Spaces:
Sleeping
Sleeping
File size: 2,306 Bytes
4d02290 9f60291 4d02290 ba1881f 4d02290 ba1881f 4d02290 ba1881f 4d02290 ba1881f 4d02290 ba1881f 4d02290 2472855 ba1881f 2472855 4d02290 53a4d7c 4d02290 ba1881f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | 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("HuggingFaceH4/zephyr-7b-beta")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
personality,
):
# Incorporate the personality into the system message
system_message = f"{system_message} You are a {personality} chatbot."
# Add the system message to the conversation
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]})
# Add the latest user message
messages.append({"role": "user", "content": message})
response = ""
# Generate the response from the model
for message_chunk in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message_chunk.choices[0].delta.content
response += token
# Update the chat history by appending the new message and the response
history.append((message, response))
return history # Return the updated chat history
def clear_chat():
return "", []
"""
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)",
),
gr.Dropdown(choices=["friendly", "professional", "humorous", "serious"], value="friendly", label="Personality"),
]
)
if __name__ == "__main__":
demo.launch()
|