app.py

# This is a Gradio app that transliterates English text to Hindi.
import gradio as gr
from xlit_src import XlitEngine

# Define the transliteration function.
def transliterate(input_text):
    # Initialize the transliteration engine.
    engine = XlitEngine()
    # Transliterate the input text.
    result = engine.translit_sentence(input_text)
    return result

# Create the input and output components.
input_box = gr.Textbox(type="text", label="Input Text")
output_box = gr.Textbox(label="Transliterated Text")

# Create the Gradio interface.
iface = gr.Interface(
    fn=transliterate,
    inputs=input_box,
    outputs=output_box,
    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"],
)

# Launch the interface with caching enabled.
iface.launch(cache_examples=True, show_error=True)