src/main.py

import display_gloss as dg
import synonyms_preprocess as sp
from NLP_Spacy_base_translator import NlpSpacyBaseTranslator 
from flask import Flask, render_template, Response, request
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqGeneration
import torch
import os

app = Flask(__name__)
app.config['TITLE'] = 'ASL Translator'

# 캐시 디렉토리 설정
cache_dir = "/tmp/huggingface"
if not os.path.exists(cache_dir):
    os.makedirs(cache_dir, exist_ok=True)
os.environ['TRANSFORMERS_CACHE'] = cache_dir
os.environ['HF_HOME'] = cache_dir

# CPU 설정
device = torch.device('cpu')
os.environ['CUDA_VISIBLE_DEVICES'] = ''

# 번역 모델 초기화
model_name = "Helsinki-NLP/opus-mt-ko-en"
tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir)
model = AutoModelForSeq2SeqGeneration.from_pretrained(model_name, cache_dir=cache_dir)
model = model.to(device)

nlp, dict_docs_spacy = sp.load_spacy_values()
dataset, list_2000_tokens = dg.load_data()

def translate_korean_to_english(text):
    if any('\u3131' <= char <= '\u318F' or '\uAC00' <= char <= '\uD7A3' for char in text):
        inputs = tokenizer(text, return_tensors="pt", padding=True)
        outputs = model.generate(**inputs)
        translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
        return translation
    return text

@app.route('/')
def index():
    return render_template('index.html', title=app.config['TITLE'])

@app.route('/translate/', methods=['POST'])
def result():
    if request.method == 'POST':
        input_text = request.form['inputSentence']
        try:
            english_text = translate_korean_to_english(input_text)
            eng_to_asl_translator = NlpSpacyBaseTranslator(sentence=english_text)
            generated_gloss = eng_to_asl_translator.translate_to_gloss()
            
            gloss_list_lower = [gloss.lower() for gloss in generated_gloss.split() if gloss.isalnum()]
            gloss_sentence_before_synonym = " ".join(gloss_list_lower)
            
            gloss_list = [sp.find_synonyms(gloss, nlp, dict_docs_spacy, list_2000_tokens) 
                         for gloss in gloss_list_lower]
            gloss_sentence_after_synonym = " ".join(gloss_list)
            
            return render_template('result.html',
                                title=app.config['TITLE'],
                                original_sentence=input_text,
                                english_translation=english_text,
                                gloss_sentence_before_synonym=gloss_sentence_before_synonym,
                                gloss_sentence_after_synonym=gloss_sentence_after_synonym)
        except Exception as e:
            return render_template('error.html', error=str(e))

@app.route('/video_feed')
def video_feed():
    sentence = request.args.get('gloss_sentence_to_display', '')
    gloss_list = sentence.split()
    return Response(dg.generate_video(gloss_list, dataset, list_2000_tokens), 
                   mimetype='multipart/x-mixed-replace; boundary=frame')

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860, debug=True)