import streamlit as st
from plotly import graph_objs as go
import pandas as pd
from pandas.core.groupby.groupby import DataError
from pytrends.request import TrendReq
from datetime import datetime, timedelta, date
import numpy as np
from plotly.subplots import make_subplots
from metodos import colores_corporativos
import pybase64 as base64
import io
from logs_portal import log
from Scheduler import Scheduler_Covid as sc
import os

def button_style():
    style_button = """
        <style>
      button {
        margin-top:-100px;
        display: inline-block;
        background-color: #e8e8e8;
        border-radius: 15px;
        border: 4px  #cccccc;
        color: #4a4a4a;
        text-align: center;
        font-size: 15px;
        padding: 2px;
        width: 260px;
        transition: all 0.5s;
        cursor: pointer;
        margin: 5px;
      }
      button span {
        cursor: pointer;
        display: inline-block;
        position: relative;
        transition: 0.5s;
      }
      button span:after {
        content: '\00bb';
        position: absolute;
        opacity: 0;
        top: 0;
        right: -20px;
        transition: 0.5s;
      }
      button:hover {
        background-color: #bb1114;
        color:#e8e8e8;
      }
      button:hover span {
        padding-right: 25px;
      }
      button:hover span:after {
        opacity: 1;
        right: 0;
      }
    </style>
    """
    st.markdown(style_button, unsafe_allow_html=True)


def get_table_download_link(df):
    """Generates a link allowing the data in a given panda dataframe to be
    downloaded
    in:  dataframe
    out: href string
    """
    csv = df.to_csv(index=False)
    b64 = base64.b64encode(csv.encode()).decode()
    name_arch = "Scoring_filtrado.csv"
    name_mark = "Descargar .csv "
    style = '"color:black;text-decoration: none;font-size:18px;"'
    href = f'<center><a href="data:file/csv;base64,{b64}" style='  + style+' download="'+name_arch+'" ><button>'+name_mark+'</button></a></center>'
    return href


def get_table_excel_link(df, name_arch):
    towrite = io.BytesIO()
    downloaded_file = df.to_excel(towrite, encoding='utf-8', index=False,
                                  header=True)
    towrite.seek(0)  # reset pointer
    file_name = name_arch
    style = 'style="color:black;text-decoration: none; font-size:18px;" '
    name_mark = "Descargar "+name_arch
    b64 = base64.b64encode(towrite.read()).decode()  # some strings
    linko= f'<center><a href="data:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;base64,{b64}" '+style+'download="'+file_name+'"><button>'+name_mark+'</button></a></center>'
    return linko


@st.cache(show_spinner=True)
def charged_data():
    regiones = {}
    regiones['Latam'] = ['Argentina', 'Brazil', 'Chile', 'Colombia',
                         'Mexico', 'Peru']
    regiones['Europa'] = ['Italy', 'Spain', 'Germany', 'United Kingdom',
                          'France']
    regiones['Asia Emergente'] = ['South Korea', 'Taiwan', 'Hong Kong',
                                  'India', 'Thailand', 'Indonesia']
    regiones['USA'] = ['United States']
    data_dict = np.load('Scheduler/dict_movilidad.npy',
                        allow_pickle='TRUE').item()
    return data_dict, regiones


@st.cache(show_spinner=True)
def charged_data2():
    covid_data = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv')
    paises = {'CL': 'Chile', 'AR': 'Argentina', 'BR': 'Brazil',
              'MX': 'Mexico'}
    covid_data = covid_data.loc[covid_data['location'].isin(paises.values())]
    covid_data['date'] = pd.to_datetime(covid_data['date'])
    covid_data.set_index(['date', 'location'], inplace=True)
    # Creamos diccionario con cada una de las variables para distintos países
    data_dict = {}
    for col in covid_data.columns:
        try:
            data_dict[col] = covid_data[col].unstack().fillna(0).rolling(1).mean()
        except DataError:
            pass
    # Descargamos la data de google trends
    pytrends = TrendReq(retries=5, backoff_factor=0.2,
                        requests_args={'verify': False})
    start = (datetime.today() - timedelta(180)).strftime("%Y-%m-%d")
    start = datetime(2020, 2, 1).strftime("%Y-%m-%d")
    end = datetime.today().strftime("%Y-%m-%d")
    tf = f'{start} {end}'
    kw_lists = {
                'CL': ['PCR', 'sintomas covid', 'examen covid',
                       'covid positivo'],
                'AR': ['PCR', 'olfato', 'sintomas covid', 'perdida gusto',
                       'covid positivo'],
                'MX': ['PCR', 'olfato', 'sintomas covid', 'covid positivo',
                       'perdida gusto'],
                'BR': ['PCR', 'sintomas covid', 'exame covid', 'covid positivo']
                }
    gt_data = {}
    for p, kw in kw_lists.items():
        pytrends.build_payload(kw, timeframe=tf, geo=p)
        df = pytrends.interest_over_time().drop(columns='isPartial')
        gt_data[paises[p]] = df.div(df.mean(0).values)
    data_dict['GT Index'] = pd.DataFrame({p: gt_data[p].mean(1).rolling(1).mean()
                                          for p in gt_data.keys()})
    return data_dict, paises


@log
def Movilidad():
    largo = 400
    ancho = 550
    button_style()
    placebar = st.empty()
    percent_complete = 0
    my_bar = placebar.progress(percent_complete)
    data_cargada = charged_data()
    data_dict = data_cargada[0]
    regiones = data_cargada[1]
    europa = data_dict['Mobility Index'][regiones.keys()]["Europa"]
    latam = data_dict['Mobility Index'][regiones.keys()]["Latam"]
    asia = data_dict['Mobility Index'][regiones.keys()]["Asia Emergente"]
    USA = data_dict['Mobility Index'][regiones.keys()]["USA"]
    mov_region = data_dict['Mobility Index'][regiones.keys()][["USA", "Europa","Asia Emergente", "Latam"]]
    percent_complete = percent_complete+33
    placebar.progress(percent_complete)
    colores = list(colores_corporativos().values())
    colores2 = []
    for i in range(len(colores)):
        colores2.append("rgb"+str(colores[i]))
    def plot_raw_data():
        fig = go.Figure()
        europa_ = go.Scatter(x=europa.index, y=europa.values, name="Europa",
                             line=dict(color=colores2[0]))
        latam_ = go.Scatter(x=latam.index, y=latam.values, name="Latam",
                            line=dict(color=colores2[1]))
        USA_ = go.Scatter(x=USA.index, y=USA.values, name="USA",
                          line=dict(color=colores2[2]))
        asia_ = go.Scatter(x=asia.index, y=asia.values, name="Asia Emergente",
                           line=dict(color=colores2[3]))
        fig.add_trace(europa_)
        fig.add_trace(latam_)
        fig.add_trace(USA_)
        fig.add_trace(asia_)
        fig.layout.update(title_text="Evolución por region",
                          xaxis_rangeslider_visible=True,
                          margin_b=20,
                          margin_r=20,
                          margin_l=20,
                          width=ancho,
                          height=largo,
                          legend=dict(orientation="h",
                                      yanchor="bottom",
                                      y=1.02,
                                      xanchor="right",
                                      x=1))
        fig2 = go.Figure()
        i = 0
        for pais in regiones["Latam"]:
            data_pais = data_dict['Mobility Index'][regiones['Latam']][pais]
            pais_gr = go.Scatter(x=data_pais.index,
                                 y=data_pais.values, name=pais,
                                 line=dict(color=colores2[i]))
            fig2.add_trace(pais_gr)
            i = i+1
        fig2.layout.update(title_text="Evolución LATAM",
                           xaxis_rangeslider_visible=True, margin_b=20,
                           margin_r=20,margin_l=20,
                           width=ancho, height=largo,
                           legend=dict(orientation="h",
                                       yanchor="bottom",
                                       y=1.0,
                                       xanchor="right",
                                       x=1))
        col1, col2 = st.columns(2)
        col1.plotly_chart(fig, use_container_width=True)
        col2.plotly_chart(fig2, use_container_width=True)
        link_excel_1 = get_table_excel_link(data_dict['Mobility Index'][regiones['Latam']], "Movilidad Latam.xlsx")
        link_excel_2 = get_table_excel_link(mov_region, "Movilidad por region.xlsx")
        col1.markdown(link_excel_1, unsafe_allow_html=True)
        col2.markdown(link_excel_2, unsafe_allow_html=True)
    percent_complete = percent_complete + 33
    placebar.progress(percent_complete)
    placebar.empty()
    plot_raw_data()
    percent_complete = percent_complete + 34
    my_bar.progress(percent_complete)
    my_bar.empty()
    data_desag = pd.read_excel("Scheduler/Movilidad_desagrada.xlsx",
                               engine="openpyxl")
    st.markdown(get_table_excel_link(data_desag, "Movilidad desagregada.xlsx"),
                unsafe_allow_html=True)
    try:
        user = os.getlogin()
        if user == 'bullm':
            act = st.button('Actualizar')
            if act:
                sc.run_data_covid()
                ud = pd.read_excel('Data/update_data.xlsx')
                ud = ud[ud['View'] != 'Covid19']
                today = date.today().strftime('%d-%m-%Y')
                ud = ud.append({"View": "Covid19",
                                "Last_Update": today}, ignore_index=True)
                ud.to_excel('Data/update_data.xlsx', index=False)
    except Exception:
        pass


@log
def Correlacion_GT():
    largo = 400
    ancho = 550
    button_style()
    # Cargamos la data relevante
    percent_complete = 0
    my_bar = st.progress(percent_complete)
    percent_complete = percent_complete + 33
    my_bar.progress(percent_complete)
    data_cargada = charged_data2()
    data_dict = data_cargada[0]
    paises = data_cargada[1]
    corr_df = pd.DataFrame(index=paises.values(), columns=np.arange(-3, 1))
    percent_complete = percent_complete + 33
    my_bar.progress(percent_complete)
    i = 0
    cols = st.columns(2)
    col1, col2, col3, col4 = st.columns((1.5, 7, 2, 7))
    for p in corr_df.index:
        df = pd.concat([data_dict['GT Index'][p],
                        data_dict['new_cases_per_million'][p]],
                       axis=1).dropna()
        df.columns = ['GT Index', 'Nuevos Casos Confirmados']
        fig = make_subplots(specs=[[{"secondary_y": True}]])
        CC = go.Scatter(x=df['GT Index'].index,
                        y=df['GT Index'].values, name='GT index',
                        line=dict(color='dimgrey'))
        GT = go.Scatter(x=df['Nuevos Casos Confirmados'].index,
                        y=df['Nuevos Casos Confirmados'].values,
                        name='Casos confirmados', line=dict(color='darkred'))
        fig.add_trace(CC, secondary_y=False,)
        fig.add_trace(GT, secondary_y=True,)
        fig.layout.update(title_text="Evolución {}".format(p),
                          xaxis_rangeslider_visible=True, margin_b=20,
                          margin_r=20, margin_l=20,
                          width=ancho, height=largo,
                          legend=dict(orientation="h",
                                      yanchor="bottom",
                                      y=1.02,
                                      xanchor="right",
                                      x=1))
        link_excel = get_table_excel_link(df, "Correlacion GT.xlsx")
        if i % 2 == 0:
            cols[0].plotly_chart(fig, use_container_width=True)
            cols[0].markdown(link_excel, unsafe_allow_html=True)
        else:
            cols[1].plotly_chart(fig, use_container_width=True)
            cols[1].markdown(link_excel, unsafe_allow_html=True)
            cols = st.columns(2)
            col1, col2, col3, col4 = st.columns((1.5, 7, 2, 7))
        i = i + 1
    percent_complete = percent_complete + 34
    my_bar.progress(percent_complete)
    my_bar.empty()



@log
def vacunas():
    largo = 400
    ancho = 550
    button_style()
    vac_data = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv').set_index(['date','location'])
    country_pop = (vac_data['population'].reset_index().set_index('location')
                   .drop(columns='date').squeeze().drop_duplicates())
    min_pop = 1000000
    idx = country_pop[country_pop > min_pop].index
    vac_data = vac_data['total_vaccinations_per_hundred'].unstack().ffill().fillna(0)
    vac_data.index = pd.to_datetime(vac_data.index)
    N = 15
    top_vac = vac_data[idx].iloc[-1].nlargest(N).sort_values()
    regiones = {}
    regiones['Latam'] = ['Argentina', 'Brazil', 'Chile', 'Colombia',
                         'Mexico', 'Peru']
    regiones['Europa'] = ['Italy', 'Spain', 'Germany', 'United Kingdom',
                          'France', 'Russia']
    regiones['Asia Emergente'] = ['South Korea', 'Taiwan', 'Hong Kong',
                                  'China', 'Japan']
    regiones['Norteamérica'] = ['United States', 'Canada']
    inicio = datetime(2020, 11, 15)
    vac_data = vac_data.loc[vac_data.index > inicio].resample('W').last()
    vac_data.index.name = ''
    colores = colores_corporativos().values()
    colores = list(colores_corporativos().values())
    colores2 = []
    for i in range(len(colores)):
        colores2.append("rgb"+str(colores[i]))

    def plot_raw_data():
        i = 0
        cols = st.columns(2)
        col1, col2, col3, col4 = st.columns((1.5, 7, 2, 7))
        for region in list(regiones.keys()):
            fig = go.Figure()
            j = 0
            for pais in regiones[region]:
                data_pais = vac_data[regiones[region]][pais]
                pais_gr = go.Scatter(x=data_pais.index,
                                     y=data_pais.values, name=pais,
                                     line=dict(color=colores2[j]))
                fig.add_trace(pais_gr)
                j = j+1
            fig.layout.update(title_text="Evolución "+region,
                              xaxis_rangeslider_visible=True, height=largo,
                              width=ancho, margin_b=20,
                              legend=dict(orientation="h",
                                          yanchor="bottom",
                                          y=1.0,
                                          xanchor="right",
                                          x=1))
            link_excel = get_table_excel_link(data_pais, "Vacunacion.xlsx")
            if i % 2 == 0:
                cols[0].plotly_chart(fig, use_column_width=True)
                cols[0].markdown(link_excel, unsafe_allow_html=True)
            else:
                cols[1].plotly_chart(fig, use_column_width=True)
                cols[1].markdown(link_excel, unsafe_allow_html=True)
                cols = st.columns(2)
                col1, col2, col3, col4 = st.columns((1.5, 7, 2, 7))
            i = i+1
    plot_raw_data()