from dataclasses import dataclass
from flask import Flask, request, current_app
# from flask_cors import CORS
import subprocess
import os
import zipfile

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data_utils
import torchvision
import torchvision.datasets as datasets

from matplotlib import pyplot as plt
from numpy import save, load
from tqdm import trange
torch.manual_seed(0)

# import agents and its functions
from ..library.MetaAugment import UCB1_JC_py as UCB1_JC
from ..library.MetaAugment import Evo_learner as Evo
print('@@@ import successful')

app = Flask(__name__)


# it is used to collect user input and store them in the app
@app.route('/home', methods=["GET", "POST"])
def home():
    print('@@@ in Flask Home')
    form_data = request.get_json() 
    # form_data = request.files
    # form_data = request.form.get('test')
    print('@@@ this is form data', form_data)

    # required input
    ds = form_data['select_dataset'] # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)
    IsLeNet = form_data["select_network"]   # using LeNet or EasyNet or SimpleNet ->> default 
    auto_aug_leanrer = form_data["select_learner"] # augmentation methods to be excluded

    print('@@@ required user input:', 'ds', ds, 'IsLeNet:', IsLeNet, 'auto_aug_leanrer:',auto_aug_leanrer)
    # advanced input
    if 'batch_size' in form_data.keys(): 
        batch_size = form_data['batch_size']       # size of batch the inner NN is trained with
    else: 
        batch_size = 1 # this is for demonstration purposes
    if 'learning_rate' in form_data.keys(): 
        learning_rate =  form_data['learning_rate']  # fix learning rate
    else: 
        learning_rate = 10-1
    if 'toy_size' in form_data.keys(): 
        toy_size = form_data['toy_size']      # total propeortion of training and test set we use
    else: 
        toy_size = 1 # this is for demonstration purposes
    if 'iterations' in form_data.keys(): 
        iterations = form_data['iterations']      # total iterations, should be more than the number of policies
    else: 
        iterations = 10
    print('@@@ advanced search: batch_size:', batch_size, 'learning_rate:', learning_rate, 'toy_size:', toy_size, 'iterations:', iterations)

    # default values 
    max_epochs = 10      # max number of epochs that is run if early stopping is not hit
    early_stop_num = 10   # max number of worse validation scores before early stopping is triggered
    num_policies = 5      # fix number of policies
    num_sub_policies = 5  # fix number of sub-policies in a policy
    
    
    # if user upload datasets and networks, save them in the database
    if ds == 'Other':
        ds_folder = request.files #['ds_upload'] 
        print('!!!ds_folder', ds_folder)
        ds_name_zip = ds_folder.filename
        ds_name = ds_name_zip.split('.')[0]
        ds_folder.save('./datasets/'+ ds_name_zip)
        with zipfile.ZipFile('./datasets/'+ ds_name_zip, 'r') as zip_ref:
            zip_ref.extractall('./datasets/upload_dataset/')
        if not current_app.debug:
            os.remove(f'./datasets/{ds_name_zip}')
    else: 
        ds_name = None

    # test if uploaded dataset meets the criteria 
    for (dirpath, dirnames, filenames) in os.walk(f'./datasets/upload_dataset/{ds_name}/'):
        for dirname in dirnames:
            if dirname[0:6] != 'class_':
                return None # neet to change render to a 'failed dataset webpage'

    # save the user uploaded network
    if IsLeNet == 'Other':
        childnetwork = request.files['network_upload']
        childnetwork.save('./child_networks/'+childnetwork.filename)

    
    # generate random policies at start
   
    current_app.config['AAL'] = auto_aug_leanrer
    current_app.config['NP'] = num_policies
    current_app.config['NSP'] = num_sub_policies
    current_app.config['BS'] = batch_size
    current_app.config['LR'] = learning_rate
    current_app.config['TS'] = toy_size
    current_app.config['ME'] = max_epochs
    current_app.config['ESN'] = early_stop_num
    current_app.config['IT'] = iterations
    current_app.config['ISLENET'] = IsLeNet
    current_app.config['DSN'] = ds_name
    current_app.config['ds'] = ds

    
    print("@@@ user input has all stored in the app")

    return {'try': 'Hello'}

@app.route('/api')
def index():
    return {'name': 'Hello'}