Merge branch 'master' into frontier

提升PDF翻译插件的效果

README.md

@@ -101,9 +101,11 @@ cd gpt_academic
 
 2. 配置API_KEY
 
-在`config.py`中，配置API KEY等设置，[点击查看特殊网络环境设置方法](https://github.com/binary-husky/gpt_academic/issues/1) 。
+在`config.py`中，配置API KEY等设置，[点击查看特殊网络环境设置方法](https://github.com/binary-husky/gpt_academic/issues/1) 。[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/%E9%A1%B9%E7%9B%AE%E9%85%8D%E7%BD%AE%E8%AF%B4%E6%98%8E)。
 
-(P.S. 程序运行时会优先检查是否存在名为`config_private.py`的私密配置文件，并用其中的配置覆盖`config.py`的同名配置。因此，如果您能理解我们的配置读取逻辑，我们强烈建议您在`config.py`旁边创建一个名为`config_private.py`的新配置文件，并把`config.py`中的配置转移（复制）到`config_private.py`中（仅复制您修改过的配置条目即可）。`config_private.py`不受git管控，可以让您的隐私信息更加安全。P.S.项目同样支持通过`环境变量`配置大多数选项，环境变量的书写格式参考`docker-compose`文件。读取优先级: `环境变量` > `config_private.py` > `config.py`)
+「 程序会优先检查是否存在名为`config_private.py`的私密配置文件，并用其中的配置覆盖`config.py`的同名配置。如您能理解该读取逻辑，我们强烈建议您在`config.py`旁边创建一个名为`config_private.py`的新配置文件，并把`config.py`中的配置转移（复制）到`config_private.py`中（仅复制您修改过的配置条目即可）。 」
+
+「 支持通过`环境变量`配置项目，环境变量的书写格式参考`docker-compose.yml`文件或者我们的[Wiki页面](https://github.com/binary-husky/gpt_academic/wiki/%E9%A1%B9%E7%9B%AE%E9%85%8D%E7%BD%AE%E8%AF%B4%E6%98%8E)。配置读取优先级: `环境变量` > `config_private.py` > `config.py`。 」
 
 
 3. 安装依赖
@@ -111,7 +113,7 @@ cd gpt_academic
 # （选择I: 如熟悉python）（python版本3.9以上，越新越好），备注：使用官方pip源或者阿里pip源,临时换源方法：python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
 python -m pip install -r requirements.txt
 
-# （选择II: 如不熟悉python）使用anaconda，步骤也是类似的 (https://www.bilibili.com/video/BV1rc411W7Dr)：
+# （选择II: 使用Anaconda）步骤也是类似的 (https://www.bilibili.com/video/BV1rc411W7Dr)：
 conda create -n gptac_venv python=3.11    # 创建anaconda环境
 conda activate gptac_venv                 # 激活anaconda环境
 python -m pip install -r requirements.txt # 这个步骤和pip安装一样的步骤
@@ -149,26 +151,25 @@ python main.py
 
 ### 安装方法II：使用Docker
 
+0. 部署项目的全部能力（这个是包含cuda和latex的大型镜像。如果您网速慢、硬盘小或没有显卡，则不推荐使用这个，建议使用方案1）（需要熟悉[Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian)运行时）
 [![fullcapacity](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-all-capacity.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
 
-1. 仅ChatGPT（推荐大多数人选择，等价于docker-compose方案1）
+``` sh
+# 修改docker-compose.yml，保留方案0并删除其他方案。修改docker-compose.yml中方案0的配置，参考其中注释即可
+docker-compose up
+```
+
+1. 仅ChatGPT+文心一言+spark等在线模型（推荐大多数人选择）
 [![basic](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
 [![basiclatex](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
 [![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
 
-
 ``` sh
-git clone --depth=1 https://github.com/binary-husky/gpt_academic.git  # 下载项目
-cd gpt_academic                                 # 进入路径
-nano config.py                                      # 用任意文本编辑器编辑config.py, 配置 “Proxy”， “API_KEY” 以及 “WEB_PORT” (例如50923) 等
-docker build -t gpt-academic .                      # 安装
-
-#（最后一步-Linux操作系统）用`--net=host`更方便快捷
-docker run --rm -it --net=host gpt-academic
-#（最后一步-MacOS/Windows操作系统）只能用-p选项将容器上的端口(例如50923)暴露给主机上的端口
-docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
+# 修改docker-compose.yml，保留方案1并删除其他方案。修改docker-compose.yml中方案1的配置，参考其中注释即可
+docker-compose up
 ```
-P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以直接使用docker-compose获取Latex功能（修改docker-compose.yml，保留方案4并删除其他方案）。
+
+P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以直接使用方案4或者方案0获取Latex功能。
 
 2. ChatGPT + ChatGLM2 + MOSS + LLAMA2 + 通义千问（需要熟悉[Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian)运行时）
 [![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
@@ -309,6 +310,7 @@ Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史h
 
 ### II：版本:
 - version 3.60（todo）: 优化虚空终端，引入code interpreter和更多插件
+- version 3.53: 支持动态选择不同界面主题，提高稳定性&解决多用户冲突问题
 - version 3.50: 使用自然语言调用本项目的所有函数插件（虚空终端），支持插件分类，改进UI，设计新主题
 - version 3.49: 支持百度千帆平台和文心一言
 - version 3.48: 支持阿里达摩院通义千问，上海AI-Lab书生，讯飞星火

config.py

@@ -46,7 +46,7 @@ DEFAULT_WORKER_NUM = 3
 # 色彩主题, 可选 ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast"]
 # 更多主题, 请查阅Gradio主题商店: https://huggingface.co/spaces/gradio/theme-gallery 可选 ["Gstaff/Xkcd", "NoCrypt/Miku", ...]
 THEME = "Default"
-
+AVAIL_THEMES = ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast", "Gstaff/Xkcd", "NoCrypt/Miku"]
 
 # 对话窗的高度 （仅在LAYOUT="TOP-DOWN"时生效）
 CHATBOT_HEIGHT = 1115

crazy_functions/pdf_fns/parse_pdf.py

@@ -1,6 +1,14 @@
+from functools import lru_cache
+from toolbox import gen_time_str
+from toolbox import promote_file_to_downloadzone
+from toolbox import write_history_to_file, promote_file_to_downloadzone
+from colorful import *
 import requests
 import random
-from functools import lru_cache
+import copy
+import os
+import math
+
 class GROBID_OFFLINE_EXCEPTION(Exception): pass
 
 def get_avail_grobid_url():
@@ -28,3 +36,133 @@ def parse_pdf(pdf_path, grobid_url):
         raise RuntimeError("解析PDF失败，请检查PDF是否损坏。")
     return article_dict
 
+
+def produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chatbot, fp, generated_conclusion_files):
+    # -=-=-=-=-=-=-=-= 写出第1个文件：翻译前后混合 -=-=-=-=-=-=-=-=
+    res_path = write_history_to_file(meta +  ["# Meta Translation" , paper_meta_info] + gpt_response_collection, file_basename=f"{gen_time_str()}translated_and_original.md", file_fullname=None)
+    promote_file_to_downloadzone(res_path, rename_file=os.path.basename(res_path)+'.md', chatbot=chatbot)
+    generated_conclusion_files.append(res_path)
+
+    # -=-=-=-=-=-=-=-= 写出第2个文件：仅翻译后的文本 -=-=-=-=-=-=-=-=
+    translated_res_array = []
+    # 记录当前的大章节标题：
+    last_section_name = ""
+    for index, value in enumerate(gpt_response_collection):
+        # 先挑选偶数序列号：
+        if index % 2 != 0:
+            # 先提取当前英文标题：
+            cur_section_name = gpt_response_collection[index-1].split('\n')[0].split(" Part")[0]
+            # 如果index是1的话，则直接使用first section name：
+            if cur_section_name != last_section_name:
+                cur_value = cur_section_name + '\n'
+                last_section_name = copy.deepcopy(cur_section_name)
+            else:
+                cur_value = ""
+            # 再做一个小修改：重新修改当前part的标题，默认用英文的
+            cur_value += value
+            translated_res_array.append(cur_value)
+    res_path = write_history_to_file(meta +  ["# Meta Translation" , paper_meta_info] + translated_res_array, 
+                                     file_basename = f"{gen_time_str()}-translated_only.md", 
+                                     file_fullname = None,
+                                     auto_caption = False)
+    promote_file_to_downloadzone(res_path, rename_file=os.path.basename(res_path)+'.md', chatbot=chatbot)
+    generated_conclusion_files.append(res_path)
+    return res_path
+
+def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG):
+    from crazy_functions.crazy_utils import construct_html
+    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
+
+    prompt = "以下是一篇学术论文的基本信息:\n"
+    # title
+    title = article_dict.get('title', '无法获取 title'); prompt += f'title:{title}\n\n'
+    # authors
+    authors = article_dict.get('authors', '无法获取 authors'); prompt += f'authors:{authors}\n\n'
+    # abstract
+    abstract = article_dict.get('abstract', '无法获取 abstract'); prompt += f'abstract:{abstract}\n\n'
+    # command
+    prompt += f"请将题目和摘要翻译为{DST_LANG}。"
+    meta = [f'# Title:\n\n', title, f'# Abstract:\n\n', abstract ]
+
+    # 单线，获取文章meta信息
+    paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
+        inputs=prompt,
+        inputs_show_user=prompt,
+        llm_kwargs=llm_kwargs,
+        chatbot=chatbot, history=[],
+        sys_prompt="You are an academic paper reader。",
+    )
+
+    # 多线，翻译
+    inputs_array = []
+    inputs_show_user_array = []
+
+    # get_token_num
+    from request_llm.bridge_all import model_info
+    enc = model_info[llm_kwargs['llm_model']]['tokenizer']
+    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
+
+    def break_down(txt):
+        raw_token_num = get_token_num(txt)
+        if raw_token_num <= TOKEN_LIMIT_PER_FRAGMENT:
+            return [txt]
+        else:
+            # raw_token_num > TOKEN_LIMIT_PER_FRAGMENT
+            # 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_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
+        section_frags = break_down(section['text'])
+        for i, fragment in enumerate(section_frags):
+            heading = section['heading']
+            if len(section_frags) > 1: heading += f' Part-{i+1}'
+            inputs_array.append(
+                f"你需要翻译{heading}章节，内容如下: \n\n{fragment}"
+            )
+            inputs_show_user_array.append(
+                f"# {heading}\n\n{fragment}"
+            )
+
+    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+        inputs_array=inputs_array,
+        inputs_show_user_array=inputs_show_user_array,
+        llm_kwargs=llm_kwargs,
+        chatbot=chatbot,
+        history_array=[meta for _ in inputs_array],
+        sys_prompt_array=[
+            "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in inputs_array],
+    )
+    # -=-=-=-=-=-=-=-= 写出Markdown文件 -=-=-=-=-=-=-=-=
+    produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chatbot, fp, generated_conclusion_files)
+
+    # -=-=-=-=-=-=-=-= 写出HTML文件 -=-=-=-=-=-=-=-=
+    ch = construct_html() 
+    orig = ""
+    trans = ""
+    gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
+    for i,k in enumerate(gpt_response_collection_html): 
+        if i%2==0:
+            gpt_response_collection_html[i] = inputs_show_user_array[i//2]
+        else:
+            # 先提取当前英文标题：
+            cur_section_name = gpt_response_collection[i-1].split('\n')[0].split(" Part")[0]
+            cur_value = cur_section_name + "\n" + gpt_response_collection_html[i]
+            gpt_response_collection_html[i] = cur_value
+
+    final = ["", "", "一、论文概况",  "", "Abstract", paper_meta_info,  "二、论文翻译",  ""]
+    final.extend(gpt_response_collection_html)
+    for i, k in enumerate(final): 
+        if i%2==0:
+            orig = k
+        if i%2==1:
+            trans = k
+            ch.add_row(a=orig, b=trans)
+    create_report_file_name = f"{os.path.basename(fp)}.trans.html"
+    html_file = ch.save_file(create_report_file_name)
+    generated_conclusion_files.append(html_file)
+    promote_file_to_downloadzone(html_file, rename_file=os.path.basename(html_file), chatbot=chatbot)

crazy_functions/批量翻译PDF文档_NOUGAT.py

@@ -1,11 +1,12 @@
-from toolbox import CatchException, report_execption, gen_time_str
+from toolbox import CatchException, report_execption, get_log_folder, gen_time_str
 from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
-from toolbox import write_history_to_file, get_log_folder
+from toolbox import write_history_to_file, promote_file_to_downloadzone
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
 from .crazy_utils import read_and_clean_pdf_text
-from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url
+from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url, translate_pdf
 from colorful import *
+import copy
 import os
 import math
 import logging
@@ -92,7 +93,7 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
 def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
     import copy
     import tiktoken
-    TOKEN_LIMIT_PER_FRAGMENT = 1280
+    TOKEN_LIMIT_PER_FRAGMENT = 1024
     generated_conclusion_files = []
     generated_html_files = []
     DST_LANG = "中文"
@@ -101,101 +102,12 @@ def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwa
     for index, fp in enumerate(file_manifest):
         chatbot.append(["当前进度：", f"正在解析论文，请稍候。（第一次运行时，需要花费较长时间下载NOUGAT参数）"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         fpp = yield from nougat_handle.NOUGAT_parse_pdf(fp, chatbot, history)
-
+        promote_file_to_downloadzone(fpp, rename_file=os.path.basename(fpp)+'.nougat.mmd', chatbot=chatbot)
         with open(fpp, 'r', encoding='utf8') as f:
             article_content = f.readlines()
         article_dict = markdown_to_dict(article_content)
         logging.info(article_dict)
-
-        prompt = "以下是一篇学术论文的基本信息:\n"
-        # title
-        title = article_dict.get('title', '无法获取 title'); prompt += f'title:{title}\n\n'
-        # authors
-        authors = article_dict.get('authors', '无法获取 authors'); prompt += f'authors:{authors}\n\n'
-        # abstract
-        abstract = article_dict.get('abstract', '无法获取 abstract'); prompt += f'abstract:{abstract}\n\n'
-        # command
-        prompt += f"请将题目和摘要翻译为{DST_LANG}。"
-        meta = [f'# Title:\n\n', title, f'# Abstract:\n\n', abstract ]
-
-        # 单线，获取文章meta信息
-        paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=prompt,
-            inputs_show_user=prompt,
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot, history=[],
-            sys_prompt="You are an academic paper reader。",
-        )
-
-        # 多线，翻译
-        inputs_array = []
-        inputs_show_user_array = []
-
-        # get_token_num
-        from request_llm.bridge_all import model_info
-        enc = model_info[llm_kwargs['llm_model']]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-
-        def break_down(txt):
-            raw_token_num = get_token_num(txt)
-            if raw_token_num <= TOKEN_LIMIT_PER_FRAGMENT:
-                return [txt]
-            else:
-                # raw_token_num > TOKEN_LIMIT_PER_FRAGMENT
-                # 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_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
-            section_frags = break_down(section['text'])
-            for i, fragment in enumerate(section_frags):
-                heading = section['heading']
-                if len(section_frags) > 1: heading += f' Part-{i+1}'
-                inputs_array.append(
-                    f"你需要翻译{heading}章节，内容如下: \n\n{fragment}"
-                )
-                inputs_show_user_array.append(
-                    f"# {heading}\n\n{fragment}"
-                )
-
-        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-            inputs_array=inputs_array,
-            inputs_show_user_array=inputs_show_user_array,
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history_array=[meta for _ in inputs_array],
-            sys_prompt_array=[
-                "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in inputs_array],
-        )
-        res_path = write_history_to_file(meta +  ["# Meta Translation" , paper_meta_info] + gpt_response_collection, file_basename=None, file_fullname=None)
-        promote_file_to_downloadzone(res_path, rename_file=os.path.basename(fp)+'.md', chatbot=chatbot)
-        generated_conclusion_files.append(res_path)
-
-        ch = construct_html() 
-        orig = ""
-        trans = ""
-        gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
-        for i,k in enumerate(gpt_response_collection_html): 
-            if i%2==0:
-                gpt_response_collection_html[i] = inputs_show_user_array[i//2]
-            else:
-                gpt_response_collection_html[i] = gpt_response_collection_html[i]
-
-        final = ["", "", "一、论文概况",  "", "Abstract", paper_meta_info,  "二、论文翻译",  ""]
-        final.extend(gpt_response_collection_html)
-        for i, k in enumerate(final): 
-            if i%2==0:
-                orig = k
-            if i%2==1:
-                trans = k
-                ch.add_row(a=orig, b=trans)
-        create_report_file_name = f"{os.path.basename(fp)}.trans.html"
-        html_file = ch.save_file(create_report_file_name)
-        generated_html_files.append(html_file)
-        promote_file_to_downloadzone(html_file, rename_file=os.path.basename(html_file), chatbot=chatbot)
+        yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG)
 
     chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

crazy_functions/批量翻译PDF文档_多线程.py

@@ -1,12 +1,12 @@
-from toolbox import CatchException, report_execption, get_log_folder
+from toolbox import CatchException, report_execption, get_log_folder, gen_time_str
 from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
 from toolbox import write_history_to_file, promote_file_to_downloadzone
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
 from .crazy_utils import read_and_clean_pdf_text
-from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url
+from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url, translate_pdf
 from colorful import *
-import glob
+import copy
 import os
 import math
 
@@ -58,8 +58,8 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
 
 
 def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url):
-    import copy
-    TOKEN_LIMIT_PER_FRAGMENT = 1280
+    import copy, json
+    TOKEN_LIMIT_PER_FRAGMENT = 1024
     generated_conclusion_files = []
     generated_html_files = []
     DST_LANG = "中文"
@@ -67,104 +67,23 @@ def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwa
     for index, fp in enumerate(file_manifest):
         chatbot.append(["当前进度：", f"正在连接GROBID服务，请稍候: {grobid_url}\n如果等待时间过长，请修改config中的GROBID_URL，可修改成本地GROBID服务。"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
         article_dict = parse_pdf(fp, grobid_url)
+        grobid_json_res = os.path.join(get_log_folder(), gen_time_str() + "grobid.json")
+        with open(grobid_json_res, 'w+', encoding='utf8') as f:
+            f.write(json.dumps(article_dict, indent=4, ensure_ascii=False))
+        promote_file_to_downloadzone(grobid_json_res, chatbot=chatbot)
+        
         if article_dict is None: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。")
-        prompt = "以下是一篇学术论文的基本信息:\n"
-        # title
-        title = article_dict.get('title', '无法获取 title'); prompt += f'title:{title}\n\n'
-        # authors
-        authors = article_dict.get('authors', '无法获取 authors'); prompt += f'authors:{authors}\n\n'
-        # abstract
-        abstract = article_dict.get('abstract', '无法获取 abstract'); prompt += f'abstract:{abstract}\n\n'
-        # command
-        prompt += f"请将题目和摘要翻译为{DST_LANG}。"
-        meta = [f'# Title:\n\n', title, f'# Abstract:\n\n', abstract ]
-
-        # 单线，获取文章meta信息
-        paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=prompt,
-            inputs_show_user=prompt,
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot, history=[],
-            sys_prompt="You are an academic paper reader。",
-        )
-
-        # 多线，翻译
-        inputs_array = []
-        inputs_show_user_array = []
-
-        # get_token_num
-        from request_llm.bridge_all import model_info
-        enc = model_info[llm_kwargs['llm_model']]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-
-        def break_down(txt):
-            raw_token_num = get_token_num(txt)
-            if raw_token_num <= TOKEN_LIMIT_PER_FRAGMENT:
-                return [txt]
-            else:
-                # raw_token_num > TOKEN_LIMIT_PER_FRAGMENT
-                # 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_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
-            section_frags = break_down(section['text'])
-            for i, fragment in enumerate(section_frags):
-                heading = section['heading']
-                if len(section_frags) > 1: heading += f' Part-{i+1}'
-                inputs_array.append(
-                    f"你需要翻译{heading}章节，内容如下: \n\n{fragment}"
-                )
-                inputs_show_user_array.append(
-                    f"# {heading}\n\n{fragment}"
-                )
-
-        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-            inputs_array=inputs_array,
-            inputs_show_user_array=inputs_show_user_array,
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history_array=[meta for _ in inputs_array],
-            sys_prompt_array=[
-                "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in inputs_array],
-        )
-        res_path = write_history_to_file(meta +  ["# Meta Translation" , paper_meta_info] + gpt_response_collection, file_basename=None, file_fullname=None)
-        promote_file_to_downloadzone(res_path, rename_file=os.path.basename(fp)+'.md', chatbot=chatbot)
-        generated_conclusion_files.append(res_path)
-
-        ch = construct_html() 
-        orig = ""
-        trans = ""
-        gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
-        for i,k in enumerate(gpt_response_collection_html): 
-            if i%2==0:
-                gpt_response_collection_html[i] = inputs_show_user_array[i//2]
-            else:
-                gpt_response_collection_html[i] = gpt_response_collection_html[i]
-
-        final = ["", "", "一、论文概况",  "", "Abstract", paper_meta_info,  "二、论文翻译",  ""]
-        final.extend(gpt_response_collection_html)
-        for i, k in enumerate(final): 
-            if i%2==0:
-                orig = k
-            if i%2==1:
-                trans = k
-                ch.add_row(a=orig, b=trans)
-        create_report_file_name = f"{os.path.basename(fp)}.trans.html"
-        html_file = ch.save_file(create_report_file_name)
-        generated_html_files.append(html_file)
-        promote_file_to_downloadzone(html_file, rename_file=os.path.basename(html_file), chatbot=chatbot)
-
+        yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG)
     chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
 
 
 def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    """
+    此函数已经弃用
+    """
     import copy
-    TOKEN_LIMIT_PER_FRAGMENT = 1280
+    TOKEN_LIMIT_PER_FRAGMENT = 1024
     generated_conclusion_files = []
     generated_html_files = []
     from crazy_functions.crazy_utils import construct_html

docker-compose.yml

@@ -1,5 +1,54 @@
 #【请修改完参数后，删除此行】请在以下方案中选择一种，然后删除其他的方案，最后docker-compose up运行 | Please choose from one of these options below, delete other options as well as This Line
 
+## ===================================================
+## 【方案零】 部署项目的全部能力（这个是包含cuda和latex的大型镜像。如果您网速慢、硬盘小或没有显卡，则不推荐使用这个）
+## ===================================================
+version: '3'
+services:
+  gpt_academic_full_capability:
+    image: ghcr.io/binary-husky/gpt_academic_with_all_capacity:master
+    environment:
+    # 请查阅 `config.py`或者 github wiki 以查看所有的配置信息
+      API_KEY:                  '  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                        '
+    # USE_PROXY:                '  True                                                                       '
+    # proxies:                  '  { "http": "http://localhost:10881", "https": "http://localhost:10881", }   '
+      LLM_MODEL:                '  gpt-3.5-turbo                                                              '
+      AVAIL_LLM_MODELS:         '  ["gpt-3.5-turbo", "gpt-4", "qianfan", "sparkv2", "spark", "chatglm"]       '
+      BAIDU_CLOUD_API_KEY :     '  bTUtwEAveBrQipEowUvDwYWq                                                   '
+      BAIDU_CLOUD_SECRET_KEY :  '  jqXtLvXiVw6UNdjliATTS61rllG8Iuni                                           '
+      XFYUN_APPID:              '  53a8d816                                                                   '
+      XFYUN_API_SECRET:         '  MjMxNDQ4NDE4MzM0OSNlNjQ2NTlhMTkx                                           '
+      XFYUN_API_KEY:            '  95ccdec285364869d17b33e75ee96447                                           '
+      ENABLE_AUDIO:             '  False                                                                      '
+      DEFAULT_WORKER_NUM:       '  20                                                                         '
+      WEB_PORT:                 '  12345                                                                      '
+      ADD_WAIFU:                '  False                                                                      '
+      ALIYUN_APPKEY:            '  RxPlZrM88DnAFkZK                                                           '
+      THEME:                    '  Chuanhu-Small-and-Beautiful                                                '
+      ALIYUN_ACCESSKEY:         '  LTAI5t6BrFUzxRXVGUWnekh1                                                   '
+      ALIYUN_SECRET:            '  eHmI20SVWIwQZxCiTD2bGQVspP9i68                                             '
+    # LOCAL_MODEL_DEVICE:       '  cuda                                                                       '
+
+    # 加载英伟达显卡运行时
+    # runtime: nvidia
+    # deploy:
+    #     resources:
+    #       reservations:
+    #         devices:
+    #           - driver: nvidia
+    #             count: 1
+    #             capabilities: [gpu]
+
+    # 与宿主的网络融合
+    network_mode: "host"
+
+    # 不使用代理网络拉取最新代码
+    command: >
+      bash -c "python3 -u main.py"
+
+
+
+
 ## ===================================================
 ## 【方案一】 如果不需要运行本地模型（仅 chatgpt, azure, 星火, 千帆, claude 等在线大模型服务）
 ## ===================================================

docs/use_azure.md

@@ -107,6 +107,12 @@ AZURE_API_KEY = "填入azure openai api的密钥"
 AZURE_API_VERSION = "2023-05-15"  # 默认使用 2023-05-15 版本，无需修改
 AZURE_ENGINE = "填入部署名" # 见上述图片
 
+
+# 例如
+API_KEY = '6424e9d19e674092815cea1cb35e67a5'
+AZURE_ENDPOINT = 'https://rhtjjjjjj.openai.azure.com/'
+AZURE_ENGINE = 'qqwe'
+LLM_MODEL = "azure-gpt-3.5" # 可选 ↓↓↓
 ```
 
 

main.py

@@ -8,12 +8,13 @@ def main():
     # 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 = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING')
+    ENABLE_AUDIO, AUTO_CLEAR_TXT, PATH_LOGGING, AVAIL_THEMES, THEME = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING', 'AVAIL_THEMES', 'THEME')
 
     # 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
+
     initial_prompt = "Serve me as a writing and programming assistant."
     title_html = f"<h1 align=\"center\">GPT 学术优化 {get_current_version()}</h1>{theme_declaration}"
     description =  "代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic)，"
@@ -59,6 +60,7 @@ def main():
     cancel_handles = []
     with gr.Blocks(title="GPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo:
         gr.HTML(title_html)
+        secret_css, secret_font = gr.Textbox(visible=False), gr.Textbox(visible=False)
         cookies = gr.State(load_chat_cookies())
         with gr_L1():
             with gr_L2(scale=2, elem_id="gpt-chat"):
@@ -123,7 +125,8 @@ def main():
                     max_length_sl = gr.Slider(minimum=256, maximum=8192, value=4096, step=1, interactive=True, label="Local LLM MaxLength",)
                     checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "底部输入区", "输入清除键", "插件参数区"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区")
                     md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False)
-                    dark_mode_btn = gr.Button("Toggle Dark Mode ☀", variant="secondary").style(size="sm")
+                    theme_dropdown = gr.Dropdown(AVAIL_THEMES, value=THEME, label="更换UI主题").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="""() => {
                             if (document.querySelectorAll('.dark').length) {
                                 document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
@@ -197,9 +200,37 @@ def main():
                 ret.update({plugin_advanced_arg: gr.update(visible=False, label=f"插件[{k}]不需要高级参数。")})
             return ret
         dropdown.select(on_dropdown_changed, [dropdown], [switchy_bt, plugin_advanced_arg] )
+
         def on_md_dropdown_changed(k):
             return {chatbot: gr.update(label="当前模型："+k)}
         md_dropdown.select(on_md_dropdown_changed, [md_dropdown], [chatbot] )
+
+        def on_theme_dropdown_changed(theme, secret_css):
+            adjust_theme, css_part1, _, adjust_dynamic_theme = load_dynamic_theme(theme)
+            if adjust_dynamic_theme:
+                css_part2 = adjust_dynamic_theme._get_theme_css()
+            else:
+                css_part2 = adjust_theme()._get_theme_css()
+            return css_part2 + css_part1
+        
+        theme_handle = theme_dropdown.select(on_theme_dropdown_changed, [theme_dropdown, secret_css], [secret_css])
+        theme_handle.then(
+            None,
+            [secret_css],
+            None,
+            _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):
             if k in [r"打开插件列表", r"请先从插件列表中选择"]: return

request_llm/bridge_all.py

@@ -52,6 +52,7 @@ API_URL_REDIRECT, AZURE_ENDPOINT, AZURE_ENGINE = get_conf("API_URL_REDIRECT", "A
 openai_endpoint = "https://api.openai.com/v1/chat/completions"
 api2d_endpoint = "https://openai.api2d.net/v1/chat/completions"
 newbing_endpoint = "wss://sydney.bing.com/sydney/ChatHub"
+if not AZURE_ENDPOINT.endswith('/'): AZURE_ENDPOINT += '/'
 azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
 # 兼容旧版的配置
 try:

themes/common.css

@@ -19,3 +19,8 @@
 .wrap.svelte-xwlu1w {
     min-height: var(--size-32);
 }
+
+/* status bar height */
+.min.svelte-1yrv54 {
+    min-height: var(--size-12);
+}

themes/gradios.py

@@ -3,6 +3,15 @@ import logging
 from toolbox import get_conf, ProxyNetworkActivate
 CODE_HIGHLIGHT, ADD_WAIFU, LAYOUT = get_conf('CODE_HIGHLIGHT', 'ADD_WAIFU', 'LAYOUT')
 
+def dynamic_set_theme(THEME):
+    set_theme = gr.themes.ThemeClass()
+    with ProxyNetworkActivate():
+        logging.info('正在下载Gradio主题，请稍等。')
+        if THEME.startswith('Huggingface-'): THEME = THEME.lstrip('Huggingface-')
+        if THEME.startswith('huggingface-'): THEME = THEME.lstrip('huggingface-')
+        set_theme = set_theme.from_hub(THEME.lower())
+    return set_theme
+
 def adjust_theme():
 
     try:

themes/theme.py

@@ -2,17 +2,22 @@ import gradio as gr
 from toolbox import get_conf
 THEME, = get_conf('THEME')
 
-if THEME == 'Chuanhu-Small-and-Beautiful':
-    from .green import adjust_theme, advanced_css
-    theme_declaration = "<h2 align=\"center\"  class=\"small\">[Chuanhu-Small-and-Beautiful主题]</h2>"
-elif THEME == 'High-Contrast':
-    from .contrast import adjust_theme, advanced_css
-    theme_declaration = ""
-elif '/' in THEME:
-    from .gradios import adjust_theme, advanced_css
-    theme_declaration = ""
-else:
-    from .default import adjust_theme, advanced_css
-    theme_declaration = ""
-
+def load_dynamic_theme(THEME):
+    adjust_dynamic_theme = None
+    if THEME == 'Chuanhu-Small-and-Beautiful':
+        from .green import adjust_theme, advanced_css
+        theme_declaration = "<h2 align=\"center\"  class=\"small\">[Chuanhu-Small-and-Beautiful主题]</h2>"
+    elif THEME == 'High-Contrast':
+        from .contrast import adjust_theme, advanced_css
+        theme_declaration = ""
+    elif '/' in THEME:
+        from .gradios import adjust_theme, advanced_css
+        from .gradios import dynamic_set_theme
+        adjust_dynamic_theme = dynamic_set_theme(THEME)
+        theme_declaration = ""
+    else:
+        from .default import adjust_theme, advanced_css
+        theme_declaration = ""
+    return adjust_theme, advanced_css, theme_declaration, adjust_dynamic_theme
 
+adjust_theme, advanced_css, theme_declaration, _ = load_dynamic_theme(THEME)

toolbox.py

@@ -216,7 +216,7 @@ def get_reduce_token_percent(text):
         return 0.5, '不详'
 
 
-def write_history_to_file(history, file_basename=None, file_fullname=None):
+def write_history_to_file(history, file_basename=None, file_fullname=None, auto_caption=True):
     """
     将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
     """
@@ -235,7 +235,7 @@ def write_history_to_file(history, file_basename=None, file_fullname=None):
                 if type(content) != str: content = str(content)
             except:
                 continue
-            if i % 2 == 0:
+            if i % 2 == 0 and auto_caption:
                 f.write('## ')
             try:
                 f.write(content)

version

@@ -1,5 +1,5 @@
 {
-  "version": 3.52,
+  "version": 3.53,
   "show_feature": true,
-  "new_feature": "提高稳定性&解决多用户冲突问题 <-> 支持插件分类和更多UI皮肤外观 <-> 支持用户使用自然语言调度各个插件(虚空终端) ！ <-> 改进UI，设计新主题 <-> 支持借助GROBID实现PDF高精度翻译 <-> 接入百度千帆平台和文心一言 <-> 接入阿里通义千问、讯飞星火、上海AI-Lab书生 <-> 优化一键升级 <-> 提高arxiv翻译速度和成功率"
+  "new_feature": "支持动态选择不同界面主题 <-> 提高稳定性&解决多用户冲突问题 <-> 支持插件分类和更多UI皮肤外观 <-> 支持用户使用自然语言调度各个插件(虚空终端) ！ <-> 改进UI，设计新主题 <-> 支持借助GROBID实现PDF高精度翻译 <-> 接入百度千帆平台和文心一言 <-> 接入阿里通义千问、讯飞星火、上海AI-Lab书生 <-> 优化一键升级 <-> 提高arxiv翻译速度和成功率"
 }