app.py

import torch
import random
import gradio as gr
from language_bpe import BPETokenizer

tokenizer = BPETokenizer()
tokenizer.load('models/english_5000.model')

def inference(input_text):
    encoding = tokenizer.encode_ordinary(input_text)
    # sentence = [tokenizer.decode([x]) for x in encoding]
    # color_sentence = ""
    # for word in sentence:
    #     background_color = random.randint(40, 47)
    #     color_sentence += f"\033[0;37;{background_color}m {word}"
    color_sentence = f"\033[0;37;{41}m Black" + f"\033[0;37;{42}m Black"
    return len(encoding), color_sentence, encoding

title = "Bilingual tokenizer"
description = "A simple Gradio interface to see tokenization of Hindi and English(Hinglish) text"
examples = [["He walked into the basement with the horror movie from the night before playing in his head."], 
           ["Henry couldn't decide if he was an auto mechanic or a priest."], 
           ["Poison ivy grew through the fence they said was impenetrable."], 
           ]
demo = gr.Interface(
    inference, 
    inputs = [
        gr.Textbox(label="Enter any sentence in Hindi, English or both language", type="text"),
        ], 
    outputs = [
        gr.Label(label="Token count"),
        gr.Textbox(label="Sentence after tokenization", type="text"),
        gr.Textbox(label="Encoding", type="text")
        ],
    title = title,
    description = description,
    examples = examples,
)
demo.launch()