# %% from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline from langchain.vectorstores import Chroma from langchain.embeddings import HuggingFaceEmbeddings 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( "ytyeung/Qwen1.5-0.5B-Chat-SFT-riddles", ) #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 = 0.6 candidate = 1 # %% # 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" for i, tip in enumerate(context): #if i==0: # tips +="最佳答案\n" #else: # tips +="較差答案\n" tips += f"{i+1}. 谜面：{tip[0]} 谜底是：{tip[1]} " tips +="\n" print (f"====\n{input_text}\n{context[0][0]} 谜底是：{context[0][1]} {context[0][2]}") if context[0][2] >=0.9: return f"谜底是：{context[0][1]}" else: tips="" prompt = f"{input_text}\n\n{tips}\n\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, temperature=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=candidate) #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((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_input) 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（猜一成语）"], ] 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/"]) # %%