File size: 24,118 Bytes
5f735a0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
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)