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| import json | |
| import os | |
| import numpy as np | |
| # os.environ['http_proxy'] = "http://127.0.0.1:1450" | |
| # os.environ['https_proxy'] = "http://127.0.0.1:1450" | |
| import argparse | |
| import openai | |
| import tiktoken | |
| import torch | |
| from scipy.spatial.distance import cosine | |
| from langchain.chat_models import ChatOpenAI | |
| import gradio as gr | |
| import random | |
| import time | |
| import collections | |
| import pickle | |
| from argparse import Namespace | |
| import torch | |
| from PIL import Image | |
| from torch import cosine_similarity | |
| from transformers import AutoTokenizer, AutoModel | |
| from langchain.prompts.chat import ( | |
| ChatPromptTemplate, | |
| SystemMessagePromptTemplate, | |
| AIMessagePromptTemplate, | |
| HumanMessagePromptTemplate, | |
| ) | |
| from langchain.schema import ( | |
| AIMessage, | |
| HumanMessage, | |
| SystemMessage | |
| ) | |
| # OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY2") | |
| openai.proxy = "http://127.0.0.1:7890" | |
| openai.api_key = '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' # 在这里输入你的OpenAI API Token | |
| os.environ["OPENAI_API_KEY"] = openai.api_key | |
| folder_name = "Suzumiya" | |
| current_directory = os.getcwd() | |
| new_directory = os.path.join(current_directory, folder_name) | |
| device = torch.device("cpu") | |
| # device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| if not os.path.exists(new_directory): | |
| os.makedirs(new_directory) | |
| print(f"文件夹 '{folder_name}' 创建成功！") | |
| else: | |
| print(f"文件夹 '{folder_name}' 已经存在。") | |
| enc = tiktoken.get_encoding("cl100k_base") | |
| class Run: | |
| def __init__(self, **params): | |
| """ | |
| * 命令行参数的接入 | |
| * 台词folder,记录台词 | |
| * system prompt存成txt文件，支持切换 | |
| * 支持设定max_len_story 和max_len_history | |
| * 支持设定save_path | |
| * 实现一个colab脚本，可以clone转换后的项目并运行，方便其他用户体验 | |
| """ | |
| self.title_to_text_pkl_path = params['title_to_text_pkl_path'] | |
| self.text_image_pkl_path = params['text_image_pkl_path'] | |
| self.dict_text_pkl_path = params['dict_text_pkl_path'] | |
| self.num_steps = params['num_steps'] | |
| self.texts_pkl_path = params['texts_pkl_path'] | |
| self.embeds_path = params['embeds_path'] | |
| self.embeds2_path = params['embeds2_path'] | |
| self.dict_path = params['dict_path'] | |
| self.image_path = params['image_path'] | |
| self.maps_pkl_path = params['maps_pkl_path'] | |
| self.folder = params['folder'] | |
| self.system_prompt = params['system_prompt'] | |
| self.max_len_story = params['max_len_story'] | |
| self.max_len_history = params['max_len_history'] | |
| self.save_path = params['save_path'] | |
| def read_text(self): | |
| """抽取、预存""" | |
| text_embeddings = [] | |
| title_to_text = {} | |
| texts = [] | |
| data = [] | |
| id = 0 | |
| for file in os.listdir(self.folder): | |
| if file.endswith('.txt'): | |
| title_name = file[:-4] | |
| with open(os.path.join(self.folder, file), 'r') as fr: | |
| title_to_text[title_name] = fr.read() | |
| for line in title_to_text[title_name].strip().split('\n'): | |
| line = line.strip() | |
| category = {} | |
| ch = '：' if '：' in line else ':' | |
| if '旁白' in line: | |
| text = line.split(ch)[1].strip() | |
| else: | |
| text = ''.join(list(line.split(ch)[1])[1:-1]) # 提取「」内的文本 | |
| if title_name + "_" + text in texts: # 避免重复的text，导致embeds 和 maps形状不一致 | |
| continue | |
| texts.append(title_name+"_"+text) | |
| category["titles"] = file.split('.')[0] | |
| category["id"] = str(id) | |
| category["text"] = text | |
| id = id + 1 | |
| data.append(dict(category)) | |
| embeddings = self.get_embedding(texts) | |
| with open(self.texts_pkl_path, 'w+', encoding='utf-8') as f1: | |
| i = 0 | |
| for text in texts: | |
| item = {} | |
| item[text] = i | |
| json.dump(item, f1, ensure_ascii=False) | |
| f1.write('\n') | |
| i+=1 | |
| with open(self.embeds_path, 'w+', encoding='utf-8') as f2, open(self.embeds2_path, 'w+', encoding='utf-8') as f3: | |
| i = 0 | |
| for embed in embeddings: | |
| item = {} | |
| embed = embed.numpy().tolist() | |
| item[i] = embed | |
| if i < len(embeddings)/2: | |
| json.dump(item, f2, ensure_ascii=False) | |
| f2.write('\n') | |
| else: | |
| json.dump(item, f3, ensure_ascii=False) | |
| f3.write('\n') | |
| i += 1 | |
| # self.store(self.texts_pkl_path, text_embeddings) | |
| self.store(self.title_to_text_pkl_path, title_to_text) | |
| # self.store(self.embeds_pkl_path, embeddings) | |
| self.store(self.maps_pkl_path, data) | |
| return text_embeddings, data | |
| def store(self, path, data): | |
| with open(path, 'wb+') as f: | |
| pickle.dump(data, f) | |
| def load(self, load_texts=False, load_maps=False, load_dict_text=False, | |
| load_text_image=False, load_title_to_text=False): | |
| if load_texts: | |
| if self.texts_pkl_path: | |
| text_embeddings = {} | |
| texts = [] | |
| embeds1 = [] | |
| embeds2 = [] | |
| with open(self.texts_pkl_path, 'r') as f: | |
| for line in f: | |
| data = json.loads(line) | |
| texts.append(list(data.keys())[0]) | |
| with open(self.embeds_path, 'r') as f: | |
| for line in f: | |
| data = json.loads(line) | |
| embeds1.append(list(data.values())) | |
| with open(self.embeds2_path, 'r') as f: | |
| for line in f: | |
| data = json.loads(line) | |
| embeds2.append(list(data.values())) | |
| embeds = embeds1 + embeds2 | |
| for text, embed in zip(texts, embeds): | |
| text_embeddings[text] = embed | |
| return text_embeddings | |
| else: | |
| print("No texts_pkl_path") | |
| elif load_maps: | |
| if self.maps_pkl_path: | |
| with open(self.maps_pkl_path, 'rb') as f: | |
| return pickle.load(f) | |
| else: | |
| print("No maps_pkl_path") | |
| elif load_dict_text: | |
| if self.dict_text_pkl_path: | |
| with open(self.dict_text_pkl_path, 'rb') as f: | |
| return pickle.load(f) | |
| else: | |
| print("No dict_text_pkl_path") | |
| elif load_text_image: | |
| if self.text_image_pkl_path: | |
| with open(self.text_image_pkl_path, 'rb') as f: | |
| return pickle.load(f) | |
| else: | |
| print("No text_image_pkl_path") | |
| elif load_title_to_text: | |
| if self.title_to_text_pkl_path: | |
| with open(self.title_to_text_pkl_path, 'rb') as f: | |
| return pickle.load(f) | |
| else: | |
| print("No title_to_text_pkl_path") | |
| else: | |
| print("Please specify the loading file！") | |
| def text_to_image(self, text, save_dict_text=False): | |
| """ | |
| 给定文本出图片 | |
| 计算query 和 texts 的相似度，取最高的作为new_query 查询image | |
| 到text_image_dict 读取图片名 | |
| 然后到images里面加载该图片然后返回 | |
| """ | |
| if save_dict_text: | |
| text_image = collections.defaultdict() | |
| with open(self.dict_path, 'r') as f: | |
| data = f.readlines() | |
| for sub_text, image in zip(data[::2], data[1::2]): | |
| text_image[sub_text.strip()] = image.strip() | |
| self.store(self.text_image_pkl_path, text_image) | |
| keys_embeddings = collections.defaultdict(str) | |
| for key in text_image.keys(): | |
| keys_embeddings[key] = self.get_embedding(key) | |
| self.store(self.dict_text_pkl_path, keys_embeddings) | |
| if self.dict_path and self.image_path: | |
| # 加载 text-imageName | |
| text_image = self.load(load_text_image=True) | |
| keys = list(text_image.keys()) | |
| keys.insert(0, text) | |
| query_similarity = self.get_cosine_similarity(keys, get_image=True) | |
| key_index = query_similarity.argmax(dim=0) | |
| text = list(text_image.keys())[key_index] | |
| image = text_image[text] + '.jpg' | |
| if image in os.listdir(self.image_path): | |
| res = Image.open(self.image_path + '/' + image) | |
| # res.show() | |
| return res | |
| else: | |
| print("Image doesn't exist") | |
| else: | |
| print("No path") | |
| def text_to_text(self, text): | |
| pkl = self.load(load_texts=True) | |
| texts = [title_text.split('_')[1] for title_text in list(pkl.keys())] | |
| texts.insert(0, text) | |
| texts_similarity = self.get_cosine_similarity(texts, get_texts=True) | |
| key_index = texts_similarity.argmax(dim=0) | |
| value = list(pkl.keys())[key_index] | |
| return value | |
| # 一个封装 OpenAI 接口的函数，参数为 Prompt，返回对应结果 | |
| def get_completion_from_messages(self, messages, model="gpt-3.5-turbo", temperature=0): | |
| response = openai.ChatCompletion.create( | |
| model=model, | |
| messages=messages, | |
| temperature=temperature, # 控制模型输出的随机程度 | |
| ) | |
| # print(str(response.choices[0].message)) | |
| return response.choices[0].message["content"] | |
| def download_models(self): | |
| # Import our models. The package will take care of downloading the models automatically | |
| model_args = Namespace(do_mlm=None, pooler_type="cls", temp=0.05, mlp_only_train=False, | |
| init_embeddings_model=None) | |
| model = AutoModel.from_pretrained("silk-road/luotuo-bert", trust_remote_code=True, model_args=model_args).to(device) | |
| return model | |
| def get_embedding(self, texts): | |
| tokenizer = AutoTokenizer.from_pretrained("silk-road/luotuo-bert") | |
| model = self.download_models() | |
| # str or strList | |
| texts = texts if isinstance(texts, list) else [texts] | |
| # 截断 | |
| for i in range(len(texts)): | |
| if len(texts[i]) > self.num_steps: | |
| texts[i] = texts[i][:self.num_steps] | |
| # Tokenize the texts | |
| inputs = tokenizer(texts, padding=True, truncation=False, return_tensors="pt") | |
| # Extract the embeddings | |
| # Get the embeddings | |
| inputs = inputs.to(device) | |
| with torch.no_grad(): | |
| embeddings = model(**inputs, output_hidden_states=True, return_dict=True, sent_emb=True).pooler_output | |
| return embeddings[0] if len(texts) == 1 else embeddings | |
| def get_cosine_similarity(self, texts, get_image=False, get_texts=False): | |
| """ | |
| 计算文本列表的相似度避免重复计算query_similarity | |
| texts[0] = query | |
| """ | |
| if get_image: | |
| pkl = self.load(load_dict_text=True) | |
| elif get_texts: | |
| pkl = self.load(load_texts=True) | |
| else: | |
| # 计算query_embed | |
| pkl = {} | |
| embeddings = self.get_embedding(texts[1:]).reshape(-1, 1536) | |
| for text, embed in zip(texts, embeddings): | |
| pkl[text] = embed | |
| query_embedding = self.get_embedding(texts[0]).reshape(1, -1) | |
| texts_embeddings = np.array([np.array(value).reshape(-1, 1536) for value in pkl.values()]).squeeze(1) | |
| return cosine_similarity(query_embedding, torch.from_numpy(texts_embeddings)) | |
| def retrieve_title(self, query_text, k): | |
| # compute cosine similarity between query_embed and embeddings | |
| embed_to_title = [] | |
| texts = [query_text] | |
| texts_pkl = self.load(load_texts=True) | |
| for title_text in texts_pkl.keys(): | |
| res = title_text.split('_') | |
| embed_to_title.append(res[0]) | |
| cosine_similarities = self.get_cosine_similarity(texts, get_texts=True).numpy().tolist() | |
| # sort cosine similarity | |
| sorted_cosine_similarities = sorted(cosine_similarities, reverse=True) | |
| top_k_index = [] | |
| top_k_title = [] | |
| for i in range(len(sorted_cosine_similarities)): | |
| current_title = embed_to_title[cosine_similarities.index(sorted_cosine_similarities[i])] | |
| if current_title not in top_k_title: | |
| top_k_title.append(current_title) | |
| top_k_index.append(cosine_similarities.index(sorted_cosine_similarities[i])) | |
| if len(top_k_title) == k: | |
| break | |
| return top_k_title | |
| def organize_story_with_maxlen(self, selected_sample): | |
| maxlen = self.max_len_story | |
| title_to_text = self.load(load_title_to_text=True) | |
| story = "凉宫春日的经典桥段如下:\n" | |
| count = 0 | |
| final_selected = [] | |
| print(selected_sample) | |
| for sample_topic in selected_sample: | |
| # find sample_answer in dictionary | |
| sample_story = title_to_text[sample_topic] | |
| sample_len = len(enc.encode(sample_story)) | |
| # print(sample_topic, ' ' , sample_len) | |
| if sample_len + count > maxlen: | |
| break | |
| story += sample_story | |
| story += '\n' | |
| count += sample_len | |
| final_selected.append(sample_topic) | |
| return story, final_selected | |
| def organize_message(self, story, history_chat, history_response, new_query): | |
| messages = [{'role': 'system', 'content': self.system_prompt}, | |
| {'role': 'user', 'content': story}] | |
| n = len(history_chat) | |
| if n != len(history_response): | |
| print('warning, unmatched history_char length, clean and start new chat') | |
| # clean all | |
| history_chat = [] | |
| history_response = [] | |
| n = 0 | |
| for i in range(n): | |
| messages.append({'role': 'user', 'content': history_chat[i]}) | |
| messages.append({'role': 'user', 'content': history_response[i]}) | |
| messages.append({'role': 'user', 'content': new_query}) | |
| return messages | |
| def keep_tail(self, history_chat, history_response): | |
| max_len = self.max_len_history | |
| n = len(history_chat) | |
| if n == 0: | |
| return [], [] | |
| if n != len(history_response): | |
| print('warning, unmatched history_char length, clean and start new chat') | |
| return [], [] | |
| token_len = [] | |
| for i in range(n): | |
| chat_len = len(enc.encode(history_chat[i])) | |
| res_len = len(enc.encode(history_response[i])) | |
| token_len.append(chat_len + res_len) | |
| keep_k = 1 | |
| count = token_len[n - 1] | |
| for i in range(1, n): | |
| count += token_len[n - 1 - i] | |
| if count > max_len: | |
| break | |
| keep_k += 1 | |
| return history_chat[-keep_k:], history_response[-keep_k:] | |
| def organize_message_langchain(self, story, history_chat, history_response, new_query): | |
| # messages = [{'role':'system', 'content':SYSTEM_PROMPT}, {'role':'user', 'content':story}] | |
| messages = [ | |
| SystemMessage(content=self.system_prompt), | |
| HumanMessage(content=story) | |
| ] | |
| n = len(history_chat) | |
| if n != len(history_response): | |
| print('warning, unmatched history_char length, clean and start new chat') | |
| # clean all | |
| history_chat = [] | |
| history_response = [] | |
| n = 0 | |
| for i in range(n): | |
| messages.append(HumanMessage(content=history_chat[i])) | |
| messages.append(AIMessage(content=history_response[i])) | |
| # messages.append( {'role':'user', 'content':new_query }) | |
| messages.append(HumanMessage(content=new_query)) | |
| return messages | |
| def get_response(self, user_message, chat_history_tuple): | |
| history_chat = [] | |
| history_response = [] | |
| if len(chat_history_tuple) > 0: | |
| for cha, res in chat_history_tuple: | |
| history_chat.append(cha) | |
| history_response.append(res) | |
| history_chat, history_response = self.keep_tail(history_chat, history_response) | |
| print('history done') | |
| new_query = user_message | |
| selected_sample = self.retrieve_title(new_query, 7) | |
| print("备选辅助：", selected_sample) | |
| story, selected_sample = self.organize_story_with_maxlen(selected_sample) | |
| ## TODO: visualize seletected sample later | |
| print('当前辅助sample:', selected_sample) | |
| messages = self.organize_message_langchain(story, history_chat, history_response, new_query) | |
| print(f"messages:{messages}") | |
| chat = ChatOpenAI(temperature=0) | |
| return_msg = chat(messages) | |
| response = return_msg.content | |
| return response | |
| def save_response(self, chat_history_tuple): | |
| with open(f"{self.save_path}/conversation_{time.time()}.txt", "w") as file: | |
| for cha, res in chat_history_tuple: | |
| file.write(cha) | |
| file.write("\n---\n") | |
| file.write(res) | |
| file.write("\n---\n") | |
| def create_gradio(self): | |
| # from google.colab import drive | |
| # drive.mount(drive_path) | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| ## Chat凉宫春日 ChatHaruhi | |
| 项目地址 [https://github.com/LC1332/Chat-Haruhi-Suzumiya](https://github.com/LC1332/Chat-Haruhi-Suzumiya) | |
| 骆驼项目地址 [https://github.com/LC1332/Luotuo-Chinese-LLM](https://github.com/LC1332/Luotuo-Chinese-LLM) | |
| 此版本为图文版本，非最终版本，将上线更多功能，敬请期待 | |
| """ | |
| ) | |
| image_input = gr.Textbox(visible=False) | |
| with gr.Row(): | |
| chatbot = gr.Chatbot() | |
| image_output = gr.Image() | |
| role_name = gr.Textbox(label="角色名", placeholde="输入角色名") | |
| msg = gr.Textbox(label="输入") | |
| with gr.Row(): | |
| clear = gr.Button("Clear") | |
| sub = gr.Button("Submit") | |
| image_button = gr.Button("给我一个图") | |
| def respond(role_name, user_message, chat_history): | |
| role_name = "阿虚" if role_name in ['', ' '] else role_name | |
| role_name = role_name[:10] if len(role_name) > 10 else role_name | |
| user_message = user_message[:200] if len(user_message) > 200 else user_message | |
| special_chars = [':', '：', '「', '」', '\n'] | |
| for char in special_chars: | |
| role_name = role_name.replace(char, 'x') | |
| user_message = user_message.replace(char, ' ') | |
| replacement_rules = {'凉': '马', '宫': '宝', '春': '国', '日': '啊'} | |
| # for char, replacement in replacement_rules.items(): | |
| # role_name = role_name.replace(char, replacement) | |
| # user_message = user_message.replace(char, replacement) | |
| input_message = role_name + ':「' + user_message + '」' | |
| print(f"chat_history:{chat_history}") | |
| bot_message = self.get_response(input_message, chat_history) | |
| chat_history.append((input_message, bot_message)) | |
| self.save_response(chat_history) | |
| # time.sleep(1) | |
| return "", chat_history, bot_message | |
| msg.submit(respond, [role_name, msg, chatbot], [msg, chatbot, image_input]) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| sub.click(fn=respond, inputs=[role_name, msg, chatbot], outputs=[msg, chatbot, image_input]) | |
| image_button.click(self.text_to_image, inputs=image_input, outputs=image_output) | |
| demo.launch(debug=True, share=True) | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser(description="-----[Chat凉宫春日]-----") | |
| parser.add_argument("--folder", default="../characters/haruhi/texts", help="text folder") | |
| parser.add_argument("--system_prompt", default="../characters/haruhi/system_prompt.txt", help="store system_prompt") | |
| parser.add_argument("--max_len_story", default=1500, type=int) | |
| parser.add_argument("--max_len_history", default=1200, type=int) | |
| # parser.add_argument("--save_path", default="/content/drive/MyDrive/GPTData/Haruhi-Lulu/") | |
| parser.add_argument("--save_path", default=os.getcwd() + "/Suzumiya") | |
| parser.add_argument("--texts_pkl_path", default="./pkl/texts.jsonl") | |
| parser.add_argument("--embeds_path", default="./pkl/embeds.jsonl") | |
| parser.add_argument("--embeds2_path", default="./pkl/embeds2.jsonl") | |
| parser.add_argument("--maps_pkl_path", default="./pkl/maps.pkl") | |
| parser.add_argument("--title_to_text_pkl_path", default='./pkl/title_to_text.pkl') | |
| parser.add_argument("--dict_text_pkl_path", default="./pkl/dict_text.pkl") | |
| parser.add_argument("--text_image_pkl_path", default="./pkl/text_image.pkl") | |
| parser.add_argument("--dict_path", default="../characters/haruhi/text_image_dict.txt") | |
| parser.add_argument("--image_path", default="../characters/haruhi/images") | |
| parser.add_argument("--num_steps", default=510, type=int) | |
| options = parser.parse_args() | |
| params = { | |
| "folder": options.folder, | |
| "system_prompt": options.system_prompt, | |
| "max_len_story": options.max_len_story, | |
| "max_len_history": options.max_len_history, | |
| "save_path": options.save_path, | |
| "texts_pkl_path": options.texts_pkl_path, | |
| "embeds_path": options.embeds_path, | |
| "embeds2_path": options.embeds2_path, | |
| "title_to_text_pkl_path": options.title_to_text_pkl_path, | |
| "maps_pkl_path": options.maps_pkl_path, | |
| "dict_text_pkl_path": options.dict_text_pkl_path, | |
| "text_image_pkl_path": options.text_image_pkl_path, | |
| "dict_path": options.dict_path, | |
| "image_path": options.image_path, | |
| "num_steps": options.num_steps, | |
| } | |
| run = Run(**params) | |
| # selected_samples = run.retrieve_title("hello", 7) | |
| # story, selected_samples = run.organize_story_with_maxlen(selected_samples) | |
| # print(story, selected_samples) | |
| run.read_text() | |
| # run.text_to_image("hello", save_dict_text=True) | |
| run.create_gradio() | |
| # a = run.load(load_texts=True) | |
| # print(len(a)) | |
| # for item in a: | |
| # print(item) | |
| # print(len(a)) | |
| # a = run.load(load_dict_text=True) | |
| # print(a) | |
| # print(len(a)) | |
| # a = run.load(load_text_image=True) | |
| # print(a) | |
| # print(len(a)) | |
| # a = run.load(load_title_to_text=True) | |
| # print(a) | |
| # print(len(a)) | |
| # b = run.load(load_maps=True) | |
| # print(len(b)) | |
| # print(run.load(load_title_to_text) | |
| # history_chat = [] | |
| # history_response = [] | |
| # chat_timer = 5 | |
| # new_query = '鲁鲁:你好我是新同学鲁鲁' | |
| # | |
| # | |
| # selected_sample = run.retrieve_title(new_query, 7) | |
| # | |
| # print('限制长度之前:', selected_sample) | |
| # | |
| # story, selected_sample = run.organize_story_with_maxlen(selected_sample) | |
| # | |
| # print('当前辅助sample:', selected_sample) | |
| # | |
| # messages = run.organize_message(story, history_chat, history_response, new_query) | |
| # | |
| # response = run.get_completion_from_messages(messages) | |
| # | |
| # print(response) | |
| # | |
| # history_chat.append(new_query) | |
| # history_response.append(response) | |
| # | |
| # history_chat, history_response = run.keep_tail(history_chat, history_response) | |
| # print(history_chat, history_response) |