# Imports
import pandas as pd
import streamlit as st
import pybase64 as base64
# from modules.tables import RealEstateMaste
from sqlalchemy import create_engine
import plotly.express as px
import io
import urllib.request
import bs4 as bs
import scipy.stats as stats 
import plotly.figure_factory as ff
import scipy.stats as stats 
import plotly.graph_objects as go
import numpy as np
from datetime import date
from datetime import timedelta
from Data.credentials import credentials_postgresql as credpost

def formatnum(numero):
    '''
    Esta función permite dar formato a los montos de saldo y valor cuota en 
    las cartolas.
    '''
    return '{:,.0f}'.format(numero).replace(",", "@").replace(".", ",").replace("@", ".")


def get_UF():
    link = "https://valoruf.cl/"
    req = urllib.request.Request(link)
    res = urllib.request.urlopen(req)
    resData = res.read()
    soup = bs.BeautifulSoup(resData)
    uf = soup.find(class_="vpr").contents[0]
    uf_value = uf.split(' ')[1]
    uf_value = uf_value.replace('.', '')
    uf_value = uf_value.replace(',', '.')
    return uf_value


def button_style():
    style_button = """
        <style>
          button {
            display: inline-block;
            background-color: #e8e8e8;
            border-radius: 15px;
            border: 4px  #cccccc;
            color: #4a4a4a;
            text-align: center;
            font-size: 18px;
            padding: 2px;
            width: 300px;
            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 plot_column(data, column, nom_var_inv, placeholder, sigmas=3, limite=None):
    data = data.rename(columns=nom_var_inv)
    column = nom_var_inv[column]
    limit = data[column].mean() + sigmas * data[column].std()
    data = data.loc[data[column] <= limit]
    if limite is not None:
        data = data.loc[data[column] <= limite]
    fig = px.scatter_mapbox(data_frame=data, lat='latitud', lon='longitud',
                            zoom=10, color=column,
                            hover_data=['codigo', 'Superficie', 'dormitorios', 'banos', 'Precio en CLP'],
                            color_continuous_scale=px.colors.diverging.RdYlBu,
                            width=1000, height=800)
    placeholder.plotly_chart(fig, use_container_width=True)

def plot_column2(data, column, nom_var_inv, placeholder, zoom, sigmas=10, limite=None):
    data = data.rename(columns=nom_var_inv)
    column = nom_var_inv[column]
    limit = data[column].mean() + sigmas * data[column].std()
    data = data.loc[data[column] <= limit]
    if limite is not None:
        data = data.loc[data[column] <= limite]
    fig = px.scatter_mapbox(data_frame=data, lat='latitud', lon='longitud',
                            zoom=zoom, color=column,
                            hover_data=['codigo', 'Superficie', 'dormitorios', 'banos', 'Precio en CLP', 'Comuna'],
                            color_continuous_scale=px.colors.diverging.RdYlBu,
                            width=1000, height=800,size='Superficie',size_max=25)
    placeholder.plotly_chart(fig, use_container_width=True)


def get_comuna(direction):
    values = direction.split(',')
    return values[-2]

def unit_separator(string):
    values = string.split(" ")
    return values[0]


def transform_df(scraping_df):
    # Extract numbers from string
    # Change datatype
    # Add 'Comuna' and 'Región'
    scraping_df[["Comuna"]] = scraping_df[["direccion"]].applymap(lambda x: get_comuna(x))
    # Scrap UF value for date
    #scraping_df[["Valor UF"]] = get_UF()
    return scraping_df


def get_table_excel_link(df, name):
    towrite = io.BytesIO()
    downloaded_file = df.to_excel(towrite, encoding='utf-8', index=False,
                                  header=True)
    towrite.seek(0)  # reset pointer
    file_name = 'Data' + name + '.xlsx'
    style = 'style="color:black;text-decoration: none; font-size:18px;"'
    name_mark = "Descargar " + name + ".xlsx"
    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(allow_output_mutation=True)
def query_inmob():
    url = credpost["POSTGRESQL"]
    engine = create_engine(url, echo=False)
    semana_pasada = date.today() - timedelta(7)
    semana_pasada = semana_pasada.strftime("%Y-%m-%d")
    data = pd.read_sql_query("""select * from scraping_inmob where Fecha >
                             '{Fecha} 00:00:00' ORDER BY Fecha desc""".format(Fecha=semana_pasada)  , con=engine)
    return data

def run_scrapping():
    button_style()
    px.set_mapbox_access_token('pk.eyJ1IjoibW9rc2VuYmVyZyIsImEiOiJja3QwOTc3dHgyNzBhMnFsczJ2Y2w3bWJlIn0.m8c3duvR5hQVjbjEByorWQ')
    data = query_inmob()
    precio_uf = get_UF()
    data["valor_uf"] = data["valor_peso"]/float(precio_uf)
    data['predicted_venta_por_m2_UF']=data['predicted_venta_por_m2']/float(precio_uf)
    nom_var = {"Precio en UF": "valor_uf",
               "Precio en CLP": "valor_peso",
               "Superficie": "superficie",
               "Predicted venta por M2 en CLP": "predicted_venta_por_m2",
               "Predicted venta por M2 en UF": "predicted_venta_por_m2_UF",
               "Predicted arriendo por M2": "predicted_arriendo_por_m2",
               "Predicted yield anual": "predicted_yield_anual"
               }
    nom_var_inv = {v: k for k, v in nom_var.items()}
    var = list(nom_var.keys())
    with st.form(key='my_form'):
        col1, col2, col3, col4 = st.columns((9, 1, 9, 1))
        cols = st.columns((9, 1, 5, 5))
        pre_selection = col1.selectbox("Variable", var)
        selection = nom_var[pre_selection]
        mercado = col3.selectbox("Mercado", ["Venta", "Arriendo", "Todos"])
        if mercado != "Todos":
            mercado = mercado.lower()
            data_mercado = data[data["mercado"] == mercado]
        else:
            data_mercado = data
        cifras = len(str(int(max(data_mercado[selection]))))
        tipo_prop = cols[0].selectbox("Tipo de propiedad",
                                      ["Todos", "Casa", "Departamento"])
        if tipo_prop != "Todos":
            tipo_prop2 = tipo_prop.lower()
            data_mercado = data_mercado[data_mercado["tipo_propiedad"] == tipo_prop2]
        if cifras > 3:
            corte = 10 ** (cifras - 3)
            cota_final = (int(max(data_mercado[selection].dropna()))//corte+1) *corte
            cota_inf = cols[2].number_input(label="Valor Mínimo",
                                            min_value=0,
                                            value=0,
                                            max_value=cota_final
                                            )
            cota_sup = cols[3].number_input(label="Valor Máximo",
                                            min_value=0,
                                            value=cota_final,
                                            max_value=cota_final
                                            )
        else:
            cota_final = max(data_mercado[selection].dropna())+1
            cota_inf = cols[2].number_input(label="Valor Mínimo",
                                            min_value=0.0,
                                            value=0.0,
                                            step=0.1,
                                            max_value=cota_final
                                            )
            cota_sup = cols[3].number_input(label="Valor Máximo",
                                            min_value=0.0,
                                            step=0.1,
                                            value=cota_final,
                                            max_value=cota_final
                                            )
        data_final = data_mercado[data_mercado[selection] < cota_sup]
        data_final = data_final[data_final[selection] > cota_inf]
        submit_button = st.form_submit_button(label='Actualizar')
    col1, col2 = st.columns((0.65, 1))
    placeholder = st.empty()
    placeholder2 = st.empty()
    placeholder2.markdown(get_table_excel_link(data, "Data Completa"),
            unsafe_allow_html=True)
    plot_column(data, selection, nom_var_inv, placeholder)
    
    if submit_button:
        plot_column(data_final, selection, nom_var_inv, placeholder)

        st.markdown(get_table_excel_link(data_final, " Data filtrada"),
                    unsafe_allow_html=True)



def scraping_localizado():
    button_style()
    data = query_inmob()
    data = transform_df(data)
    precio_uf = get_UF()
    data["valor_uf"] = data["valor_peso"]/float(precio_uf)
    data['predicted_venta_por_m2_UF'] = data['predicted_venta_por_m2']/float(precio_uf)
    nom_var = {"Precio en UF": "valor_uf",
               "Precio en CLP": "valor_peso",
               "Superficie": "superficie",
               "Predicted venta por M2 en CLP": "predicted_venta_por_m2",
               "Predicted venta por M2 en UF": "predicted_venta_por_m2_UF",
               "Predicted arriendo por M2": "predicted_arriendo_por_m2",
               "Predicted yield anual": "predicted_yield_anual"
               }
    nom_var_inv = {v : k for k, v in nom_var.items()}
    var = list(nom_var.keys())
    with st.form(key='my_form'):
        col1, col2, col3, col4 = st.columns((9, 1, 9, 1))
        cols = st.columns((9, 1, 5, 5))
        pre_selection = col1.selectbox("Variable", var)
        selection = nom_var[pre_selection]
        mercado = col3.selectbox("Mercado", ["Venta", "Arriendo", "Todos"])
        banos = col1.slider("Baños", value=(1, 5), min_value=0, max_value=10)
        dormitorios = col3.slider("Dormitorios", value=(1, 5), min_value=0,
                                  max_value=10)
        com = ["Todas"] + sorted(list(dict.fromkeys(data["Comuna"])))
        comuna = col1.selectbox("Comuna", com)
        zoom = col3.number_input("Zoom", value=12)
        if mercado != "Todos":
            mercado = mercado.lower()
            data_mercado = data[data["mercado"] == mercado]
        else:
            data_mercado = data
        if comuna != "Todas":
            data_mercado = data_mercado[data_mercado["Comuna"] == comuna]
        if len(data_mercado) > 0:
            cifras = len(str(int(max(data_mercado[selection].dropna()))))
        else:
            cifras = 0
        tipo_prop = cols[0].selectbox("Tipo de Propiedad",
                                      ["Todos", "Casa", "Departamento"])
        if tipo_prop != "Todos":
            tipo_prop2 = tipo_prop.lower()
            data_mercado = data_mercado[data_mercado["tipo_propiedad"] == tipo_prop2]
        if cifras > 3:
            corte = 10 ** (cifras - 3)
            cota_final = (int(max(data_mercado[selection].dropna()))//corte+1)*corte
            cota_inf = cols[2].number_input(label="Valor Mínimo",
                                            min_value=0,
                                            value=0,
                                            max_value=cota_final
                                            )
            cota_sup = cols[3].number_input(label="Valor Máximo",
                                            min_value=0,
                                            value=cota_final,
                                            max_value=cota_final
                                            )
        else:
            cota_final = max(data_mercado[selection].dropna())+1
            cota_inf = cols[2].number_input(label="Valor Mínimo",
                                            min_value=0.0,
                                            value=0.0,
                                            step=0.1,
                                            max_value=cota_final
                                            )
            cota_sup = cols[3].number_input(label="Valor Máximo",
                                            min_value=0.0,
                                            step=0.1,
                                            value=cota_final,
                                            max_value=cota_final
                                            )
            
        data_final = data_mercado[data_mercado[selection] < cota_sup]
        data_final = data_final[data_final[selection] > cota_inf]
        data_final = data_final[data_final["banos"] > banos[0]]
        data_final = data_final[data_final["banos"] < banos[1]]
        data_final = data_final[data_final["dormitorios"] > dormitorios[0]]
        data_final = data_final[data_final["dormitorios"] < dormitorios[1]]
        st.write('Stats principales del análisis')
        st.write('Valor promedio en UF por metro cuadrado: {:.2f} UF'.format((data_final['valor_uf']/data_final['superficie']).mean()))
        st.write('Superficie promedio: {:.0f} m2'.format(data_final['superficie'].mean()))
        st.write('N° de dormitorios promedio: {:.2f}'.format(data_final['dormitorios'].mean()))
        st.write('N° de baños promedio: {:.2f}'.format(data_final['banos'].mean()))

        submit_button = st.form_submit_button(label='Actualizar')
    col1, col2 = st.columns((0.65, 1))
    placeholder = st.empty()
    placeholder2 = st.empty()
    placeholder3 = st.empty()
    placeholder2.markdown(get_table_excel_link(data, "Data Completa"),
            unsafe_allow_html=True)
    plot_column2(data, selection, nom_var_inv, placeholder, zoom)
    dist_gamma = stats.gamma.rvs(1, scale=250000, size=2965) 
    if submit_button:
        plot_column2(data_final, selection, nom_var_inv, placeholder, zoom)
        st.markdown(get_table_excel_link(data_final, " Data filtrada"),
                    unsafe_allow_html=True)
        data_hist = data_final[data_final["mercado"] == "venta"]
        data_hist = data_hist[data_hist["valor_peso"] > 40000000]
        data_hist = data_hist[data_hist["valor_peso"] < 1000000000]["valor_peso"] * 0.2
        fig2 = px.histogram((data_hist), x='valor_peso')
        #placeholder3.plotly_chart(fig2)
        
# streamlit run analisis_inmob.py
# scraping_localizado()