Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,965 Bytes
9c078e7 4a8131a 9c078e7 aecc737 9c078e7 0e24f0d b1a9b5c 9c078e7 aecc737 0e24f0d aecc737 9c078e7 0e24f0d 7c5f508 0e24f0d aecc737 e1ae004 aecc737 e1ae004 aecc737 9c078e7 aecc737 9c078e7 0e24f0d 9c078e7 0e24f0d aecc737 0e24f0d aecc737 9c078e7 0e24f0d 9c078e7 0e24f0d |
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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
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("HuggingFaceH4/zephyr-7b-beta")
@spaces.GPU
def respond(
message,
task_type,
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 = []
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)",
# ),
# ],
# )
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",
]
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()
|