import streamlit as st
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import plotly.express as px 

st.set_page_config(layout="wide")

st.markdown(
    """
    <style>
    .stTable {
        max-width: 500px;  // Adjust the width as per your layout needs
    }
    </style>
    """,
    unsafe_allow_html=True
)


df = pd.read_csv('last_results_8.csv')

temp_data = pd.read_csv('temp_data(2).csv')
temp_data['Data_Completa'] = pd.to_datetime(temp_data['Data_Completa'])
temp_data.sort_values(['Instituição', 'Conta', 'Data_Completa'], inplace=True)
temp_data['Últimos 12 meses'] = temp_data.groupby(['Instituição', 'Conta'])['Valor'].transform(lambda x: x.rolling(window=12, min_periods=1).sum())
last_dates = temp_data.groupby(['Instituição', 'Conta'])['Data_Completa'].transform(max)
last_rows = temp_data[temp_data['Data_Completa'] == last_dates]
ultimo_ano = last_rows[['Instituição', 'Conta', 'Últimos 12 meses']]

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)]

# Initial filtering based on selected 'Instituição'
instituicao_df = df[df['Instituição'] == selected_instituicao]

# Container for adjusted DataFrame rows
adjusted_rows = []

# Iterate through each unique 'Conta' within the selected 'Instituição'
for conta in instituicao_df['Conta'].unique():
    conta_df = instituicao_df[instituicao_df['Conta'] == selected_conta]
    
    # Check if 'Linear Regression' is available for this 'Conta'
    if len(conta_df['Modelo'].unique()) > 1 and "Linear Regression" in conta_df['Modelo'].unique():
        lr_rows = conta_df[conta_df['Modelo'] == 'Linear Regression']
        adjusted_rows.append(lr_rows)
    else:
        # If not, include all models' results for this 'Conta'
        adjusted_rows.append(conta_df)

# 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')

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
    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:
    col5.warning('No data available for the selected filters.')


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')
col4.write(fig)

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)