import argparse
import os
from importlib import import_module
import gradio as gr
from tqdm import tqdm
import models.TextCNN
import torch
import pickle as pkl
from utils import build_dataset

classes = ['金融类', '房地产类', '股票类', '教育类', '科技类', '社会类', '政治类', '体育类', '游戏类',
           '娱乐类']

MAX_VOCAB_SIZE = 10000  # 词表长度限制
UNK, PAD = '<UNK>', '<PAD>'  # 未知字，padding符号


def build_vocab(file_path, tokenizer, max_size, min_freq):
    vocab_dic = {}
    with open(file_path, 'r', encoding='UTF-8') as f:
        for line in tqdm(f):
            lin = line.strip()
            if not lin:
                continue
            content = lin.split('\t')[0]
            for word in tokenizer(content):
                vocab_dic[word] = vocab_dic.get(word, 0) + 1
        vocab_list = sorted([_ for _ in vocab_dic.items() if _[1] >= min_freq], key=lambda x: x[1], reverse=True)[
                     :max_size]
        vocab_dic = {word_count[0]: idx for idx, word_count in enumerate(vocab_list)}
        vocab_dic.update({UNK: len(vocab_dic), PAD: len(vocab_dic) + 1})
    return vocab_dic








def greet(text):
    parser = argparse.ArgumentParser(description='Chinese Text Classification')
    parser.add_argument('--word', default=False, type=bool, help='True for word, False for char')
    args = parser.parse_args()
    model_name = 'TextCNN'
    dataset = 'THUCNews'  # 数据集
    embedding = 'embedding_SougouNews.npz'
    x = import_module('models.' + model_name)

    config = x.Config(dataset, embedding)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    model = models.TextCNN.Model(config)

    # vocab, train_data, dev_data, test_data = build_dataset(config, args.word)
    model.load_state_dict(torch.load('THUCNews/saved_dict/TextCNN.ckpt', map_location=torch.device('cpu')))
    model.to(device)
    model.eval()

    tokenizer = lambda x: [y for y in x]  # char-level
    if os.path.exists(config.vocab_path):
        vocab = pkl.load(open(config.vocab_path, 'rb'))
    else:
        vocab = build_vocab(config.train_path, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
        pkl.dump(vocab, open(config.vocab_path, 'wb'))
    # print(f"Vocab size: {len(vocab)}")

    # content='时评：“国学小天才”录取缘何少佳话'
    content = text

    words_line = []
    token = tokenizer(content)
    seq_len = len(token)
    pad_size = 32
    contents = []

    if pad_size:
        if len(token) < pad_size:
            token.extend([PAD] * (pad_size - len(token)))
        else:
            token = token[:pad_size]
            seq_len = pad_size
    # word to id
    for word in token:
        words_line.append(vocab.get(word, vocab.get(UNK)))

    contents.append((words_line, seq_len))
    # print(words_line)
    # input = torch.LongTensor(words_line).unsqueeze(1).to(device)  # convert words_line to LongTensor and add batch dimension
    x = torch.LongTensor([_[0] for _ in contents]).to(device)

    # pad前的长度(超过pad_size的设为pad_size)
    seq_len = torch.LongTensor([_[1] for _ in contents]).to(device)
    input = (x, seq_len)
    # print(input)
    with torch.no_grad():
        output = model(input)
        predic = torch.max(output.data, 1)[1].cpu().numpy()
    # print(predic)
    # print('类别为：{}'.format(classes[predic[0]]))
    return classes[predic[0]]



examples = [
    ["苹果发布iPhone18"],
    ["小明高考考了700分"],
    ["英雄联盟世界赛即将开始"]
]

demo = gr.Interface(fn=greet, inputs="text", outputs="text", title="text-classification app", 
                     layout="vertical", description="This is a demo for text classification.",examples=examples)
demo.launch()

#