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7d8fcc6 178c00a 7d8fcc6 e43bc83 7d8fcc6 e43bc83 7d8fcc6 e43bc83 7d8fcc6 e43bc83 7d8fcc6 e43bc83 7d8fcc6 bde11d0 7d8fcc6 e43bc83 7d8fcc6 e43bc83 7d8fcc6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | import gradio as gr
from huggingface_hub import InferenceClient
client = InferenceClient("google/med-gemma-7b", token=os.getenv("HF_TOKEN"))
def format_prompt(history, user_input, system_message):
prompt = f"### System:\n{system_message.strip()}\n\n"
for user_msg, bot_msg in history:
prompt += f"### User:\n{user_msg.strip()}\n\n"
prompt += f"### Assistant:\n{bot_msg.strip()}\n\n"
prompt += f"### User:\n{user_input.strip()}\n\n### Assistant:\n"
return prompt
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
prompt = format_prompt(history, message, system_message)
response = ""
for token in client.text_generation(
prompt=prompt,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=True,
):
response += token.token
yield response
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a helpful medical assistant.", 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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