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# Run the script and open the link in the browser. | |
import os | |
import gradio as gr | |
import streamlit as st | |
import torch | |
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
# scratch with latbert tokenizer | |
CHECKPOINT_PATH= 'scratch_2-nodes_tokenizer_latbert-original_packing_fcocchi/model.safetensors' | |
CHECKPOINT_PATH= 'itserr/latin_llm_alpha' | |
print(f"Loading model from: {CHECKPOINT_PATH}") | |
tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_PATH, token=os.environ['HF_TOKEN']) | |
model = AutoModelForCausalLM.from_pretrained(CHECKPOINT_PATH, token=os.environ['HF_TOKEN']) | |
description=""" | |
This is a Latin Language Model (LLM) based on GPT-2 and it was trained on a large corpus of Latin texts and can generate text in Latin. | |
Please enter a prompt in Latin to generate text. | |
""" | |
title= "(L<sup>3</sup>) - Latin Large Language Model" | |
article= "hello world ..." | |
examples= ['Accidere ex una scintilla', 'Audacter calumniare,', 'Consolatium misero comites'] | |
logo_image= 'ITSERR_row_logo.png' | |
def generate_text(prompt): | |
if torch.cuda.is_available(): device = torch.device("cuda") | |
else: | |
device = torch.device("cpu") | |
print("No GPU available") | |
print("***** Generate *****") | |
text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device) | |
generated_text = text_generator(prompt, max_length=50, do_sample=True, temperature=1.0, repetition_penalty=2.0, truncation=True) | |
return generated_text[0]['generated_text'] | |
custom_css = """ | |
#logo { | |
display: block; | |
margin-left: auto; | |
margin-right: auto; | |
width: 512px; | |
height: 256px; | |
} | |
""" | |
with gr.Blocks(css=custom_css) as demo: | |
gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>") | |
gr.Image(logo_image, elem_id="logo") | |
with gr.Row(): | |
with gr.Column(): | |
input_text = gr.Textbox(lines=5, placeholder="Enter latin text here...", label="Input Text") | |
with gr.Column(): | |
output_text = gr.Textbox(lines=5, placeholder="Output text will appear here...", label="Output Text") | |
clean_button = gr.Button("Generate Text") | |
clean_button.click(fn=generate_text, inputs=input_text, outputs=output_text) | |
demo.launch(share=True) | |