import os
import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from datetime import timedelta
import sqlite3
from sqlite3 import Connection
import plotly.express as px

userDir = os.path.expanduser('~')
URI_SQLITE_DB = userDir + '/BirdNET-Pi/scripts/birds.db'

st.set_page_config(layout='wide')

# Remove whitespace from the top of the page and sidebar
st.markdown("""
        <style>
               .css-18e3th9 {
                    padding-top: 2.5rem;
                    padding-bottom: 10rem;
                    padding-left: 5rem;
                    padding-right: 5rem;
                }
               .css-1d391kg {
                    padding-top: 3.5rem;
                    padding-right: 1rem;
                    padding-bottom: 3.5rem;
                    padding-left: 1rem;
                }
        </style>
        """, unsafe_allow_html=True)


@st.cache(hash_funcs={Connection: id})
def get_connection(path: str):
    return sqlite3.connect(path, check_same_thread=False)


def get_data(conn: Connection):
    df1 = pd.read_sql("SELECT * FROM detections", con=conn)
    return df1


conn = get_connection(URI_SQLITE_DB)
# Read in the cereal data
# df = load_data()
df = get_data(conn)
df2 = df.copy()
df2['DateTime'] = pd.to_datetime(df2['Date'] + " " + df2['Time'])
df2 = df2.set_index('DateTime')


# Filter on date range
# Date as calendars
# Start_Date = pd.to_datetime(st.sidebar.date_input('Which date do you want to start?', value = df2.index.min()))
# End_Date =   pd.to_datetime(st.sidebar.date_input('Which date do you want to end?',  value = df2.index.max()))

# Date as slider
Start_Date = pd.to_datetime(df2.index.min()).date()
End_Date = pd.to_datetime(df2.index.max()).date()
cols1, cols2 = st.columns((1, 1))
Date_Slider = cols1.slider('Date Range',
                           min_value=Start_Date - timedelta(days=1),
                           max_value=End_Date,
                           value=(Start_Date,
                                  End_Date)
                           )


filt = (df2.index >= pd.Timestamp(Date_Slider[0])) & (df2.index <= pd.Timestamp(Date_Slider[1] + timedelta(days=1)))
df2 = df2[filt]

st.write('<style>div.row-widget.stRadio > div{flex-direction:row;justify-content: left;} </style>', unsafe_allow_html=True)
st.write('<style>div.st-bf{flex-direction:column;} div.st-ag{font-weight:bold;padding-left:2px;}</style>', unsafe_allow_html=True)

resample_sel = cols2.radio(
    '''
    Select Resample Resolution - To downsample and make run faster select longer period,
    Daily provides a view on detections at 15 min intervals through the day
    ''',
    ('1 minute',
     '5 minutes',
     '10 minutes',
     'Hourly',
     'Daily'))

resample_times = {'1 minute': '1min',
                  '5 minutes': '5min',
                  '10 minutes': '10min',
                  'Hourly': '1H',
                  'Daily': '1D'
                  }
resample_time = resample_times[resample_sel]

df5 = df2.resample(resample_time)['Com_Name'].aggregate('unique').explode()

# Create species count for selected date range

Specie_Count = df5.value_counts()

# Create species treemap

# Create Hourly Crosstab
hourly = pd.crosstab(df5, df5.index.hour, dropna=False)

# Filter on species
species = list(hourly.index)

cols1, cols2 = st.columns((1, 1))
top_N = cols1.slider(
    'Select Number of Birds to Show',
    min_value=1,
    value=min(10, len(Specie_Count))
)

top_N_species = (df5.value_counts()[:top_N])


specie = cols2.selectbox('Which bird would you like to explore for the dates ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '?', species,
                         index=species.index(list(top_N_species.index)[0]))


font_size = 15


# specie filter
filt = df2['Com_Name'] == specie

df_counts = sum(df5 == specie)


if resample_time != '1D':
    fig = make_subplots(
        rows=3, cols=2,
        specs=[[{"type": "xy", "rowspan": 3}, {"type": "polar", "rowspan": 2}], [{"rowspan": 1}, {"rowspan": 1}], [None, {"type": "xy", "rowspan": 1}]],
        subplot_titles=('<b>Top ' +
                        str(top_N) +
                        ' Species in Date Range ' +
                        str(Date_Slider[0]) +
                        ' to ' +
                        str(Date_Slider[1]) +
                        ' for ' +
                        str(resample_sel) +
                        ' sampling interval.' +
                        '</b>',
                        'Total Detect:' + str('{:,}'.format(df_counts)) +
                        '   Confidence Max:' + str('{:.2f}%'.format(max(df2[df2['Com_Name'] == specie]['Confidence']) * 100)) +
                        '   ' + '   Median:' + str('{:.2f}%'.format(np.median(df2[df2['Com_Name'] == specie]['Confidence']) * 100))
                        )
    )
    fig.layout.annotations[1].update(x=0.7, y=0.25, font_size=15)

    # Plot seen species for selected date range and number of species
    fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1, col=1)

    fig.update_layout(
        margin=dict(l=0, r=0, t=50, b=0),
        yaxis={'categoryorder': 'total ascending'})

    # Set 360 degrees, 24 hours for polar plot
    theta = np.linspace(0.0, 360, 24, endpoint=False)

    specie_filt = df5 == specie
    df3 = df5[specie_filt]

    detections2 = pd.crosstab(df3, df3.index.hour)

    d = pd.DataFrame(np.zeros((23, 1))).squeeze()
    detections = hourly.loc[specie]
    detections = (d + detections).fillna(0)
    fig.add_trace(go.Barpolar(r=detections, theta=theta), row=1, col=2)
    fig.update_layout(
        autosize=False,
        width=1000,
        height=500,
        showlegend=False,
        polar=dict(
            radialaxis=dict(
                tickfont_size=font_size,
                showticklabels=False,
                hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
            ),
            angularaxis=dict(
                tickfont_size=font_size,
                rotation=-90,
                direction='clockwise',
                tickmode='array',
                tickvals=[0, 15, 35, 45, 60, 75, 90, 105, 120, 135, 150, 165, 180, 195, 210, 225, 240, 255, 270, 285, 300, 315, 330, 345],
                ticktext=['12am', '1am', '2am', '3am', '4am', '5am', '6am', '7am', '8am', '9am', '10am', '11am',
                          '12pm', '1pm', '2pm', '3pm', '4pm', '5pm', '6pm', '7pm', '8pm', '9pm', '10pm', '11pm'],
                hoverformat="#%{theta}: <br>Popularity: %{percent} </br> %{r}"
            ),
        ),
    )

    daily = pd.crosstab(df5, df5.index.date, dropna=False)

    fig.add_trace(go.Bar(x=daily.columns, y=daily.loc[specie]), row=3, col=2)

else:
    fig = make_subplots(
        rows=1, cols=2,
        specs=[[{"type": "xy", "rowspan": 1}, {"type": "xy", "rowspan": 1}]],


        subplot_titles=('<b>Daily Top ' + str(top_N) + ' Species in Date Range ' + str(Date_Slider[0]) + ' to ' + str(Date_Slider[1]) + '</b>',
                        '<b>Daily ' + specie + ' Detections on 15 minute intervals </b>'),
        #                                     'Total Detect:'+str('{:,}'.format(df_counts))+
        #                                     '   Confidence Max:'+str('{:.2f}%'.format(max(df2[df2['Com_Name']==specie]['Confidence'])*100))+
        #                                     '   '+'   Median:'+str('{:.2f}%'.format(np.median(df2[df2['Com_Name']==specie]['Confidence'])*100))
        #                                     )
    )

    fig.add_trace(go.Bar(y=top_N_species.index, x=top_N_species, orientation='h'), row=1, col=1)
    df4 = df2['Com_Name'][df2['Com_Name'] == specie].resample('15min').count()
    df4.index = [df4.index.date, df4.index.time]
    day_hour_freq = df4.unstack().fillna(0)

    fig_x = [d.strftime('%d-%m-%Y') for d in day_hour_freq.index.tolist()]
    fig_y = [h.strftime('%H:%M') for h in day_hour_freq.columns.tolist()]
    fig_z = day_hour_freq.values.transpose()
    fig_heatmap = go.Figure(data=go.Heatmap(x=fig_x, y=fig_y, z=fig_z))

    fig.update_layout(
        margin=dict(l=0, r=0, t=50, b=0),
        yaxis={'categoryorder': 'total ascending'})
    color_pals = px.colors.named_colorscales()
    selected_pal = cols2.selectbox('Select Color Pallet for Daily Detections', color_pals)
    fig.add_trace(go.Heatmap(x=fig_x, y=fig_y, z=fig_z, autocolorscale=False, colorscale=selected_pal), row=1, col=2)
# container=st.container()
# config={'displayModelBar': False}
st.plotly_chart(fig, use_container_width=True)  # , config=config)

# cols3,cols4=st.columns((1,1))
#
# extract_date=Date_Slider
#
# audio_file = open('/home/*/BirdSongs/Extracted/By_Date/2022-03-22/Yellow-streaked_Greenbul/Yellow-streaked_Greenbul-77-2022-03-22-birdnet-15:04:28.mp3', 'rb')
# audio_bytes = audio_file.read()
# cols4.audio(audio_bytes, format='audio/mp3')