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, AutoModelForSeq2SeqTranslation
import torch
import os

app = Flask(__name__, static_folder='static')
app.config['TITLE'] = 'Sign Language Translate'

# Set cache directory
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

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

# Load pre-trained Korean-English translation model
model_name = "Helsinki-NLP/opus-mt-ko-en"
tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir)
model = AutoModelForSeq2SeqTranslation.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):
   try:
       # Check if input is Korean
       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)
           print(f"Translated text: {translation}")  # Debug log
           return translation
       return text
   except Exception as e:
       print(f"Translation error: {e}")
       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:
           # Translate to English
           english_text = translate_korean_to_english(input_text)
           
           # Check if translation failed
           if english_text == input_text and any('\u3131' <= char <= '\u318F' or '\uAC00' <= char <= '\uD7A3' for char in input_text):
               raise Exception("Translation failed")
           
           # Convert to ASL gloss
           eng_to_asl_translator = NlpSpacyBaseTranslator(sentence=english_text)
           generated_gloss = eng_to_asl_translator.translate_to_gloss()
           
           # Process 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:
           print(f"Error in translation process: {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)