curse_word_to_emoji.py

import random
import pandas as pd

emoji = pd.read_csv("emoji_category.csv")
#모델이랑 tokenizer 불러오기

def sentence_classification(sentence):
    columns = ['악플/욕설', '인종/국적', '개인지칭', '연령', '남성', '여성/가족', '성소수자', '지역','기타 혐오', '종교']
    tokenized_sent = tokenizer(
        sentence,
        return_tensors="pt",
        padding=True,
        truncation=True,
        max_length=128,
        add_special_tokens=True,
    )
    
    tokenized_sent.to(device)
    
    model.eval()
    with torch.no_grad():
        output = model(**tokenized_sent)
        
    outputs = output['logits'].detach().cpu().numpy()
    final_outputs = np.array(outputs) >=0.5
    
    return (pd.DataFrame(final_outputs,columns=columns))

def curse_word_to_emoji(word):
    columns = ['악플/욕설', '인종/국적', '개인지칭', '연령', '남성', '여성/가족', '성소수자', '지역','기타 혐오', '종교']
    random_emoji = []
    final_outputs = sentence_classification(word)

    for col in columns:
        if final_outputs.loc[0,col] == True:
                random_emoji.extend(emoji.loc[emoji["category"] == col,"Browser"].tolist())
    
    if random_emoji == []:
        random_emoji.extend(emoji.loc[emoji["category"] == '기타',"Browser"].tolist())
    
    return random.sample(random_emoji,1)[0]
    
    

    
    
curse_word_to_emoji("페미")