conta': ['TOTAL (RLIT)'],
-            'Modelo': [''],
-            'Próximos 12 meses': [f'R$ {total_sum:,.2f}'],
-            'Últimos 12 meses': [f'R$ {total_sum_prev:,.2f}']
-        })
-        table_data = pd.concat([table_data, total_row], ignore_index=True)
-
-        # Additional rows calculations and appending
-        # Assuming percentages for health and education as previously mentioned
-        saude_value = total_sum * 0.15
-        educacao_value = total_sum * 0.25
-        saude_value_prev = total_sum_prev * 0.15
-        educacao_value_prev = total_sum_prev * 0.25
-        saude_row = pd.DataFrame({'Conta': ['Saúde (15% da RLIT)'], 'Modelo': [''], 'Próximos 12 meses': [f'R$ {saude_value:,.2f}'], 'Últimos 12 meses': [f'R$ {saude_value_prev:,.2f}']})
-        educacao_row = pd.DataFrame({'Conta': ['Educação (25% da RLIT)'], 'Modelo': [''], 'Próximos 12 meses': [f'R$ {educacao_value:,.2f}'], 'Últimos 12 meses': [f'R$ {educacao_value_prev:,.2f}']})
-
-        # Append these rows to the table data
-        table_data = pd.concat([table_data, saude_row, educacao_row], ignore_index=True)
-
-        table_data.fillna('-', inplace=True)
-    
-        # Display the table using Streamlit
-        st.table(table_data)
+    labels = table_data['Conta']
+    sizes = [(i / total_sum) * 100 for i in table_data['Próximos 12 meses']]
+
+    fig1, ax1 = plt.subplots()
+    ax1.pie(sizes, labels=labels, autopct='%1.1f%%',)
+    ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
 
-    except Exception as e:
-        st.error(f"Error in processing data: {str(e)}")
+    st.pyplot(fig1)
 
-else:
-    col5.warning('No data available for the selected filters.')
+with tab1:
 
+    if not filtered_df.empty:
+    data_string = filtered_df['Forecasts'].iloc[0]
+
+        # Split the string into lines
+        lines = data_string.split('\n')
 
-data = {
-    "Últimos 12 meses": [saude_value_prev, educacao_value_prev],  # Placeholder data for 'Last 12 Months'
-    "Próximos 12 meses": [saude_value, educacao_value]  # Placeholder data for 'Next 12 Months'
-}
+        mes = 0
 
-# Define the index names
-index_names = ["Saúde", "Educação"]  # 'Health' and 'Education'
+        # Iterate through the lines and extract the values
+        for line in lines[:-1]:  # Skip the last two lines which might not contain forecast data
+            period, value = line.split()
+            num_float = float(value)
+            monetary_value = f'R$ {num_float:,.2f}'  # Adding commas for thousands separator
+            mes += 1
+            st.write(f"Mês {mes}: {monetary_value}")
+    else:
+        st.warning('No data available for the selected filters.')
 
-df = pd.DataFrame(data, index=index_names).reset_index().melt(id_vars='index', var_name='Period', value_name='Value')
+    # Display the Forecasts values as line plots in the second column
 
-# Create the bar chart
-fig = px.bar(df, x='index', y='Value', color='Period', barmode='group')
-col4.write(fig)
+    if not filtered_df.empty:
+        data_string = filtered_df['Forecasts'].iloc[0]
 
-st.markdown("""
+        # Create a list to store data for each period
+        data = []
+
+        # Split the string into lines
+        lines = data_string.split('\n')
+
+        mes = 0
+
+        # Iterate through the lines and extract the values
+        for line in lines[:-1]:
+            period, value = line.split()
+            num_float = float(value)
+            monetary_value = f'R$ {num_float:,.2f}'  # Adding commas for thousands separator
+            mes += 1
+            data.append({'Period': int(mes), 'Monetary Value': num_float})
+
+        # Create a DataFrame from the list
+        chart_data = pd.DataFrame(data)
+
+        # Sort the DataFrame by 'Period'
+        chart_data = chart_data.sort_values(by='Period')
+
+        # Display line chart with "period" on X-axis and "Monetary Value" on Y-axis
+        st.line_chart(chart_data.set_index('Period'))
+
+    else:
+        st.warning('No data available for the selected filters.')
+
+
+with tab3:
+    if not filtered_df.empty:
+        # Filter the DataFrame for the selected institution
+        tab_df = df[df['Instituição'] == selected_instituicao]
+
+        # Create an empty list to store data
+        data = []
+
+        # 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]
+
+            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)
+
+        last_df = ultimo_ano[ultimo_ano['Instituição'] == selected_instituicao]
+        last_df.drop(['Instituição'], axis=1, inplace=True)
+        print(last_df)
+        last_sum = last_df.iloc[:,-1].sum()
+        def format_currency(x):
+            return "R${:,.2f}".format(x)
+        last_df['Últimos 12 meses'] = last_df['Últimos 12 meses'].apply(format_currency)
+        table_data = pd.merge(table_data, last_df)
+
+        print(table_data)
+
+        try:
+            # Calculate the grand total sum of 'Próximos 12 meses' and 'Últimos 12 meses' values
+            total_sum =  sum(float(row['Próximos 12 meses'].replace('R$ ', '').replace(',', '')) for row in data)
+            total_sum_prev = last_sum
+
+            # Append the "Total" row
+            total_row = pd.DataFrame({
+                'Conta': ['TOTAL (RLIT)'],
+                'Modelo': [''],
+                'Próximos 12 meses': [f'R$ {total_sum:,.2f}'],
+                'Últimos 12 meses': [f'R$ {total_sum_prev:,.2f}']
+            })
+            table_data = pd.concat([table_data, total_row], ignore_index=True)
+
+            # Additional rows calculations and appending
+            # Assuming percentages for health and education as previously mentioned
+            saude_value = total_sum * 0.15
+            educacao_value = total_sum * 0.25
+            saude_value_prev = total_sum_prev * 0.15
+            educacao_value_prev = total_sum_prev * 0.25
+            saude_row = pd.DataFrame({'Conta': ['Saúde (15% da RLIT)'], 'Modelo': [''], 'Próximos 12 meses': [f'R$ {saude_value:,.2f}'], 'Últimos 12 meses': [f'R$ {saude_value_prev:,.2f}']})
+            educacao_row = pd.DataFrame({'Conta': ['Educação (25% da RLIT)'], 'Modelo': [''], 'Próximos 12 meses': [f'R$ {educacao_value:,.2f}'], 'Últimos 12 meses': [f'R$ {educacao_value_prev:,.2f}']})
+
+            # Append these rows to the table data
+            table_data = pd.concat([table_data, saude_row, educacao_row], ignore_index=True)
+
+            table_data.fillna('-', inplace=True)
+    
+            # Display the table using Streamlit
+            st.table(table_data)
+
+        except Exception as e:
+            st.error(f"Error in processing data: {str(e)}")
+
+    else:
+        st.warning('No data available for the selected filters.')
+
+    st.markdown("""
     <b>Observação:</b> Previsões realizadas com dados extraídos do Relatório Resumido de Execução Orçamentária (RREO) até o 6º bimestre de 2023 no Sistema de Informações Contábeis e Fiscais do Setor Público Brasileiro (SICONFI). 
     [Link](https://siconfi.tesouro.gov.br/)
-    """, unsafe_allow_html=True)
+    """, unsafe_allow_html=True)
+
+
+with tab4:
+    data = {
+        "Últimos 12 meses": [saude_value_prev, educacao_value_prev],  # Placeholder data for 'Last 12 Months'
+        "Próximos 12 meses": [saude_value, educacao_value]  # Placeholder data for 'Next 12 Months'
+    }
+
+    # Define the index names
+    index_names = ["Saúde", "Educação"]  # 'Health' and 'Education'
+
+    df = pd.DataFrame(data, index=index_names).reset_index().melt(id_vars='index', var_name='Period', value_name='Value')
+
+    # Create the bar chart
+    fig = px.bar(df, x='index', y='Value', color='Period', barmode='group')
+    st.write(fig)