File size: 13,246 Bytes
936a3fd f23a967 936a3fd fb5904b 936a3fd f23a967 936a3fd f23a967 936a3fd cd15210 936a3fd cd15210 936a3fd cd15210 f23a967 cd15210 936a3fd f23a967 cd15210 936a3fd cd15210 936a3fd cd15210 936a3fd f23a967 cd15210 936a3fd f23a967 936a3fd f23a967 936a3fd f23a967 936a3fd f23a967 936a3fd f23a967 936a3fd f23a967 936a3fd f23a967 936a3fd f23a967 936a3fd f23a967 936a3fd 0fbfc96 936a3fd f23a967 936a3fd f23a967 936a3fd f23a967 936a3fd f23a967 936a3fd f23a967 936a3fd f23a967 936a3fd 4c0c8f1 936a3fd 4c0c8f1 936a3fd 4c0c8f1 936a3fd f23a967 936a3fd f23a967 936a3fd f23a967 936a3fd 79c744e fb5904b 936a3fd |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 |
# %%
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from peft import AutoPeftModelForCausalLM
import gradio as gr
import hanzidentifier
import re
import chinese_converter
import pathlib
current_path=str(pathlib.Path(__file__).parent.resolve())
# %%
#Load the LLM model and pipeline directly
llm_model_name="Qwen/Qwen1.5-0.5B-Chat"
#pipe = pipeline("text2text-generation", model=model)
model = AutoModelForCausalLM.from_pretrained(
llm_model_name
)
#model = AutoPeftModelForCausalLM.from_pretrained(
# "Qwen1.5_0.5B_Chat_sft_full/checkpoint-300",
# low_cpu_mem_usage=True,
#)
tokenizer = AutoTokenizer.from_pretrained(llm_model_name)
# %%
# %%
# loading the vector encoder
vec_model_name = "shibing624/text2vec-base-chinese"
encode_kwargs = {'normalize_embeddings': False}
model_kwargs = {'device': 'cpu'}
huggingface_embeddings= HuggingFaceEmbeddings(
model_name=vec_model_name,
model_kwargs=model_kwargs,
encode_kwargs = encode_kwargs
)
# %%
persist_directory = 'chroma/'
vectordb = Chroma(embedding_function=huggingface_embeddings,persist_directory=persist_directory)
print(vectordb._collection.count())
# %%
text_input_label=["谜面","謎面","Riddle"]
text_output_label=["谜底","謎底","Answer"]
clear_label = ["清除","清除","Clear"]
submit_label = ["提交","提交","Submit"]
threshold = 0.7
# %%
# helper functions for prompt processing for this LLM
# def preprocess(text):
# text = text.replace("\n", "\\n").replace("\t", "\\t")
# return text
# def postprocess(text):
# return text.replace("\\n", "\n").replace("\\t", "\t").replace('%20',' ')
# get answer from LLM with prompt input
def answer(input_text,context=None):
if context:
tips = "提示:\n"
tips += "\n".join([x[0] for x in context])
print (f"====\n{input_text}\n{context[0][0]} {context[0][1]}")
if context[0][1] >=0.9:
ans = re.search(r"谜底:(\w+)", context[0][0])
if ans:
return f"谜底是:{ans.group(1)}"
else:
tips=""
prompt = f"{input_text}\n{tips}\n谜底是什么?"
prompt = prompt.strip()
print(f"===\n{prompt}")
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device="cpu")
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=128,
do_sample=False,
top_p=0.0
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
#return out_text[0]["generated_text"]
return response
#return postprocess(out_text[0]["generated_text"])
# helper function for RAG
def helper_rag(text):
docs_out = vectordb.similarity_search_with_relevance_scores(text,k=1)
#docs_out = vectordb.max_marginal_relevance_search(text,k=5,fetch_k = 20, lambda_mult = 0.5)
context = []
for doc in docs_out:
if doc[1] > threshold:
context.append((f"{doc[0].page_content}{doc[0].metadata['answer']}", doc[1]))
return context
# helper function for prompt
def helper_text(text_input,radio=None):
chinese_type = "simplified"
if hanzidentifier.is_traditional(text_input):
text_input = chinese_converter.to_simplified(text_input)
chinese_type = "traditional"
text_input = re.sub(r'hint',"猜",text_input,flags=re.I)
#if not any(c in text_input for c in ["猜", "打"]):
# warning = "请给一个提示,提示格式,例子:猜一水果,打一字。"
# if chinese_type == "traditional" or radio == "繁體中文":
# warning = chinese_converter.to_traditional(warning)
# return warning
text=f"""猜谜语:\n谜面:{text_input}
"""
context = helper_rag(text)
output = answer(text,context=context)
print(output)
if chinese_type == "traditional":
output = chinese_converter.to_traditional(output)
#output = re.split(r'\s+',output)
return output
#return output[0]
# get answer from LLM with prompt input
def translate(input_text):
'''Use LLM for translation'''
prompt = f"""翻译以下內容成英语:
{input_text}
"""
print(prompt)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device="cpu")
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=128,
do_sample=False,
top_p=0.0
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
#return out_text[0]["generated_text"]
return response
#return postprocess(out_text[0]["generated_text"])
# Gradio function for configure the language of UI
def change_language(radio,text_input,text_output,markdown,
markdown_msg1, markdown_msg2,translate_btn):
if radio == "简体中文":
index = 0
text_input=gr.Textbox(value = chinese_converter.to_simplified(text_input), label = text_input_label[index])
text_output=gr.Textbox(value = chinese_converter.to_simplified(text_output),label = text_output_label[index])
markdown=chinese_converter.to_simplified(markdown)
markdown_msg1=chinese_converter.to_simplified(markdown_msg1)
markdown_msg2=chinese_converter.to_simplified(markdown_msg2)
translate_btn=gr.Button(visible=False)
elif radio == "繁體中文":
index = 1
text_input=gr.Textbox(value = chinese_converter.to_traditional(text_input),label = text_input_label[index])
text_output=gr.Textbox(value = chinese_converter.to_traditional(text_output),label = text_output_label[index])
markdown=chinese_converter.to_traditional(markdown)
markdown_msg1=chinese_converter.to_traditional(markdown_msg1)
markdown_msg2=chinese_converter.to_traditional(markdown_msg2)
translate_btn=gr.Button(visible=False)
elif radio == "English":
index = 2
text_input=gr.Textbox(label = text_input_label[index])
text_output=gr.Textbox(label = text_output_label[index])
translate_btn=gr.Button(visible=True)
else:
index = 0
text_input=gr.Textbox(label = text_input_label[index])
text_output=gr.Textbox(label = text_output_label[index])
markdown=chinese_converter.to_simplified(markdown)
markdown_msg1=chinese_converter.to_simplified(markdown_msg1)
markdown_msg2=chinese_converter.to_simplified(markdown_msg2)
translate_btn=gr.Button(visible=False)
clear_btn = clear_label[index]
submit_btn = submit_label[index]
return [text_input,text_output,clear_btn,submit_btn,markdown,
markdown_msg1 ,markdown_msg2,translate_btn]
def clear_text():
text_input_update=""
text_output_update=""
return [text_input_update,text_output_update]
def translate_text(text_input,text_output):
text_input = translate(f"{text_input}")
text_output = translate(f"{text_output}")
return text_input,text_output
# %%
# css = """
# #markdown { background-image: url("file/data/DSC_0105.jpg");
# background-size: cover;
# }
# """
with gr.Blocks() as demo:
index = 0
example_list = [
["小家伙穿黄袍,花丛中把房造。飞到西来飞到东,人人夸他爱劳动。(猜一动物)"],
["一物生来身穿三百多件衣,每天脱一件,年底剩张皮。(猜一物品)"],
["A thousand threads, a million strands. Reaching the water, vanishing all at once. (Hint: natural phenomenon)"],
["无底洞"],
]
radio = gr.Radio(
["简体中文","繁體中文", "English"],show_label=False,value="简体中文"
)
markdown = gr.Markdown(
"""
# Chinese Lantern Riddles Solver with LLM
## 用大语言模型来猜灯谜
""",elem_id="markdown")
with gr.Row():
with gr.Column():
text_input = gr.Textbox(label=text_input_label[index],
value="小家伙穿黄袍,花丛中把房造。飞到西来飞到东,人人夸他爱劳动。(猜一动物)", lines = 2)
with gr.Row():
clear_btn = gr.ClearButton(value=clear_label[index],components=[text_input])
submit_btn = gr.Button(value=submit_label[index], variant = "primary")
text_output = gr.Textbox(label=text_output_label[index])
translate_btn = gr.Button(value="Translate", variant = "primary", scale=0, visible=False)
examples = gr.Examples(
examples=example_list,
inputs=text_input,
outputs=text_output,
fn=helper_text,
cache_examples=True,
)
markdown_msg1 = gr.Markdown(
"""
灯谜是中华文化特色文娱活动,自北宋盛行。每年逢正月十五元宵节,将谜语贴在花灯上,让大家可一起猜谜。
Lantern riddle is a traditional Chinese cultural activity. Being popular since the Song Dynasty (960-1276), it \
is held in the Lantern Festival (15th day of the first lunar month). \
When people are viewing the flower lanterns, they can guess the riddles on the lanterns together.
"""
)
with gr.Column():
markdown_msg2 = gr.Markdown(
"""
![lantern](file/data/DSC_0105.jpg)
---
# 声明 Disclaimer
本应用输出的文本为机器基于模型生成的结果,不代表任何人观点,请谨慎辨别和参考。请在法律允许的范围内使用。
本应用调用了 [Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) 对话语言大模型,\
使用本应用前请务必阅读和同意遵守其[使用授权许可证](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat/blob/main/LICENSE)。
本应用仅供非商业用途。
The outputs of this application are machine-generated with a statistical model. \
The outputs do not reflect any opinions of any human subjects. You must identify the outputs in caution. \
It is your responsbility to decide whether to accept the outputs. You must use the applicaiton in obedience to the Law.
This application utilizes [Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) \
Conversational Large Language Model. Before using this application, you must read and accept to follow \
the [LICENSE](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat/blob/main/LICENSE).
This application is for non-commercial use only.
---
# 感谢 Acknowledgement
本应用调用了 [text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese) 生成 text vector embeddings.
该模型是以 [apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) 发行。
This application utilizes [text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese) to generate text vector embeddings.
The model is released under [apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)。
""")
submit_btn.click(fn=helper_text, inputs=[text_input,radio], outputs=text_output)
translate_btn.click(fn=translate_text, inputs=[text_input,text_output], outputs=[text_input,text_output])
clear_btn.click(fn=clear_text,outputs=[text_input,text_output])
radio.change(fn=change_language,inputs=[radio,text_input,text_output,
markdown, markdown_msg1,markdown_msg2,translate_btn],
outputs=[text_input,text_output,clear_btn,submit_btn,
markdown, markdown_msg1,markdown_msg2,translate_btn])
#demo = gr.Interface(fn=helper_text, inputs=text_input, outputs=text_output,
# flagging_options=["Inappropriate"],allow_flagging="never",
# title="aaa",description="aaa",article="aaa")
#demo.queue(api_open=False)
demo.launch(show_api=False,allowed_paths=[current_path+"/data/"])
# %%
|