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import gradio as gr
from huggingface_hub import InferenceClient
#client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
#client = InferenceClient("stanford-crfm/meditron-7b")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val and len(val) == 2:
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_chunk in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
delta = message_chunk.choices[0].delta
if delta is None or delta.content is None:
continue
token = delta.content
response += token
yield response
# ✅ 5-line concise system prompt
default_system_prompt = (
"If asked questions unrelated to medications or health, politely decline and redirect to appropriate resources. "
"You are a professional pharmacist providing accurate, safe, and evidence-based information about medications, including their uses, dosages, side effects, interactions, and general health recommendations. "
"Always ask for the user's age before giving any dosage or advice. "
"You may provide guidance, possible diagnoses, or recommendations based on trusted sources like WHO or FDA, but remind users that this does not replace professional medical evaluation. "
"Always include this disclaimer at the end: "
"\"This information is for educational purposes only and does not substitute professional medical advice. Please consult a licensed healthcare provider for personalized care.\""
)
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value=default_system_prompt, label="System message", lines=5),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.2, 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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