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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| df = pd.read_csv('last_results_4.csv') | |
| image1 = 'images/rs_pmpa.PNG' | |
| title_html = """ | |
| <style> | |
| @font-face { | |
| font-family: 'Quicksand'; | |
| src: url('font/Quicksand-VariableFont_wght.ttf') format('truetype'); | |
| } | |
| body { | |
| font-family: 'Quicksand', sans-serif; | |
| } | |
| .custom-title { | |
| color: darkgreen; | |
| font-size: 30px; | |
| font-weight: bold; | |
| } | |
| </style> | |
| <span class='custom-title'>PREVISÕES DE RECEITAS</span> | |
| """ | |
| # Set a fixed width for the sidebar | |
| st.markdown( | |
| """ | |
| <style> | |
| .sidebar .sidebar-content { | |
| width: 300px; | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| with st.sidebar: | |
| st.image(image1, use_column_width=True) | |
| st.markdown(title_html, unsafe_allow_html=True) | |
| selected_instituicao = st.selectbox('Seleciona Instituição', df['Instituição'].unique()) | |
| selected_conta = st.selectbox('Seleciona Conta', df['Conta'].unique()) | |
| # 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 | |
| col1, col2 = st.columns([col1_width, col2_width]) | |
| # Display the Forecasts values in the first column | |
| col1.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') | |
| # 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 | |
| col1.write(f"Período {period}: {monetary_value}") | |
| else: | |
| col1.warning('No data available for the selected filters.') | |
| # Display the Forecasts values as line plots in the second column | |
| col2.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') | |
| # 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({'Period': int(period), '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 | |
| col2.line_chart(chart_data.set_index('Period')) | |
| else: | |
| col2.warning('No data available for the selected filters.') | |