app.py

from transformers import MarianMTModel, MarianTokenizer

# Load translation models
en_to_hi_model_name = 'Helsinki-NLP/opus-mt-en-hi'  # English to Hindi
hi_to_en_model_name = 'Helsinki-NLP/opus-mt-hi-en'  # Hindi to English

# Load the models and tokenizers
en_to_hi_model = MarianMTModel.from_pretrained(en_to_hi_model_name)
en_to_hi_tokenizer = MarianTokenizer.from_pretrained(en_to_hi_model_name)

hi_to_en_model = MarianMTModel.from_pretrained(hi_to_en_model_name)
hi_to_en_tokenizer = MarianTokenizer.from_pretrained(hi_to_en_model_name)

# Function to perform back-translation (English -> Hindi -> English)
def back_translate(text, from_model, to_model, from_tokenizer, to_tokenizer):
    """
    Perform back translation:
    1. Translate from English to Hindi
    2. Translate back from Hindi to English
    """
    # Step 1: Translate from English to Hindi
    encoded = from_tokenizer.encode(text, return_tensors="pt", truncation=True, padding=True)
    translated = from_model.generate(encoded, num_beams=4, max_length=50, early_stopping=True)
    hindi_text = to_tokenizer.decode(translated[0], skip_special_tokens=True)

    # Step 2: Translate back from Hindi to English
    encoded_back = to_tokenizer.encode(hindi_text, return_tensors="pt", truncation=True, padding=True)
    back_translated = hi_to_en_model.generate(encoded_back, num_beams=4, max_length=50, early_stopping=True)
    back_translated_text = en_to_hi_tokenizer.decode(back_translated[0], skip_special_tokens=True)

    return back_translated_text

# Example usage:
original_text = "What is your address?"
back_translated_text = back_translate(original_text, en_to_hi_model, hi_to_en_model, en_to_hi_tokenizer, hi_to_en_tokenizer)
print("Original text:", original_text)
print("Back-translated text:", back_translated_text)