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# Run the script and open the link in the browser.
import os
import json
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/'
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. \n
Demo instructions:
- Enter a prompt in Latin in the Input Text box.
- Select the temperature value to control the randomness of the generated text (higher value produce a more creative and unstable answer).
- Click the 'Generate Text' button to trigger model generation.
- (Optional) insert a Feedback text in the box.
- Click the 'Like' or 'Dislike' button to judge the generation correctness.
"""
title= "(L<sup>2</sup>) - Latin 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, slider):
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=100)
generated_text = text_generator(prompt, max_length=50, do_sample=True, temperature=slider, repetition_penalty=2.0, truncation=True)
return generated_text[0]['generated_text']
# Function to handle user preferences
def handle_preference(preference, input, output, feedback, temp_value, preferences_file="preferences.json"):
"""
Format values stored in preferences:
- input text
- output generated text
- user feedback
- float temperature value
"""
if os.path.exists(preferences_file):
with open(preferences_file, "r") as file:
preferences = json.load(file)
else:
preferences = {"like": [], "dislike": [], "count_like": 0, "count_dislike": 0}
if input == output:
output_tuple= ("", "", feedback)
else:
output_tuple= (input, output.split(input)[-1], feedback, temp_value)
if preference == "like":
preferences["like"].append(output_tuple)
if output_tuple[1] != "" :
preferences["count_like"] += 1
elif preference == "dislike":
preferences["dislike"].append(output_tuple)
if output_tuple[1] != "" :
preferences["count_dislike"] += 1
with open(preferences_file, "w") as file:
json.dump(preferences, file)
print(f"Admin log: like: {preferences['count_like']} and dislike: {preferences['count_dislike']}")
return f"You select '{preference}' as answer of the model generation. Thank you for your time!"
custom_css = """
#logo {
display: block;
margin-left: auto;
margin-right: auto;
width: 280px;
height: 140px;
}
"""
with gr.Blocks(css=custom_css) as demo:
gr.Image(logo_image, elem_id="logo")
gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>")
gr.Markdown(description)
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")
gr.Examples(examples=examples, inputs=input_text)
temperature_slider = gr.Slider(minimum=0.1, maximum=5.0, step=0.1, value=1.0, label="Temperature")
clean_button = gr.Button("Generate Text")
clean_button.click(fn=generate_text, inputs=[input_text, temperature_slider], outputs=output_text)
feedback_output = gr.Textbox(lines=1, placeholder="If you want to provide a feedback, please fill this box ...", label="Feedback")
with gr.Row():
like_button = gr.Button("Like")
dislike_button = gr.Button("Dislike")
button_output = gr.Textbox(lines=1, placeholder="Please submit your choice", label="Latin Language Model Demo")
like_button.click(fn=lambda x,y,z,v: handle_preference("like", x, y, z, v), inputs=[input_text, output_text, feedback_output, temperature_slider], outputs=button_output)
dislike_button.click(fn=lambda x,y,z,v: handle_preference("dislike", x, y, z, v), inputs=[input_text, output_text, feedback_output, temperature_slider], outputs=button_output)
#gr.Markdown(article)
demo.launch(share=True)
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