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
import plotly.graph_objects as go

st.set_page_config(layout="wide")

st.markdown("""
    <style>
    @media (max-width: 768px) {
        /* Smaller devices, less space */
        .block-container>div>div {
            flex: 1 1 100%; /* Make all columns take full width */
        }
    }
    @media (min-width: 769px) and (max-width: 1024px) {
        /* Medium devices */
        .block-container>div>div {
            flex: 1; /* Default flex behavior */
        }
        .block-container>div>div:nth-child(1) {
            flex: 0 1 50%; /* First column takes half of the space */
        }
    }
    @media (min-width: 1025px) {
        /* Larger devices */
        .block-container>div>div:nth-child(1) {
            flex: 0 1 33%; /* First column takes about a third of the space */
        }
    }
    </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)

# Set custom width for columns
tab1, tab2, tab3, tab4 = st.tabs(["Composição RLIT", "Valores Previstos", "Tabela Resumo", "Comparativo - Saúde e Eduacação"])

tab_df = df[df['Instituição'] == selected_instituicao]

data = []

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"]

        # 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.
    fig = go.Figure(data=[go.Pie(labels=labels, values=sizes)])
    #st.pyplot(fig1)
    st.plotly_chart(fig, theme=None, use_container_width=True)

with tab2:

    col1, col2= st.columns(2)

    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
            col1.write(f"Mês {mes}: {monetary_value}")
    else:
        col1.warning('No data available for the selected filters.')

    # Display the Forecasts values as line plots in the second column

    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
        #col2.line_chart(chart_data.set_index('Period'))
        fig = px.line(chart_data, x="period", y="Monetary Value")
        st.plotly_chart(fig, theme=None, use_container_width=True)

    else:
        col2.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)


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)