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import gradio as gr
from huggingface_hub import InferenceClient
from typing import List, Tuple, Dict
client = InferenceClient("AuriLab/gpt-bi-instruct-cesar")
def format_messages(history: List[Tuple[str, str]], system_message: str, user_message: str) -> List[Dict[str, str]]:
messages = [{"role": "system", "content": system_message}]
messages.extend([
{"role": "user" if i % 2 == 0 else "assistant", "content": msg}
for turn in history
for i, msg in enumerate(turn)
if msg
])
messages.append({"role": "user", "content": user_message})
return messages
def respond(message: str, history: List[Tuple[str, str]], system_message: str, max_tokens: int, temperature: float, top_p: float) -> str:
messages = format_messages(history, system_message, message)
response = ""
for msg in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
repetition_penalty=1.2, # Add repetition penalty
presence_penalty=0.5, # Penalize presence of repeated tokens
frequency_penalty=0.5, # Penalize frequency of repeated tokens
):
token = msg.choices[0].delta.content
response += token
yield response
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=256, 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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