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import gradio as gr
from huggingface_hub import InferenceClient, OAuthToken
MODEL_ID = "Bocklitz-Lab/lit2vec-tldr-bart-model"
def respond(
message: str,
history: list[dict[str, str]],
system_message: str,
max_tokens: int,
temperature: float,
top_p: float,
hf_token: OAuthToken | None,
):
"""Summarise a chemistry abstract with the HF Inference API."""
client = InferenceClient(
model=MODEL_ID,
token=None if hf_token is None else hf_token.token,
)
prompt = f"{system_message.strip()}\n\n{message.strip()}"
# stream=False → one-shot; stream=True → token generator
for chunk in client.text_generation(
prompt,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=True, # change to False if you prefer
):
yield chunk
with gr.Blocks(title="🧪 Chemistry Abstract Summariser") as demo:
with gr.Sidebar():
login_btn = gr.LoginButton()
chatbot = gr.ChatInterface(
respond,
chatbot=gr.Chatbot(type="messages"),
textbox=gr.Textbox(
placeholder="Paste abstract of a chemistry paper…",
lines=8,
),
additional_inputs=[
gr.Textbox(
value="Summarise this chemistry paper abstract:",
label="System message",
),
gr.Slider(16, 1024, value=256, step=8, label="Max new tokens"),
gr.Slider(0.1, 4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p"),
login_btn, # 👈 passes OAuthToken to respond()
],
type="messages",
)
if __name__ == "__main__":
demo.launch() # Hugging Face Spaces picks this up automatically
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