import streamlit as st
from transformers import MarianTokenizer, MarianMTModel

# Preload default model for English to French
@st.cache_resource
def _load_default_model():
    """Load default MarianMT model (en-fr)."""
    model_name = "Helsinki-NLP/opus-mt-en-fr"
    tokenizer = MarianTokenizer.from_pretrained(model_name)
    model = MarianMTModel.from_pretrained(model_name)
    return tokenizer, model

# Cache other models dynamically
@st.cache_resource
def load_model(src_lang, tgt_lang):
    """Load MarianMT model for a specific language pair."""
    model_name = f"Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}"
    try:
        tokenizer = MarianTokenizer.from_pretrained(model_name)
        model = MarianMTModel.from_pretrained(model_name)
        return tokenizer, model
    except Exception as e:
        raise Exception(f"Model for {src_lang} to {tgt_lang} not available: {str(e)}")

# Preload default model globally
DEFAULT_TOKENIZER, DEFAULT_MODEL = _load_default_model()

def translate(text, source_lang, target_lang):
    """Translate text from source to target language."""
    if not text:
        return "Please provide text to translate."
    
    src_code = LANGUAGES.get(source_lang)
    tgt_code = LANGUAGES.get(target_lang)
    
    # Use preloaded model if en-fr, else load dynamically
    if src_code == "en" and tgt_code == "fr":
        tokenizer, model = DEFAULT_TOKENIZER, DEFAULT_MODEL
    else:
        tokenizer, model = load_model(src_code, tgt_code)
    
    # Perform translation
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=400)
    translated = model.generate(**inputs)
    return tokenizer.decode(translated[0], skip_special_tokens=True)

# Language dictionary (limited for speed)
LANGUAGES = {
   "English": "en",
    "French": "fr",
    "Spanish": "es",
    "German": "de",
    "Chinese": "zh",
    "Arabic": "ar",
    "Russian": "ru",
    "Hindi": "hi",
    "Japanese": "ja"
}