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import gradio as gr
from huggingface_hub import InferenceClient
# Initialize the InferenceClient with the model ID from Hugging Face
client = InferenceClient(model="HuggingFaceH4/zephyr-7b-beta")
def respond(
message: str,
history: list[tuple[str, str]],
system_message: str,
max_tokens: int,
temperature: float,
top_p: float,
):
"""
Generates a response from the AI model based on the user's message and chat history.
Args:
message (str): The user's input message.
history (list): A list of tuples representing the conversation history (user, assistant).
system_message (str): A system-level message guiding the AI's behavior.
max_tokens (int): The maximum number of tokens for the output.
temperature (float): Sampling temperature for controlling the randomness.
top_p (float): Top-p (nucleus sampling) for controlling diversity.
Yields:
str: The AI's response as it is generated.
"""
# Prepare the conversation history for the API call
messages = [{"role": "system", "content": system_message}]
for user_input, assistant_response in history:
if user_input:
messages.append({"role": "user", "content": user_input})
if assistant_response:
messages.append({"role": "assistant", "content": assistant_response})
# Add the latest user message to the conversation
messages.append({"role": "user", "content": message})
# Initialize an empty response
response = ""
try:
# Generate a response from the model with streaming
for message in client.chat_completion(
messages=messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
except Exception as e:
yield f"An error occurred: {str(e)}"
# Define the ChatInterface with additional input components for user customization
demo = gr.ChatInterface(
fn=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)",
),
],
title="Chatbot Interface",
description="A customizable chatbot interface using Hugging Face's Inference API.",
)
# Launch the Gradio interface
if __name__ == "__main__":
demo.launch()
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