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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("microsoft/Phi-3.5-mini-instruct")
# Specialized prompt for the system message
ophthalmology_prompt = (
"Act as an experienced ophthalmologist with extensive knowledge in clinical diagnosis, "
"surgical treatments, and current research trends. Explain your answers with detailed insights "
"and clear medical terminology, providing up-to-date information and guidance. When appropriate, "
"outline differential diagnoses, treatment options, or advanced procedural steps. Additionally, "
"summarize any relevant clinical studies or guidelines that support your responses, making sure to "
"keep explanations clear and tailored to both professionals and non-specialists."
)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Set the system message to the ophthalmology prompt
system_message = ophthalmology_prompt if not system_message else system_message
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
"""
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=ophthalmology_prompt, 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() |