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import gradio as gr
from huggingface_hub import InferenceClient
from typing import List, Dict
# Response function for the chatbot
def respond(
message: str,
history: List[Dict[str, str]],
system_message: str,
max_tokens: int,
temperature: float,
top_p: float,
hf_token: gr.OAuthToken,
):
"""
Sends a chat message to the Hugging Face Inference API using the provided token and parameters.
"""
client = InferenceClient(
token=hf_token.token,
model="Bocklitz-Lab/lit2vec-tldr-bart-model"
)
messages = [{"role": "system", "content": system_message}] + history
messages.append({"role": "user", "content": message})
response = ""
for message_chunk in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
if message_chunk.choices and message_chunk.choices[0].delta.content:
token = message_chunk.choices[0].delta.content
response += token
yield response
# Define the Gradio interface
chatbot = gr.ChatInterface(
fn=respond,
type="messages",
additional_inputs=[
gr.Textbox(
value="You are a friendly chatbot.",
label="System message",
lines=1
),
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)"
),
],
)
# Set up the full Gradio Blocks layout with login
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1):
gr.LoginButton()
chatbot.render()
# Run the app
if __name__ == "__main__":
demo.launch()
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