| import gradio as gr | |
| from xlit_src import XlitEngine | |
| def transliterate(input_text): | |
| engine = XlitEngine() | |
| result = engine.translit_sentence(input_text) | |
| return result | |
| input_box = gr.inputs.Textbox(type="str", label="Input Text") | |
| target = gr.outputs.Textbox() | |
| iface = gr.Interface( | |
| transliterate, | |
| input_box, | |
| target, | |
| title="English to Hindi Transliteration", | |
| description='Model for Transliterating English to Hindi using a Character-level recurrent sequence-to-sequence trained using <a href="http://workshop.colips.org/news2018/dataset.html">NEWS2018 DATASET_04</a>', | |
| article='Author: <a href="https://huggingface.co/anuragshas">Anurag Singh</a> . Using training and inference script from <a href="https://github.com/AI4Bharat/IndianNLP-Transliteration.git">AI4Bharat/IndianNLP-Transliteration</a><p><center><img src="https://visitor-badge.glitch.me/badge?page_id=anuragshas/en-hi-transliteration" alt="visitor badge"></center></p>', | |
| examples=["Namaste"], | |
| ) | |
| iface.launch(enable_queue=True, cache_examples=True) |