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import gradio as gr
from transformers import AutoTokenizer
import json
from huggingface_hub import hf_hub_download
def get_tokenizer_names(model_name):
try:
# First attempt: Try to get names from model_index.json
model_info_path = hf_hub_download(model_name, filename="model_index.json")
with open(model_info_path, "r") as f:
model_info = json.load(f)
# Extract tokenizer class names from the JSON
tokenizer_1_class = model_info.get("tokenizer", ["", "Unknown"])[1]
tokenizer_2_class = model_info.get("tokenizer_2", ["", "Unknown"])[1]
return tokenizer_1_class, tokenizer_2_class
except Exception:
# Second attempt: Fall back to original method
try:
model_info = AutoTokenizer.from_pretrained(model_name, subfolder="tokenizer", _from_auto=True)
config = model_info.init_kwargs
return config.get('tokenizer_class', 'Unknown'), config.get('tokenizer_2_class', 'Unknown')
except Exception:
return "Unknown", "Unknown"
def count_tokens(model_name, text):
# Load the tokenizers from the specified model
tokenizer_1 = AutoTokenizer.from_pretrained(f"{model_name}", subfolder="tokenizer")
tokenizer_2 = AutoTokenizer.from_pretrained(f"{model_name}", subfolder="tokenizer_2")
# Get tokenizer names
tokenizer_1_name, tokenizer_2_name = get_tokenizer_names(model_name)
# Tokenize the input text
tokens_1 = tokenizer_1.tokenize(text)
tokens_2 = tokenizer_2.tokenize(text)
# Count the tokens
count_1 = len(tokens_1)
count_2 = len(tokens_2)
return f"{tokenizer_1_name}: {count_1} tokens", f"{tokenizer_2_name}: {count_2} tokens"
# Create a Gradio interface
iface = gr.Interface(
fn=count_tokens,
inputs=[
gr.Textbox(label="Model Name", placeholder="e.g., black-forest-labs/FLUX.1-dev"),
gr.Textbox(label="Text", placeholder="Enter text here...")
],
outputs=[
gr.Textbox(label="Tokenizer 1"),
gr.Textbox(label="Tokenizer 2")
],
title="Token Counter",
description="Enter a Hugging Face model name and text to count tokens using the model's tokenizers."
)
# Launch the app
iface.launch()
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