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import gradio as gr
from huggingface_hub import InferenceClient
# Initialize the InferenceClient with your chat model.
client = InferenceClient("amusktweewt/tiny-model-500M-chat-v2")
def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
"""
Builds a chat prompt using a simple template:
- Optionally includes a system message.
- Iterates over conversation history (each exchange as a tuple of (user, assistant)).
- Adds the new user message and appends an empty assistant turn.
Then it streams the response from the model.
"""
messages = []
# Include the system prompt if provided.
if system_message:
messages.append({"role": "system", "content": system_message})
# Append conversation history.
if history:
for user_msg, bot_msg in history:
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": bot_msg})
# Add the new user message and an empty assistant response
# (this mimics your template where the assistant turn is empty to be filled).
messages.append({"role": "user", "content": message})
messages.append({"role": "assistant", "content": ""})
response_text = ""
# Stream the response token-by-token.
for resp in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = resp.choices[0].delta.content
response_text += token
yield response_text
# Create a 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)"),
],
)
if __name__ == "__main__":
demo.launch()
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