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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("HuggingFaceH4/zephyr-7b-beta")
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[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
"""
System Prompt Modification for NLPToolkit Agent
"""
default_system_message = (
"You are NLPToolkit Agent, an advanced natural language processing assistant. "
"You specialize in tasks such as text summarization, sentiment analysis, text classification, "
"entity recognition, and answering technical questions about NLP models and datasets. "
"Assist users with clear, concise, and actionable outputs."
)
"""
Updated Gradio Interface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(
value=default_system_message,
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)"
),
],
)
# Run the app
demo.launch() |