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, datetime
from pathlib import Path
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')