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import gradio as gr
from huggingface_hub import InferenceClient
def respond(
message,
history: list[dict[str, str]],
system_message,
max_tokens,
temperature,
top_p,
hf_token: gr.OAuthToken,
):
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient(token=hf_token.token, model="deepseek-ai/DeepSeek-V3.2")
messages = [{"role": "system", "content": system_message}]
messages.extend(history)
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
choices = message.choices
token = ""
if len(choices) and choices[0].delta.content:
token = choices[0].delta.content
response += token
yield response
theme = gr.themes.Soft(
primary_hue="indigo",
secondary_hue="violet",
).set(
button_primary_background_fill="*primary_500",
button_primary_text_color="white",
)
css = """
h1 { text-align: center; color: #4f46e5; font-size: 2em; margin-bottom: 0.5em; }
.login-text { font-size: 0.9em; color: #666; text-align: center; margin-top: 5px; }
"""
chatbot = gr.ChatInterface(
respond,
type="messages",
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
with gr.Blocks(theme=theme, css=css) as demo:
gr.Markdown("# ✨ Le Chatbot qu'il vous faut")
gr.Markdown("Une interface intelligente propulsée par DeepSeek AI.", elem_classes="description")
with gr.Sidebar():
gr.Markdown("### 🔐 Authentification")
gr.LoginButton(value="Connexion HF")
gr.Markdown("Connectez-vous pour utiliser le modèle.")
chatbot.render()
if __name__ == "__main__":
demo.launch(share=True) |