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12
README.md
12
README.md
@ -1,3 +1,15 @@
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---
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title: ChatImprovement
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emoji: 😻
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colorFrom: blue
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colorTo: blue
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sdk: gradio
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sdk_version: 3.32.0
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app_file: app.py
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pinned: false
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---
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# ChatGPT 学术优化
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> **Note**
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>
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> 2023.5.27 对Gradio依赖进行了调整,Fork并解决了官方Gradio的若干Bugs。请及时**更新代码**并重新更新pip依赖。安装依赖时,请严格选择`requirements.txt`中**指定的版本**:
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@ -1,13 +1,15 @@
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import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
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def main():
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import subprocess, sys
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subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'gradio-stable-fork'])
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import gradio as gr
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if gr.__version__ not in ['3.28.3','3.32.2']: assert False, "需要特殊依赖,请务必用 pip install -r requirements.txt 指令安装依赖,详情信息见requirements.txt"
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if gr.__version__ not in ['3.28.3','3.32.3']: assert False, "请用 pip install -r requirements.txt 安装依赖"
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from request_llm.bridge_all import predict
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from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith
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# 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
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proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = \
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get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
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proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY, AVAIL_LLM_MODELS = \
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get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY', 'AVAIL_LLM_MODELS')
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# 如果WEB_PORT是-1, 则随机选取WEB端口
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PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
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@ -54,6 +56,7 @@ def main():
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cancel_handles = []
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with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo:
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gr.HTML(title_html)
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gr.HTML('''<center><a href="https://huggingface.co/spaces/qingxu98/gpt-academic?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>请您打开此页面后务必点击上方的“复制空间”(Duplicate Space)按钮!<font color="#FF00FF">使用时,先在输入框填入API-KEY然后回车。</font><br/>切忌在“复制空间”(Duplicate Space)之前填入API_KEY或进行提问,否则您的API_KEY将极可能被空间所有者攫取!<br/>支持任意数量的OpenAI的密钥和API2D的密钥共存,例如输入"OpenAI密钥1,API2D密钥2",然后提交,即可同时使用两种模型接口。</center>''')
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cookies = gr.State({'api_key': API_KEY, 'llm_model': LLM_MODEL})
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with gr_L1():
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with gr_L2(scale=2):
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@ -63,7 +66,7 @@ def main():
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with gr_L2(scale=1):
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with gr.Accordion("输入区", open=True) as area_input_primary:
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with gr.Row():
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txt = gr.Textbox(show_label=False, placeholder="Input question here.").style(container=False)
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txt = gr.Textbox(show_label=False, lines=2, placeholder="输入问题或API密钥,输入多个密钥时,用英文逗号间隔。支持OpenAI密钥和API2D密钥共存。").style(container=False)
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with gr.Row():
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submitBtn = gr.Button("提交", variant="primary")
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with gr.Row():
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@ -104,7 +107,7 @@ def main():
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system_prompt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt)
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top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
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temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
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max_length_sl = gr.Slider(minimum=256, maximum=8192, value=4096, step=1, interactive=True, label="Local LLM MaxLength",)
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max_length_sl = gr.Slider(minimum=256, maximum=4096, value=512, step=1, interactive=True, label="Local LLM MaxLength",)
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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")
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md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False)
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@ -144,11 +147,6 @@ def main():
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resetBtn2.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])
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clearBtn.click(lambda: ("",""), None, [txt, txt2])
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clearBtn2.click(lambda: ("",""), None, [txt, txt2])
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if AUTO_CLEAR_TXT:
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submitBtn.click(lambda: ("",""), None, [txt, txt2])
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submitBtn2.click(lambda: ("",""), None, [txt, txt2])
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txt.submit(lambda: ("",""), None, [txt, txt2])
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txt2.submit(lambda: ("",""), None, [txt, txt2])
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# 基础功能区的回调函数注册
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for k in functional:
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if ("Visible" in functional[k]) and (not functional[k]["Visible"]): continue
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@ -202,10 +200,7 @@ def main():
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threading.Thread(target=warm_up_modules, name="warm-up", daemon=True).start()
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auto_opentab_delay()
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demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
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server_name="0.0.0.0", server_port=PORT,
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favicon_path="docs/logo.png", auth=AUTHENTICATION,
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blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile"])
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demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", share=False, favicon_path="docs/logo.png", blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile"])
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# 如果需要在二级路径下运行
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# CUSTOM_PATH, = get_conf('CUSTOM_PATH')
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10
config.py
10
config.py
@ -45,10 +45,9 @@ WEB_PORT = -1
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# 如果OpenAI不响应(网络卡顿、代理失败、KEY失效),重试的次数限制
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MAX_RETRY = 2
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# 模型选择是 (注意: LLM_MODEL是默认选中的模型, 同时它必须被包含在AVAIL_LLM_MODELS切换列表中 )
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LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
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AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt35", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss", "newbing", "newbing-free", "stack-claude"]
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# P.S. 其他可用的模型还包括 ["gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "newbing-free", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
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# OpenAI模型选择是(gpt4现在只对申请成功的人开放)
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LLM_MODEL = "gpt-3.5-turbo" # 可选 "chatglm"
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AVAIL_LLM_MODELS = ["newbing-free", "gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "api2d-gpt-3.5-turbo"]
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# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
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LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
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@ -56,9 +55,6 @@ LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
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# 设置gradio的并行线程数(不需要修改)
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CONCURRENT_COUNT = 100
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# 是否在提交时自动清空输入框
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AUTO_CLEAR_TXT = False
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# 加一个live2d装饰
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ADD_WAIFU = False
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@ -63,7 +63,6 @@ def get_core_functions():
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"Prefix": r"我需要你找一张网络图片。使用Unsplash API(https://source.unsplash.com/960x640/?<英语关键词>)获取图片URL," +
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r"然后请使用Markdown格式封装,并且不要有反斜线,不要用代码块。现在,请按以下描述给我发送图片:" + "\n\n",
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"Suffix": r"",
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"Visible": False,
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},
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"解释代码": {
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"Prefix": r"请解释以下代码:" + "\n```\n",
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@ -74,5 +73,6 @@ def get_core_functions():
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r"Note that, reference styles maybe more than one kind, you should transform each item correctly." +
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r"Items need to be transformed:",
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"Suffix": r"",
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"Visible": False,
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}
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}
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@ -193,9 +193,8 @@ def test_Latex():
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# txt = r"https://arxiv.org/abs/2212.10156"
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# txt = r"https://arxiv.org/abs/2211.11559"
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# txt = r"https://arxiv.org/abs/2303.08774"
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# txt = r"https://arxiv.org/abs/2303.12712"
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txt = r"https://arxiv.org/abs/2303.12712"
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# txt = r"C:\Users\fuqingxu\arxiv_cache\2303.12712\workfolder"
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txt = r"2306.17157" # 这个paper有个input命令文件名大小写错误!
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for cookies, cb, hist, msg in (Latex翻译中文并重新编译PDF)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
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@ -189,18 +189,6 @@ def rm_comments(main_file):
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main_file = re.sub(r'(?<!\\)%.*', '', main_file) # 使用正则表达式查找半行注释, 并替换为空字符串
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return main_file
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def find_tex_file_ignore_case(fp):
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dir_name = os.path.dirname(fp)
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base_name = os.path.basename(fp)
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if not base_name.endswith('.tex'): base_name+='.tex'
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if os.path.exists(pj(dir_name, base_name)): return pj(dir_name, base_name)
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# go case in-sensitive
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import glob
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for f in glob.glob(dir_name+'/*.tex'):
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base_name_s = os.path.basename(fp)
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if base_name_s.lower() == base_name.lower(): return f
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return None
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def merge_tex_files_(project_foler, main_file, mode):
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"""
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Merge Tex project recrusively
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@ -209,11 +197,14 @@ def merge_tex_files_(project_foler, main_file, mode):
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for s in reversed([q for q in re.finditer(r"\\input\{(.*?)\}", main_file, re.M)]):
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f = s.group(1)
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fp = os.path.join(project_foler, f)
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fp = find_tex_file_ignore_case(fp)
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if fp:
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with open(fp, 'r', encoding='utf-8', errors='replace') as fx: c = fx.read()
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else:
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raise RuntimeError(f'找不到{fp},Tex源文件缺失!')
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if os.path.exists(fp):
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# e.g., \input{srcs/07_appendix.tex}
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with open(fp, 'r', encoding='utf-8', errors='replace') as fx:
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c = fx.read()
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else:
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# e.g., \input{srcs/07_appendix}
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with open(fp+'.tex', 'r', encoding='utf-8', errors='replace') as fx:
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c = fx.read()
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c = merge_tex_files_(project_foler, c, mode)
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main_file = main_file[:s.span()[0]] + c + main_file[s.span()[1]:]
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return main_file
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@ -27,10 +27,8 @@ def gen_image(llm_kwargs, prompt, resolution="256x256"):
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}
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response = requests.post(url, headers=headers, json=data, proxies=proxies)
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print(response.content)
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try:
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image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
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except:
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raise RuntimeError(response.content.decode())
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image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
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# 文件保存到本地
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r = requests.get(image_url, proxies=proxies)
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file_path = 'gpt_log/image_gen/'
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@ -1,5 +1,5 @@
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from toolbox import CatchException, report_execption, write_results_to_file
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from toolbox import update_ui, promote_file_to_downloadzone
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from toolbox import update_ui
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from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
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from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
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from .crazy_utils import read_and_clean_pdf_text
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@ -147,14 +147,23 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
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print('writing html result failed:', trimmed_format_exc())
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# 准备文件的下载
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import shutil
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for pdf_path in generated_conclusion_files:
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# 重命名文件
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rename_file = f'翻译-{os.path.basename(pdf_path)}'
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promote_file_to_downloadzone(pdf_path, rename_file=rename_file, chatbot=chatbot)
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rename_file = f'./gpt_log/翻译-{os.path.basename(pdf_path)}'
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if os.path.exists(rename_file):
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os.remove(rename_file)
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shutil.copyfile(pdf_path, rename_file)
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if os.path.exists(pdf_path):
|
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os.remove(pdf_path)
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for html_path in generated_html_files:
|
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# 重命名文件
|
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rename_file = f'翻译-{os.path.basename(html_path)}'
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promote_file_to_downloadzone(html_path, rename_file=rename_file, chatbot=chatbot)
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rename_file = f'./gpt_log/翻译-{os.path.basename(html_path)}'
|
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if os.path.exists(rename_file):
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os.remove(rename_file)
|
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shutil.copyfile(html_path, rename_file)
|
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if os.path.exists(html_path):
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os.remove(html_path)
|
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chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
|
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
|
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@ -13,11 +13,11 @@ def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
|
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web_port 当前软件运行的端口号
|
||||
"""
|
||||
history = [] # 清空历史,以免输入溢出
|
||||
chatbot.append((txt, "正在同时咨询ChatGPT和ChatGLM……"))
|
||||
chatbot.append((txt, "正在同时咨询gpt-3.5和gpt-4……"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间,我们先及时地做一次界面更新
|
||||
|
||||
# llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口,用&符号分隔
|
||||
llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口,用&符号分隔
|
||||
llm_kwargs['llm_model'] = 'gpt-3.5-turbo&gpt-4' # 支持任意数量的llm接口,用&符号分隔
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=txt, inputs_show_user=txt,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
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||||
|
||||
@ -104,7 +104,7 @@ def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
meta_paper_info_list = meta_paper_info_list[batchsize:]
|
||||
|
||||
chatbot.append(["状态?",
|
||||
"已经全部完成,您可以试试让AI写一个Related Works,例如您可以继续输入Write a \"Related Works\" section about \"你搜索的研究领域\" for me."])
|
||||
"已经全部完成,您可以试试让AI写一个Related Works,例如您可以继续输入Write an academic \"Related Works\" section about \"你搜索的研究领域\" for me."])
|
||||
msg = '正常'
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
|
||||
res = write_results_to_file(history)
|
||||
|
||||
Binary file not shown.
@ -1,4 +1,3 @@
|
||||
./docs/gradio-3.32.2-py3-none-any.whl
|
||||
tiktoken>=0.3.3
|
||||
requests[socks]
|
||||
transformers
|
||||
@ -15,4 +14,4 @@ pymupdf
|
||||
openai
|
||||
numpy
|
||||
arxiv
|
||||
rich
|
||||
rich
|
||||
|
||||
@ -842,4 +842,4 @@ def objload(file='objdump.tmp'):
|
||||
return
|
||||
with open(file, 'rb') as f:
|
||||
return pickle.load(f)
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user