import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import locale

# Set the locale to Brazilian Portuguese
locale.setlocale(locale.LC_ALL, 'pt_BR.UTF-8')

df = pd.read_csv('last_results.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())
    # Generate some example data
    x = np.linspace(0, 10, 100)
    y1 = np.sin(x)
    y2 = np.cos(x)
    y3 = np.tan(x)

    # Plot the lines using Matplotlib
    fig, ax = plt.subplots()
    ax.plot(x, y1, label='Green Line', color='green')
    ax.plot(x, y2, label='Red Line', color='red')
    ax.plot(x, y3, label='Yellow Line', color='yellow')

    # Set plot properties
    ax.legend()
    ax.set_xlabel('X-axis')
    ax.set_ylabel('Y-axis')

    # Display the Matplotlib plot in the Streamlit sidebar
    st.sidebar.pyplot(fig)

# Filter the DataFrame based on selected values
filtered_df = df[(df['Instituição'] == selected_instituicao) & (df['Conta'] == selected_conta)]

# Display the filtered DataFrame
st.write('Filtered DataFrame:')
st.write(filtered_df)

# Display the Forecasts values
if not filtered_df.empty:
    forecasts_values = filtered_df['Forecasts'].values

    data_string = forecasts_values

    # Split the string into lines
    lines = data_string.split('\n')

    # Iterate through the lines and extract the values
    for line in lines[:-2]:  # Exclude the last two elements (empty and the 'Name: Valor, dtype: float64' line)
        period, value = line.split()
        num_float = float(value)
    
        # Format as monetary value with dots and comma
        monetary_value = locale.currency(num_float, grouping=True)
    
        st.write(f"Período {period}: {monetary_value}")
else:
    st.warning('No data available for the selected filters.')