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import gradio as gr
import spaces
import torch
from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM, AutoModelForSeq2SeqLM
model_id = "textcleanlm/textclean-4B"
model = None
tokenizer = None
def load_model():
global model, tokenizer
if model is None:
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Add padding token if needed
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# Try different model classes
for model_class in [AutoModelForSeq2SeqLM, AutoModelForCausalLM, AutoModel]:
try:
model = model_class.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
break
except:
continue
if model is None:
raise ValueError(f"Could not load model {model_id}")
return model, tokenizer
@spaces.GPU(duration=60)
def clean_text(text):
model, tokenizer = load_model()
inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
inputs = {k: v.cuda() for k, v in inputs.items()}
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=512,
num_beams=4,
early_stopping=True
)
cleaned_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return cleaned_text
iface = gr.Interface(
fn=clean_text,
inputs=gr.Textbox(
lines=5,
placeholder="Enter text to clean...",
label="Input Text"
),
outputs=gr.Textbox(
lines=5,
label="Cleaned Text"
),
title="TextClean-4B Demo",
description="Simple demo for text cleaning using textcleanlm/textclean-4B model"
)
if __name__ == "__main__":
iface.launch() |