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import os
import requests
import json
import gradio as gr
from transformers import AutoTokenizer
DESCRIPTION = """
# Demo: Breeze-7B-Instruct-v0.1
Breeze-7B is a language model family that builds on top of [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1), specifically intended for Traditional Chinese use.
[Breeze-7B-Base](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v0.1) is the base model for the Breeze-7B series.
It is suitable for use if you have substantial fine-tuning data to tune it for your specific use case.
[Breeze-7B-Instruct](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v0.1) derives from the base model Breeze-7B-Base, making the resulting model amenable to be used as-is for commonly seen tasks.
[Breeze-7B-Instruct-64k](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-64k-v0.1) is a slightly modified version of
Breeze-7B-Instruct to enable a 64k-token context length. Roughly speaking, that is equivalent to 88k Traditional Chinese characters.
The current release version of Breeze-7B is v0.1.
*A project by the members (in alphabetical order): Chan-Jan Hsu 許湛然, Chang-Le Liu 劉昶樂, Feng-Ting Liao 廖峰挺, Po-Chun Hsu 許博竣, Yi-Chang Chen 陳宜昌, and the supervisor Da-Shan Shiu 許大山.*
**免責聲明: Breeze-7B-Instruct 和 Breeze-7B-Instruct-64k 並未針對問答進行安全保護,因此語言模型的任何回應不代表 MediaTek Research 立場。**
"""
LICENSE = """
"""
DEFAULT_SYSTEM_PROMPT = "You are a helpful AI assistant built by MediaTek Research. The user you are helping speaks Traditional Chinese and comes from Taiwan."
API_URL = os.environ.get("API_URL")
TOKEN = os.environ.get("TOKEN")
HEADERS = {
"accept": "application/json",
"Authorization": f"Bearer {TOKEN}",
"Content-Type": "application/json",
}
MODEL_NAME="breeze-7b-instruct-v01"
PRESENCE_PENALTY=0
FREQUENCY_PENALTY=0
model_name = "MediaTek-Research/Breeze-7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
def refusal_condition(query):
# 不要再問這些問題啦!
query_remove_space = query.replace(' ', '').lower()
is_including_tw = False
for x in ['台灣', '台湾', 'taiwan', 'tw', '中華民國', '中华民国']:
if x in query_remove_space:
is_including_tw = True
is_including_cn = False
for x in ['中國', '中国', 'china', 'cn', '大陸', '內地', '大陆', '内地', '中華人民共和國', '中华人民共和国']:
if x in query_remove_space:
is_including_cn = True
if is_including_tw and is_including_cn:
return True
for x in ['一個中國', '兩岸', '一中原則', '一中政策', '一个中国', '两岸', '一中原则']:
if x in query_remove_space:
return True
return False
with gr.Blocks() as demo:
gr.Markdown(DESCRIPTION)
chatbot = gr.Chatbot()
with gr.Row():
msg = gr.Textbox(
container=False,
show_label=False,
placeholder='Type a message...',
scale=10,
)
submit_button = gr.Button('Submit',
variant='primary',
scale=1,
min_width=0)
with gr.Row():
retry_button = gr.Button('🔄 Retry', variant='secondary')
undo_button = gr.Button('↩️ Undo', variant='secondary')
clear = gr.Button('🗑️ Clear', variant='secondary')
saved_input = gr.State()
with gr.Accordion(label='Advanced options', open=False):
system_prompt = gr.Textbox(label='System prompt',
value=DEFAULT_SYSTEM_PROMPT,
lines=6)
max_new_tokens = gr.Slider(
label='Max new tokens',
minimum=32,
maximum=1024,
step=1,
value=512,
)
temperature = gr.Slider(
label='Temperature',
minimum=0.01,
maximum=0.5,
step=0.01,
value=0.01,
)
top_p = gr.Slider(
label='Top-p (nucleus sampling)',
minimum=0.01,
maximum=1.0,
step=0.01,
value=0.01,
)
def user(user_message, history):
return "", history + [[user_message, None]]
def bot(history, max_new_tokens, temperature, top_p, system_prompt):
chat_data = []
system_prompt = system_prompt.strip()
if system_prompt:
chat_data.append({"role": "system", "content": system_prompt})
for user_msg, assistant_msg in history:
if user_msg is not None:
chat_data.append({"role": "user", "content": user_msg})
if assistant_msg is not None:
chat_data.append({"role": "assistant", "content": assistant_msg})
message = tokenizer.apply_chat_template(chat_data, tokenize=False)
message = message[3:] # remove SOT token
if refusal_condition(history[-1][0]):
history = [['[安全拒答啟動]', '[安全拒答啟動] 請清除再開啟對話']]
yield history
else:
data = {
"model": MODEL_NAME,
"prompt": str(message),
"temperature": float(temperature) + 0.01,
"n": 1,
"max_tokens": int(max_new_tokens),
"stop": "",
"top_p": float(top_p),
"logprobs": 0,
"echo": False,
"presence_penalty": PRESENCE_PENALTY,
"frequency_penalty": FREQUENCY_PENALTY,
"stream": True,
}
with requests.post(API_URL, headers=HEADERS, data=json.dumps(data), stream=True) as r:
for response in r.iter_lines():
if len(response) > 0:
text = response.decode()
if text != "data: [DONE]":
if text.startswith("data: "):
text = text[5:]
delta = json.loads(text)["choices"][0]["text"]
if history[-1][1] is None:
history[-1][1] = delta
else:
history[-1][1] += delta
yield history
if history[-1][1].endswith('</s>'):
history[-1][1] = history[-1][1][:-4]
yield history
print('== Record ==\nQuery: {query}\nResponse: {response}'.format(query=repr(message), response=repr(history[-1][1])))
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
fn=bot,
inputs=[
chatbot,
max_new_tokens,
temperature,
top_p,
system_prompt,
],
outputs=chatbot
)
submit_button.click(
user, [msg, chatbot], [msg, chatbot], queue=False
).then(
fn=bot,
inputs=[
chatbot,
max_new_tokens,
temperature,
top_p,
system_prompt,
],
outputs=chatbot
)
def delete_prev_fn(
history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]:
try:
message, _ = history.pop()
except IndexError:
message = ''
return history, message or ''
def display_input(message: str,
history: list[tuple[str, str]]) -> list[tuple[str, str]]:
history.append((message, ''))
return history
retry_button.click(
fn=delete_prev_fn,
inputs=chatbot,
outputs=[chatbot, saved_input],
api_name=False,
queue=False,
).then(
fn=display_input,
inputs=[saved_input, chatbot],
outputs=chatbot,
api_name=False,
queue=False,
).then(
fn=bot,
inputs=[
chatbot,
max_new_tokens,
temperature,
top_p,
system_prompt,
],
outputs=chatbot,
)
undo_button.click(
fn=delete_prev_fn,
inputs=chatbot,
outputs=[chatbot, saved_input],
api_name=False,
queue=False,
).then(
fn=lambda x: x,
inputs=[saved_input],
outputs=msg,
api_name=False,
queue=False,
)
clear.click(lambda: None, None, chatbot, queue=False)
gr.Markdown(LICENSE)
demo.queue(concurrency_count=1, max_size=16)
demo.launch()
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