Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,111 Bytes
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import gradio as gr
import spaces
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("BatsResearch/bonito-v1")
@spaces.GPU
def respond(
message,
task_type,
max_tokens,
temperature,
top_p,
):
task_type = task_type.lower()
input_text = "<|tasktype|>\n" + task_type.strip()
input_text += "\n<|context|>\n" + message.strip() + "\n<|task|>\n"
response = client.text_generation(input_text, max_new_tokens=max_tokens, temperature=temperature, top_p=top_p)
return response
# messages = []
# messages.append({"role": "user", "content": message})
# response = ""
# for message in client.text_generation(
# 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)",
# ),
# ],
# )
task_types = [
"extractive question answering",
"multiple-choice question answering",
"question generation",
"question answering without choices",
"yes-no question answering",
"coreference resolution",
"paraphrase generation",
"paraphrase identification",
"sentence completion",
"sentiment",
"summarization",
"text generation",
"topic classification",
"word sense disambiguation",
"textual entailment",
"natural language inference",
]
# capitalize for better readability
task_types = [task_type.capitalize() for task_type in task_types]
demo = gr.Interface(
fn=respond,
inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Dropdown(task_types, label="Task type"),
],
outputs=gr.Textbox(label="Response"),
additional_inputs=[
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)",
),
],
title="Zephyr Chatbot",
description="A chatbot that uses the Hugging Face Zephyr model.",
)
if __name__ == "__main__":
demo.launch()
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