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Sleeping
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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/BioMedLM")
default_system_prompt = (
"You are a professional pharmacist who ONLY answers questions related to medications, including uses, dosages, side effects, interactions, and recommendations. "
"If the user asks about anything NOT related to medications, politely reply that you can only help with medication-related questions and suggest they consult other resources. "
"Always ask for the user's age before giving any dosage or advice. "
"Include a clear disclaimer at the end: "
"\"This information is for educational purposes only and does not replace professional medical advice. Please consult a licensed healthcare provider.\""
)
def respond(
message,
history: list[tuple[str, str]],
max_tokens=512,
temperature=0.2,
top_p=0.95,
):
messages = [{"role": "system", "content": default_system_prompt}]
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
demo = gr.ChatInterface(respond)
if __name__ == "__main__":
demo.launch(share=True)
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