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import gradio as gr
from huggingface_hub import InferenceClient
def respond(
message,
history: list[dict[str, str]],
system_message,
max_tokens,
temperature,
top_p,
hf_token: gr.OAuthToken,
):
client = InferenceClient(
token=hf_token.token,
model="Bocklitz-Lab/lit2vec-tldr-bart-model"
)
full_input = f"{system_message.strip()}\n\n{message.strip()}"
response = client.text_generation(
full_input,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=False
)
yield response
chatbot = gr.ChatInterface(
respond,
chatbot=gr.Chatbot(),
textbox=gr.Textbox(placeholder="Paste abstract of a chemistry paper...", container=False, scale=7),
additional_inputs=[
gr.Textbox(value="Summarize this chemistry paper abstract:", label="System message"),
gr.Slider(minimum=16, maximum=1024, value=256, step=8, 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"),
],
type="messages",
)
demo = gr.Blocks()
with demo:
with gr.Sidebar():
gr.LoginButton()
chatbot.render()
# 👇 This MUST be called at the module level for Hugging Face Spaces to work
demo.launch()
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