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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("unsloth/Llama-3.2-1B-Instruct")
def respond(
message,
history: list[tuple[str, str]],
# system_message,
# max_tokens,
# temperature,
# top_p,
):
system_message = "You are a Dietician Assistant specializing in providing general guidance on diet, "
"nutrition, and healthy eating habits. Answer questions thoroughly with scientifically "
"backed advice, practical tips, and easy-to-understand explanations. Keep in mind that "
"your role is to assist, not replace a registered dietitian, so kindly remind users to "
"consult a professional for personalized advice when necessary."
max_tokens = 512
temperature = 0.7
top_p = 0.95
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="You are a friendly Chatbot.", 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)",
# # ),
# # ],
# )
def default_message():
"""Function to return initial default message."""
return [("Hi there! I'm your Dietician Assistant, here to help with general advice "
"on diet, nutrition, and healthy eating habits. Let's explore your questions.", "")]
# Set up the Gradio ChatInterface with an initial default message
with gr.Blocks() as demo:
chatbot = gr.ChatInterface(
respond,
# additional_inputs=[
# gr.Textbox(value="You are a friendly Chatbot.", 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)",
# ),
# ],
)
# Display the default message on load
gr.State(default_message()) # Store initial chat history
chatbot.history = default_message() # Set the chat history to show the greeting
if __name__ == "__main__":
demo.launch() |