File size: 2,905 Bytes
a2026d4 1ac2733 6477e62 33f480a 698c5b4 6477e62 a2026d4 6477e62 a2026d4 6477e62 a2026d4 1ac2733 a2026d4 1ac2733 a2026d4 6477e62 a2026d4 6477e62 a2026d4 6477e62 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | import gradio as gr
from huggingface_hub import InferenceClient
AVAILABLE_MODELS = [
"openai/gpt-oss-20b",
"meta-llama/Llama-3.3-70B-Instruct",
"meta-llama/Llama-3.1-8B-Instruct",
"Qwen/Qwen2.5-72B-Instruct",
"Qwen/Qwen2.5-7B-Instruct",
"mistralai/Mistral-7B-Instruct-v0.3",
"mistralai/Mixtral-8x7B-Instruct-v0.1",
"google/gemma-2-27b-it",
"google/gemma-2-9b-it",
"hydffgg/HOS-OSS-270M",
"Hyggshi-AI/HOS-OSS-200M",
]
def respond(
message,
history: list[dict[str, str]],
system_message,
max_tokens,
temperature,
top_p,
model_name,
hf_token: gr.OAuthToken,
):
client = InferenceClient(token=hf_token.token, model=model_name)
messages = [{"role": "system", "content": system_message}]
messages.extend(history)
messages.append({"role": "user", "content": message})
response = ""
try:
# Thử streaming trước
for chunk in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
try:
choices = chunk.choices
if choices and choices[0].delta.content:
response += choices[0].delta.content
yield response
except (AttributeError, IndexError):
continue
except Exception as e:
# Nếu stream lỗi, fallback sang non-streaming
try:
result = client.chat_completion(
messages,
max_tokens=max_tokens,
stream=False,
temperature=temperature,
top_p=top_p,
)
response = result.choices[0].message.content
yield response
except Exception as e2:
yield f"❌ Lỗi: {str(e2)}\n\nModel `{model_name}` có thể không hỗ trợ chat completion."
chatbot = gr.ChatInterface(
respond,
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)",
),
gr.Dropdown(
choices=AVAILABLE_MODELS,
value=AVAILABLE_MODELS[0],
label="🤖 Model",
info="Chọn model để chat",
),
],
)
with gr.Blocks() as demo:
with gr.Sidebar():
gr.LoginButton()
gr.Markdown("### ⚙️ Cài đặt")
gr.Markdown("Đăng nhập để sử dụng các model HuggingFace.")
chatbot.render()
if __name__ == "__main__":
demo.launch() |