import gradio as gr import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer import os import sys # Add local IndicTransToolkit path sys.path.append(os.path.abspath("libs/IndicTransToolkit")) from IndicTransToolkit.processor import IndicProcessor # Load processor and model ip = IndicProcessor(inference=True) tokenizer = AutoTokenizer.from_pretrained("ai4bharat/indictrans2-en-indic-dist-200M", trust_remote_code=True) model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/indictrans2-en-indic-dist-200M", trust_remote_code=True) LANG_OPTIONS = [ "hin_Deva", # Hindi "ben_Beng", # Bengali "pan_Guru", # Punjabi "guj_Gujr", # Gujarati "tam_Taml", # Tamil "tel_Telu", # Telugu "mal_Mlym", # Malayalam "mar_Deva", # Marathi "kan_Knda", # Kannada "asm_Beng", # Assamese "kas_Arab", # Kashmiri (Arabic) "kas_Deva", # Kashmiri (Devanagari) "san_Deva", # Sanskrit "brx_Deva", # Bodo "mai_Deva", # Maithili "sat_Olck", # Santali "eng_Latn", # English "urd_Arab" # Urdu ] def translate(text, target_lang): if not text.strip(): return "Please enter some text." try: batch = ip.preprocess_batch([text], src_lang="eng_Latn", tgt_lang=target_lang) batch = tokenizer(batch, padding="longest", truncation=True, max_length=256, return_tensors="pt") with torch.inference_mode(): outputs = model.generate(**batch, num_beams=5, max_length=256) with tokenizer.as_target_tokenizer(): decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenization_spaces=True) translated = ip.postprocess_batch(decoded, lang=target_lang)[0] return translated except Exception as e: return f"Error: {e}" demo = gr.Interface( fn=translate, inputs=[ gr.Textbox(label="Enter text in English", lines=5), gr.Dropdown(choices=LANG_OPTIONS, label="Select Target Language") ], outputs="text", title="IndicTrans Translator", description="Translate English text into Indian languages using IndicTrans2." ) if __name__ == "__main__": demo.launch()