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import gradio as gr
from huggingface_hub import InferenceClient
import os
# 🔹 Load HF token from Space Secrets
HF_TOKEN = os.environ.get('telemedpro')
# 🔹 Fixed persona system message
SYSTEM_MESSAGE = (
"You are Dr. Alex, a highly knowledgeable yet empathetic doctor. "
"You always provide clear, safe, and well-structured medical advice in simple language. "
"You avoid making unsafe claims and encourage users to seek professional help when needed. "
"You behave politely, patiently, and with care, like a trusted family doctor."
)
# 🔹 Initialize InferenceClient once
client = InferenceClient(token=HF_TOKEN, model="m42-health/Llama3-Med42-70B")
# 🔹 Respond function
def respond(message, history, system_message=SYSTEM_MESSAGE, max_tokens=512, temperature=0.7, top_p=0.95):
try:
# Start with system message
messages = [{"role": "system", "content": system_message}]
# Append previous conversation safely
if history:
messages.extend(history) # ✅ safe, don't manipulate
# Append current user message
messages.append({"role": "user", "content": message})
# Stream model output
response = ""
for msg in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
if msg.choices and hasattr(msg.choices[0].delta, "content") and msg.choices[0].delta.content:
token = msg.choices[0].delta.content
response += token
yield response
except Exception as e:
yield f"⚠️ Space error: {e}"
# 🔹 Gradio Chat Interface
chatbot = gr.ChatInterface(
fn=respond,
type="messages",
additional_inputs=[
gr.Textbox(value=SYSTEM_MESSAGE, label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max tokens"),
gr.Slider(minimum=0.1, maximum=2.0, 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"),
],
)
# 🔹 Layout
with gr.Blocks() as demo:
gr.Markdown("## 🩺 AI Health Mentor — Dr. Alex")
chatbot.render()
# 🔹 Launch
if __name__ == "__main__":
demo.launch(show_error=True)
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