Merge pull request #841 from KelvinF97/master

Optimize some code and fix some bugs

README.md

@@ -1,15 +1,3 @@
----
-title: ChatImprovement
-emoji: 😻
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: false
----
-
-# ChatGPT 学术优化
 > **Note**
 >
 > 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**： 

check_proxy.py

@@ -1,10 +1,10 @@
 
-def check_proxy(proxies):
+def check_proxy(proxies: dict):
     import requests
-    proxies_https = proxies['https'] if proxies is not None else '无'
+    proxies_https = proxies.get('https') if proxies is not None else '无'
     try:
         response = requests.get("https://ipapi.co/json/",
-                                proxies=proxies, timeout=4)
+                                proxies=proxies, timeout=30)
         data = response.json()
         print(f'查询代理的地理位置，返回的结果是{data}')
         if 'country_name' in data:
@@ -12,10 +12,12 @@ def check_proxy(proxies):
             result = f"代理配置 {proxies_https}, 代理所在地：{country}"
         elif 'error' in data:
             result = f"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限"
+        else:
+            result = f"代理配置 {proxies_https}, 代理数据解析失败：{data}"
         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"
+    except Exception as e:
+        result = f"代理 {proxies_https} 查询出现异常: {e}，代理可能无效"
         print(result)
         return result
 

config.py

@@ -45,9 +45,10 @@ 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
 
-# OpenAI模型选择是（gpt4现在只对申请成功的人开放）
-LLM_MODEL = "gpt-3.5-turbo" # 可选 "chatglm"
-AVAIL_LLM_MODELS = ["newbing-free", "gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "api2d-gpt-3.5-turbo"]
+# 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 )
+LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
+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"]
+# 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"]
 
 # 本地LLM模型如ChatGLM的执行方式 CPU/GPU
 LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
@@ -55,6 +56,9 @@ LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
 # 设置gradio的并行线程数（不需要修改）
 CONCURRENT_COUNT = 100
 
+# 是否在提交时自动清空输入框
+AUTO_CLEAR_TXT = False
+
 # 加一个live2d装饰
 ADD_WAIFU = False
 

core_functional.py

@@ -63,6 +63,7 @@ def get_core_functions():
             "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，" +
                         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",
             "Suffix":   r"",
+            "Visible": False,
         },
         "解释代码": {
             "Prefix":   r"请解释以下代码：" + "\n```\n",
@@ -73,6 +74,5 @@ def get_core_functions():
                         r"Note that, reference styles maybe more than one kind, you should transform each item correctly." +
                         r"Items need to be transformed:",
             "Suffix":   r"",
-            "Visible": False,
         }
     }

crazy_functions/crazy_functions_test.py

@@ -193,8 +193,9 @@ def test_Latex():
     # txt = r"https://arxiv.org/abs/2212.10156"
     # txt = r"https://arxiv.org/abs/2211.11559"
     # txt = r"https://arxiv.org/abs/2303.08774"
-    txt = r"https://arxiv.org/abs/2303.12712"
+    # txt = r"https://arxiv.org/abs/2303.12712"
     # txt = r"C:\Users\fuqingxu\arxiv_cache\2303.12712\workfolder"
+    txt = r"2306.17157" # 这个paper有个input命令文件名大小写错误！
     
 
     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):

crazy_functions/crazy_utils.py

@@ -1,16 +1,19 @@
 from toolbox import update_ui, get_conf, trimmed_format_exc
 import threading
 
+
 def input_clipping(inputs, history, max_token_limit):
     import numpy as np
     from request_llm.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=()))
+
+    def get_token_num(txt):
+        return len(enc.encode(txt, disallowed_special=()))
 
     mode = 'input-and-history'
     # 当 输入部分的token占比 小于 全文的一半时，只裁剪历史
     input_token_num = get_token_num(inputs)
-    if input_token_num < max_token_limit//2: 
+    if input_token_num < max_token_limit // 2:
         mode = 'only-history'
         max_token_limit = max_token_limit - input_token_num
 
@@ -18,13 +21,13 @@ def input_clipping(inputs, history, max_token_limit):
     everything.extend(history)
     n_token = get_token_num('\n'.join(everything))
     everything_token = [get_token_num(e) for e in everything]
-    delta = max(everything_token) // 16 # 截断时的颗粒度
-        
+    delta = max(everything_token) // 16  # 截断时的颗粒度
+
     while n_token > max_token_limit:
         where = np.argmax(everything_token)
         encoded = enc.encode(everything[where], disallowed_special=())
-        clipped_encoded = encoded[:len(encoded)-delta]
-        everything[where] = enc.decode(clipped_encoded)[:-1]    # -1 to remove the may-be illegal char
+        clipped_encoded = encoded[:len(encoded) - delta]
+        everything[where] = enc.decode(clipped_encoded)[:-1]  # -1 to remove the may-be illegal char
         everything_token[where] = get_token_num(everything[where])
         n_token = get_token_num('\n'.join(everything))
 
@@ -35,12 +38,13 @@ def input_clipping(inputs, history, max_token_limit):
     history = everything[1:]
     return inputs, history
 
+
 def request_gpt_model_in_new_thread_with_ui_alive(
-        inputs, inputs_show_user, llm_kwargs, 
+        inputs, inputs_show_user, llm_kwargs,
         chatbot, history, sys_prompt, refresh_interval=0.2,
-        handle_token_exceed=True, 
+        handle_token_exceed=True,
         retry_times_at_unknown_error=2,
-        ):
+):
     """
     Request GPT model，请求GPT模型同时维持用户界面活跃。
 
@@ -64,15 +68,16 @@ def request_gpt_model_in_new_thread_with_ui_alive(
     from request_llm.bridge_all import predict_no_ui_long_connection
     # 用户反馈
     chatbot.append([inputs_show_user, ""])
-    yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
+    yield from update_ui(chatbot=chatbot, history=[])  # 刷新界面
     executor = ThreadPoolExecutor(max_workers=16)
     mutable = ["", time.time(), ""]
+
     def _req_gpt(inputs, history, sys_prompt):
         retry_op = retry_times_at_unknown_error
         exceeded_cnt = 0
         while True:
             # watchdog error
-            if len(mutable) >= 2 and (time.time()-mutable[1]) > 5: 
+            if len(mutable) >= 2 and (time.time() - mutable[1]) > 5:
                 raise RuntimeError("检测到程序终止。")
             try:
                 # 【第一种情况】：顺利完成
@@ -89,14 +94,14 @@ def request_gpt_model_in_new_thread_with_ui_alive(
                     p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
                     MAX_TOKEN = 4096
                     EXCEED_ALLO = 512 + 512 * exceeded_cnt
-                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
+                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN - EXCEED_ALLO)
                     mutable[0] += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
-                    continue # 返回重试
+                    continue  # 返回重试
                 else:
                     # 【选择放弃】
                     tb_str = '```\n' + trimmed_format_exc() + '```'
                     mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                    return mutable[0] # 放弃
+                    return mutable[0]  # 放弃
             except:
                 # 【第三种情况】：其他错误：重试几次
                 tb_str = '```\n' + trimmed_format_exc() + '```'
@@ -104,14 +109,15 @@ def request_gpt_model_in_new_thread_with_ui_alive(
                 mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
                 if retry_op > 0:
                     retry_op -= 1
-                    mutable[0] += f"[Local Message] 重试中，请稍等 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}：\n\n"
+                    mutable[
+                        0] += f"[Local Message] 重试中，请稍等 {retry_times_at_unknown_error - retry_op}/{retry_times_at_unknown_error}：\n\n"
                     if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str):
                         time.sleep(30)
                     time.sleep(5)
-                    continue # 返回重试
+                    continue  # 返回重试
                 else:
                     time.sleep(5)
-                    return mutable[0] # 放弃
+                    return mutable[0]  # 放弃
 
     # 提交任务
     future = executor.submit(_req_gpt, inputs, history, sys_prompt)
@@ -123,21 +129,21 @@ def request_gpt_model_in_new_thread_with_ui_alive(
         if future.done():
             break
         chatbot[-1] = [chatbot[-1][0], mutable[0]]
-        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
+        yield from update_ui(chatbot=chatbot, history=[])  # 刷新界面
 
     final_result = future.result()
     chatbot[-1] = [chatbot[-1][0], final_result]
-    yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
+    yield from update_ui(chatbot=chatbot, history=[])  # 如果最后成功了，则删除报错信息
     return final_result
 
 
 def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array, inputs_show_user_array, llm_kwargs, 
-        chatbot, history_array, sys_prompt_array, 
+        inputs_array, inputs_show_user_array, llm_kwargs,
+        chatbot, history_array, sys_prompt_array,
         refresh_interval=0.2, max_workers=-1, scroller_max_len=30,
         handle_token_exceed=True, show_user_at_complete=False,
         retry_times_at_unknown_error=2,
-        ):
+):
     """
     Request GPT model using multiple threads with UI and high efficiency
     请求GPT模型的[多线程]版。
@@ -170,19 +176,21 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
     from request_llm.bridge_all import predict_no_ui_long_connection
     assert len(inputs_array) == len(history_array)
     assert len(inputs_array) == len(sys_prompt_array)
-    if max_workers == -1: # 读取配置文件
-        try: max_workers, = get_conf('DEFAULT_WORKER_NUM')
-        except: max_workers = 8
+    if max_workers == -1:  # 读取配置文件
+        try:
+            max_workers, = get_conf('DEFAULT_WORKER_NUM')
+        except:
+            max_workers = 8
         if max_workers <= 0: max_workers = 3
     # 屏蔽掉 chatglm的多线程，可能会导致严重卡顿
     if not (llm_kwargs['llm_model'].startswith('gpt-') or llm_kwargs['llm_model'].startswith('api2d-')):
         max_workers = 1
-        
+
     executor = ThreadPoolExecutor(max_workers=max_workers)
     n_frag = len(inputs_array)
     # 用户反馈
     chatbot.append(["请开始多线程操作。", ""])
-    yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
+    yield from update_ui(chatbot=chatbot, history=[])  # 刷新界面
     # 跨线程传递
     mutable = [["", time.time(), "等待中"] for _ in range(n_frag)]
 
@@ -194,13 +202,13 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
         mutable[index][2] = "执行中"
         while True:
             # watchdog error
-            if len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > 5: 
+            if len(mutable[index]) >= 2 and (time.time() - mutable[index][1]) > 5:
                 raise RuntimeError("检测到程序终止。")
             try:
                 # 【第一种情况】：顺利完成
                 # time.sleep(10); raise RuntimeError("测试")
                 gpt_say = predict_no_ui_long_connection(
-                    inputs=inputs, llm_kwargs=llm_kwargs, history=history, 
+                    inputs=inputs, llm_kwargs=llm_kwargs, history=history,
                     sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True
                 )
                 mutable[index][2] = "已成功"
@@ -214,24 +222,26 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
                     p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
                     MAX_TOKEN = 4096
                     EXCEED_ALLO = 512 + 512 * exceeded_cnt
-                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
+                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN - EXCEED_ALLO)
                     gpt_say += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
                     mutable[index][2] = f"截断重试"
-                    continue # 返回重试
+                    continue  # 返回重试
                 else:
                     # 【选择放弃】
                     tb_str = '```\n' + trimmed_format_exc() + '```'
                     gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                    if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
+                    if len(mutable[index][0]) > 0:
+                        gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
                     mutable[index][2] = "输入过长已放弃"
-                    return gpt_say # 放弃
-            except:
+                    return gpt_say  # 放弃
+            except Exception as e:
                 # 【第三种情况】：其他错误
                 tb_str = '```\n' + trimmed_format_exc() + '```'
-                print(tb_str)
+                print(f"发生异常：{e}, 调用栈信息：{tb_str}")
                 gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
-                if retry_op > 0: 
+                if len(mutable[index][0]) > 0:
+                    gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
+                if retry_op > 0:
                     retry_op -= 1
                     wait = random.randint(5, 20)
                     if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str):
@@ -241,19 +251,22 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
                         fail_info = ""
                     # 也许等待十几秒后，情况会好转
                     for i in range(wait):
-                        mutable[index][2] = f"{fail_info}等待重试 {wait-i}"; time.sleep(1)
+                        mutable[index][2] = f"{fail_info}等待重试 {wait - i}";
+                        time.sleep(1)
                     # 开始重试
-                    mutable[index][2] = f"重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}"
-                    continue # 返回重试
+                    mutable[index][
+                        2] = f"重试中 {retry_times_at_unknown_error - retry_op}/{retry_times_at_unknown_error}"
+                    continue  # 返回重试
                 else:
                     mutable[index][2] = "已失败"
                     wait = 5
                     time.sleep(5)
-                    return gpt_say # 放弃
+                    return gpt_say  # 放弃
 
     # 异步任务开始
-    futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(
-        range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
+    futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in
+               zip(
+                   range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
     cnt = 0
     while True:
         # yield一次以刷新前端页面
@@ -267,17 +280,17 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
             mutable[thread_index][1] = time.time()
         # 在前端打印些好玩的东西
         for thread_index, _ in enumerate(worker_done):
-            print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
+            print_something_really_funny = "[ ...`" + mutable[thread_index][0][-scroller_max_len:]. \
                 replace('\n', '').replace('```', '...').replace(
-                    ' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
+                ' ', '.').replace('<br/>', '.....').replace('$', '.') + "`... ]"
             observe_win.append(print_something_really_funny)
         # 在前端打印些好玩的东西
-        stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n' 
-                            if not done else f'`{mutable[thread_index][2]}`\n\n' 
+        stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n'
+                            if not done else f'`{mutable[thread_index][2]}`\n\n'
                             for thread_index, done, obs in zip(range(len(worker_done)), worker_done, observe_win)])
         # 在前端打印些好玩的东西
-        chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))]
-        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
+        chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.'] * (cnt % 10 + 1))]
+        yield from update_ui(chatbot=chatbot, history=[])  # 刷新界面
         if all(worker_done):
             executor.shutdown()
             break
@@ -287,13 +300,13 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
     for inputs_show_user, f in zip(inputs_show_user_array, futures):
         gpt_res = f.result()
         gpt_response_collection.extend([inputs_show_user, gpt_res])
-    
+
     # 是否在结束时，在界面上显示结果
     if show_user_at_complete:
         for inputs_show_user, f in zip(inputs_show_user_array, futures):
             gpt_res = f.result()
             chatbot.append([inputs_show_user, gpt_res])
-            yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
+            yield from update_ui(chatbot=chatbot, history=[])  # 刷新界面
             time.sleep(0.3)
     return gpt_response_collection
 
@@ -306,6 +319,7 @@ def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
             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] != "":
@@ -322,6 +336,7 @@ def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
             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:
@@ -337,9 +352,10 @@ def force_breakdown(txt, limit, get_token_fn):
             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):  
+    def cut(txt_tocut, must_break_at_empty_line, break_anyway=False):
         if get_token_fn(txt_tocut) <= limit:
             return [txt_tocut]
         else:
@@ -365,6 +381,7 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
             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)
@@ -375,7 +392,7 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
         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
+                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:
@@ -387,7 +404,6 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
                     return cut(txt, must_break_at_empty_line=False, break_anyway=True)
 
 
-
 def read_and_clean_pdf_text(fp):
     """
     这个函数用于分割pdf，用了很多trick，逻辑较乱，效果奇好
@@ -415,8 +431,9 @@ def read_and_clean_pdf_text(fp):
     fc = 0  # Index 0 文本
     fs = 1  # Index 1 字体
     fb = 2  # Index 2 框框
-    REMOVE_FOOT_NOTE = True # 是否丢弃掉 不是正文的内容 （比正文字体小，如参考文献、脚注、图注等）
-    REMOVE_FOOT_FFSIZE_PERCENT = 0.95 # 小于正文的？时，判定为不是正文（有些文章的正文部分字体大小不是100%统一的，有肉眼不可见的小变化）
+    REMOVE_FOOT_NOTE = True  # 是否丢弃掉 不是正文的内容 （比正文字体小，如参考文献、脚注、图注等）
+    REMOVE_FOOT_FFSIZE_PERCENT = 0.95  # 小于正文的？时，判定为不是正文（有些文章的正文部分字体大小不是100%统一的，有肉眼不可见的小变化）
+
     def primary_ffsize(l):
         """
         提取文本块主字体
@@ -426,12 +443,12 @@ def read_and_clean_pdf_text(fp):
             if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0
             fsize_statiscs[wtf['size']] += len(wtf['text'])
         return max(fsize_statiscs, key=fsize_statiscs.get)
-        
-    def ffsize_same(a,b):
+
+    def ffsize_same(a, b):
         """
         提取字体大小是否近似相等
         """
-        return abs((a-b)/max(a,b)) < 0.02
+        return abs((a - b) / max(a, b)) < 0.02
 
     with fitz.open(fp) as doc:
         meta_txt = []
@@ -451,18 +468,19 @@ def read_and_clean_pdf_text(fp):
                         if len(txt_line) == 0: continue
                         pf = primary_ffsize(l)
                         meta_line.append([txt_line, pf, l['bbox'], l])
-                        for wtf in l['spans']: # for l in t['lines']:
+                        for wtf in l['spans']:  # for l in t['lines']:
                             meta_span.append([wtf['text'], wtf['size'], len(wtf['text'])])
                     # meta_line.append(["NEW_BLOCK", pf])
-            # 块元提取                           for each word segment with in line                       for each line         cross-line words                          for each block
+            # 块元提取                           for each word segment with in line                       for each line
+            # cross-line words                          for each block
             meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
                 '- ', '') for t in text_areas['blocks'] if 'lines' in t])
             meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']])
-                             for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])
+                                       for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])
             if index == 0:
                 page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
                     '- ', '') for t in text_areas['blocks'] if 'lines' in t]
-                
+
         ############################## <第 2 步，获取正文主字体> ##################################
         fsize_statiscs = {}
         for span in meta_span:
@@ -476,32 +494,33 @@ def read_and_clean_pdf_text(fp):
         mega_sec = []
         sec = []
         for index, line in enumerate(meta_line):
-            if index == 0: 
+            if index == 0:
                 sec.append(line[fc])
                 continue
             if REMOVE_FOOT_NOTE:
                 if meta_line[index][fs] <= give_up_fize_threshold:
                     continue
-            if ffsize_same(meta_line[index][fs], meta_line[index-1][fs]):
+            if ffsize_same(meta_line[index][fs], meta_line[index - 1][fs]):
                 # 尝试识别段落
-                if meta_line[index][fc].endswith('.') and\
-                    (meta_line[index-1][fc] != 'NEW_BLOCK') and \
-                    (meta_line[index][fb][2] - meta_line[index][fb][0]) < (meta_line[index-1][fb][2] - meta_line[index-1][fb][0]) * 0.7:
+                if meta_line[index][fc].endswith('.') and \
+                        (meta_line[index - 1][fc] != 'NEW_BLOCK') and \
+                        (meta_line[index][fb][2] - meta_line[index][fb][0]) < (
+                        meta_line[index - 1][fb][2] - meta_line[index - 1][fb][0]) * 0.7:
                     sec[-1] += line[fc]
                     sec[-1] += "\n\n"
                 else:
                     sec[-1] += " "
                     sec[-1] += line[fc]
             else:
-                if (index+1 < len(meta_line)) and \
-                    meta_line[index][fs] > main_fsize:
+                if (index + 1 < len(meta_line)) and \
+                        meta_line[index][fs] > main_fsize:
                     # 单行 + 字体大
                     mega_sec.append(copy.deepcopy(sec))
                     sec = []
                     sec.append("# " + line[fc])
                 else:
                     # 尝试识别section
-                    if meta_line[index-1][fs] > meta_line[index][fs]:
+                    if meta_line[index - 1][fs] > meta_line[index][fs]:
                         sec.append("\n" + line[fc])
                     else:
                         sec.append(line[fc])
@@ -520,13 +539,15 @@ def read_and_clean_pdf_text(fp):
                 if len(block_txt) < 100:
                     meta_txt[index] = '\n'
             return meta_txt
+
         meta_txt = 把字符太少的块清除为回车(meta_txt)
 
         def 清理多余的空行(meta_txt):
             for index in reversed(range(1, len(meta_txt))):
-                if meta_txt[index] == '\n' and meta_txt[index-1] == '\n':
+                if meta_txt[index] == '\n' and meta_txt[index - 1] == '\n':
                     meta_txt.pop(index)
             return meta_txt
+
         meta_txt = 清理多余的空行(meta_txt)
 
         def 合并小写开头的段落块(meta_txt):
@@ -537,16 +558,18 @@ def read_and_clean_pdf_text(fp):
                     return True
                 else:
                     return False
+
             for _ in range(100):
                 for index, block_txt in enumerate(meta_txt):
                     if starts_with_lowercase_word(block_txt):
-                        if meta_txt[index-1] != '\n':
-                            meta_txt[index-1] += ' '
+                        if meta_txt[index - 1] != '\n':
+                            meta_txt[index - 1] += ' '
                         else:
-                            meta_txt[index-1] = ''
-                        meta_txt[index-1] += meta_txt[index]
+                            meta_txt[index - 1] = ''
+                        meta_txt[index - 1] += meta_txt[index]
                         meta_txt[index] = '\n'
             return meta_txt
+
         meta_txt = 合并小写开头的段落块(meta_txt)
         meta_txt = 清理多余的空行(meta_txt)
 
@@ -566,7 +589,7 @@ def read_and_clean_pdf_text(fp):
     return meta_txt, page_one_meta
 
 
-def get_files_from_everything(txt, type): # type='.md'
+def get_files_from_everything(txt, type):  # type='.md'
     """
     这个函数是用来获取指定目录下所有指定类型（如.md）的文件，并且对于网络上的文件，也可以获取它。
     下面是对每个参数和返回值的说明：
@@ -588,9 +611,10 @@ def get_files_from_everything(txt, type): # type='.md'
         from toolbox import get_conf
         proxies, = get_conf('proxies')
         r = requests.get(txt, proxies=proxies)
-        with open('./gpt_log/temp'+type, 'wb+') as f: f.write(r.content)
+        with open('./gpt_log/temp' + type, 'wb+') as f:
+            f.write(r.content)
         project_folder = './gpt_log/'
-        file_manifest = ['./gpt_log/temp'+type]
+        file_manifest = ['./gpt_log/temp' + type]
     elif txt.endswith(type):
         # 直接给定文件
         file_manifest = [txt]
@@ -598,7 +622,7 @@ def get_files_from_everything(txt, type): # type='.md'
     elif os.path.exists(txt):
         # 本地路径，递归搜索
         project_folder = txt
-        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*'+type, recursive=True)]
+        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*' + type, recursive=True)]
         if len(file_manifest) == 0:
             success = False
     else:
@@ -609,16 +633,14 @@ def get_files_from_everything(txt, type): # type='.md'
     return success, file_manifest, project_folder
 
 
-
-
 def Singleton(cls):
     _instance = {}
- 
+
     def _singleton(*args, **kargs):
         if cls not in _instance:
             _instance[cls] = cls(*args, **kargs)
         return _instance[cls]
- 
+
     return _singleton
 
 
@@ -637,31 +659,30 @@ class knowledge_archive_interface():
             from toolbox import ProxyNetworkActivate
             print('Checking Text2vec ...')
             from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-            with ProxyNetworkActivate():    # 临时地激活代理网络
+            with ProxyNetworkActivate():  # 临时地激活代理网络
                 self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
 
         return self.text2vec_large_chinese
 
-
     def feed_archive(self, file_manifest, id="default"):
         self.threadLock.acquire()
         # import uuid
         self.current_id = id
         from zh_langchain import construct_vector_store
-        self.qa_handle, self.kai_path = construct_vector_store(   
-            vs_id=self.current_id, 
-            files=file_manifest, 
+        self.qa_handle, self.kai_path = construct_vector_store(
+            vs_id=self.current_id,
+            files=file_manifest,
             sentence_size=100,
             history=[],
             one_conent="",
             one_content_segmentation="",
-            text2vec = self.get_chinese_text2vec(),
+            text2vec=self.get_chinese_text2vec(),
         )
         self.threadLock.release()
 
     def get_current_archive_id(self):
         return self.current_id
-    
+
     def get_loaded_file(self):
         return self.qa_handle.get_loaded_file()
 
@@ -670,30 +691,31 @@ class knowledge_archive_interface():
         if not self.current_id == id:
             self.current_id = id
             from zh_langchain import construct_vector_store
-            self.qa_handle, self.kai_path = construct_vector_store(   
-                vs_id=self.current_id, 
-                files=[], 
+            self.qa_handle, self.kai_path = construct_vector_store(
+                vs_id=self.current_id,
+                files=[],
                 sentence_size=100,
                 history=[],
                 one_conent="",
                 one_content_segmentation="",
-                text2vec = self.get_chinese_text2vec(),
+                text2vec=self.get_chinese_text2vec(),
             )
         VECTOR_SEARCH_SCORE_THRESHOLD = 0
         VECTOR_SEARCH_TOP_K = 4
         CHUNK_SIZE = 512
         resp, prompt = self.qa_handle.get_knowledge_based_conent_test(
-            query = txt,
-            vs_path = self.kai_path,
+            query=txt,
+            vs_path=self.kai_path,
             score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
-            vector_search_top_k=VECTOR_SEARCH_TOP_K, 
+            vector_search_top_k=VECTOR_SEARCH_TOP_K,
             chunk_conent=True,
             chunk_size=CHUNK_SIZE,
-            text2vec = self.get_chinese_text2vec(),
+            text2vec=self.get_chinese_text2vec(),
         )
         self.threadLock.release()
         return resp, prompt
 
+
 def try_install_deps(deps):
     for dep in deps:
         import subprocess, sys

crazy_functions/latex_utils.py

@@ -203,6 +203,7 @@ def merge_tex_files_(project_foler, main_file, mode):
                 c = fx.read()
         else:  
             # e.g., \input{srcs/07_appendix}
+            assert os.path.exists(fp+'.tex'), f'即找不到{fp}，也找不到{fp}.tex，Tex源文件缺失！'
             with open(fp+'.tex', 'r', encoding='utf-8', errors='replace') as fx:
                 c = fx.read()
         c = merge_tex_files_(project_foler, c, mode)

crazy_functions/图片生成.py

@@ -27,8 +27,10 @@ def gen_image(llm_kwargs, prompt, resolution="256x256"):
     }
     response = requests.post(url, headers=headers, json=data, proxies=proxies)
     print(response.content)
-    image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
-
+    try:
+        image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
+    except:
+        raise RuntimeError(response.content.decode())
     # 文件保存到本地
     r = requests.get(image_url, proxies=proxies)
     file_path = 'gpt_log/image_gen/'

crazy_functions/批量翻译PDF文档_多线程.py

@@ -1,5 +1,5 @@
 from toolbox import CatchException, report_execption, write_results_to_file
-from toolbox import update_ui
+from toolbox import update_ui, 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
@@ -147,23 +147,14 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
             print('writing html result failed:', trimmed_format_exc())
 
     # 准备文件的下载
-    import shutil
     for pdf_path in generated_conclusion_files:
         # 重命名文件
-        rename_file = f'./gpt_log/翻译-{os.path.basename(pdf_path)}'
-        if os.path.exists(rename_file):
-            os.remove(rename_file)
-        shutil.copyfile(pdf_path, rename_file)
-        if os.path.exists(pdf_path):
-            os.remove(pdf_path)
+        rename_file = f'翻译-{os.path.basename(pdf_path)}'
+        promote_file_to_downloadzone(pdf_path, rename_file=rename_file, chatbot=chatbot)
     for html_path in generated_html_files:
         # 重命名文件
-        rename_file = f'./gpt_log/翻译-{os.path.basename(html_path)}'
-        if os.path.exists(rename_file):
-            os.remove(rename_file)
-        shutil.copyfile(html_path, rename_file)
-        if os.path.exists(html_path):
-            os.remove(html_path)
+        rename_file = f'翻译-{os.path.basename(html_path)}'
+        promote_file_to_downloadzone(html_path, rename_file=rename_file, chatbot=chatbot)
     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/询问多个大语言模型.py

@@ -13,11 +13,11 @@ def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
     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gpt-3.5和gpt-4……"))
+    chatbot.append((txt, "正在同时咨询ChatGPT和ChatGLM……"))
     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'] = 'gpt-3.5-turbo&gpt-4' # 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
     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, 

crazy_functions/读文章写摘要.py

@@ -1,67 +1,78 @@
-from toolbox import update_ui
 from toolbox import CatchException, report_execption, write_results_to_file
+from toolbox import update_ui
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+
 fast_debug = False
 
 
 def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, glob, os
+    import time
+    import os
     print('begin analysis on:', file_manifest)
     for index, fp in enumerate(file_manifest):
         with open(fp, 'r', encoding='utf-8', errors='replace') as f:
             file_content = f.read()
 
-        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
+        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index == 0 else ""
         i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
         i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
         chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
 
-        if not fast_debug: 
+        if not fast_debug:
             msg = '正常'
             # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt)   # 带超时倒计时
+            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, llm_kwargs,
+                                                                               chatbot, history=[],
+                                                                               sys_prompt=system_prompt)  # 带超时倒计时
 
             chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+            history.append(i_say_show_user);
+            history.append(gpt_say)
+            yield from update_ui(chatbot=chatbot, history=history, msg=msg)  # 刷新界面
             if not fast_debug: time.sleep(2)
 
     all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
     i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
     chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
 
-    if not fast_debug: 
+    if not fast_debug:
         msg = '正常'
         # ** gpt request **
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say, llm_kwargs, chatbot, history=history, sys_prompt=system_prompt)   # 带超时倒计时
+        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say, llm_kwargs, chatbot,
+                                                                           history=history,
+                                                                           sys_prompt=system_prompt)  # 带超时倒计时
 
         chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+        history.append(i_say)
+        history.append(gpt_say)
+        yield from update_ui(chatbot=chatbot, history=history, msg=msg)  # 刷新界面
         res = write_results_to_file(history)
         chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
+        yield from update_ui(chatbot=chatbot, history=history, msg=msg)  # 刷新界面
 
 
 @CatchException
-def 读文章写摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
+def 读文章写摘要(txt, llm_kwargs, plugin_kwargs, chatbot, system_prompt, web_port, history=None):
+    # history = []    # 清空历史，以免输入溢出
+    if history is None:
+        history = []  # 清空历史，以免输入溢出
+    import glob
+    import os
     if os.path.exists(txt):
         project_folder = txt
     else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        if txt == "":
+            txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
         return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]  # + \
+    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
+    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
     if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
         return
     yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/谷歌检索小助手.py

@@ -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 an academic \"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 a \"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)

crazy_functions/辅助回答.py

@@ -0,0 +1,28 @@
+# encoding: utf-8
+# @Time   : 2023/4/19
+# @Author : Spike
+# @Descr   :
+from toolbox import update_ui
+from toolbox import CatchException, report_execption, write_results_to_file
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+
+
+@CatchException
+def 猜你想问(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    if txt:
+        show_say = txt
+        prompt = txt+'\n回答完问题后，再列出用户可能提出的三个问题。'
+    else:
+        prompt = history[-1]+"\n分析上述回答，再列出用户可能提出的三个问题。"
+        show_say = '分析上述回答，再列出用户可能提出的三个问题。'
+    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+        inputs=prompt,
+        inputs_show_user=show_say,
+        llm_kwargs=llm_kwargs,
+        chatbot=chatbot,
+        history=history,
+        sys_prompt=system_prompt
+    )
+    chatbot[-1] = (show_say, gpt_say)
+    history.extend([show_say, gpt_say])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面

main.py

@@ -1,15 +1,13 @@
 import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
 
 def main():
-    import subprocess, sys
-    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'gradio-stable-fork'])
     import gradio as gr
-    if gr.__version__ not in ['3.28.3','3.32.3']: assert False, "请用 pip install -r requirements.txt 安装依赖"
+    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"
     from request_llm.bridge_all import predict
     from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith
     # 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, CHATBOT_HEIGHT, LAYOUT, API_KEY, AVAIL_LLM_MODELS = \
-        get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY', 'AVAIL_LLM_MODELS')
+    proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = \
+        get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
 
     # 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
@@ -56,7 +54,6 @@ def main():
     cancel_handles = []
     with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo:
         gr.HTML(title_html)
-        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>''')
         cookies = gr.State({'api_key': API_KEY, 'llm_model': LLM_MODEL})
         with gr_L1():
             with gr_L2(scale=2):
@@ -66,7 +63,7 @@ def main():
             with gr_L2(scale=1):
                 with gr.Accordion("输入区", open=True) as area_input_primary:
                     with gr.Row():
-                        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)
+                        txt = gr.Textbox(show_label=False, placeholder="Input question here.").style(container=False)
                     with gr.Row():
                         submitBtn = gr.Button("提交", variant="primary")
                     with gr.Row():
@@ -107,7 +104,7 @@ def main():
                     system_prompt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt)
                     top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
                     temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
-                    max_length_sl = gr.Slider(minimum=256, maximum=4096, value=512, step=1, interactive=True, label="Local LLM MaxLength",)
+                    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)
 
@@ -147,6 +144,11 @@ def main():
         resetBtn2.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])
         clearBtn.click(lambda: ("",""), None, [txt, txt2])
         clearBtn2.click(lambda: ("",""), None, [txt, txt2])
+        if AUTO_CLEAR_TXT:
+            submitBtn.click(lambda: ("",""), None, [txt, txt2])
+            submitBtn2.click(lambda: ("",""), None, [txt, txt2])
+            txt.submit(lambda: ("",""), None, [txt, txt2])
+            txt2.submit(lambda: ("",""), None, [txt, txt2])
         # 基础功能区的回调函数注册
         for k in functional:
             if ("Visible" in functional[k]) and (not functional[k]["Visible"]): continue
@@ -200,7 +202,10 @@ def main():
         threading.Thread(target=warm_up_modules, name="warm-up", daemon=True).start()
 
     auto_opentab_delay()
-    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"])
+    demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
+        server_name="0.0.0.0", server_port=PORT,
+        favicon_path="docs/logo.png", auth=AUTHENTICATION,
+        blocked_paths=["config.py","config_private.py","docker-compose.yml","Dockerfile"])
 
     # 如果需要在二级路径下运行
     # CUSTOM_PATH, = get_conf('CUSTOM_PATH')

request_llm/bridge_chatgpt.py

@@ -28,6 +28,7 @@ proxies, API_KEY, TIMEOUT_SECONDS, MAX_RETRY = \
 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 get_full_error(chunk, stream_response):
     """
         获取完整的从Openai返回的报错
@@ -40,7 +41,9 @@ def get_full_error(chunk, stream_response):
     return chunk
 
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
+def predict_no_ui_long_connection(
+        inputs, llm_kwargs, history=None, sys_prompt="", observe_window=None, console_slience=False
+):
     """
     发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
     inputs：
@@ -54,45 +57,59 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     observe_window = None：
         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
+    if history is None:
+        history = []
+    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)
     retry = 0
+    from bridge_all import model_info
     while True:
         try:
             # make a POST request to the API endpoint, stream=False
-            from .bridge_all import model_info
             endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
             response = requests.post(endpoint, headers=headers, proxies=proxies,
-                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
-        except requests.exceptions.ReadTimeout as e:
+                                     json=payload, stream=True, timeout=TIMEOUT_SECONDS)
+            stream_response = response.iter_lines()
+            break
+        except (requests.exceptions.ReadTimeout, requests.exceptions.ConnectionError):
             retry += 1
             traceback.print_exc()
-            if retry > MAX_RETRY: raise TimeoutError
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+            if retry > MAX_RETRY:
+                raise TimeoutError
+            if MAX_RETRY != 0:
+                print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+        except Exception as e:
+            print(f"出现异常：{e}")
+            raise e
 
-    stream_response =  response.iter_lines()
     result = ''
     while True:
-        try: chunk = next(stream_response).decode()
+        try:
+            chunk = next(stream_response).decode()
         except StopIteration: 
             break
-        except requests.exceptions.ConnectionError:
-            chunk = next(stream_response).decode() # 失败了，重试一次？再失败就没办法了。
-        if len(chunk)==0: continue
+        # except requests.exceptions.ConnectionError:
+        #     chunk = next(stream_response).decode()  # 失败了，重试一次？再失败就没办法了。
+        if len(chunk) == 0:
+            continue
         if not chunk.startswith('data:'): 
             error_msg = get_full_error(chunk.encode('utf8'), stream_response).decode()
             if "reduce the length" in error_msg:
                 raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg)
             else:
                 raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
-        if ('data: [DONE]' in chunk): break # api2d 正常完成
+        if 'data: [DONE]' in chunk:
+            break  # api2d 正常完成
         json_data = json.loads(chunk.lstrip('data:'))['choices'][0]
         delta = json_data["delta"]
-        if len(delta) == 0: break
-        if "role" in delta: continue
+        if len(delta) == 0:
+            break
+        if "role" in delta:
+            continue
         if "content" in delta: 
             result += delta["content"]
-            if not console_slience: print(delta["content"], end='')
+            if not console_slience:
+                print(delta["content"], end='')
             if observe_window is not None: 
                 # 观测窗，把已经获取的数据显示出去
                 if len(observe_window) >= 1: observe_window[0] += delta["content"]
@@ -100,7 +117,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
                 if len(observe_window) >= 2:  
                     if (time.time()-observe_window[1]) > watch_dog_patience:
                         raise RuntimeError("用户取消了程序。")
-        else: raise RuntimeError("意外Json结构："+delta)
+        else:
+            raise RuntimeError("意外Json结构："+delta)
     if json_data['finish_reason'] == 'length':
         raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
     return result
@@ -228,6 +246,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
                     return
 
+
 def generate_payload(inputs, llm_kwargs, 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
@@ -247,23 +266,19 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
     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]
+            what_i_have_asked = {"role": "user", "content": history[index]}
+            what_gpt_answer = {"role": "assistant", "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
+                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
+    what_i_ask_now = {"role": "user", "content": inputs}
     messages.append(what_i_ask_now)
 
     payload = {
@@ -278,8 +293,8 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
     }
     try:
         print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
-    except:
-        print('输入中可能存在乱码。')
-    return headers,payload
+    except Exception as e:
+        print(f'输入中可能存在乱码。抛出异常: {e}')
+    return headers, payload
 
 

requirements.txt

@@ -1,3 +1,4 @@
+gradio>=3.33.1
 tiktoken>=0.3.3
 requests[socks]
 transformers
@@ -15,3 +16,5 @@ openai
 numpy
 arxiv
 rich
+langchain
+zh_langchain

toolbox.py

@@ -21,6 +21,7 @@ pj = os.path.join
 ========================================================================
 """
 
+
 class ChatBotWithCookies(list):
     def __init__(self, cookie):
         self._cookies = cookie
@@ -71,11 +72,13 @@ def update_ui(chatbot, history, msg='正常', **kwargs):  # 刷新界面
     assert isinstance(chatbot, ChatBotWithCookies), "在传递chatbot的过程中不要将其丢弃。必要时，可用clear将其清空，然后用for+append循环重新赋值。"
     yield chatbot.get_cookies(), chatbot, history, msg
 
+
 def update_ui_lastest_msg(lastmsg, chatbot, history, delay=1):  # 刷新界面
     """
     刷新用户界面
     """
-    if len(chatbot) == 0: chatbot.append(["update_ui_last_msg", lastmsg])
+    if len(chatbot) == 0:
+        chatbot.append(["update_ui_last_msg", lastmsg])
     chatbot[-1] = list(chatbot[-1])
     chatbot[-1][-1] = lastmsg
     yield from update_ui(chatbot=chatbot, history=history)
@@ -83,24 +86,25 @@ def update_ui_lastest_msg(lastmsg, chatbot, history, delay=1):  # 刷新界面
 
 
 def trimmed_format_exc():
-    import os, traceback
-    str = traceback.format_exc()
+    import os
+    import traceback
+    _str = traceback.format_exc()
     current_path = os.getcwd()
     replace_path = "."
-    return str.replace(current_path, replace_path)
+    return _str.replace(current_path, replace_path)
+
 
 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=-1):
         try:
             yield from f(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT)
         except Exception as e:
             from check_proxy import check_proxy
-            from toolbox import get_conf
+            # from toolbox import get_conf  # 不需要导入本文件内容
             proxies, = get_conf('proxies')
             tb_str = '```\n' + trimmed_format_exc() + '```'
             if len(chatbot) == 0:
@@ -108,7 +112,7 @@ def CatchException(f):
                 chatbot.append(["插件调度异常", "异常原因"])
             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 from update_ui(chatbot=chatbot, history=history, msg=f'异常 {e}') # 刷新界面
+            yield from update_ui(chatbot=chatbot, history=history, msg=f'异常 {e}')  # 刷新界面
     return decorated
 
 
@@ -148,6 +152,7 @@ def HotReload(f):
 ========================================================================
 """
 
+
 def get_reduce_token_percent(text):
     """
         * 此函数未来将被弃用
@@ -207,8 +212,6 @@ def regular_txt_to_markdown(text):
     return text
 
 
-
-
 def report_execption(chatbot, history, a, b):
     """
     向chatbot中添加错误信息
@@ -238,6 +241,7 @@ def text_divide_paragraph(text):
         text = "</br>".join(lines)
         return pre + text + suf
 
+
 @lru_cache(maxsize=128) # 使用 lru缓存 加快转换速度
 def markdown_convertion(txt):
     """
@@ -440,6 +444,7 @@ def find_recent_files(directory):
 
     return recent_files
 
+
 def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
     # 将文件复制一份到下载区
     import shutil
@@ -452,6 +457,7 @@ def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
         else: current = []
         chatbot._cookies.update({'file_to_promote': [new_path] + current})
 
+
 def on_file_uploaded(files, chatbot, txt, txt2, checkboxes):
     """
     当文件被上传时的回调函数
@@ -505,17 +511,20 @@ def on_report_generated(cookies, files, chatbot):
     chatbot.append(['报告如何远程获取？', f'报告已经添加到右侧“文件上传区”（可能处于折叠状态），请查收。{file_links}'])
     return cookies, report_files, chatbot
 
+
 def is_openai_api_key(key):
     API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
     API_MATCH_AZURE = re.match(r"[a-zA-Z0-9]{32}$", key)
     return bool(API_MATCH_ORIGINAL) or bool(API_MATCH_AZURE)
 
+
 def is_api2d_key(key):
     if key.startswith('fk') and len(key) == 41:
         return True
     else:
         return False
 
+
 def is_any_api_key(key):
     if ',' in key:
         keys = key.split(',')
@@ -525,6 +534,7 @@ def is_any_api_key(key):
     else:
         return is_openai_api_key(key) or is_api2d_key(key)
 
+
 def what_keys(keys):
     avail_key_list = {'OpenAI Key':0, "API2D Key":0}
     key_list = keys.split(',')
@@ -539,6 +549,7 @@ def what_keys(keys):
 
     return f"检测到： OpenAI Key {avail_key_list['OpenAI Key']} 个，API2D Key {avail_key_list['API2D Key']} 个"
 
+
 def select_api_key(keys, llm_model):
     import random
     avail_key_list = []
@@ -558,6 +569,7 @@ def select_api_key(keys, llm_model):
     api_key = random.choice(avail_key_list) # 随机负载均衡
     return api_key
 
+
 def read_env_variable(arg, default_value):
     """
     环境变量可以是 `GPT_ACADEMIC_CONFIG`(优先)，也可以直接是`CONFIG`
@@ -612,6 +624,7 @@ def read_env_variable(arg, default_value):
     print亮绿(f"[ENV_VAR] 成功读取环境变量{arg}")
     return r
 
+
 @lru_cache(maxsize=128)
 def read_single_conf_with_lru_cache(arg):
     from colorful import print亮红, print亮绿, print亮蓝
@@ -676,6 +689,7 @@ class DummyWith():
     def __exit__(self, exc_type, exc_value, traceback):
         return
 
+
 def run_gradio_in_subpath(demo, auth, port, custom_path):
     """
     把gradio的运行地址更改到指定的二次路径上
@@ -770,6 +784,7 @@ def clip_history(inputs, history, tokenizer, max_token_limit):
 ========================================================================
 """
 
+
 def zip_folder(source_folder, dest_folder, zip_name):
     import zipfile
     import os
@@ -801,6 +816,7 @@ def zip_folder(source_folder, dest_folder, zip_name):
 
     print(f"Zip file created at {zip_file}")
 
+
 def zip_result(folder):
     import time
     t = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
@@ -811,6 +827,7 @@ def gen_time_str():
     import time
     return time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
 
+
 class ProxyNetworkActivate():
     """
     这段代码定义了一个名为TempProxy的空上下文管理器, 用于给一小段代码上代理
@@ -830,16 +847,18 @@ class ProxyNetworkActivate():
         if 'HTTPS_PROXY' in os.environ: os.environ.pop('HTTPS_PROXY')
         return
 
+
 def objdump(obj, file='objdump.tmp'):
     import pickle
     with open(file, 'wb+') as f:
         pickle.dump(obj, f)
     return
 
+
 def objload(file='objdump.tmp'):
     import pickle, os
     if not os.path.exists(file): 
         return
     with open(file, 'rb') as f:
         return pickle.load(f)
-    
+