import pandas as pd
import streamlit as st
import pybase64 as base64
import io
from logs_portal import log
import os
from datetime import date
from modules import tables
import boto3
from Data.credentials import credentials_s3 as creds3
from streamlit_echarts import st_echarts
from st_aggrid import GridOptionsBuilder, AgGrid, GridUpdateMode, DataReturnMode, JsCode
import numpy as np
from streamlit_lottie import st_lottie
import json

def generador_variable_pond(name, col_s1):
    col_s1.markdown("""<p style="margin-top:35px;
                                    font-size:20px;
                                    text-align:center;
                                    margin-bottom:30px;
                                    ">{Var}</p>""".format(Var=name),
                    unsafe_allow_html=True)


def button_style():
    style_button = """
        <style>
          button {
            display: inline-block;
            background-color: white;
            border-radius: 15px;
            border: 4px  #cccccc;
            color: #4a4a4a;
            text-align: center;
            font-size: 18px;
            padding: 2px;
            width: 200px;
            transition: all 0.5s;
            cursor: pointer;
            margin-top: 25px;
          }
          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_excel_link(df, name):
    towrite = io.BytesIO()
    writer = pd.ExcelWriter(towrite, engine='xlsxwriter')
    downloaded_file = df.to_excel(writer, encoding='utf-8', index=False, 
                                  header=True)
    workbook = writer.book
    worksheet = writer.sheets["Sheet1"]
    #set the column width as per your requirement
    worksheet.set_column('A:BZ', 18)
    writer.save()
    towrite.seek(0)  # reset pointer
    file_name = 'Scoring.xlsx'
    style = 'style="color:black;text-decoration: none; font-size:18px;" '
    name_mark = name
    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





def read_excel_s3(key, secret_key, bucket, path):
    s3_client = boto3.client('s3', aws_access_key_id = key, aws_secret_access_key= secret_key)
    response =  s3_client.get_object(Bucket=bucket, Key=path)
    data = response["Body"].read()
    df = pd.read_excel(io.BytesIO(data), engine='openpyxl')
    return df


def display_table(df: pd.DataFrame, name):
    # Configure AgGrid options
    gb = GridOptionsBuilder.from_dataframe(df)
    gb.configure_selection(selection_mode="multiple", use_checkbox=True,)
    gb.configure_column(name, headerCheckboxSelection = True)
    gb.configure_columns(("TICKER", "COUNTRY", "LV1"), pinned=True)
    return AgGrid(
        df, gridOptions=gb.build(),
        update_mode=GridUpdateMode.SELECTION_CHANGED,
        enable_enterprise_modules=True)

@st.experimental_memo
def read_scoring():
    key = creds3["S3_KEY_ID"]
    secret_key = creds3["S3_SECRET_KEY"]
    bucket = creds3["S3_BUCKET"]
    path ="scoring.xlsx"
    scoring = read_excel_s3(key, secret_key, bucket, path)
    return scoring





# @log
def general():

    with open("Data/lotties/99268-laading-22.json", "r") as f:
        spinner = json.load(f)
    cols1, cols2= st.sidebar.columns((3,1))
    place = cols2.empty()
    with place:
        st_lottie(spinner)
    # scoring.index = scoring['Ticker']
    button_style()
    scoring_completo = read_scoring()
    col1, col2, col3 = st.columns((4,1,1))
    col1.write("Last Update: " + scoring_completo.iloc[0]["TODAY"])
    col1.write("Valores en MM USD")
    place1=col2.empty()
    place2 =col3.empty()
    scoring_completo = scoring_completo.drop(columns=["TODAY"])
    scoring_completo[["Nota", 'W Latino', "W Small", 'Nota ESG']] = scoring_completo[["Nota", "W Latino", 'W Small', 'Nota ESG']].fillna(0)
    scoring = scoring_completo.copy()
    # convert just columns "a" and "b"
    metrics = ['MOMENTUM Precio', 'MOMENTUM Fundamental',
               'VALUE','PROF', 'Distres_Prom', 'PROF', 'Delta 1M', 'QUALITY', 'Nota ESG']
    metrics2 = ['Market_Cap'] +  metrics
    
    scoring[metrics2] = scoring[metrics2].round()
    scoring[["W Latino", "W Small", "BM Latino", "BM Small"]] = scoring[["W Latino", "W Small", "BM Latino", "BM Small"]] * 100
    scoring[["W Latino", "W Small", "BM Latino", "BM Small"]] = scoring[["W Latino", "W Small", "BM Latino", "BM Small"]].round(2).fillna(0)
    metrics_aggrid = ['Ticker', 'Portfolio_Country', 'LV1', 'Market_Cap',
                       'ADTV','Large/Small', 'Delta 1M','Distres_Prom', 'MOMENTUM Precio', 'MOMENTUM Fundamental',
                       'VALUE','PROF', 'QUALITY', 'Score', "Nota",
                        'Nota ESG', 'Stop Loss', "W Latino", "W Small", "BM Latino","BM Small"]
    scoring = scoring[metrics_aggrid]
    scoring[metrics_aggrid] = scoring[metrics_aggrid].replace(np.nan, -1)
    
    metrics3 = ['TICKER', 'COUNTRY', 'LV1', 'MKT CAP', 'ADTV', 'L/S', 'Δ 1M',
                'DISTRES', 'MOM PREC',
                'MOM FUND', 'VALUE','PROF', 'QUALITY', 'SCORE',
                "NOTA", 'ESG', 'STOP LOSS', "W LAT", "W SMALL", "BM LAT", "BM SMALL"]
    metrics4 = [
        'MOM PREC', 'MOM FUND', 'VALUE', 'QUALITY','DISTRES', 'PROF']
    scoring.columns = metrics3
    button = st.button("Refresh")
    r = display_table(scoring, 'TICKER')
    rad = 'Home'
    if button:
        st.experimental_memo.clear()
        st.experimental_rerun()
    with place1:
        link = get_table_excel_link(scoring_completo, "Scoring completo")
        st.markdown(link, unsafe_allow_html=True)
    with place2:
        link2 = get_table_excel_link(scoring, "Scoring resumen")
        st.markdown(link2, unsafe_allow_html=True)
    w_lat = []
    w_small = []
    bm_lat = []
    bm_small = []
    col1, col2, col3, col4, col5 = st.columns((1,1,1,1,3))
    large = col1.checkbox("LUXMEXEQ", True)
    small = col2.checkbox("LUXLATSML", True)
    m1la = col3.checkbox("M1LA")
    msm = col4.checkbox("MSLUELAN")
    
    if "large" not in st.session_state:
        st.session_state.large=False
    if "small" not in st.session_state:
        st.session_state.small=False
    if "bm_sm" not in st.session_state:
        st.session_state.bm_sm=False
    if "bm_lat" not in st.session_state:
        st.session_state.bm_lat=False
    
    if large:
        st.session_state.large = True
    else:
        st.session_state.large = False
    if small:
        st.session_state.small =True
    else:
        st.session_state.small = False
    if m1la:
        st.session_state.bm_lat = True
    else:
        st.session_state.bm_lat = False
    if msm:
        st.session_state.bm_sm =True
    else:
        st.session_state.bm_sm = False
    col1, col2, col3 = st.columns((2.5, 1, 1))
    try:
        series_data = []
        names=[]
        for metric in metrics4:
            w_lat.append((scoring[metric]*scoring["W LAT"]/100).sum())
            w_small.append((scoring[metric]*scoring["W SMALL"]/100).sum())
            bm_lat.append((scoring[metric]*scoring["BM LAT"]/100).sum())
            bm_small.append((scoring[metric]*scoring["BM SMALL"]/100).sum())
        if st.session_state.large:
            series_data.append({"value":w_lat,
                        "name": "LUXMEXEQ"})
            names.append("LUXMEXEQ")
        if st.session_state.small:
            series_data.append({"value":w_small,
                        "name": "LUXLATSML"})
            names.append("LUXLATSML")
        if st.session_state.bm_lat:
            series_data.append({"value":bm_lat,
                        "name": "M1LA"})
            names.append("M1LA")
        if st.session_state.bm_sm:
            series_data.append({"value":bm_small,
                        "name": "MSLUELAN"})
            names.append("MSLUELAN")




        if len(r['selected_rows'])>0:
            for emp in r['selected_rows']:
                selected = emp.copy()
                name = selected['TICKER']
                names.append(name)
                indicators = []
                series_value = []
                for met in metrics4:
                    indicators.append({'name': met, "max": 100})
                    series_value.append(selected[met])
                series_data.append({"value": series_value,
                                    "name": name})
                with col2:
                    st.metric('SCORE PROMEDIO - ' + name,
                              int(np.array(series_value).mean()))
        else:
            indicators = []
            for met in metrics4:
                indicators.append({'name': met, "max": 100})
        option = {
            "title": {"text": 'Score'},
            "legend": {"data": names},
            "radar": {
                "indicator": indicators
            },
            "series": [
                {
                    "name": "",
                    "type": "radar",
                    "data": series_data,
                }
            ],
        }
        with col1:
            st_echarts(option, height="400px", width="80%")
    except Exception as exc:
        st.write(exc)
        pass
    
    # st.image("img/Scoring.png", width="100%")
    place.empty()



def diagrama():
    
    import pandas as pd
    import pandas_profiling
    import streamlit as st
    
    from streamlit_pandas_profiling import st_profile_report
    st.image("img/Scoring.png")
    df = read_scoring()
    df=df[['MOMENTUM Precio','MOMENTUM Fundamental', 'VALUE','QUALITY', 'PROF','Distres_Prom','Score' ]]
    pr = df.profile_report()
    
    st_profile_report(pr)