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import gradio as gr
from huggingface_hub import InferenceClient
# Initialize the InferenceClient
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def respond(
message: str,
history: list[tuple[str, str]],
system_message: str,
max_tokens: int,
temperature: float,
top_p: float,
) -> str:
"""
Generate a response based on the user's message and chat history.
Args:
message (str): The user's message.
history (list[tuple[str, str]]): The chat history.
system_message (str): The system message.
max_tokens (int): The maximum number of tokens in the response.
temperature (float): The temperature for sampling.
top_p (float): The top-p (nucleus) sampling value.
Returns:
str: The generated response.
"""
messages = [{"role": "system", "content": system_message}]
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})
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,
):
token = message.choices[0].delta.content
response += token
yield response
# Create the Gradio 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)",
),
],
theme="default", # Apply the default theme
css=".gradio-container {background-color: #E0F7FA;}" # Set a light blue background
)
if __name__ == "__main__":
demo.launch() |