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import gradio as gr
from huggingface_hub import InferenceClient
"""
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("meta-llama/Meta-Llama-3.1-405B")
## None type
def respond(
message: str,
history: list[tuple[str, str]], # This will not be used
system_message: str,
max_tokens: int,
temperature: float,
top_p: float,
):
messages = [{"role": "system", "content": system_message}]
# Append only the latest user message
messages.append({"role": "user", "content": message})
response = ""
try:
# Generate response from the model
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
if message.choices[0].delta.content is not None:
token = message.choices[0].delta.content
response += token
yield response
except Exception as e:
yield f"An error occurred: {e}"
"""
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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