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import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import gradio as gr
# Load pre-trained model and tokenizer
model_name = "PleIAs/OCRonos-Vintage"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
# Set the device to GPU if available, otherwise use CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
def historical_generation(prompt, max_new_tokens=600):
prompt = f"### Text ###\n{prompt}"
inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
input_ids = inputs["input_ids"].to(device)
attention_mask = inputs["attention_mask"].to(device)
# Generate text
output = model.generate(
input_ids,
attention_mask=attention_mask,
max_new_tokens=max_new_tokens,
pad_token_id=tokenizer.eos_token_id,
top_k=50,
temperature=0.3,
top_p=0.95,
do_sample=True,
repetition_penalty=1.5,
bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.eos_token_id
)
# Decode the generated text
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
# Remove the prompt from the generated text
generated_text = generated_text.replace("### Text ###\n", "").strip()
# Tokenize the generated text
tokens = tokenizer.tokenize(generated_text)
# Create highlighted text output
highlighted_text = []
for token in tokens:
# Remove special tokens and get the token type
clean_token = token.replace("Δ ", "").replace("</w>", "")
token_type = tokenizer.convert_ids_to_tokens([tokenizer.convert_tokens_to_ids(token)])[0]
highlighted_text.append((clean_token, token_type))
return highlighted_text
# Create Gradio interface
iface = gr.Interface(
fn=historical_generation,
inputs=[
gr.Textbox(
label="Prompt",
placeholder="Enter a prompt for historical text generation...",
lines=3
),
gr.Slider(
label="Max New Tokens",
minimum=50,
maximum=1000,
step=50,
value=600
)
],
outputs=gr.HighlightedText(
label="Generated Historical Text",
combine_adjacent=True,
show_legend=True
),
title="Historical Text Generation with OCRonos-Vintage",
description="Generate historical-style text using the OCRonos-Vintage model. The output shows token types as highlights.",
theme=gr.themes.Base()
)
if __name__ == "__main__":
iface.launch() |