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import os
import threading
from collections import defaultdict
import gradio as gr
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
TextIteratorStreamer,
)
# Define model paths
model_name_to_path = {
"LeCarnet-3M": "MaxLSB/LeCarnet-3M",
"LeCarnet-8M": "MaxLSB/LeCarnet-8M",
"LeCarnet-21M": "MaxLSB/LeCarnet-21M",
}
# Load Hugging Face token
hf_token = os.environ["HUGGINGFACEHUB_API_TOKEN"]
# Preload models and tokenizers
loaded_models = defaultdict(dict)
for name, path in model_name_to_path.items():
loaded_models[name]["tokenizer"] = AutoTokenizer.from_pretrained(path, token=hf_token)
loaded_models[name]["model"] = AutoModelForCausalLM.from_pretrained(path, token=hf_token)
loaded_models[name]["model"].eval()
def respond(
prompt: str,
chat_history,
model_name: str,
max_tokens: int,
temperature: float,
top_p: float,
):
# Select the appropriate model and tokenizer
tokenizer = loaded_models[model_name]["tokenizer"]
model = loaded_models[model_name]["model"]
# Tokenize input
inputs = tokenizer(prompt, return_tensors="pt")
# Set up streaming
streamer = TextIteratorStreamer(
tokenizer,
skip_prompt=False,
skip_special_tokens=True,
)
# Configure generation parameters
generate_kwargs = dict(
**inputs,
streamer=streamer,
max_new_tokens=max_tokens,
do_sample=True,
temperature=temperature,
top_p=top_p,
eos_token_id=tokenizer.eos_token_id,
)
# Run generation in a background thread
thread = threading.Thread(target=model.generate, kwargs=generate_kwargs)
thread.start()
# Stream results
accumulated = ""
for new_text in streamer:
accumulated += new_text
yield accumulated
# Create Gradio Chat Interface
with gr.Blocks() as demo:
# Custom title with logo
with gr.Row():
gr.HTML(
'<div style="display: flex; align-items: center;">'
f'<img src="file/{os.path.abspath("media/le-carnet.png")}" style="height: 50px; margin-right: 10px;" />'
'<h1 style="margin: 0;">LeCarnet</h1>'
'</div>'
)
# Chat interface
chatbot = gr.ChatInterface(
fn=respond,
title=None, # Remove default title
description=None, # Remove default description
examples=[
["Il était une fois un petit garçon qui vivait dans un village paisible."],
["Il était une fois une grenouille qui rêvait de toucher les étoiles chaque nuit depuis son étang."],
["Il était une fois un petit lapin perdu"],
],
cache_examples=False,
)
# Sidebar for model selection and parameters
with gr.Column(elem_classes="sidebar", variant="panel"):
gr.Markdown("### Model Configuration")
model_dropdown = gr.Dropdown(
choices=["LeCarnet-3M", "LeCarnet-8M", "LeCarnet-21M"],
value="LeCarnet-8M",
label="Model",
)
max_tokens_slider = gr.Slider(1, 512, value=512, step=1, label="Max New Tokens")
temperature_slider = gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature")
top_p_slider = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-p")
# Pass parameters to the chatbot
chatbot.load(
fn=lambda x, y, z, w: None,
inputs=[model_dropdown, max_tokens_slider, temperature_slider, top_p_slider],
outputs=None,
)
chatbot.config.update({
"model_name": model_dropdown,
"max_tokens": max_tokens_slider,
"temperature": temperature_slider,
"top_p": top_p_slider,
})
# Launch the app
if __name__ == "__main__":
demo.queue(default_concurrency_limit=10, max_size=10).launch(ssr_mode=False, max_threads=10) |