from pathlib import Path
from tzlocal import get_localzone
import datetime
import sqlite3
import requests
import json
import time
import math
import numpy as np
import librosa
import operator
import socket
import threading
import os
import sys
import argparse
import datetime

try:
    import tflite_runtime.interpreter as tflite
except BaseException:
    from tensorflow import lite as tflite



def loadMetaModel():

    global M_INTERPRETER
    global M_INPUT_LAYER_INDEX
    global M_OUTPUT_LAYER_INDEX
    global CLASSES

    # Load TFLite model and allocate tensors.
    M_INTERPRETER = tflite.Interpreter(model_path=userDir + '/BirdNET-Pi/model/BirdNET_GLOBAL_3K_V2.3_MData_Model_FP16.tflite')
    M_INTERPRETER.allocate_tensors()

    # Get input and output tensors.
    input_details = M_INTERPRETER.get_input_details()
    output_details = M_INTERPRETER.get_output_details()

    # Get input tensor index
    M_INPUT_LAYER_INDEX = input_details[0]['index']
    M_OUTPUT_LAYER_INDEX = output_details[0]['index']

    # Load labels
    CLASSES = []
    labelspath = userDir + '/BirdNET-Pi/model/labels.txt'
    with open(labelspath, 'r') as lfile:
        for line in lfile.readlines():
            CLASSES.append(line.replace('\n', ''))

    print("loaded META model")

def predictFilter(lat, lon, week):

    global M_INTERPRETER

    # Does interpreter exist?
    try:
        if M_INTERPRETER == None:
            loadMetaModel()
    except Exception as e:
        loadMetaModel()

    # Prepare mdata as sample
    sample = np.expand_dims(np.array([lat, lon, week], dtype='float32'), 0)

    # Run inference
    M_INTERPRETER.set_tensor(M_INPUT_LAYER_INDEX, sample)
    M_INTERPRETER.invoke()

    return M_INTERPRETER.get_tensor(M_OUTPUT_LAYER_INDEX)[0]

def explore(lat, lon, week, threshold):

    # Make filter prediction
    l_filter = predictFilter(lat, lon, week)

    # Apply threshold
    l_filter = np.where(l_filter >= threshold, l_filter, 0)

    # Zip with labels
    l_filter = list(zip(l_filter, CLASSES))

    # Sort by filter value
    l_filter = sorted(l_filter, key=lambda x: x[0], reverse=True)

    return l_filter

def getSpeciesList(lat, lon, week, threshold=0.05, sort=False):

    print('Getting species list for {}/{}, Week {}...'.format(lat, lon, week), end='', flush=True)

    # Extract species from model
    pred = explore(lat, lon, week, threshold)

    # Make species list
    slist = []
    for p in pred:
        if p[0] >= threshold:
            slist.append([p[1],p[0]])

    return slist


userDir = os.path.expanduser('~')
DB_PATH = userDir + '/BirdNET-Pi/scripts/birds.db'
with open(userDir + '/BirdNET-Pi/scripts/thisrun.txt', 'r') as f:

    this_run = f.readlines()
    lat = str(str(str([i for i in this_run if i.startswith('LATITUDE')]).split('=')[1]).split('\\')[0])
    lon = str(str(str([i for i in this_run if i.startswith('LONGITUDE')]).split('=')[1]).split('\\')[0])

weekofyear = datetime.datetime.today().isocalendar()[1]
if __name__ == '__main__':    

    # Parse arguments
    parser = argparse.ArgumentParser(description='Get list of species for a given location with BirdNET. Sorted by occurrence frequency.')
    #parser.add_argument('--o', default='/home/pi/BirdNET-Pi/include_species_list.txt', help='Path to output file or folder. If this is a folder, file will be named \'species_list.txt\'.')
    #parser.add_argument('--lat', type=float, default=##, help='Recording location latitude. Set -1 to ignore.')
    #parser.add_argument('--lon', type=float, default=##, help='Recording location longitude. Set -1 to ignore.')
    #parser.add_argument('--week', type=int, default=dayofweek, help='Week of the year when the recording was made. Values in [1, 48] (4 weeks per month). Set -1 for year-round species list.')
    parser.add_argument('--threshold', type=float, default=0.05, help='Occurrence frequency threshold. Defaults to 0.05.')
    #parser.add_argument('--sortby', default='freq', help='Sort species by occurrence frequency or alphabetically. Values in [\'freq\', \'alpha\']. Defaults to \'freq\'.')
    
    args = parser.parse_args()

    LOCATION_FILTER_THRESHOLD = args.threshold

    # Get species list
    species_list = getSpeciesList(lat, lon, weekofyear, LOCATION_FILTER_THRESHOLD, False)
    for x in range(len(species_list)):
        print(species_list[x][0] + " - "+ str(species_list[x][1]))
    
    print("\nThe above species list describes all the species that the model will attempt to detect. If you don't see a species you want detected on this list, decrease your threshold.")
    print("\nNOTE: no actual changes to your BirdNET-Pi species list were made by running this command. To set your desired frequency threshold, do it through the BirdNET-Pi web interface (Tools -> Settings -> Model)")