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import os
import threading
import gradio as gr
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
TextIteratorStreamer,
)
# Configuration
MODEL_NAMES = ["LeCarnet-3M", "LeCarnet-8M", "LeCarnet-21M"]
HF_TOKEN = os.environ.get("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HUGGINGFACEHUB_API_TOKEN")
MEDIA_PATH = "media/le-carnet.png" # Relative path to logo
# Pre-load all tokenizers and models
models = {}
tokenizers = {}
for name in MODEL_NAMES:
hub_id = f"MaxLSB/LeCarnet-{name.split('-')[-1]}M"
tokenizers[name] = AutoTokenizer.from_pretrained(hub_id, token=HF_TOKEN)
models[name] = AutoModelForCausalLM.from_pretrained(hub_id, token=HF_TOKEN)
models[name].eval()
def respond(
prompt: str,
chat_history,
selected_model: str,
max_tokens: int,
temperature: float,
top_p: float,
):
"""
Generate a streaming response from the chosen LeCarnet model,
prepending the logo and model name in the chat bubble.
"""
tokenizer = tokenizers[selected_model]
model = models[selected_model]
inputs = tokenizer(prompt, return_tensors="pt")
streamer = TextIteratorStreamer(
tokenizer,
skip_prompt=False,
skip_special_tokens=True,
)
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,
)
# Start generation in background thread
thread = threading.Thread(target=model.generate, kwargs=generate_kwargs)
thread.start()
prefix = f"<img src='{MEDIA_PATH}' alt='logo' width='20' style='vertical-align: middle;'/> <strong>{selected_model}</strong>: "
accumulated = ""
first = True
for new_text in streamer:
if first:
# include prefix only once at start
accumulated = prefix + new_text
first = False
else:
accumulated += new_text
yield accumulated
# Build Gradio ChatInterface
with gr.Blocks() as demo:
gr.Markdown("# LeCarnet: Short French Stories")
with gr.Row():
with gr.Column():
chat = gr.ChatInterface(
fn=respond,
additional_inputs=[
gr.Dropdown(MODEL_NAMES, value="LeCarnet-8M", label="Model"),
gr.Slider(1, 512, value=512, step=1, label="Max new tokens"),
gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top‑p"),
],
title="LeCarnet Chat",
description="Type the beginning of a sentence and watch the model finish it.",
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,
)
if __name__ == "__main__":
demo.queue()
demo.launch()
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