Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+import re
+
+# GPU check
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+# Load model and tokenizer
+model_name = "facebook/mbart-large-50-many-to-many-mmt"
+model = MBartForConditionalGeneration.from_pretrained(model_name).to(device)
+tokenizer = MBart50TokenizerFast.from_pretrained(model_name)
+
+# Set languages
+src_lang, tgt_lang = "en_XX", "ta_IN"
+
+# Define a set of technical terms (expand this list as needed)
+technical_terms = {
+    "machine translation", "natural language processing", "nlp", "transformer architecture",
+    "machine learning", "deep learning", "artificial intelligence", "ai", "neural network",
+    "algorithms", "data science", "big data", "cloud computing", "internet of things", "iot",
+    "blockchain", "cybersecurity", "virtual reality", "vr", "augmented reality", "ar",
+    "robotics", "automation", "quantum computing", "5g", "edge computing", "devops",
+    "microservices", "api", "serverless", "container", "docker", "kubernetes", "ml",
+    "computer vision", "natural language understanding", "nlu", "speech recognition",
+    "sentiment analysis", "chatbot", "reinforcement learning", "supervised learning",
+    "unsupervised learning", "convolutional neural network", "cnn", "recurrent neural network", "rnn",
+    "long short-term memory", "lstm", "generative adversarial network", "gan",
+    "transfer learning", "federated learning", "explainable ai", "xai"
+}
+
+def preprocess_text(text):
+    # Wrap technical terms with special tokens
+    for term in sorted(technical_terms, key=len, reverse=True):
+        pattern = re.compile(r'\b' + re.escape(term) + r'\b', re.IGNORECASE)
+        text = pattern.sub(lambda m: f"<keep>{m.group()}</keep>", text)
+    return text
+
+def postprocess_text(text):
+    # Replace special tokens with original terms
+    return re.sub(r'<keep>(.*?)</keep>', r'**\1**', text)
+
+def translate(text, src_lang=src_lang, tgt_lang=tgt_lang):
+    # Preprocess the text
+    preprocessed_text = preprocess_text(text)
+    
+    # Tokenize the preprocessed text
+    inputs = tokenizer(preprocessed_text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    
+    # Generate translation
+    translated = model.generate(
+        **inputs,
+        forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang],
+        max_length=512,
+        num_beams=5,
+        length_penalty=1.0,
+        early_stopping=True
+    )
+    
+    # Decode the generated tokens
+    translated_text = tokenizer.batch_decode(translated, skip_special_tokens=True)[0]
+    
+    # Postprocess the translated text
+    return postprocess_text(translated_text)
+
+# Gradio interface
+def gradio_translate(text):
+    return translate(text)
+
+iface = gr.Interface(
+    fn=gradio_translate,
+    inputs=gr.Textbox(lines=5, label="English Text"),
+    outputs=gr.Textbox(lines=5, label="Tamil Translation"),
+    title="English to Tamil Translation with Technical Terms Preserved",
+    description="This app translates English text to Tamil while preserving technical terms."
+)
+
+iface.launch()