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import gradio as gr
from huggingface_hub import InferenceClient
import os
# Setze deinen Hugging Face API-Token hier
HF_TOKEN = os.getenv("HF_TOKEN")
#print("HF_TOKEN:", HF_TOKEN)
"""
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(
#"Qwen/Qwen2.5-72B-Instruct",
"XiaomiMiMo/MiMo-V2-Flash",
token=HF_TOKEN # Token from Environment Variable or passed directly
)
def respond(
message,
history: list,
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
# Unterstütze altes Tuple-Format und neues Message-Format
if history:
first = history[0]
if isinstance(first, (list, tuple)):
for user_msg, assistant_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if assistant_msg:
messages.append({"role": "assistant", "content": assistant_msg})
elif isinstance(first, dict):
for item in history:
role = item.get("role")
content = item.get("content")
if role and content:
messages.append({"role": role, "content": content})
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,
):
if message and message.choices and message.choices[0].delta and message.choices[0].delta.content:
token = message.choices[0].delta.content
response += str(token)
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = 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)",
),
],
)
if __name__ == "__main__":
demo.launch()
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