import datetime 
import numpy as np
import pandas as pd
import re
import json
import os
import glob

import torch
import torch.nn.functional as F
from torch.optim import Adam
from tqdm import tqdm
from torch import nn
from transformers import BertModel

from transformers import AutoTokenizer

import argparse

def split_essay_to_sentence(origin_essay):
    origin_essay_sentence = sum([[a.strip() for a in i.split('.')] for i in origin_essay.split('\n')], [])
    essay_sent = [a for a in origin_essay_sentence if len(a) > 0]
    return essay_sent

def get_first_extraction(text_sentence):
    row_dict = {}
    for row in tqdm(text_sentence):
        question = 'what is the feeling?'
        answer = question_answerer(question=question, context=row)
        row_dict[row] = answer
    return row_dict


def get_sent_labeldata():
    label =pd.read_csv('./rawdata/sentimental_label.csv', encoding = 'cp949', header = None)
    label[1] = label[1].apply(lambda x : re.findall(r'[가-힣]+', x)[0])
    label_dict =label[label.index % 10 == 0].set_index(0).to_dict()[1]
    emo2idx = {v : k for k, v in enumerate(label_dict.items())}
    idx2emo = {v : k[1] for k, v in emo2idx.items()}
    return emo2idx, idx2emo


class myDataset_for_infer(torch.utils.data.Dataset):
    def __init__(self, X):
        self.X = X

    def __len__(self):
        return len(self.X)

    def __getitem__(self,idx):
        sentences =  tokenizer(self.X[idx], return_tensors = 'pt', padding = 'max_length', max_length = 96, truncation = True)
        return sentences
    
    
def infer_data(model, main_feeling_keyword):
    #ds = myDataset_for_infer()
    df_infer = myDataset_for_infer(main_feeling_keyword)

    infer_dataloader = torch.utils.data.DataLoader(df_infer, batch_size= 16)
    device = 'cuda' if torch.cuda.is_available() else 'cpu'

    if device == 'cuda':
        model = model.cuda()

    result_list = []
    with torch.no_grad():
        for idx, infer_input in tqdm(enumerate(infer_dataloader)):
            mask = infer_input['attention_mask'].to(device)
            input_id = infer_input['input_ids'].squeeze(1).to(device)

            output = clsmodel(input_id, mask)
            result = np.argmax(output.logits, axis=1).numpy()
            result_list.extend(result)
    return result_list

def get_word_emotion_pair(cls_model, origin_essay_sentence, idx2emo):

    import re
    def get_noun(sent):
        return [re.sub(r'[을를]+', '', vocab) for (vocab, pos) in nlp(sent) if len(vocab) > 1 and pos == 'NOUN']
    def get_adj(sent):
        return [re.sub(r'[을를]+', '', vocab) for (vocab, pos) in nlp(sent) if len(vocab) > 1 and pos == 'ADJ']
    def get_verb(sent):
        return [re.sub(r'[을를]+', '', vocab) for (vocab, pos) in nlp(sent) if len(vocab) > 1 and pos == 'VERB']

    result_list = infer_data(cls_model, origin_essay_sentence)
    final_result = pd.DataFrame(data = {'text': origin_essay_sentence , 'label' : result_list})
    final_result['emotion'] = final_result['label'].map(idx2emo)
    
    nlp=lambda x:[(x[t["start"]:t["end"]],t["entity_group"]) for t in pipeline(x)]
    #essay_sent_pos = [nlp(i) for i in tqdm(essay_sent)]
    #final_result['text_pos'] = essay_sent_pos
    final_result['noun_list'] = final_result['text'].map(get_noun)
    final_result['adj_list'] = final_result['text'].map(get_adj)
    final_result['verb_list'] = final_result['text'].map(get_verb)
    
    final_result['title'] = 'none'
    file_made_dt = datetime.datetime.now()
    file_made_dt_str = datetime.datetime.strftime(file_made_dt, '%Y%m%d_%H%M%d')
    os.makedirs(f'./result/{file_made_dt_str}/', exist_ok = True)
    final_result.to_csv(f"./result/{file_made_dt_str}/essay_result.csv", index = False)

    return final_result, file_made_dt_str


def get_essay_base_analysis(file_made_dt_str):
    essay1 = pd.read_csv(f"./result/{file_made_dt_str}/essay_result.csv")
    essay1['noun_list_len'] = essay1['noun_list'].apply(lambda x : len(x))
    essay1['noun_list_uniqlen'] = essay1['noun_list'].apply(lambda x : len(set(x)))
    essay1['adj_list_len'] = essay1['adj_list'].apply(lambda x : len(x))
    essay1['adj_list_uniqlen'] = essay1['adj_list'].apply(lambda x : len(set(x)))
    essay1['vocab_all'] = essay1[['noun_list','adj_list']].apply(lambda x : sum((eval(x[0]),eval(x[1])), []), axis=1)
    essay1['vocab_cnt'] = essay1['vocab_all'].apply(lambda x : len(x))
    essay1['vocab_unique_cnt'] = essay1['vocab_all'].apply(lambda x : len(set(x)))
    essay1['noun_list'] = essay1['noun_list'].apply(lambda x : eval(x))
    essay1['adj_list'] = essay1['adj_list'].apply(lambda x : eval(x))
    d = essay1.groupby('title')[['noun_list','adj_list']].sum([]).reset_index()
    d['noun_cnt'] = d['noun_list'].apply(lambda x : len(set(x)))
    d['adj_cnt'] = d['adj_list'].apply(lambda x : len(set(x)))

    # 문장 기준 최고 감정
    essay_summary =essay1.groupby(['title'])['emotion'].value_counts().unstack(level =1)

    emo_vocab_dict = {}
    for k, v in essay1[['emotion','noun_list']].values:
      for vocab in v:
        if (k, 'noun', vocab) not in emo_vocab_dict:
          emo_vocab_dict[(k, 'noun', vocab)] = 0

        emo_vocab_dict[(k, 'noun', vocab)] += 1

    for k, v in essay1[['emotion','adj_list']].values:
      for vocab in v:
        if (k, 'adj', vocab) not in emo_vocab_dict:
          emo_vocab_dict[(k, 'adj', vocab)] = 0

        emo_vocab_dict[(k, 'adj', vocab)] += 1
    vocab_emo_cnt_dict = {}
    for k, v in essay1[['emotion','noun_list']].values:
      for vocab in v:
        if (vocab, 'noun') not in vocab_emo_cnt_dict:
          vocab_emo_cnt_dict[('noun', vocab)] = {}
        if k not in vocab_emo_cnt_dict[( 'noun', vocab)]:
          vocab_emo_cnt_dict[( 'noun', vocab)][k] = 0

        vocab_emo_cnt_dict[('noun', vocab)][k] += 1

    for k, v in essay1[['emotion','adj_list']].values:
      for vocab in v:
        if ('adj', vocab) not in vocab_emo_cnt_dict:
          vocab_emo_cnt_dict[( 'adj', vocab)] = {}
        if k not in vocab_emo_cnt_dict[( 'adj', vocab)]:
          vocab_emo_cnt_dict[( 'adj', vocab)][k] = 0

        vocab_emo_cnt_dict[('adj', vocab)][k] += 1

    vocab_emo_cnt_df = pd.DataFrame(vocab_emo_cnt_dict).T
    vocab_emo_cnt_df['total'] = vocab_emo_cnt_df.sum(axis=1)
    # 단어별 최고 감정 및 감정 개수
    all_result=vocab_emo_cnt_df.sort_values(by = 'total', ascending = False)

    # 단어별 최고 감정 및 감정 개수 , 형용사 포함 시
    adj_result=vocab_emo_cnt_df.sort_values(by = 'total', ascending = False)

    # 명사만 사용 시
    noun_result=vocab_emo_cnt_df[vocab_emo_cnt_df.index.get_level_values(0) == 'noun'].sort_values(by = 'total', ascending = False)

    final_file_name = f"essay_all_vocab_result.csv"
    adj_file_name = f"essay_adj_vocab_result.csv"
    noun_file_name = f"essay_noun_vocab_result.csv"
    
    os.makedirs(f'./result/{file_made_dt_str}/', exist_ok = True)
    
    final_result.to_csv(f"./result/{file_made_dt_str}/essay_all_vocab_result.csv", index = False)
    adj_result.to_csv(f"./result/{file_made_dt_str}/essay_adj_vocab_result.csv", index = False)
    noun_result.to_csv(f"./result/{file_made_dt_str}/essay_noun_vocab_result.csv", index = False)
    
    return final_result, adj_result, noun_result, essay_summary, file_made_dt_str


from transformers import pipeline
#model_name = 'AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru'
model_name = 'monologg/koelectra-base-v2-finetuned-korquad'
question_answerer = pipeline("question-answering", model=model_name)

from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-large-korean-upos")
posmodel=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-large-korean-upos")

pipeline=TokenClassificationPipeline(tokenizer=tokenizer,model=posmodel,aggregation_strategy="simple")
nlp=lambda x:[(x[t["start"]:t["end"]],t["entity_group"]) for t in pipeline(x)]

from transformers import AutoModelForSequenceClassification
device = 'cuda' if torch.cuda.is_available() else 'cpu'

def all_process(origin_essay):
    essay_sent =split_essay_to_sentence(origin_essay)
    row_dict = {}
    for row in tqdm(essay_sent):
        question = 'what is the feeling?'
        answer = question_answerer(question=question, context=row)
        row_dict[row] = answer
    emo2idx, idx2emo = get_sent_labeldata()
    tokenizer = AutoTokenizer.from_pretrained('bert-base-multilingual-cased')
    cls_model = AutoModelForSequenceClassification.from_pretrained('seriouspark/bert-base-multilingual-cased-finetuning-sentimental-6label')
    #cls_model = AutoModelForSequenceClassification.from_pretrained('bert-base-multilingual-cased', num_labels = 6)
    
    final_result, file_name_dt = get_word_emotion_pair(cls_model, essay_sent)
    all_result, adj_\result, noun_result, essay_summary, file_made_dt_str = get_essay_base_analysis(file_name_dt)
    
    summary_result = pd.concat([adj_result, noun_result]).fillna(0).sort_values(by = 'total', ascending = False).fillna(0).reset_index()[:30]
    with open(f'./result/{file_name_dt}/summary.json','w') as f:
        json.dump( essay_summary.to_json(),f)
    with open(f'./result/{file_made_dt_str}/all_result.json','w') as f:
        json.dump( all_result.to_json(),f)    
    with open(f'./result/{file_made_dt_str}/adj_result.json','w') as f:
        json.dump( adj_result.to_json(),f)  
    with open(f'./result/{file_made_dt_str}/noun_result.json','w') as f:
        json.dump( noun_result.to_json(),f)  
    return essay_summary

import gradio as gr
outputs = [gr.Dataframe(row_count = (6, "dynamic"),
                        col_count=(2, "dynamic"),
                        label="Essay Summary based on Words"),
                        title = 'MooGeulMooGeul'
                   
                        #headers=['type','word','슬픔', '분노', '기쁨', '불안', '상처', '당황', 'total'])
    
                        ]

        
iface = gr.Interface(
   fn=all_process,
   inputs = gr.Textbox(lines=2, placeholder= '당신의 글을 넣어보세요'),
   outputs = outputs,
)
iface.launch(share=True)