Merge branch 'frontier' into production

.github/workflows/build-with-all-capacity-beta.yml

@@ -1,44 +0,0 @@
-# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
-name: build-with-all-capacity-beta
-
-on:
-  push:
-    branches:
-      - 'master'
-
-env:
-  REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}_with_all_capacity_beta
-
-jobs:
-  build-and-push-image:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v3
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@v2
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for Docker
-        id: meta
-        uses: docker/metadata-action@v4
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v4
-        with:
-          context: .
-          push: true
-          file: docs/GithubAction+AllCapacityBeta
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}

crazy_functional.py

@@ -590,19 +590,19 @@ def get_crazy_functions():
         print(trimmed_format_exc())
         print('Load function plugin failed')
 
-    try:
+    # try:
-        from crazy_functions.互动小游戏 import 随机小游戏
+    #     from crazy_functions.互动小游戏 import 随机小游戏
-        function_plugins.update({
+    #     function_plugins.update({
-            "随机互动小游戏（仅供测试）": {
+    #         "随机小游戏": {
-                "Group": "智能体",
+    #             "Group": "智能体",
-                "Color": "stop",
+    #             "Color": "stop",
-                "AsButton": False,
+    #             "AsButton": True,
-                "Function": HotReload(随机小游戏)
+    #             "Function": HotReload(随机小游戏)
-            }
+    #         }
-        })
+    #     })
-    except:
+    # except:
-        print(trimmed_format_exc())
+    #     print(trimmed_format_exc())
-        print('Load function plugin failed')
+    #     print('Load function plugin failed')
 
     # try:
     #     from crazy_functions.chatglm微调工具 import 微调数据集生成

crazy_functions/Latex全文润色.py

@@ -26,8 +26,8 @@ class PaperFileGroup():
                 self.sp_file_index.append(index)
                 self.sp_file_tag.append(self.file_paths[index])
             else:
-                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
+                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
                 for j, segment in enumerate(segments):
                     self.sp_file_contents.append(segment)
                     self.sp_file_index.append(index)

crazy_functions/Latex全文翻译.py

@@ -26,8 +26,8 @@ class PaperFileGroup():
                 self.sp_file_index.append(index)
                 self.sp_file_tag.append(self.file_paths[index])
             else:
-                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
+                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
                 for j, segment in enumerate(segments):
                     self.sp_file_contents.append(segment)
                     self.sp_file_index.append(index)

crazy_functions/crazy_utils.py

@@ -312,6 +312,95 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
     return gpt_response_collection
 
 
+def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
+    def cut(txt_tocut, must_break_at_empty_line):  # 递归
+        if get_token_fn(txt_tocut) <= limit:
+            return [txt_tocut]
+        else:
+            lines = txt_tocut.split('\n')
+            estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
+            estimated_line_cut = int(estimated_line_cut)
+            for cnt in reversed(range(estimated_line_cut)):
+                if must_break_at_empty_line:
+                    if lines[cnt] != "":
+                        continue
+                print(cnt)
+                prev = "\n".join(lines[:cnt])
+                post = "\n".join(lines[cnt:])
+                if get_token_fn(prev) < limit:
+                    break
+            if cnt == 0:
+                raise RuntimeError("存在一行极长的文本！")
+            # print(len(post))
+            # 列表递归接龙
+            result = [prev]
+            result.extend(cut(post, must_break_at_empty_line))
+            return result
+    try:
+        return cut(txt, must_break_at_empty_line=True)
+    except RuntimeError:
+        return cut(txt, must_break_at_empty_line=False)
+
+
+def force_breakdown(txt, limit, get_token_fn):
+    """
+    当无法用标点、空行分割时，我们用最暴力的方法切割
+    """
+    for i in reversed(range(len(txt))):
+        if get_token_fn(txt[:i]) < limit:
+            return txt[:i], txt[i:]
+    return "Tiktoken未知错误", "Tiktoken未知错误"
+
+def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
+    # 递归
+    def cut(txt_tocut, must_break_at_empty_line, break_anyway=False):  
+        if get_token_fn(txt_tocut) <= limit:
+            return [txt_tocut]
+        else:
+            lines = txt_tocut.split('\n')
+            estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
+            estimated_line_cut = int(estimated_line_cut)
+            cnt = 0
+            for cnt in reversed(range(estimated_line_cut)):
+                if must_break_at_empty_line:
+                    if lines[cnt] != "":
+                        continue
+                prev = "\n".join(lines[:cnt])
+                post = "\n".join(lines[cnt:])
+                if get_token_fn(prev) < limit:
+                    break
+            if cnt == 0:
+                if break_anyway:
+                    prev, post = force_breakdown(txt_tocut, limit, get_token_fn)
+                else:
+                    raise RuntimeError(f"存在一行极长的文本！{txt_tocut}")
+            # print(len(post))
+            # 列表递归接龙
+            result = [prev]
+            result.extend(cut(post, must_break_at_empty_line, break_anyway=break_anyway))
+            return result
+    try:
+        # 第1次尝试，将双空行（\n\n）作为切分点
+        return cut(txt, must_break_at_empty_line=True)
+    except RuntimeError:
+        try:
+            # 第2次尝试，将单空行（\n）作为切分点
+            return cut(txt, must_break_at_empty_line=False)
+        except RuntimeError:
+            try:
+                # 第3次尝试，将英文句号（.）作为切分点
+                res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
+                return [r.replace('。\n', '.') for r in res]
+            except RuntimeError as e:
+                try:
+                    # 第4次尝试，将中文句号（。）作为切分点
+                    res = cut(txt.replace('。', '。。\n'), must_break_at_empty_line=False)
+                    return [r.replace('。。\n', '。') for r in res]
+                except RuntimeError as e:
+                    # 第5次尝试，没办法了，随便切一下敷衍吧
+                    return cut(txt, must_break_at_empty_line=False, break_anyway=True)
+
+
 
 def read_and_clean_pdf_text(fp):
     """
@@ -542,6 +631,7 @@ def get_files_from_everything(txt, type): # type='.md'
 
 
 
+    
 @Singleton
 class nougat_interface():
     def __init__(self):

crazy_functions/game_fns/game_ascii_art.py

@@ -1,42 +0,0 @@
-from toolbox import CatchException, update_ui, update_ui_lastest_msg
-from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from request_llms.bridge_all import predict_no_ui_long_connection
-from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
-import random
-
-
-class MiniGame_ASCII_Art(GptAcademicGameBaseState):
-    def step(self, prompt, chatbot, history):
-        if self.step_cnt == 0:  
-            chatbot.append(["我画你猜（动物）", "请稍等..."])
-        else:
-            if prompt.strip() == 'exit':
-                self.delete_game = True
-                yield from update_ui_lastest_msg(lastmsg=f"谜底是{self.obj}，游戏结束。", chatbot=chatbot, history=history, delay=0.)
-                return
-            chatbot.append([prompt, ""])
-        yield from update_ui(chatbot=chatbot, history=history)
-
-        if self.step_cnt == 0:
-            self.lock_plugin(chatbot)
-            self.cur_task = 'draw'
-
-        if self.cur_task == 'draw':
-            avail_obj = ["狗","猫","鸟","鱼","老鼠","蛇"]
-            self.obj = random.choice(avail_obj)
-            inputs = "I want to play a game called Guess the ASCII art. You can draw the ASCII art and I will try to guess it. " + \
-                f"This time you draw a {self.obj}. Note that you must not indicate what you have draw in the text, and you should only produce the ASCII art wrapped by ```. "
-            raw_res = predict_no_ui_long_connection(inputs=inputs, llm_kwargs=self.llm_kwargs, history=[], sys_prompt="")
-            self.cur_task = 'identify user guess'
-            res = get_code_block(raw_res)
-            history += ['', f'the answer is {self.obj}', inputs, res]
-            yield from update_ui_lastest_msg(lastmsg=res, chatbot=chatbot, history=history, delay=0.)
-
-        elif self.cur_task == 'identify user guess':
-            if is_same_thing(self.obj, prompt, self.llm_kwargs):
-                self.delete_game = True
-                yield from update_ui_lastest_msg(lastmsg="你猜对了！", chatbot=chatbot, history=history, delay=0.)
-            else:
-                self.cur_task = 'identify user guess'
-                yield from update_ui_lastest_msg(lastmsg="猜错了，再试试，输入“exit”获取答案。", chatbot=chatbot, history=history, delay=0.)

crazy_functions/game_fns/game_interactive_story.py

@@ -1,212 +0,0 @@
-prompts_hs = """ 请以“{headstart}”为开头，编写一个小说的第一幕。
-
-- 尽量短，不要包含太多情节，因为你接下来将会与用户互动续写下面的情节，要留出足够的互动空间。
-- 出现人物时，给出人物的名字。
-- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
-- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
-- 字数要求：第一幕的字数少于300字，且少于2个段落。
-"""
-
-prompts_interact = """ 小说的前文回顾：
-「
-{previously_on_story}
-」
-
-你是一个作家，根据以上的情节，给出4种不同的后续剧情发展方向，每个发展方向都精明扼要地用一句话说明。稍后，我将在这4个选择中，挑选一种剧情发展。
-
-输出格式例如：
-1. 后续剧情发展1
-2. 后续剧情发展2
-3. 后续剧情发展3
-4. 后续剧情发展4
-"""
-
-
-prompts_resume = """小说的前文回顾：
-「
-{previously_on_story}
-」
-
-你是一个作家，我们正在互相讨论，确定后续剧情的发展。
-在以下的剧情发展中，
-「
-{choice}
-」
-我认为更合理的是：{user_choice}。
-请在前文的基础上（不要重复前文），围绕我选定的剧情情节，编写小说的下一幕。
-
-- 禁止杜撰不符合我选择的剧情。
-- 尽量短，不要包含太多情节，因为你接下来将会与用户互动续写下面的情节，要留出足够的互动空间。
-- 不要重复前文。
-- 出现人物时，给出人物的名字。
-- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
-- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
-- 小说的下一幕字数少于300字，且少于2个段落。
-"""
-
-
-prompts_terminate = """小说的前文回顾：
-「
-{previously_on_story}
-」
-
-你是一个作家，我们正在互相讨论，确定后续剧情的发展。
-现在，故事该结束了，我认为最合理的故事结局是：{user_choice}。
-
-请在前文的基础上（不要重复前文），编写小说的最后一幕。
-
-- 不要重复前文。
-- 出现人物时，给出人物的名字。
-- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
-- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
-- 字数要求：最后一幕的字数少于1000字。
-"""
-
-
-from toolbox import CatchException, update_ui, update_ui_lastest_msg
-from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from request_llms.bridge_all import predict_no_ui_long_connection
-from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
-import random
-
-
-class MiniGame_ResumeStory(GptAcademicGameBaseState):
-    story_headstart = [
-        '先行者知道，他现在是全宇宙中唯一的一个人了。',
-        '深夜，一个年轻人穿过天安门广场向纪念堂走去。在二十二世纪编年史中，计算机把他的代号定为M102。',
-        '他知道，这最后一课要提前讲了。又一阵剧痛从肝部袭来，几乎使他晕厥过去。',
-        '在距地球五万光年的远方，在银河系的中心，一场延续了两万年的星际战争已接近尾声。那里的太空中渐渐隐现出一个方形区域，仿佛灿烂的群星的背景被剪出一个方口。',
-        '伊依一行三人乘坐一艘游艇在南太平洋上做吟诗航行，他们的目的地是南极，如果几天后能顺利到达那里，他们将钻出地壳去看诗云。',
-        '很多人生来就会莫名其妙地迷上一样东西，仿佛他的出生就是要和这东西约会似的，正是这样，圆圆迷上了肥皂泡。'
-    ]
-
-
-    def begin_game_step_0(self, prompt, chatbot, history):
-        # init game at step 0
-        self.headstart = random.choice(self.story_headstart)
-        self.story = []
-        chatbot.append(["互动写故事", f"这次的故事开头是：{self.headstart}"])
-        self.sys_prompt_ = '你是一个想象力丰富的杰出作家。正在与你的朋友互动，一起写故事，因此你每次写的故事段落应少于300字（结局除外）。'
-        
-        
-    def generate_story_image(self, story_paragraph):
-        try:
-            from crazy_functions.图片生成 import gen_image
-            prompt_ = predict_no_ui_long_connection(inputs=story_paragraph, llm_kwargs=self.llm_kwargs, history=[], sys_prompt='你需要根据用户给出的小说段落，进行简短的环境描写。要求：80字以内。')
-            image_url, image_path = gen_image(self.llm_kwargs, prompt_, '512x512', model="dall-e-2", quality='standard', style='natural')
-            return f'<br/><div align="center"><img src="file={image_path}"></div>'
-        except:
-            return ''
-        
-    def step(self, prompt, chatbot, history):
-        
-        """
-        首先，处理游戏初始化等特殊情况
-        """
-        if self.step_cnt == 0:  
-            self.begin_game_step_0(prompt, chatbot, history)
-            self.lock_plugin(chatbot)
-            self.cur_task = 'head_start'
-        else:
-            if prompt.strip() == 'exit' or prompt.strip() == '结束剧情':
-                # should we terminate game here?
-                self.delete_game = True
-                yield from update_ui_lastest_msg(lastmsg=f"游戏结束。", chatbot=chatbot, history=history, delay=0.)
-                return
-            if '剧情收尾' in prompt:
-                self.cur_task = 'story_terminate'
-            # # well, game resumes
-            # chatbot.append([prompt, ""])
-        # update ui, don't keep the user waiting
-        yield from update_ui(chatbot=chatbot, history=history)
-
-
-        """
-        处理游戏的主体逻辑
-        """
-        if self.cur_task == 'head_start':
-            """
-            这是游戏的第一步
-            """
-            inputs_ = prompts_hs.format(headstart=self.headstart)
-            history_ = []
-            story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs_, '故事开头', self.llm_kwargs, 
-                chatbot, history_, self.sys_prompt_
-            )
-            self.story.append(story_paragraph)
-            # # 配图
-            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
-            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
-
-            # # 构建后续剧情引导
-            previously_on_story = ""
-            for s in self.story:
-                previously_on_story += s + '\n'
-            inputs_ = prompts_interact.format(previously_on_story=previously_on_story)
-            history_ = []
-            self.next_choices = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs_, '请在以下几种故事走向中，选择一种（当然，您也可以选择给出其他故事走向）：', self.llm_kwargs, 
-                chatbot,
-                history_,
-                self.sys_prompt_
-            )
-            self.cur_task = 'user_choice'
-
-
-        elif self.cur_task == 'user_choice':
-            """
-            根据用户的提示，确定故事的下一步
-            """
-            if '请在以下几种故事走向中，选择一种' in chatbot[-1][0]: chatbot.pop(-1)
-            previously_on_story = ""
-            for s in self.story:
-                previously_on_story += s + '\n'
-            inputs_ = prompts_resume.format(previously_on_story=previously_on_story, choice=self.next_choices, user_choice=prompt)
-            history_ = []
-            story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs_, f'下一段故事（您的选择是：{prompt}）。', self.llm_kwargs, 
-                chatbot, history_, self.sys_prompt_
-            )
-            self.story.append(story_paragraph)
-            # # 配图
-            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
-            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
-
-            # # 构建后续剧情引导
-            previously_on_story = ""
-            for s in self.story:
-                previously_on_story += s + '\n'
-            inputs_ = prompts_interact.format(previously_on_story=previously_on_story)
-            history_ = []
-            self.next_choices = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs_, 
-                '请在以下几种故事走向中，选择一种。当然，您也可以给出您心中的其他故事走向。另外，如果您希望剧情立即收尾，请输入剧情走向，并以“剧情收尾”四个字提示程序。', self.llm_kwargs, 
-                chatbot, 
-                history_, 
-                self.sys_prompt_
-            )
-            self.cur_task = 'user_choice'
-
-
-        elif self.cur_task == 'story_terminate':
-            """
-            根据用户的提示，确定故事的结局
-            """
-            previously_on_story = ""
-            for s in self.story:
-                previously_on_story += s + '\n'
-            inputs_ = prompts_terminate.format(previously_on_story=previously_on_story, user_choice=prompt)
-            history_ = []
-            story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs_, f'故事收尾（您的选择是：{prompt}）。', self.llm_kwargs, 
-                chatbot, history_, self.sys_prompt_
-            )
-            # # 配图
-            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
-            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
-
-            # terminate game
-            self.delete_game = True
-            return

crazy_functions/ipc_fns/mp.py

@@ -1,37 +0,0 @@
-import platform 
-import pickle
-import multiprocessing
-
-def run_in_subprocess_wrapper_func(v_args):
-    func, args, kwargs, return_dict, exception_dict = pickle.loads(v_args)
-    import sys
-    try:
-        result = func(*args, **kwargs)
-        return_dict['result'] = result
-    except Exception as e:
-        exc_info = sys.exc_info()
-        exception_dict['exception'] = exc_info
-
-def run_in_subprocess_with_timeout(func, timeout=60):
-    if platform.system() == 'Linux':
-        def wrapper(*args, **kwargs):
-            return_dict = multiprocessing.Manager().dict()
-            exception_dict = multiprocessing.Manager().dict()
-            v_args = pickle.dumps((func, args, kwargs, return_dict, exception_dict))
-            process = multiprocessing.Process(target=run_in_subprocess_wrapper_func, args=(v_args,))
-            process.start()
-            process.join(timeout)
-            if process.is_alive():
-                process.terminate()
-                raise TimeoutError(f'功能单元{str(func)}未能在规定时间内完成任务')
-            process.close()
-            if 'exception' in exception_dict:
-                # ooops, the subprocess ran into an exception
-                exc_info = exception_dict['exception']
-                raise exc_info[1].with_traceback(exc_info[2])
-            if 'result' in return_dict.keys():
-                # If the subprocess ran successfully, return the result
-                return return_dict['result']
-        return wrapper
-    else:
-        return func

crazy_functions/latex_fns/latex_actions.py

@@ -175,6 +175,7 @@ class LatexPaperFileGroup():
         self.sp_file_contents = []
         self.sp_file_index = []
         self.sp_file_tag = []
+
         # count_token
         from request_llms.bridge_all import model_info
         enc = model_info["gpt-3.5-turbo"]['tokenizer']
@@ -191,12 +192,13 @@ class LatexPaperFileGroup():
                 self.sp_file_index.append(index)
                 self.sp_file_tag.append(self.file_paths[index])
             else:
-                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+                from ..crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
+                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
                 for j, segment in enumerate(segments):
                     self.sp_file_contents.append(segment)
                     self.sp_file_index.append(index)
                     self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
+        print('Segmentation: done')
 
     def merge_result(self):
         self.file_result = ["" for _ in range(len(self.file_paths))]
@@ -402,7 +404,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
             result_pdf = pj(work_folder_modified, f'merge_diff.pdf')    # get pdf path
             promote_file_to_downloadzone(result_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI
         if modified_pdf_success:
-            yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 正在尝试生成对比PDF, 请稍候 ...', chatbot, history)    # 刷新Gradio前端界面
+            yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 即将退出 ...', chatbot, history)    # 刷新Gradio前端界面
             result_pdf = pj(work_folder_modified, f'{main_file_modified}.pdf') # get pdf path
             origin_pdf = pj(work_folder_original, f'{main_file_original}.pdf') # get pdf path
             if os.path.exists(pj(work_folder, '..', 'translation')):

crazy_functions/pdf_fns/breakdown_txt.py

@@ -1,125 +0,0 @@
-from crazy_functions.ipc_fns.mp import run_in_subprocess_with_timeout
-
-def force_breakdown(txt, limit, get_token_fn):
-    """ 当无法用标点、空行分割时，我们用最暴力的方法切割
-    """
-    for i in reversed(range(len(txt))):
-        if get_token_fn(txt[:i]) < limit:
-            return txt[:i], txt[i:]
-    return "Tiktoken未知错误", "Tiktoken未知错误"
-
-
-def maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage):
-    """ 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
-    当 remain_txt_to_cut < `_min` 时，我们再把 remain_txt_to_cut_storage 中的部分文字取出
-    """
-    _min = int(5e4)
-    _max = int(1e5)
-    # print(len(remain_txt_to_cut), len(remain_txt_to_cut_storage))
-    if len(remain_txt_to_cut) < _min and len(remain_txt_to_cut_storage) > 0:
-        remain_txt_to_cut = remain_txt_to_cut + remain_txt_to_cut_storage
-        remain_txt_to_cut_storage = ""
-    if len(remain_txt_to_cut) > _max:
-        remain_txt_to_cut_storage = remain_txt_to_cut[_max:] + remain_txt_to_cut_storage
-        remain_txt_to_cut = remain_txt_to_cut[:_max]
-    return remain_txt_to_cut, remain_txt_to_cut_storage
-
-
-def cut(limit, get_token_fn, txt_tocut, must_break_at_empty_line, break_anyway=False):
-    """ 文本切分
-    """
-    res = []
-    total_len = len(txt_tocut)
-    fin_len = 0
-    remain_txt_to_cut = txt_tocut
-    remain_txt_to_cut_storage = ""
-    # 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
-    remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
-    
-    while True:
-        if get_token_fn(remain_txt_to_cut) <= limit:
-            # 如果剩余文本的token数小于限制，那么就不用切了
-            res.append(remain_txt_to_cut); fin_len+=len(remain_txt_to_cut)
-            break
-        else:
-            # 如果剩余文本的token数大于限制，那么就切
-            lines = remain_txt_to_cut.split('\n')
-
-            # 估计一个切分点
-            estimated_line_cut = limit / get_token_fn(remain_txt_to_cut) * len(lines)
-            estimated_line_cut = int(estimated_line_cut)
-
-            # 开始查找合适切分点的偏移（cnt）
-            cnt = 0
-            for cnt in reversed(range(estimated_line_cut)):
-                if must_break_at_empty_line:
-                    # 首先尝试用双空行（\n\n）作为切分点
-                    if lines[cnt] != "":
-                        continue
-                prev = "\n".join(lines[:cnt])
-                post = "\n".join(lines[cnt:])
-                if get_token_fn(prev) < limit:
-                    break
-
-            if cnt == 0:
-                # 如果没有找到合适的切分点
-                if break_anyway:
-                    # 是否允许暴力切分
-                    prev, post = force_breakdown(txt_tocut, limit, get_token_fn)
-                else:
-                    # 不允许直接报错
-                    raise RuntimeError(f"存在一行极长的文本！{txt_tocut}")
-
-            # 追加列表
-            res.append(prev); fin_len+=len(prev)
-            # 准备下一次迭代
-            remain_txt_to_cut = post
-            remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
-            process = fin_len/total_len
-            print(f'正在文本切分 {int(process*100)}%')
-            if len(remain_txt_to_cut.strip()) == 0:
-                break
-    return res
-
-
-def breakdown_text_to_satisfy_token_limit_(txt, limit, llm_model="gpt-3.5-turbo"):
-    """ 使用多种方式尝试切分文本，以满足 token 限制
-    """
-    from request_llms.bridge_all import model_info
-    enc = model_info[llm_model]['tokenizer']
-    def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))
-    try:
-        # 第1次尝试，将双空行（\n\n）作为切分点
-        return cut(limit, get_token_fn, txt, must_break_at_empty_line=True)
-    except RuntimeError:
-        try:
-            # 第2次尝试，将单空行（\n）作为切分点
-            return cut(limit, get_token_fn, txt, must_break_at_empty_line=False)
-        except RuntimeError:
-            try:
-                # 第3次尝试，将英文句号（.）作为切分点
-                res = cut(limit, get_token_fn, txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
-                return [r.replace('。\n', '.') for r in res]
-            except RuntimeError as e:
-                try:
-                    # 第4次尝试，将中文句号（。）作为切分点
-                    res = cut(limit, get_token_fn, txt.replace('。', '。。\n'), must_break_at_empty_line=False)
-                    return [r.replace('。。\n', '。') for r in res]
-                except RuntimeError as e:
-                    # 第5次尝试，没办法了，随便切一下吧
-                    return cut(limit, get_token_fn, txt, must_break_at_empty_line=False, break_anyway=True)
-
-breakdown_text_to_satisfy_token_limit = run_in_subprocess_with_timeout(breakdown_text_to_satisfy_token_limit_, timeout=60)
-
-if __name__ == '__main__':
-    from crazy_functions.crazy_utils import read_and_clean_pdf_text
-    file_content, page_one = read_and_clean_pdf_text("build/assets/at.pdf")
-
-    from request_llms.bridge_all import model_info
-    for i in range(5):
-        file_content += file_content
-
-    print(len(file_content))
-    TOKEN_LIMIT_PER_FRAGMENT = 2500
-    res = breakdown_text_to_satisfy_token_limit(file_content, TOKEN_LIMIT_PER_FRAGMENT)
-

crazy_functions/pdf_fns/parse_pdf.py

@@ -74,7 +74,7 @@ def produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chat
 
 def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG):
     from crazy_functions.pdf_fns.report_gen_html import construct_html
-    from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+    from crazy_functions.crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
     from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
     from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
 
@@ -116,7 +116,7 @@ def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_fi
             # find a smooth token limit to achieve even seperation
             count = int(math.ceil(raw_token_num / TOKEN_LIMIT_PER_FRAGMENT))
             token_limit_smooth = raw_token_num // count + count
-            return breakdown_text_to_satisfy_token_limit(txt, limit=token_limit_smooth, llm_model=llm_kwargs['llm_model'])
+            return breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn=get_token_num, limit=token_limit_smooth)
 
     for section in article_dict.get('sections'):
         if len(section['text']) == 0: continue

crazy_functions/互动小游戏.py

@@ -3,28 +3,47 @@ from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseSta
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from request_llms.bridge_all import predict_no_ui_long_connection
 from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
+import random
+
+
+class MiniGame_ASCII_Art(GptAcademicGameBaseState):
+    
+    def step(self, prompt, chatbot, history):
+        if self.step_cnt == 0:  
+            chatbot.append(["我画你猜（动物）", "请稍等..."])
+        else:
+            if prompt.strip() == 'exit':
+                self.delete_game = True
+                yield from update_ui_lastest_msg(lastmsg=f"谜底是{self.obj}，游戏结束。", chatbot=chatbot, history=history, delay=0.)
+                return
+            chatbot.append([prompt, ""])
+        yield from update_ui(chatbot=chatbot, history=history)
+
+        if self.step_cnt == 0:
+            self.lock_plugin(chatbot)
+            self.cur_task = 'draw'
+
+        if self.cur_task == 'draw':
+            avail_obj = ["狗","猫","鸟","鱼","老鼠","蛇"]
+            self.obj = random.choice(avail_obj)
+            inputs = "I want to play a game called Guess the ASCII art. You can draw the ASCII art and I will try to guess it. " + f"This time you draw a {self.obj}. Note that you must not indicate what you have draw in the text, and you should only produce the ASCII art wrapped by ```. "
+            raw_res = predict_no_ui_long_connection(inputs=inputs, llm_kwargs=self.llm_kwargs, history=[], sys_prompt="")
+            self.cur_task = 'identify user guess'
+            res = get_code_block(raw_res)
+            history += ['', f'the answer is {self.obj}', inputs, res]
+            yield from update_ui_lastest_msg(lastmsg=res, chatbot=chatbot, history=history, delay=0.)
+
+        elif self.cur_task == 'identify user guess':
+            if is_same_thing(self.obj, prompt, self.llm_kwargs):
+                self.delete_game = True
+                yield from update_ui_lastest_msg(lastmsg="你猜对了！", chatbot=chatbot, history=history, delay=0.)
+            else:
+                self.cur_task = 'identify user guess'
+                yield from update_ui_lastest_msg(lastmsg="猜错了，再试试，输入“exit”获取答案。", chatbot=chatbot, history=history, delay=0.)
+            
 
 @CatchException
 def 随机小游戏(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    from crazy_functions.game_fns.game_interactive_story import MiniGame_ResumeStory
-    # 清空历史
-    history = []
-    # 选择游戏
-    cls = MiniGame_ResumeStory
-    # 如果之前已经初始化了游戏实例，则继续该实例；否则重新初始化
-    state = cls.sync_state(chatbot, 
-                           llm_kwargs, 
-                           cls, 
-                           plugin_name='MiniGame_ResumeStory',
-                           callback_fn='crazy_functions.互动小游戏->随机小游戏',
-                           lock_plugin=True
-                           )
-    yield from state.continue_game(prompt, chatbot, history)
-
-
-@CatchException
-def 随机小游戏1(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    from crazy_functions.game_fns.game_ascii_art import MiniGame_ASCII_Art
     # 清空历史
     history = []
     # 选择游戏
@@ -34,7 +53,7 @@ def 随机小游戏1(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system
                            llm_kwargs, 
                            cls, 
                            plugin_name='MiniGame_ASCII_Art',
-                           callback_fn='crazy_functions.互动小游戏->随机小游戏1',
+                           callback_fn='crazy_functions.互动小游戏->随机小游戏',
                            lock_plugin=True
                            )
     yield from state.continue_game(prompt, chatbot, history)

crazy_functions/总结word文档.py

@@ -29,12 +29,17 @@ def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot
             except:
                 raise RuntimeError('请先将.doc文档转换为.docx文档。')
 
+        print(file_content)
         # private_upload里面的文件名在解压zip后容易出现乱码（rar和7z格式正常），故可以只分析文章内容，不输入文件名
-        from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
         from request_llms.bridge_all import model_info
         max_token = model_info[llm_kwargs['llm_model']]['max_token']
         TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4
-        paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
+        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
+            txt=file_content,  
+            get_token_fn=model_info[llm_kwargs['llm_model']]['token_cnt'], 
+            limit=TOKEN_LIMIT_PER_FRAGMENT
+        )
         this_paper_history = []
         for i, paper_frag in enumerate(paper_fragments):
             i_say = f'请对下面的文章片段用中文做概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{paper_frag}```'

crazy_functions/批量Markdown翻译.py

@@ -28,8 +28,8 @@ class PaperFileGroup():
                 self.sp_file_index.append(index)
                 self.sp_file_tag.append(self.file_paths[index])
             else:
-                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
+                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
                 for j, segment in enumerate(segments):
                     self.sp_file_contents.append(segment)
                     self.sp_file_index.append(index)

crazy_functions/批量总结PDF文档.py

@@ -20,9 +20,14 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
         
         TOKEN_LIMIT_PER_FRAGMENT = 2500
 
-        from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content,  limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
+        from request_llms.bridge_all import model_info
-        page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=str(page_one), limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
+        enc = model_info["gpt-3.5-turbo"]['tokenizer']
+        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
+        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
+            txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
+        page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
+            txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
         # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
         paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
         

crazy_functions/批量翻译PDF文档_多线程.py

@@ -91,9 +91,14 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
         page_one = str(page_one).encode('utf-8', 'ignore').decode()      # avoid reading non-utf8 chars
 
         # 递归地切割PDF文件
-        from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
+        from request_llms.bridge_all import model_info
-        page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=page_one, limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
+        enc = model_info["gpt-3.5-turbo"]['tokenizer']
+        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
+        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
+            txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
+        page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
+            txt=page_one, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
 
         # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
         paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]

crazy_functions/理解PDF文档内容.py

@@ -18,9 +18,14 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
     
     TOKEN_LIMIT_PER_FRAGMENT = 2500
 
-    from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+    from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-    paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
+    from request_llms.bridge_all import model_info
-    page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=str(page_one), limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
+    enc = model_info["gpt-3.5-turbo"]['tokenizer']
+    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
+    paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
+        txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
+    page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
+        txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
     # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
     paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
     
@@ -40,7 +45,7 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
     for i in range(n_fragment):
         NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
         i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}"
-        i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]} ...."
+        i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]}"
         gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
                                                                            llm_kwargs, chatbot, 
                                                                            history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果

crazy_functions/解析JupyterNotebook.py

@@ -12,6 +12,13 @@ class PaperFileGroup():
         self.sp_file_index = []
         self.sp_file_tag = []
 
+        # count_token
+        from request_llms.bridge_all import model_info
+        enc = model_info["gpt-3.5-turbo"]['tokenizer']
+        def get_token_num(txt): return len(
+            enc.encode(txt, disallowed_special=()))
+        self.get_token_num = get_token_num
+
     def run_file_split(self, max_token_limit=1900):
         """
         将长文本分离开来
@@ -22,8 +29,9 @@ class PaperFileGroup():
                 self.sp_file_index.append(index)
                 self.sp_file_tag.append(self.file_paths[index])
             else:
-                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
+                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(
+                    file_content, self.get_token_num, max_token_limit)
                 for j, segment in enumerate(segments):
                     self.sp_file_contents.append(segment)
                     self.sp_file_index.append(index)

docs/GithubAction+AllCapacityBeta

@@ -1,53 +0,0 @@
-# docker build -t gpt-academic-all-capacity -f docs/GithubAction+AllCapacity  --network=host --build-arg http_proxy=http://localhost:10881 --build-arg https_proxy=http://localhost:10881 .
-# docker build -t gpt-academic-all-capacity -f docs/GithubAction+AllCapacityBeta  --network=host .
-# docker run -it --net=host gpt-academic-all-capacity  bash
-
-# 从NVIDIA源，从而支持显卡（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
-FROM fuqingxu/11.3.1-runtime-ubuntu20.04-with-texlive:latest
-
-# use python3 as the system default python
-WORKDIR /gpt
-RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
-
-# # 非必要步骤，更换pip源 （以下三行，可以删除）
-# RUN echo '[global]' > /etc/pip.conf && \
-#     echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \
-#     echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
-
-# 下载pytorch
-RUN python3 -m pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113
-# 准备pip依赖
-RUN python3 -m pip install openai numpy arxiv rich
-RUN python3 -m pip install colorama Markdown pygments pymupdf
-RUN python3 -m pip install python-docx moviepy pdfminer
-RUN python3 -m pip install zh_langchain==0.2.1 pypinyin
-RUN python3 -m pip install rarfile py7zr
-RUN python3 -m pip install aliyun-python-sdk-core==2.13.3 pyOpenSSL webrtcvad scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
-# 下载分支
-WORKDIR /gpt
-RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
-WORKDIR /gpt/gpt_academic
-RUN git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss
-
-RUN python3 -m pip install -r requirements.txt
-RUN python3 -m pip install -r request_llms/requirements_moss.txt
-RUN python3 -m pip install -r request_llms/requirements_qwen.txt
-RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
-RUN python3 -m pip install -r request_llms/requirements_newbing.txt
-RUN python3 -m pip install nougat-ocr
-
-# 预热Tiktoken模块
-RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
-
-# 安装知识库插件的额外依赖
-RUN apt-get update && apt-get install libgl1 -y
-RUN pip3 install transformers protobuf langchain sentence-transformers  faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk --upgrade
-RUN pip3 install unstructured[all-docs] --upgrade
-RUN python3  -c 'from check_proxy import warm_up_vectordb; warm_up_vectordb()'
-RUN rm -rf /usr/local/lib/python3.8/dist-packages/tests
-
-
-# COPY .cache /root/.cache
-# COPY config_private.py config_private.py
-# 启动
-CMD ["python3", "-u", "main.py"]

docs/GithubAction+NoLocal+Vectordb

@@ -17,10 +17,10 @@ RUN apt-get update && apt-get install libgl1 -y
 RUN pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu
 RUN pip3 install transformers protobuf langchain sentence-transformers  faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk --upgrade
 RUN pip3 install unstructured[all-docs] --upgrade
-RUN python3  -c 'from check_proxy import warm_up_vectordb; warm_up_vectordb()'
 
 # 可选步骤，用于预热模块
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
+RUN python3  -c 'from check_proxy import warm_up_vectordb; warm_up_vectordb()'
 
 # 启动
 CMD ["python3", "-u", "main.py"]

docs/translate_english.json

@@ -2863,7 +2863,7 @@
     "加载API_KEY": "Loading API_KEY",
     "协助您编写代码": "Assist you in writing code",
     "我可以为您提供以下服务": "I can provide you with the following services",
-    "排队中请稍候 ...": "Please wait in line ...",
+    "排队中请稍后 ...": "Please wait in line ...",
     "建议您使用英文提示词": "It is recommended to use English prompts",
     "不能支撑AutoGen运行": "Cannot support AutoGen operation",
     "帮助您解决编程问题": "Help you solve programming problems",

main.py

@@ -1,17 +1,6 @@
 import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
-
+import pickle
-help_menu_description = \
+import base64
-"""Github源代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic), 
-感谢热情的[开发者们❤️](https://github.com/binary-husky/gpt_academic/graphs/contributors).
-</br></br>常见问题请查阅[项目Wiki](https://github.com/binary-husky/gpt_academic/wiki), 
-如遇到Bug请前往[Bug反馈](https://github.com/binary-husky/gpt_academic/issues).
-</br></br>普通对话使用说明: 1. 输入问题; 2. 点击提交
-</br></br>基础功能区使用说明: 1. 输入文本; 2. 点击任意基础功能区按钮
-</br></br>函数插件区使用说明: 1. 输入路径/问题, 或者上传文件; 2. 点击任意函数插件区按钮
-</br></br>虚空终端使用说明: 点击虚空终端, 然后根据提示输入指令, 再次点击虚空终端
-</br></br>如何保存对话: 点击保存当前的对话按钮
-</br></br>如何语音对话: 请阅读Wiki
-</br></br>如何临时更换API_KEY: 在输入区输入临时API_KEY后提交（网页刷新后失效）"""
 
 def main():
     import gradio as gr
@@ -19,7 +8,7 @@ def main():
         raise ModuleNotFoundError("使用项目内置Gradio获取最优体验! 请运行 `pip install -r requirements.txt` 指令安装内置Gradio及其他依赖, 详情信息见requirements.txt.")
     from request_llms.bridge_all import predict
     from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, DummyWith
-    # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址
+    # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
     proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION = get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION')
     CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = get_conf('CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
     ENABLE_AUDIO, AUTO_CLEAR_TXT, PATH_LOGGING, AVAIL_THEMES, THEME = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING', 'AVAIL_THEMES', 'THEME')
@@ -29,10 +18,20 @@ def main():
     # 如果WEB_PORT是-1, 则随机选取WEB端口
     PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
     from check_proxy import get_current_version
-    from themes.theme import adjust_theme, advanced_css, theme_declaration
+    from themes.theme import adjust_theme, advanced_css, theme_declaration, load_dynamic_theme
-    from themes.theme import js_code_for_css_changing, js_code_for_darkmode_init, js_code_for_toggle_darkmode, js_code_for_persistent_cookie_init
+
-    from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, init_cookie
     title_html = f"<h1 align=\"center\">GPT 学术优化 {get_current_version()}</h1>{theme_declaration}"
+    description =  "Github源代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic), "
+    description += "感谢热情的[开发者们❤️](https://github.com/binary-husky/gpt_academic/graphs/contributors)."
+    description += "</br></br>常见问题请查阅[项目Wiki](https://github.com/binary-husky/gpt_academic/wiki), "
+    description += "如遇到Bug请前往[Bug反馈](https://github.com/binary-husky/gpt_academic/issues)."
+    description += "</br></br>普通对话使用说明: 1. 输入问题; 2. 点击提交"
+    description += "</br></br>基础功能区使用说明: 1. 输入文本; 2. 点击任意基础功能区按钮"
+    description += "</br></br>函数插件区使用说明: 1. 输入路径/问题, 或者上传文件; 2. 点击任意函数插件区按钮"
+    description += "</br></br>虚空终端使用说明: 点击虚空终端, 然后根据提示输入指令, 再次点击虚空终端"
+    description += "</br></br>如何保存对话: 点击保存当前的对话按钮"
+    description += "</br></br>如何语音对话: 请阅读Wiki"
+    description += "</br></br>如何临时更换API_KEY: 在输入区输入临时API_KEY后提交（网页刷新后失效）"
 
     # 问询记录, python 版本建议3.9+（越新越好）
     import logging, uuid
@@ -163,10 +162,16 @@ def main():
                     checkboxes_2 = gr.CheckboxGroup(["自定义菜单"], 
                                                   value=[], label="显示/隐藏自定义菜单", elem_id='cbs').style(container=False)
                     dark_mode_btn = gr.Button("切换界面明暗 ☀", variant="secondary").style(size="sm")
-                    dark_mode_btn.click(None, None, None, _js=js_code_for_toggle_darkmode,
+                    dark_mode_btn.click(None, None, None, _js="""() => {
+                            if (document.querySelectorAll('.dark').length) {
+                                document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
+                            } else {
+                                document.querySelector('body').classList.add('dark');
+                            }
+                        }""",
                     )
                 with gr.Tab("帮助", elem_id="interact-panel"):
-                    gr.Markdown(help_menu_description)
+                    gr.Markdown(description)
 
         with gr.Floating(init_x="20%", init_y="50%", visible=False, width="40%", drag="top") as area_input_secondary:
             with gr.Accordion("浮动输入区", open=True, elem_id="input-panel2"):
@@ -181,6 +186,16 @@ def main():
                         stopBtn2 = gr.Button("停止", variant="secondary"); stopBtn2.style(size="sm")
                         clearBtn2 = gr.Button("清除", variant="secondary", visible=False); clearBtn2.style(size="sm")
 
+        def to_cookie_str(d):
+            # Pickle the dictionary and encode it as a string
+            pickled_dict = pickle.dumps(d)
+            cookie_value = base64.b64encode(pickled_dict).decode('utf-8')
+            return cookie_value
+        
+        def from_cookie_str(c):
+            # Decode the base64-encoded string and unpickle it into a dictionary
+            pickled_dict = base64.b64decode(c.encode('utf-8'))
+            return pickle.loads(pickled_dict)
 
         with gr.Floating(init_x="20%", init_y="50%", visible=False, width="40%", drag="top") as area_customize:
             with gr.Accordion("自定义菜单", open=True, elem_id="edit-panel"):
@@ -212,11 +227,11 @@ def main():
                             else:
                                 ret.update({predefined_btns[basic_btn_dropdown_]: gr.update(visible=True, value=basic_fn_title)})
                             ret.update({cookies: cookies_})
-                            try: persistent_cookie_ = from_cookie_str(persistent_cookie_)   # persistent cookie to dict
+                            try: persistent_cookie_ = from_cookie_str(persistent_cookie_)    # persistent cookie to dict
                             except: persistent_cookie_ = {}
-                            persistent_cookie_["custom_bnt"] = customize_fn_overwrite_      # dict update new value
+                            persistent_cookie_["custom_bnt"] = customize_fn_overwrite_   # dict update new value
-                            persistent_cookie_ = to_cookie_str(persistent_cookie_)          # persistent cookie to dict
+                            persistent_cookie_ = to_cookie_str(persistent_cookie_)         # persistent cookie to dict
-                            ret.update({persistent_cookie: persistent_cookie_})             # write persistent cookie
+                            ret.update({persistent_cookie: persistent_cookie_})                             # write persistent cookie
                             return ret
                         
                         def reflesh_btn(persistent_cookie_, cookies_):
@@ -237,11 +252,10 @@ def main():
                                 else: ret.update({predefined_btns[k]: gr.update(visible=True, value=v['Title'])})
                             return ret
                         
-                        basic_fn_load.click(reflesh_btn, [persistent_cookie, cookies], [cookies, *customize_btns.values(), *predefined_btns.values()])
+                        basic_fn_load.click(reflesh_btn, [persistent_cookie, cookies],[cookies, *customize_btns.values(), *predefined_btns.values()])
                         h = basic_fn_confirm.click(assign_btn, [persistent_cookie, cookies, basic_btn_dropdown, basic_fn_title, basic_fn_prefix, basic_fn_suffix], 
                                                    [persistent_cookie, cookies, *customize_btns.values(), *predefined_btns.values()])
-                        # save persistent cookie
+                        h.then(None, [persistent_cookie], None, _js="""(persistent_cookie)=>{setCookie("persistent_cookie", persistent_cookie, 5);}""") # save persistent cookie
-                        h.then(None, [persistent_cookie], None, _js="""(persistent_cookie)=>{setCookie("persistent_cookie", persistent_cookie, 5);}""")
 
         # 功能区显示开关与功能区的互动
         def fn_area_visibility(a):
@@ -291,8 +305,8 @@ def main():
             click_handle = btn.click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(btn.value)], outputs=output_combo)
             cancel_handles.append(click_handle)
         # 文件上传区，接收文件后与chatbot的互动
-        file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies]).then(None, None, None,   _js=r"()=>{toast_push('上传完毕 ...'); cancel_loading_status();}")
+        file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies])
-        file_upload_2.upload(on_file_uploaded, [file_upload_2, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies]).then(None, None, None, _js=r"()=>{toast_push('上传完毕 ...'); cancel_loading_status();}")
+        file_upload_2.upload(on_file_uploaded, [file_upload_2, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies])
         # 函数插件-固定按钮区
         for k in plugins:
             if not plugins[k].get("AsButton", True): continue
@@ -328,7 +342,18 @@ def main():
             None,
             [secret_css],
             None,
-            _js=js_code_for_css_changing
+            _js="""(css) => {
+                var existingStyles = document.querySelectorAll("style[data-loaded-css]");
+                for (var i = 0; i < existingStyles.length; i++) {
+                    var style = existingStyles[i];
+                    style.parentNode.removeChild(style);
+                }
+                var styleElement = document.createElement('style');
+                styleElement.setAttribute('data-loaded-css', css);
+                styleElement.innerHTML = css;
+                document.head.appendChild(styleElement);
+            }
+            """
         )
         # 随变按钮的回调函数注册
         def route(request: gr.Request, k, *args, **kwargs):
@@ -360,10 +385,27 @@ def main():
                 rad.feed(cookies['uuid'].hex, audio)
             audio_mic.stream(deal_audio, inputs=[audio_mic, cookies])
 
-
+        def init_cookie(cookies, chatbot):
+            # 为每一位访问的用户赋予一个独一无二的uuid编码
+            cookies.update({'uuid': uuid.uuid4()})
+            return cookies
         demo.load(init_cookie, inputs=[cookies, chatbot], outputs=[cookies])
-        darkmode_js = js_code_for_darkmode_init
+        darkmode_js = """(dark) => {
-        demo.load(None, inputs=None, outputs=[persistent_cookie], _js=js_code_for_persistent_cookie_init)
+            dark = dark == "True";
+            if (document.querySelectorAll('.dark').length) {
+                if (!dark){
+                    document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
+                }
+            } else {
+                if (dark){
+                    document.querySelector('body').classList.add('dark');
+                }
+            }
+        }"""
+        load_cookie_js = """(persistent_cookie) => {
+            return getCookie("persistent_cookie");
+        }"""
+        demo.load(None, inputs=None, outputs=[persistent_cookie], _js=load_cookie_js)
         demo.load(None, inputs=[dark_mode], outputs=None, _js=darkmode_js)    # 配置暗色主题或亮色主题
         demo.load(None, inputs=[gr.Textbox(LAYOUT, visible=False)], outputs=None, _js='(LAYOUT)=>{GptAcademicJavaScriptInit(LAYOUT);}')
         
@@ -376,7 +418,7 @@ def main():
 
         def auto_updates(): time.sleep(0); auto_update()
         def open_browser(): time.sleep(2); webbrowser.open_new_tab(f"http://localhost:{PORT}")
-        def warm_up_mods(): time.sleep(6); warm_up_modules()
+        def warm_up_mods(): time.sleep(4); warm_up_modules()
         
         threading.Thread(target=auto_updates, name="self-upgrade", daemon=True).start() # 查看自动更新
         threading.Thread(target=open_browser, name="open-browser", daemon=True).start() # 打开浏览器页面

request_llms/bridge_chatgpt.py

@@ -28,6 +28,12 @@ proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
 timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
                   '网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
 
+def report_invalid_key(key):
+    if get_conf("BLOCK_INVALID_APIKEY"): 
+        # 实验性功能，自动检测并屏蔽失效的KEY，请勿使用
+        from request_llms.key_manager import ApiKeyManager
+        api_key = ApiKeyManager().add_key_to_blacklist(key)
+
 def get_full_error(chunk, stream_response):
     """
         获取完整的从Openai返回的报错
@@ -51,8 +57,7 @@ def decode_chunk(chunk):
         chunkjson = json.loads(chunk_decoded[6:])
         has_choices = 'choices' in chunkjson
         if has_choices: choice_valid = (len(chunkjson['choices']) > 0)
-        if has_choices and choice_valid: has_content = ("content" in chunkjson['choices'][0]["delta"])
+        if has_choices and choice_valid: has_content = "content" in chunkjson['choices'][0]["delta"]
-        if has_content: has_content = (chunkjson['choices'][0]["delta"]["content"] is not None)
         if has_choices and choice_valid: has_role = "role" in chunkjson['choices'][0]["delta"]
     except: 
         pass
@@ -83,7 +88,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
         用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
     """
     watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
-    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
+    headers, payload, api_key = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
     retry = 0
     while True:
         try:
@@ -113,6 +118,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
             if "reduce the length" in error_msg:
                 raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg)
             else:
+                if "API key has been deactivated" in error_msg: report_invalid_key(api_key)
+                elif "exceeded your current quota" in error_msg: report_invalid_key(api_key)
                 raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
         if ('data: [DONE]' in chunk): break # api2d 正常完成
         json_data = json.loads(chunk.lstrip('data:'))['choices'][0]
@@ -175,7 +182,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         time.sleep(2)
 
     try:
-        headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
+        headers, payload, api_key = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
     except RuntimeError as e:
         chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
         yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
@@ -223,7 +230,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     yield from update_ui(chatbot=chatbot, history=history, msg="检测到有缺陷的非OpenAI官方接口，建议选择更稳定的接口。")
                     break
                 # 其他情况，直接返回报错
-                chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
+                chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg, api_key)
                 yield from update_ui(chatbot=chatbot, history=history, msg="非OpenAI官方接口返回了错误:" + chunk.decode()) # 刷新界面
                 return
             
@@ -265,12 +272,12 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     chunk = get_full_error(chunk, stream_response)
                     chunk_decoded = chunk.decode()
                     error_msg = chunk_decoded
-                    chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
+                    chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg, api_key)
                     yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
                     print(error_msg)
                     return
 
-def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
+def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg, api_key=""):
     from .bridge_all import model_info
     openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
     if "reduce the length" in error_msg:
@@ -281,15 +288,15 @@ def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
     elif "does not exist" in error_msg:
         chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
     elif "Incorrect API key" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website)
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website); report_invalid_key(api_key)
     elif "exceeded your current quota" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务." + openai_website)
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务." + openai_website); report_invalid_key(api_key)
     elif "account is not active" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website)
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website); report_invalid_key(api_key)
     elif "associated with a deactivated account" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website)
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website); report_invalid_key(api_key)
     elif "API key has been deactivated" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] API key has been deactivated. OpenAI以账户失效为由, 拒绝服务." + openai_website)
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] API key has been deactivated. OpenAI以账户失效为由, 拒绝服务." + openai_website); report_invalid_key(api_key)
     elif "bad forward key" in error_msg:
         chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
     elif "Not enough point" in error_msg:
@@ -372,6 +379,6 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
         print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
     except:
         print('输入中可能存在乱码。')
-    return headers,payload
+    return headers, payload, api_key
 
 

request_llms/key_manager.py

@@ -1,4 +1,6 @@
 import random
+import os
+from toolbox import get_log_folder
 
 def Singleton(cls):
     _instance = {}
@@ -12,18 +14,41 @@ def Singleton(cls):
 
 
 @Singleton
-class OpenAI_ApiKeyManager():
+class ApiKeyManager():
+    """
+    只把失效的key保存在内存中
+    """
     def __init__(self, mode='blacklist') -> None:
         # self.key_avail_list = []
         self.key_black_list = []
+        self.debug = False
+        self.log = True
+        self.remain_keys = []
     
     def add_key_to_blacklist(self, key):
         self.key_black_list.append(key)
+        if self.debug: print('black list key added', key)
+        if self.log:
+            with open(
+                os.path.join(get_log_folder(user='admin', plugin_name='api_key_manager'), 'invalid_key.log'), 'a+', encoding='utf8') as f:
+                summary = 'num blacklist keys:' + str(len(self.key_black_list)) + '\tnum valid keys:' + str(len(self.remain_keys))
+                f.write('\n\n' + summary + '\n')
+                f.write('---- <add blacklist key> ----\n')
+                f.write(key)
+                f.write('\n')
+                f.write('---- <all blacklist keys> ----\n')
+                f.write(str(self.key_black_list))
+                f.write('\n')
+                f.write('---- <remain keys> ----\n')
+                f.write(str(self.remain_keys))
+                f.write('\n')
 
     def select_avail_key(self, key_list):
         # select key from key_list, but avoid keys also in self.key_black_list, raise error if no key can be found
         available_keys = [key for key in key_list if key not in self.key_black_list]
         if not available_keys:
-            raise KeyError("No available key found.")
+            raise KeyError("所有API KEY都被OPENAI拒绝了")
         selected_key = random.choice(available_keys)
+        if self.debug: print('total keys', len(key_list), 'valid keys', len(available_keys))
+        if self.log: self.remain_keys = available_keys
         return selected_key

request_llms/local_llm_class.py

@@ -183,11 +183,11 @@ class LocalLLMHandle(Process):
     def stream_chat(self, **kwargs):
         # ⭐run in main process
         if self.get_state() == "`准备就绪`":
-            yield "`正在等待线程锁，排队中请稍候 ...`"
+            yield "`正在等待线程锁，排队中请稍后 ...`"
 
         with self.threadLock:
             if self.parent.poll():
-                yield "`排队中请稍候 ...`"
+                yield "`排队中请稍后 ...`"
                 self.clear_pending_messages()
             self.parent.send(kwargs)
             std_out = ""

request_llms/requirements_chatglm_onnx.txt

@@ -6,3 +6,5 @@ sentencepiece
 numpy
 onnxruntime
 sentencepiece
+streamlit
+streamlit-chat

request_llms/requirements_moss.txt

@@ -5,4 +5,5 @@ accelerate
 matplotlib
 huggingface_hub
 triton
+streamlit
 

requirements.txt

@@ -3,7 +3,6 @@ pypdf2==2.12.1
 tiktoken>=0.3.3
 requests[socks]
 pydantic==1.10.11
-protobuf==3.18
 transformers>=4.27.1
 scipdf_parser>=0.52
 python-markdown-math

themes/common.js

@@ -1,13 +1,9 @@
-// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-//  第 1 部分: 工具函数
-// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-
 function gradioApp() {
     // https://github.com/GaiZhenbiao/ChuanhuChatGPT/tree/main/web_assets/javascript
     const elems = document.getElementsByTagName('gradio-app');
     const elem = elems.length == 0 ? document : elems[0];
     if (elem !== document) {
-        elem.getElementById = function (id) {
+        elem.getElementById = function(id) {
             return document.getElementById(id);
         };
     }
@@ -18,9 +14,9 @@ function setCookie(name, value, days) {
     var expires = "";
   
     if (days) {
-        var date = new Date();
+      var date = new Date();
-        date.setTime(date.getTime() + (days * 24 * 60 * 60 * 1000));
+      date.setTime(date.getTime() + (days * 24 * 60 * 60 * 1000));
-        expires = "; expires=" + date.toUTCString();
+      expires = "; expires=" + date.toUTCString();
     }
   
     document.cookie = name + "=" + value + expires + "; path=/";
@@ -31,60 +27,15 @@ function getCookie(name) {
     var cookies = decodedCookie.split(';');
   
     for (var i = 0; i < cookies.length; i++) {
-        var cookie = cookies[i].trim();
+      var cookie = cookies[i].trim();
   
-        if (cookie.indexOf(name + "=") === 0) {
+      if (cookie.indexOf(name + "=") === 0) {
-            return cookie.substring(name.length + 1, cookie.length);
+        return cookie.substring(name.length + 1, cookie.length);
-        }
+      }
     }
   
     return null;
-}
+  }
-
-let toastCount = 0;
-function toast_push(msg, duration) {
-    duration = isNaN(duration) ? 3000 : duration;
-    const existingToasts = document.querySelectorAll('.toast');
-    existingToasts.forEach(toast => {
-        toast.style.top = `${parseInt(toast.style.top, 10) - 70}px`;
-    });
-    const m = document.createElement('div');
-    m.innerHTML = msg;
-    m.classList.add('toast');
-    m.style.cssText = `font-size: var(--text-md) !important; color: rgb(255, 255, 255); background-color: rgba(0, 0, 0, 0.6); padding: 10px 15px; border-radius: 4px; position: fixed; top: ${50 + toastCount * 70}%; left: 50%; transform: translateX(-50%); width: auto; text-align: center; transition: top 0.3s;`;
-    document.body.appendChild(m);
-    setTimeout(function () {
-        m.style.opacity = '0';
-        setTimeout(function () {
-            document.body.removeChild(m);
-            toastCount--;
-        }, 500);
-    }, duration);
-    toastCount++;
-}
-
-function toast_up(msg) {
-    var m = document.getElementById('toast_up');
-    if (m) {
-        document.body.removeChild(m); // remove the loader from the body
-    }
-    m = document.createElement('div');
-    m.id = 'toast_up';
-    m.innerHTML = msg;
-    m.style.cssText = "font-size: var(--text-md) !important; color: rgb(255, 255, 255); background-color: rgba(0, 0, 100, 0.6); padding: 10px 15px; margin: 0 0 0 -60px; border-radius: 4px; position: fixed; top: 50%; left: 50%; width: auto; text-align: center;";
-    document.body.appendChild(m);
-}
-function toast_down() {
-    var m = document.getElementById('toast_up');
-    if (m) {
-        document.body.removeChild(m); // remove the loader from the body
-    }
-}
-
-
-// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-//  第 2 部分: 复制按钮
-// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
   
 function addCopyButton(botElement) {
     // https://github.com/GaiZhenbiao/ChuanhuChatGPT/tree/main/web_assets/javascript
@@ -147,27 +98,23 @@ function chatbotContentChanged(attempt = 1, force = false) {
     }
 }
 
-
+function chatbotAutoHeight(){
-
-// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-//  第 3 部分: chatbot动态高度调整
-// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-
-function chatbotAutoHeight() {
     // 自动调整高度
-    function update_height() {
+    function update_height(){
         var { panel_height_target, chatbot_height, chatbot } = get_elements(true);
-        if (panel_height_target != chatbot_height) {
+        if (panel_height_target!=chatbot_height)
+        {
             var pixelString = panel_height_target.toString() + 'px';
             chatbot.style.maxHeight = pixelString; chatbot.style.height = pixelString; 
         }
     }
 
-    function update_height_slow() {
+    function update_height_slow(){
         var { panel_height_target, chatbot_height, chatbot } = get_elements();
-        if (panel_height_target != chatbot_height) {
+        if (panel_height_target!=chatbot_height)
-            new_panel_height = (panel_height_target - chatbot_height) * 0.5 + chatbot_height;
+        {
-            if (Math.abs(new_panel_height - panel_height_target) < 10) {
+            new_panel_height = (panel_height_target - chatbot_height)*0.5 + chatbot_height;
+            if (Math.abs(new_panel_height - panel_height_target) < 10){
                 new_panel_height = panel_height_target;
             }
             // console.log(chatbot_height, panel_height_target, new_panel_height);
@@ -177,12 +124,21 @@ function chatbotAutoHeight() {
     }
     monitoring_input_box()
     update_height();
-    setInterval(function () {
+    setInterval(function() {
         update_height_slow()
     }, 50); // 每100毫秒执行一次
 }
 
-function get_elements(consider_state_panel = false) {
+function GptAcademicJavaScriptInit(LAYOUT = "LEFT-RIGHT") {
+    chatbotIndicator = gradioApp().querySelector('#gpt-chatbot > div.wrap');
+    var chatbotObserver = new MutationObserver(() => {
+        chatbotContentChanged(1);
+    });
+    chatbotObserver.observe(chatbotIndicator, { attributes: true, childList: true, subtree: true });
+    if (LAYOUT === "LEFT-RIGHT") {chatbotAutoHeight();}
+}
+
+function get_elements(consider_state_panel=false) {
     var chatbot = document.querySelector('#gpt-chatbot > div.wrap.svelte-18telvq');
     if (!chatbot) {
         chatbot = document.querySelector('#gpt-chatbot');
@@ -193,13 +149,13 @@ function get_elements(consider_state_panel = false) {
     // const panel4 = document.querySelector('#interact-panel').getBoundingClientRect();
     const panel5 = document.querySelector('#input-panel2').getBoundingClientRect();
     const panel_active = document.querySelector('#state-panel').getBoundingClientRect();
-    if (consider_state_panel || panel_active.height < 25) {
+    if (consider_state_panel || panel_active.height < 25){
         document.state_panel_height = panel_active.height;
     }
     // 25 是chatbot的label高度, 16 是右侧的gap
-    var panel_height_target = panel1.height + panel2.height + panel3.height + 0 + 0 - 25 + 16 * 2;
+    var panel_height_target = panel1.height + panel2.height + panel3.height + 0 + 0 - 25 + 16*2;
     // 禁止动态的state-panel高度影响
-    panel_height_target = panel_height_target + (document.state_panel_height - panel_active.height)
+    panel_height_target = panel_height_target + (document.state_panel_height-panel_active.height)
     var panel_height_target = parseInt(panel_height_target);
     var chatbot_height = chatbot.style.height;
     var chatbot_height = parseInt(chatbot_height);
@@ -207,18 +163,6 @@ function get_elements(consider_state_panel = false) {
 }
 
 
-
-// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-//  第 4 部分: 粘贴、拖拽文件上传
-// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-
-var elem_upload = null;
-var elem_upload_float = null;
-var elem_input_main = null;
-var elem_input_float = null;
-var elem_chatbot = null;
-var exist_file_msg = '⚠️请先删除上传区（左上方）中的历史文件，再尝试上传。'
-
 function add_func_paste(input) {
     let paste_files = [];
     if (input) {
@@ -236,7 +180,7 @@ function add_func_paste(input) {
                 }
                 if (paste_files.length > 0) {
                     // 按照文件列表执行批量上传逻辑
-                    await upload_files(paste_files);
+                    await paste_upload_files(paste_files);
                     paste_files = []
 
                 }
@@ -245,43 +189,8 @@ function add_func_paste(input) {
     }
 }
 
-function add_func_drag(elem) {
-    if (elem) {
-        const dragEvents = ["dragover"];
-        const leaveEvents = ["dragleave", "dragend", "drop"];
 
-        const onDrag = function (e) {
+async function paste_upload_files(files) {
-            e.preventDefault();
-            e.stopPropagation();
-            if (elem_upload_float.querySelector("input[type=file]")) {
-                toast_up('⚠️释放以上传文件')
-            } else {
-                toast_up(exist_file_msg)
-            }
-        };
-
-        const onLeave = function (e) {
-            toast_down();
-            e.preventDefault();
-            e.stopPropagation();
-        };
-
-        dragEvents.forEach(event => {
-            elem.addEventListener(event, onDrag);
-        });
-
-        leaveEvents.forEach(event => {
-            elem.addEventListener(event, onLeave);
-        });
-
-        elem.addEventListener("drop", async function (e) {
-            const files = e.dataTransfer.files;
-            await upload_files(files);
-        });
-    }
-}
-
-async function upload_files(files) {
     const uploadInputElement = elem_upload_float.querySelector("input[type=file]");
     let totalSizeMb = 0
     if (files && files.length > 0) {
@@ -293,101 +202,59 @@ async function upload_files(files) {
             }
             // 检查文件总大小是否超过20MB
             if (totalSizeMb > 20) {
-                toast_push('⚠️文件夹大于 20MB 🚀上传文件中', 3000)
+                toast_push('⚠️文件夹大于20MB 🚀上传文件中', 2000)
                 // return;  // 如果超过了指定大小, 可以不进行后续上传操作
             }
-            // 监听change事件， 原生Gradio可以实现
+             // 监听change事件， 原生Gradio可以实现
             // uploadInputElement.addEventListener('change', function(){replace_input_string()});
             let event = new Event("change");
-            Object.defineProperty(event, "target", { value: uploadInputElement, enumerable: true });
+            Object.defineProperty(event, "target", {value: uploadInputElement, enumerable: true});
-            Object.defineProperty(event, "currentTarget", { value: uploadInputElement, enumerable: true });
+            Object.defineProperty(event, "currentTarget", {value: uploadInputElement, enumerable: true});
-            Object.defineProperty(uploadInputElement, "files", { value: files, enumerable: true });
+            Object.defineProperty(uploadInputElement, "files", {value: files, enumerable: true});
             uploadInputElement.dispatchEvent(event);
+            // toast_push('🎉上传文件成功', 2000)
         } else {
-            toast_push(exist_file_msg, 3000)
+            toast_push('⚠️请先删除上传区中的历史文件，再尝试粘贴。', 2000)
         }
     }
 }
-
+//提示信息 封装
-function begin_loading_status() {
+function toast_push(msg, duration) {
-    // Create the loader div and add styling
+    duration = isNaN(duration) ? 3000 : duration;
-    var loader = document.createElement('div');
+    const m = document.createElement('div');
-    loader.id = 'Js_File_Loading';
+    m.innerHTML = msg;
-    loader.style.position = "absolute";
+    m.style.cssText = "font-size:  var(--text-md) !important; color: rgb(255, 255, 255);background-color: rgba(0, 0, 0, 0.6);padding: 10px 15px;margin: 0 0 0 -60px;border-radius: 4px;position: fixed;    top: 50%;left: 50%;width: auto; text-align: center;";
-    loader.style.top = "50%";
+    document.body.appendChild(m);
-    loader.style.left = "50%";
+    setTimeout(function () {
-    loader.style.width = "60px";
+        var d = 0.5;
-    loader.style.height = "60px";
+        m.style.opacity = '0';
-    loader.style.border = "16px solid #f3f3f3";
+        setTimeout(function () {
-    loader.style.borderTop = "16px solid #3498db";
+            document.body.removeChild(m)
-    loader.style.borderRadius = "50%";
+        }, d * 1000);
-    loader.style.animation = "spin 2s linear infinite";
+    }, duration);
-    loader.style.transform = "translate(-50%, -50%)";
-    document.body.appendChild(loader); // Add the loader to the body
-    // Set the CSS animation keyframes
-    var styleSheet = document.createElement('style');
-    // styleSheet.type = 'text/css';
-    styleSheet.id = 'Js_File_Loading_Style'
-    styleSheet.innerText = `
-    @keyframes spin {
-        0% { transform: rotate(0deg); }
-        100% { transform: rotate(360deg); }
-    }`;
-    document.head.appendChild(styleSheet);
 }
 
-function cancel_loading_status() {
+var elem_upload = null;
-    var loadingElement = document.getElementById('Js_File_Loading');
+var elem_upload_float = null;
-    if (loadingElement) {
+var elem_input_main = null;
-        document.body.removeChild(loadingElement); // remove the loader from the body
+var elem_input_float = null;
-    }
-    var loadingStyle = document.getElementById('Js_File_Loading_Style');
-    if (loadingStyle) {
-        document.head.removeChild(loadingStyle);
-    }
-    let clearButton = document.querySelectorAll('div[id*="elem_upload"] button[aria-label="Clear"]');
-    for (let button of clearButton) {
-        button.addEventListener('click', function () {
-            setTimeout(function () {
-                register_upload_event();
-            }, 50);
-        });
-    }
-}
 
-function register_upload_event() {
-    elem_upload_float = document.getElementById('elem_upload_float')
-    const upload_component = elem_upload_float.querySelector("input[type=file]");
-    if (upload_component) {
-        upload_component.addEventListener('change', function (event) {
-            toast_push('正在上传中，请稍等。', 2000);
-            begin_loading_status();
-        });
-    }
-}
 
 function monitoring_input_box() {
-    register_upload_event();
-
     elem_upload = document.getElementById('elem_upload')
     elem_upload_float = document.getElementById('elem_upload_float')
     elem_input_main = document.getElementById('user_input_main')
     elem_input_float = document.getElementById('user_input_float')
-    elem_chatbot = document.getElementById('gpt-chatbot')
-
     if (elem_input_main) {
         if (elem_input_main.querySelector("textarea")) {
             add_func_paste(elem_input_main.querySelector("textarea"))
         }
     }
     if (elem_input_float) {
-        if (elem_input_float.querySelector("textarea")) {
+        if (elem_input_float.querySelector("textarea")){
             add_func_paste(elem_input_float.querySelector("textarea"))
         }
     }
-    if (elem_chatbot) {
-        add_func_drag(elem_chatbot)
-    }
 }
 
 
@@ -396,64 +263,3 @@ window.addEventListener("DOMContentLoaded", function () {
     // const ga = document.getElementsByTagName("gradio-app");
     gradioApp().addEventListener("render", monitoring_input_box);
 });
-
-
-
-
-
-// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-//  第 5 部分: 音频按钮样式变化
-// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-
-function audio_fn_init() {
-    let audio_component = document.getElementById('elem_audio');
-    if (audio_component) {
-        let buttonElement = audio_component.querySelector('button');
-        let specificElement = audio_component.querySelector('.hide.sr-only');
-        specificElement.remove();
-
-        buttonElement.childNodes[1].nodeValue = '启动麦克风';
-        buttonElement.addEventListener('click', function (event) {
-            event.stopPropagation();
-            toast_push('您启动了麦克风!下一步请点击“实时语音对话”启动语音对话。');
-        });
-
-        // 查找语音插件按钮
-        let buttons = document.querySelectorAll('button');
-        let audio_button = null;
-        for (let button of buttons) {
-            if (button.textContent.includes('语音')) {
-                audio_button = button;
-                break;
-            }
-        }
-        if (audio_button) {
-            audio_button.addEventListener('click', function () {
-                toast_push('您点击了“实时语音对话”启动语音对话。');
-            });
-            let parent_element = audio_component.parentElement; // 将buttonElement移动到audio_button的内部
-            audio_button.appendChild(audio_component);
-            buttonElement.style.cssText = 'border-color: #00ffe0;border-width: 2px; height: 25px;'
-            parent_element.remove();
-            audio_component.style.cssText = 'width: 250px;right: 0px;display: inline-flex;flex-flow: row-reverse wrap;place-content: stretch space-between;align-items: center;background-color: #ffffff00;';
-        }
-
-    }
-}
-
-
-
-
-// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-//  第 6 部分: JS初始化函数
-// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-
-function GptAcademicJavaScriptInit(LAYOUT = "LEFT-RIGHT") {
-    audio_fn_init();
-    chatbotIndicator = gradioApp().querySelector('#gpt-chatbot > div.wrap');
-    var chatbotObserver = new MutationObserver(() => {
-        chatbotContentChanged(1);
-    });
-    chatbotObserver.observe(chatbotIndicator, { attributes: true, childList: true, subtree: true });
-    if (LAYOUT === "LEFT-RIGHT") { chatbotAutoHeight(); }
-}

themes/theme.py

@@ -1,14 +1,6 @@
-import pickle
+import gradio as gr
-import base64
-import uuid
 from toolbox import get_conf
-
+THEME = get_conf('THEME')
-"""
--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
-第 1 部分
-加载主题相关的工具函数
--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
-"""
 
 def load_dynamic_theme(THEME):
     adjust_dynamic_theme = None
@@ -28,91 +20,4 @@ def load_dynamic_theme(THEME):
         theme_declaration = ""
     return adjust_theme, advanced_css, theme_declaration, adjust_dynamic_theme
 
-adjust_theme, advanced_css, theme_declaration, _ = load_dynamic_theme(get_conf('THEME'))
+adjust_theme, advanced_css, theme_declaration, _ = load_dynamic_theme(THEME)
-
-
-
-
-
-
-"""
--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
-第 2 部分
-cookie相关工具函数
--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
-"""
-
-def init_cookie(cookies, chatbot):
-    # 为每一位访问的用户赋予一个独一无二的uuid编码
-    cookies.update({'uuid': uuid.uuid4()})
-    return cookies
-
-def to_cookie_str(d):
-    # Pickle the dictionary and encode it as a string
-    pickled_dict = pickle.dumps(d)
-    cookie_value = base64.b64encode(pickled_dict).decode('utf-8')
-    return cookie_value
-
-def from_cookie_str(c):
-    # Decode the base64-encoded string and unpickle it into a dictionary
-    pickled_dict = base64.b64decode(c.encode('utf-8'))
-    return pickle.loads(pickled_dict)
-
-
-
-
-
-"""
--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
-第 3 部分
-内嵌的javascript代码
--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
-"""
-
-js_code_for_css_changing = """(css) => {
-    var existingStyles = document.querySelectorAll("body > gradio-app > div > style")
-    for (var i = 0; i < existingStyles.length; i++) {
-        var style = existingStyles[i];
-        style.parentNode.removeChild(style);
-    }
-    var existingStyles = document.querySelectorAll("style[data-loaded-css]");
-    for (var i = 0; i < existingStyles.length; i++) {
-        var style = existingStyles[i];
-        style.parentNode.removeChild(style);
-    }
-    var styleElement = document.createElement('style');
-    styleElement.setAttribute('data-loaded-css', 'placeholder');
-    styleElement.innerHTML = css;
-    document.body.appendChild(styleElement);
-}
-"""
-
-js_code_for_darkmode_init = """(dark) => {
-    dark = dark == "True";
-    if (document.querySelectorAll('.dark').length) {
-        if (!dark){
-            document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
-        }
-    } else {
-        if (dark){
-            document.querySelector('body').classList.add('dark');
-        }
-    }
-}
-"""
-
-js_code_for_toggle_darkmode = """() => {
-    if (document.querySelectorAll('.dark').length) {
-        document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
-    } else {
-        document.querySelector('body').classList.add('dark');
-    }
-}"""
-
-
-js_code_for_persistent_cookie_init = """(persistent_cookie) => {
-    return getCookie("persistent_cookie");
-}
-"""
-
-

toolbox.py

@@ -87,7 +87,7 @@ def ArgsGeneralWrapper(f):
         }
         chatbot_with_cookie = ChatBotWithCookies(cookies)
         chatbot_with_cookie.write_list(chatbot)
-        
+        print('[plugin is called]:', f)
         if cookies.get('lock_plugin', None) is None:
             # 正常状态
             if len(args) == 0:  # 插件通道
@@ -583,8 +583,7 @@ def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
     if chatbot is not None:
         if 'files_to_promote' in chatbot._cookies: current = chatbot._cookies['files_to_promote']
         else: current = []
-        if new_path not in current: # 避免把同一个文件添加多次
+        chatbot._cookies.update({'files_to_promote': [new_path] + current})
-            chatbot._cookies.update({'files_to_promote': [new_path] + current})
     return new_path
 
 
@@ -824,6 +823,11 @@ def select_api_key(keys, llm_model):
         raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源（右下角更换模型菜单中可切换openai,azure,claude,api2d等请求源）。")
 
     api_key = random.choice(avail_key_list) # 随机负载均衡
+
+    if get_conf("BLOCK_INVALID_APIKEY"): 
+        # 实验性功能，自动检测并屏蔽失效的KEY，请勿使用
+        from request_llms.key_manager import ApiKeyManager
+        api_key = ApiKeyManager().select_avail_key(avail_key_list)
     return api_key
 
 def read_env_variable(arg, default_value):
@@ -1008,19 +1012,14 @@ def clip_history(inputs, history, tokenizer, max_token_limit):
     def get_token_num(txt): 
         return len(tokenizer.encode(txt, disallowed_special=()))
     input_token_num = get_token_num(inputs)
-
-    if max_token_limit < 5000:   output_token_expect = 256  # 4k & 2k models
-    elif max_token_limit < 9000: output_token_expect = 512  # 8k models
-    else: output_token_expect = 1024                        # 16k & 32k models
-
     if input_token_num < max_token_limit * 3 / 4:
         # 当输入部分的token占比小于限制的3/4时，裁剪时
         # 1. 把input的余量留出来
         max_token_limit = max_token_limit - input_token_num
         # 2. 把输出用的余量留出来
-        max_token_limit = max_token_limit - output_token_expect
+        max_token_limit = max_token_limit - 128
         # 3. 如果余量太小了，直接清除历史
-        if max_token_limit < output_token_expect:
+        if max_token_limit < 128:
             history = []
             return history
     else:

version

@@ -1,5 +1,5 @@
 {
-  "version": 3.64,
+  "version": 3.62,
   "show_feature": true,
-  "new_feature": "支持直接拖拽文件到上传区 <-> 支持将图片粘贴到输入区 <-> 修复若干隐蔽的内存BUG <-> 修复多用户冲突问题 <-> 接入Deepseek Coder <-> AutoGen多智能体插件测试版"
+  "new_feature": "修复若干隐蔽的内存BUG <-> 修复多用户冲突问题 <-> 接入Deepseek Coder <-> AutoGen多智能体插件测试版 <-> 修复本地模型在Windows下的加载BUG <-> 支持文心一言v4和星火v3 <-> 支持GLM3和智谱的API <-> 解决本地模型并发BUG <-> 支持动态追加基础功能按钮"
 }