Create app.py

app.py

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+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)