Create app.py

app.py

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+import gradio as gr
+import ctranslate2
+import transformers
+
+# Load optimized components
+translator = ctranslate2.Translator("ct2_model")
+tokenizer = transformers.AutoTokenizer.from_pretrained("final_model")
+
+def transliterate(text, target_lang):
+    if not text.strip(): return ""
+    
+    lang_codes = {"Hindi": "hin", "Bengali": "ben", "Tamil": "tam"}
+    input_text = f"<{lang_codes[target_lang]}> {text.lower()}"
+    
+    source_tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(input_text))
+    results = translator.translate_batch([source_tokens])
+    
+    output_tokens = results[0].hypotheses[0]
+    # Clean up output (handle sentencepiece tokens like ' ')
+    decoded = tokenizer.decode(tokenizer.convert_tokens_to_ids(output_tokens), skip_special_tokens=True)
+    return decoded
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🇮🇳 Indic Multilingual Transliteration")
+    gr.Markdown("Transliterate English names/words into Hindi, Bengali, or Tamil using an optimized mT5 model.")
+    
+    with gr.Row():
+        with gr.Column():
+            input_box = gr.Textbox(label="English Input", placeholder="Type a word (e.g., 'Bharat')")
+            lang_drop = gr.Dropdown(["Hindi", "Bengali", "Tamil"], label="Select Language", value="Hindi")
+            btn = gr.Button("Transliterate", variant="primary")
+        with gr.Column():
+            output_box = gr.Textbox(label="Output")
+    
+    btn.click(transliterate, inputs=[input_box, lang_drop], outputs=output_box)
+    gr.Examples([["namaste", "Hindi"], ["dhanyabad", "Bengali"], ["vanakkam", "Tamil"]], 
+                inputs=[input_box, lang_drop])
+
+if __name__ == "__main__":
+    demo.launch()