Upload 8 files

check_proxy.py

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+
+def check_proxy(proxies):
+    import requests
+    proxies_https = proxies['https'] if proxies is not None else '无'
+    try:
+        response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4)
+        data = response.json()
+        print(f'查询代理的地理位置，返回的结果是{data}')
+        country = data['country_name']
+        result = f"代理配置 {proxies_https}, 代理所在地：{country}"
+        print(result)
+        return result
+    except:
+        result = f"代理配置 {proxies_https}, 代理所在地查询超时，代理可能无效"
+        print(result)
+        return result
+
+
+if __name__ == '__main__':
+    try: from config_private import proxies # 放自己的秘密如API和代理网址 os.path.exists('config_private.py')
+    except: from config import proxies
+    check_proxy(proxies)

config.py

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+# API_KEY = "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" 此key无效
+API_KEY = "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"
+API_URL = "https://api.openai.com/v1/chat/completions"
+
+# 改为True应用代理
+USE_PROXY = True
+if USE_PROXY:
+    # 代理网络的地址，打开你的科学上网软件查看代理的协议(socks5/http)、地址(localhost)和端口(11284)
+    proxies = { "http": "socks5h://127.0.0.1:7890", "https": "socks5h://127.0.0.1:7890", } 
+    print('网络代理状态：运行。')
+else:
+    proxies = None
+    print('网络代理状态：未配置。无代理状态下很可能无法访问。')
+
+# 发送请求到OpenAI后，等待多久判定为超时
+TIMEOUT_SECONDS = 20
+
+# 网页的端口, -1代表随机端口
+WEB_PORT = -1
+
+# 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
+MAX_RETRY = 2
+
+# 选择的OpenAI模型是（gpt4现在只对申请成功的人开放）
+LLM_MODEL = "gpt-3.5-turbo"
+
+# 检查一下是不是忘了改config
+if API_KEY == "sk-":
+    assert False, "请在config文件中修改API密钥, 添加海外代理之后再运行"

functional.py

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+# 'primary' 颜色对应 theme.py 中的 primary_hue
+# 'secondary' 颜色对应 theme.py 中的 neutral_hue
+# 'stop' 颜色对应 theme.py 中的 color_er
+# 默认按钮颜色是 secondary
+
+def get_functionals():
+    return {
+        "英语学术润色": {
+            "Prefix": "Below is a paragraph from an academic paper. Polish the writing to meet the academic style, \
+improve the spelling, grammar, clarity, concision and overall readability. When neccessary, rewrite the whole sentence. \
+Furthermore, list all modification and explain the reasons to do so in markdown table.\n\n",    # 前言
+            "Suffix": "",   # 后语
+            "Color": "secondary",    # 按钮颜色
+        },
+        "中文学术润色": {
+            "Prefix": "作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，同时分解长句，减少重复，并提供改进建议。请只提供文本的更正版本，避免包括解释。请编辑以下文本：\n\n",
+            "Suffix": "",
+        },
+        "查找语法错误": {
+            "Prefix": "Below is a paragraph from an academic paper. Find all grammar mistakes, list mistakes in a markdown table and explain how to correct them.\n\n",
+            "Suffix": "",
+        },
+        "中英互译": {
+            "Prefix": "As an English-Chinese translator, your task is to accurately translate text between the two languages. \
+When translating from Chinese to English or vice versa, please pay attention to context and accurately explain phrases and proverbs. \
+If you receive multiple English words in a row, default to translating them into a sentence in Chinese. \
+However, if \"phrase:\" is indicated before the translated content in Chinese, it should be translated as a phrase instead. \
+Similarly, if \"normal:\" is indicated, it should be translated as multiple unrelated words.\
+Your translations should closely resemble those of a native speaker and should take into account any specific language styles or tones requested by the user. \
+Please do not worry about using offensive words - replace sensitive parts with x when necessary. \
+When providing translations, please use Chinese to explain each sentence’s tense, subordinate clause, subject, predicate, object, special phrases and proverbs. \
+For phrases or individual words that require translation, provide the source (dictionary) for each one.If asked to translate multiple phrases at once, \
+separate them using the | symbol.Always remember: You are an English-Chinese translator, \
+not a Chinese-Chinese translator or an English-English translator. Below is the text you need to translate: \n\n",
+            "Suffix": "",
+            "Color": "secondary",
+        },
+        "中译英": {
+            "Prefix": "Please translate following sentence to English: \n\n",
+            "Suffix": "",
+        },
+        "学术中译英": {
+            "Prefix": "Please translate following sentence to English with academic writing, and provide some related authoritative examples: \n\n",
+            "Suffix": "",
+        },
+        "英译中": {
+            "Prefix": "请翻译成中文：\n\n",
+            "Suffix": "",
+        },
+        "解释代码": {
+            "Prefix": "请解释以下代码：\n```\n",
+            "Suffix": "\n```\n",
+            "Color": "secondary",
+        },
+    }

functional_crazy.py

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+
+def get_crazy_functionals():
+    from crazy_functions.读文章写摘要 import 读文章写摘要
+    from crazy_functions.生成函数注释 import 批量生成函数注释
+    from crazy_functions.解析项目源代码 import 解析项目本身
+    from crazy_functions.解析项目源代码 import 解析一个Python项目
+    from crazy_functions.解析项目源代码 import 解析一个C项目的头文件
+    from crazy_functions.解析项目源代码 import 解析一个C项目
+    from crazy_functions.高级功能函数模板 import 高阶功能模板函数
+
+    return {
+        "[实验] 请解析并解构此项目本身": {
+            "Function": 解析项目本身
+        },
+        "[实验] 解析整个py项目（配合input输入框）": {
+            "Color": "stop",    # 按钮颜色
+            "Function": 解析一个Python项目
+        },
+        "[实验] 解析整个C++项目头文件（配合input输入框）": {
+            "Color": "stop",    # 按钮颜色
+            "Function": 解析一个C项目的头文件
+        },
+        "[实验] 解析整个C++项目（配合input输入框）": {
+            "Color": "stop",    # 按钮颜色
+            "Function": 解析一个C项目
+        },
+        "[实验] 读tex论文写摘要（配合input输入框）": {
+            "Color": "stop",    # 按钮颜色
+            "Function": 读文章写摘要
+        },
+        "[实验] 批量生成函数注释（配合input输入框）": {
+            "Color": "stop",    # 按钮颜色
+            "Function": 批量生成函数注释
+        },
+        "[实验] 实验功能函数模板": {
+            "Color": "stop",    # 按钮颜色
+            "Function": 高阶功能模板函数
+        },
+    }
+
+def on_file_uploaded(files, chatbot, txt):
+    if len(files) == 0: return chatbot, txt
+    import shutil, os, time, glob
+    from toolbox import extract_archive
+    try: shutil.rmtree('./private_upload/')
+    except: pass
+    time_tag = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
+    os.makedirs(f'private_upload/{time_tag}', exist_ok=True)
+    for file in files:
+        file_origin_name = os.path.basename(file.orig_name)
+        shutil.copy(file.name, f'private_upload/{time_tag}/{file_origin_name}')
+        extract_archive(f'private_upload/{time_tag}/{file_origin_name}', 
+                        dest_dir=f'private_upload/{time_tag}/{file_origin_name}.extract')
+    moved_files = [fp for fp in glob.glob('private_upload/**/*', recursive=True)]
+    txt = f'private_upload/{time_tag}'
+    moved_files_str = '\t\n\n'.join(moved_files)
+    chatbot.append(['我上传了文件，请查收', 
+                    f'[Local Message] 收到以下文件: \n\n{moved_files_str}\n\n调用路径参数已自动修正到: \n\n{txt}\n\n现在您可以直接选择任意实现性功能'])
+    return chatbot, txt
+
+def on_report_generated(files, chatbot):
+    from toolbox import find_recent_files
+    report_files = find_recent_files('gpt_log')
+    if len(report_files) == 0: return report_files, chatbot
+    # files.extend(report_files)
+    chatbot.append(['汇总报告如何远程获取？', '汇总报告已经添加到右侧文件上传区，请查收。'])
+    return report_files, chatbot
+

predict.py

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+# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
+
+import json
+import gradio as gr
+import logging
+import traceback
+import requests
+import importlib
+
+# config_private.py放自己的秘密如API和代理网址
+# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
+try: from config_private import proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL
+except: from config import proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL
+
+timeout_bot_msg = '[local] Request timeout, network error. please check proxy settings in config.py.'
+
+def get_full_error(chunk, stream_response):
+    """
+        获取完整的从Openai返回的报错
+    """
+    while True:
+        try:
+            chunk += next(stream_response)
+        except:
+            break
+    return chunk
+
+def predict_no_ui(inputs, top_p, temperature, history=[]):
+    """
+        发送至chatGPT，等待回复，一次性完成，不显示中间过程。
+        predict函数的简化版。
+        用于payload比较大的情况，或者用于实现多线、带嵌套的复杂功能。
+
+        inputs 是本次问询的输入
+        top_p, temperature是chatGPT的内部调优参数
+        history 是之前的对话列表
+        （注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误，然后raise ConnectionAbortedError）
+    """
+    headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt="", stream=False)
+
+    retry = 0
+    while True:
+        try:
+            # make a POST request to the API endpoint, stream=False
+            response = requests.post(API_URL, headers=headers, proxies=proxies,
+                                    json=payload, stream=False, timeout=TIMEOUT_SECONDS*2); break
+        except requests.exceptions.ReadTimeout as e:
+            retry += 1
+            traceback.print_exc()
+            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+            if retry > MAX_RETRY: raise TimeoutError
+
+    try:
+        result = json.loads(response.text)["choices"][0]["message"]["content"]
+        return result
+    except Exception as e:
+        if "choices" not in response.text: print(response.text)
+        raise ConnectionAbortedError("Json解析不合常规，可能是文本过长" + response.text)
+
+
+def predict(inputs, top_p, temperature, chatbot=[], history=[], system_prompt='', 
+            stream = True, additional_fn=None):
+    """
+        发送至chatGPT，流式获取输出。
+        用于基础的对话功能。
+        inputs 是本次问询的输入
+        top_p, temperature是chatGPT的内部调优参数
+        history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
+        chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+        additional_fn代表点击的哪个按钮，按钮见functional.py
+    """
+    if additional_fn is not None:
+        import functional
+        importlib.reload(functional)
+        functional = functional.get_functionals()
+        inputs = functional[additional_fn]["Prefix"] + inputs + functional[additional_fn]["Suffix"]
+
+    if stream:
+        raw_input = inputs
+        logging.info(f'[raw_input] {raw_input}')
+        chatbot.append((inputs, ""))
+        yield chatbot, history, "等待响应"
+
+    headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt, stream)
+    history.append(inputs); history.append(" ")
+
+    retry = 0
+    while True:
+        try:
+            # make a POST request to the API endpoint, stream=True
+            response = requests.post(API_URL, headers=headers, proxies=proxies,
+                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
+        except:
+            retry += 1
+            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
+            retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
+            yield chatbot, history, "请求超时"+retry_msg
+            if retry > MAX_RETRY: raise TimeoutError
+
+    gpt_replying_buffer = ""
+    
+    is_head_of_the_stream = True
+    if stream:
+        stream_response =  response.iter_lines()
+        while True:
+            chunk = next(stream_response)
+            # print(chunk.decode()[6:])
+            if is_head_of_the_stream:
+                # 数据流的第一帧不携带content
+                is_head_of_the_stream = False; continue
+            
+            if chunk:
+                try:
+                    if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
+                        # 判定为数据流的结束，gpt_replying_buffer也写完了
+                        logging.info(f'[response] {gpt_replying_buffer}')
+                        break
+                    # 处理数据流的主体
+                    chunkjson = json.loads(chunk.decode()[6:])
+                    status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}"
+                    # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
+                    gpt_replying_buffer = gpt_replying_buffer + json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"]
+                    history[-1] = gpt_replying_buffer
+                    chatbot[-1] = (history[-2], history[-1])
+                    yield chatbot, history, status_text
+
+                except Exception as e:
+                    traceback.print_exc()
+                    yield chatbot, history, "Json解析不合常规"
+                    chunk = get_full_error(chunk, stream_response)
+                    error_msg = chunk.decode()
+                    if "reduce the length" in error_msg:
+                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Input (or history) is too long, please reduce input or clear history by refleshing this page.")
+                        history = []
+                    elif "Incorrect API key" in error_msg:
+                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key provided.")
+                    else:
+                        from toolbox import regular_txt_to_markdown
+                        tb_str = regular_txt_to_markdown(traceback.format_exc())
+                        chatbot[-1] = (chatbot[-1][0], f"[Local Message] Json Error \n\n {tb_str} \n\n {regular_txt_to_markdown(chunk.decode()[4:])}")
+                    yield chatbot, history, "Json解析不合常规" + error_msg
+                    return
+
+def generate_payload(inputs, top_p, temperature, history, system_prompt, stream):
+    """
+        整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
+    """
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {API_KEY}"
+    }
+
+    conversation_cnt = len(history) // 2
+
+    messages = [{"role": "system", "content": system_prompt}]
+    if conversation_cnt:
+        for index in range(0, 2*conversation_cnt, 2):
+            what_i_have_asked = {}
+            what_i_have_asked["role"] = "user"
+            what_i_have_asked["content"] = history[index]
+            what_gpt_answer = {}
+            what_gpt_answer["role"] = "assistant"
+            what_gpt_answer["content"] = history[index+1]
+            if what_i_have_asked["content"] != "":
+                if what_gpt_answer["content"] == "": continue
+                if what_gpt_answer["content"] == timeout_bot_msg: continue
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+            else:
+                messages[-1]['content'] = what_gpt_answer['content']
+
+    what_i_ask_now = {}
+    what_i_ask_now["role"] = "user"
+    what_i_ask_now["content"] = inputs
+    messages.append(what_i_ask_now)
+
+    payload = {
+        "model": LLM_MODEL,
+        "messages": messages, 
+        "temperature": temperature,  # 1.0,
+        "top_p": top_p,  # 1.0,
+        "n": 1,
+        "stream": stream,
+        "presence_penalty": 0,
+        "frequency_penalty": 0,
+    }
+    
+    print(f" {LLM_MODEL} : {conversation_cnt} : {inputs}")
+    return headers,payload
+
+

show_math.py

@@ -0,0 +1,80 @@
+# This program is written by: https://github.com/polarwinkel/mdtex2html
+
+from latex2mathml.converter import convert as tex2mathml
+import re
+
+incomplete = '<font style="color:orange;" class="tooltip">&#9888;<span class="tooltiptext">formula incomplete</span></font>'
+convError = '<font style="color:red" class="tooltip">&#9888;<span class="tooltiptext">LaTeX-convert-error</span></font>'
+
+def convert(mdtex, extensions=[], splitParagraphs=True):
+    ''' converts recursively the Markdown-LaTeX-mixture to HTML with MathML '''
+    found = False
+    # handle all paragraphs separately (prevents aftereffects)
+    if splitParagraphs:
+        parts = re.split("\n\n", mdtex)
+        result = ''
+        for part in parts:
+            result += convert(part, extensions, splitParagraphs=False)
+        return result
+    # find first $$-formula:
+    parts = re.split('\${2}', mdtex, 2)
+    if len(parts)>1:
+        found = True
+        result = convert(parts[0], extensions, splitParagraphs=False)+'\n'
+        try:
+            result += '<div class="blockformula">'+tex2mathml(parts[1])+'</div>\n'
+        except:
+            result += '<div class="blockformula">'+convError+'</div>'
+        if len(parts)==3:
+            result += convert(parts[2], extensions, splitParagraphs=False)
+        else:
+            result += '<div class="blockformula">'+incomplete+'</div>'
+    # else find first $-formulas:
+    else:
+        parts = re.split('\${1}', mdtex, 2)
+    if len(parts)>1 and not found:
+        found = True
+        try:
+            mathml = tex2mathml(parts[1])
+        except:
+            mathml = convError
+        if parts[0].endswith('\n\n') or parts[0]=='': # make sure textblock starts before formula!
+            parts[0]=parts[0]+'&#x200b;'
+        if len(parts)==3:
+            result = convert(parts[0]+mathml+parts[2], extensions, splitParagraphs=False)
+        else:
+            result = convert(parts[0]+mathml+incomplete, extensions, splitParagraphs=False)
+    # else find first \[..\]-equation:
+    else:
+        parts = re.split(r'\\\[', mdtex, 1)
+    if len(parts)>1 and not found:
+        found = True
+        result = convert(parts[0], extensions, splitParagraphs=False)+'\n'
+        parts = re.split(r'\\\]', parts[1], 1)
+        try:
+            result += '<div class="blockformula">'+tex2mathml(parts[0])+'</div>\n'
+        except:
+            result += '<div class="blockformula">'+convError+'</div>'
+        if len(parts)==2:
+            result += convert(parts[1], extensions, splitParagraphs=False)
+        else:
+            result += '<div class="blockformula">'+incomplete+'</div>'
+    # else find first \(..\)-equation:
+    else:
+        parts = re.split(r'\\\(', mdtex, 1)
+    if len(parts)>1 and not found:
+        found = True
+        subp = re.split(r'\\\)', parts[1], 1)
+        try:
+            mathml = tex2mathml(subp[0])
+        except:
+            mathml = convError
+        if parts[0].endswith('\n\n') or parts[0]=='': # make sure textblock starts before formula!
+            parts[0]=parts[0]+'&#x200b;'
+        if len(subp)==2:
+            result = convert(parts[0]+mathml+subp[1], extensions, splitParagraphs=False)
+        else:
+            result = convert(parts[0]+mathml+incomplete, extensions, splitParagraphs=False)
+    if not found:
+        result = mdtex
+    return result

theme.py

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+import gradio as gr 
+
+# gradio可用颜色列表
+# gr.themes.utils.colors.slate (石板色)
+# gr.themes.utils.colors.gray (灰色)
+# gr.themes.utils.colors.zinc (锌色)
+# gr.themes.utils.colors.neutral (中性色)
+# gr.themes.utils.colors.stone (石头色)
+# gr.themes.utils.colors.red (红色)
+# gr.themes.utils.colors.orange (橙色)
+# gr.themes.utils.colors.amber (琥珀色)
+# gr.themes.utils.colors.yellow (黄色)
+# gr.themes.utils.colors.lime (酸橙色)
+# gr.themes.utils.colors.green (绿色)
+# gr.themes.utils.colors.emerald (祖母绿)
+# gr.themes.utils.colors.teal (青蓝色)
+# gr.themes.utils.colors.cyan (青色)
+# gr.themes.utils.colors.sky (天蓝色)
+# gr.themes.utils.colors.blue (蓝色)
+# gr.themes.utils.colors.indigo (靛蓝色)
+# gr.themes.utils.colors.violet (紫罗兰色)
+# gr.themes.utils.colors.purple (紫色)
+# gr.themes.utils.colors.fuchsia (洋红色)
+# gr.themes.utils.colors.pink (粉红色)
+# gr.themes.utils.colors.rose (玫瑰色)
+
+def adjust_theme():
+    try: 
+        color_er = gr.themes.utils.colors.pink
+        set_theme = gr.themes.Default( 
+                        primary_hue=gr.themes.utils.colors.orange,
+                        neutral_hue=gr.themes.utils.colors.gray,
+                        font=["sans-serif", "Microsoft YaHei", "ui-sans-serif", "system-ui", "sans-serif", gr.themes.utils.fonts.GoogleFont("Source Sans Pro")], 
+                        font_mono=["ui-monospace", "Consolas", "monospace", gr.themes.utils.fonts.GoogleFont("IBM Plex Mono")])
+        set_theme.set(
+            # Colors
+            input_background_fill_dark="*neutral_800",
+            # Transition
+            button_transition="none",
+            # Shadows
+            button_shadow="*shadow_drop",
+            button_shadow_hover="*shadow_drop_lg",
+            button_shadow_active="*shadow_inset",
+            input_shadow="0 0 0 *shadow_spread transparent, *shadow_inset",
+            input_shadow_focus="0 0 0 *shadow_spread *secondary_50, *shadow_inset",
+            input_shadow_focus_dark="0 0 0 *shadow_spread *neutral_700, *shadow_inset",
+            checkbox_label_shadow="*shadow_drop",
+            block_shadow="*shadow_drop",
+            form_gap_width="1px",
+            # Button borders
+            input_border_width="1px",
+            input_background_fill="white",
+            # Gradients
+            stat_background_fill="linear-gradient(to right, *primary_400, *primary_200)",
+            stat_background_fill_dark="linear-gradient(to right, *primary_400, *primary_600)",
+            error_background_fill=f"linear-gradient(to right, {color_er.c100}, *background_fill_secondary)",
+            error_background_fill_dark="*background_fill_primary",
+            checkbox_label_background_fill="linear-gradient(to top, *neutral_50, white)",
+            checkbox_label_background_fill_dark="linear-gradient(to top, *neutral_900, *neutral_800)",
+            checkbox_label_background_fill_hover="linear-gradient(to top, *neutral_100, white)",
+            checkbox_label_background_fill_hover_dark="linear-gradient(to top, *neutral_900, *neutral_800)",
+            button_primary_background_fill="linear-gradient(to bottom right, *primary_100, *primary_300)",
+            button_primary_background_fill_dark="linear-gradient(to bottom right, *primary_500, *primary_600)",
+            button_primary_background_fill_hover="linear-gradient(to bottom right, *primary_100, *primary_200)",
+            button_primary_background_fill_hover_dark="linear-gradient(to bottom right, *primary_500, *primary_500)",
+            button_primary_border_color_dark="*primary_500",
+            button_secondary_background_fill="linear-gradient(to bottom right, *neutral_100, *neutral_200)",
+            button_secondary_background_fill_dark="linear-gradient(to bottom right, *neutral_600, *neutral_700)",
+            button_secondary_background_fill_hover="linear-gradient(to bottom right, *neutral_100, *neutral_100)",
+            button_secondary_background_fill_hover_dark="linear-gradient(to bottom right, *neutral_600, *neutral_600)",
+            button_cancel_background_fill=f"linear-gradient(to bottom right, {color_er.c100}, {color_er.c200})",
+            button_cancel_background_fill_dark=f"linear-gradient(to bottom right, {color_er.c600}, {color_er.c700})",
+            button_cancel_background_fill_hover=f"linear-gradient(to bottom right, {color_er.c100}, {color_er.c100})",
+            button_cancel_background_fill_hover_dark=f"linear-gradient(to bottom right, {color_er.c600}, {color_er.c600})",
+            button_cancel_border_color=color_er.c200,
+            button_cancel_border_color_dark=color_er.c600,
+            button_cancel_text_color=color_er.c600,
+            button_cancel_text_color_dark="white",
+        )
+    except: 
+        set_theme = None; print('gradio版本较旧, 不能自定义字体和颜色')
+    return set_theme

toolbox.py

@@ -0,0 +1,187 @@
+import markdown, mdtex2html, threading
+from show_math import convert as convert_math
+from functools import wraps
+
+def predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[]):
+    """
+        调用简单的predict_no_ui接口，但是依然保留了些许界面心跳功能，当对话太长时，会自动采用二分法截断
+    """
+    import time
+    try: from config_private import TIMEOUT_SECONDS, MAX_RETRY
+    except: from config import TIMEOUT_SECONDS, MAX_RETRY
+    from predict import predict_no_ui
+    # 多线程的时候，需要一个mutable结构在不同线程之间传递信息
+    # list就是最简单的mutable结构，我们第一个位置放gpt输出，第二个位置传递报错信息
+    mutable = [None, '']
+    # multi-threading worker
+    def mt(i_say, history): 
+        while True:
+            try:
+                mutable[0] = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature, history=history)
+                break
+            except ConnectionAbortedError as e:
+                if len(history) > 0:
+                    history = [his[len(his)//2:] for his in history if his is not None]
+                    mutable[1] = 'Warning! History conversation is too long, cut into half. '
+                else:
+                    i_say = i_say[:len(i_say)//2]
+                    mutable[1] = 'Warning! Input file is too long, cut into half. '
+            except TimeoutError as e:
+                mutable[0] = '[Local Message] Failed with timeout'
+    # 创建新线程发出http请求
+    thread_name = threading.Thread(target=mt, args=(i_say, history)); thread_name.start()
+    # 原来的线程则负责持续更新UI，实现一个超时倒计时，并等待新线程的任务完成
+    cnt = 0
+    while thread_name.is_alive():
+        cnt += 1
+        chatbot[-1] = (i_say_show_user, f"[Local Message] {mutable[1]}waiting gpt response {cnt}/{TIMEOUT_SECONDS*2*(MAX_RETRY+1)}"+''.join(['.']*(cnt%4)))
+        yield chatbot, history, '正常'
+        time.sleep(1)
+    # 把gpt的输出从mutable中取出来
+    gpt_say = mutable[0]
+    return gpt_say
+
+def write_results_to_file(history, file_name=None):
+    """
+        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。
+    """
+    import os, time
+    if file_name is None:
+        # file_name = time.strftime("chatGPT分析报告%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
+        file_name = 'chatGPT分析报告' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
+    os.makedirs('./gpt_log/', exist_ok=True)
+    with open(f'./gpt_log/{file_name}', 'w', encoding = 'utf8') as f:
+        f.write('# chatGPT 分析报告\n')
+        for i, content in enumerate(history):
+            if i%2==0: f.write('## ')
+            f.write(content)
+            f.write('\n\n')
+    res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}')
+    print(res)
+    return res
+
+def regular_txt_to_markdown(text):
+    """
+        将普通文本转换为Markdown格式的文本。
+    """
+    text = text.replace('\n', '\n\n')
+    text = text.replace('\n\n\n', '\n\n')
+    text = text.replace('\n\n\n', '\n\n')
+    return text
+
+def CatchException(f):
+    """
+        装饰器函数，捕捉函数f中的异常并封装到一个生成器中返回，并显示到聊天当中。
+    """
+    @wraps(f)
+    def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
+        try:
+            yield from f(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT)
+        except Exception as e:
+            import traceback
+            from check_proxy import check_proxy
+            try: from config_private import proxies
+            except: from config import proxies
+            tb_str = regular_txt_to_markdown(traceback.format_exc())
+            chatbot[-1] = (chatbot[-1][0], f"[Local Message] 实验性函数调用出错: \n\n {tb_str} \n\n 当前代理可用性: \n\n {check_proxy(proxies)}")
+            yield chatbot, history, f'异常 {e}'
+    return decorated
+
+def report_execption(chatbot, history, a, b):
+    """
+        向chatbot中添加错误信息
+    """
+    chatbot.append((a, b))
+    history.append(a); history.append(b)
+
+def text_divide_paragraph(text):
+    """
+        将文本按照段落分隔符分割开，生成带有段落标签的HTML代码。
+    """
+    if '```' in text:
+        # careful input
+        return text
+    else:
+        # wtf input
+        lines = text.split("\n")
+        for i, line in enumerate(lines):
+            if i!=0: lines[i] = "<p>"+lines[i].replace(" ", "&nbsp;")+"</p>"
+        text = "".join(lines)
+        return text
+
+def markdown_convertion(txt):
+    """
+        将Markdown格式的文本转换为HTML格式。如果包含数学公式，则先将公式转换为HTML格式。
+    """
+    if ('$' in txt) and ('```' not in txt):
+        return markdown.markdown(txt,extensions=['fenced_code','tables']) + '<br><br>' + \
+            markdown.markdown(convert_math(txt, splitParagraphs=False),extensions=['fenced_code','tables'])
+    else:
+        return markdown.markdown(txt,extensions=['fenced_code','tables'])
+
+
+def format_io(self, y):
+    """
+        将输入和输出解析为HTML格式。将y中最后一项的输入部分段落化，并将输出部分的Markdown和数学公式转换为HTML格式。
+    """
+    if y is None: return []
+    i_ask, gpt_reply = y[-1]
+    i_ask = text_divide_paragraph(i_ask) # 输入部分太自由，预处理一波
+    y[-1] = (
+        None if i_ask is None else markdown.markdown(i_ask, extensions=['fenced_code','tables']),
+        None if gpt_reply is None else markdown_convertion(gpt_reply)
+    )
+    return y
+
+
+def find_free_port():
+    """
+        返回当前系统中可用的未使用端口。
+    """
+    import socket
+    from contextlib import closing
+    with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
+        s.bind(('', 0))
+        s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+        return s.getsockname()[1]
+    
+
+def extract_archive(file_path, dest_dir):
+    import zipfile
+    import tarfile
+    import os
+    # Get the file extension of the input file
+    file_extension = os.path.splitext(file_path)[1]
+
+    # Extract the archive based on its extension
+    if file_extension == '.zip':
+        with zipfile.ZipFile(file_path, 'r') as zipobj:
+            zipobj.extractall(path=dest_dir)
+            print("Successfully extracted zip archive to {}".format(dest_dir))
+
+    elif file_extension in ['.tar', '.gz', '.bz2']:
+        with tarfile.open(file_path, 'r:*') as tarobj:
+            tarobj.extractall(path=dest_dir)
+            print("Successfully extracted tar archive to {}".format(dest_dir))
+    else:
+        return
+    
+def find_recent_files(directory):
+    """
+        me: find files that is created with in one minutes under a directory with python, write a function
+        gpt: here it is!
+    """
+    import os
+    import time
+    current_time = time.time()
+    one_minute_ago = current_time - 60
+    recent_files = []
+
+    for filename in os.listdir(directory):
+        file_path = os.path.join(directory, filename)
+        if file_path.endswith('.log'): continue
+        created_time = os.path.getctime(file_path)
+        if created_time >= one_minute_ago:
+            recent_files.append(file_path)
+
+    return recent_files