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import streamlit as st
from transformers import MarianTokenizer, MarianMTModel
import torch
@st.cache_resource
def _load_default_model():
model_name = "Helsinki-NLP/opus-mt-en-fr"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
return tokenizer, model
@st.cache_resource
def load_model(source_lang, target_lang):
try:
if source_lang == target_lang:
return _load_default_model()
model_name = f"Helsinki-NLP/opus-mt-{source_lang}-{target_lang}"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
return tokenizer, model
except Exception as e:
st.warning(f"No direct model for {source_lang} to {target_lang}. Using cached en-fr.")
return _load_default_model()
@st.cache_data(ttl=3600)
def translate_cached(text, source_lang, target_lang):
src_code = {"English": "en", "French": "fr", "Spanish": "es", "German": "de",
"Hindi": "hi", "Chinese": "zh", "Arabic": "ar", "Russian": "ru", "Japanese": "ja"}.get(source_lang, "en")
tgt_code = {"English": "en", "French": "fr", "Spanish": "es", "German": "de",
"Hindi": "hi", "Chinese": "zh", "Arabic": "ar", "Russian": "ru", "Japanese": "ja"}.get(target_lang, "fr")
tokenizer, model = load_model(src_code, tgt_code)
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=500)
with torch.no_grad():
translated = model.generate(**inputs, max_length=500, num_beams=4, early_stopping=True) # Increased beams for speed
return tokenizer.decode(translated[0], skip_special_tokens=True)
def translate(text, source_lang, target_lang):
if not text:
return "No text provided."
try:
return translate_cached(text, source_lang, target_lang)
except Exception as e:
st.error(f"Translation error: {str(e)}. Using input as fallback.")
return text
LANGUAGES = {"English": "en", "French": "fr", "Spanish": "es", "German": "de",
"Hindi": "hi", "Chinese": "zh", "Arabic": "ar", "Russian": "ru", "Japanese": "ja"} |