feat: 使用log代替print (#166)

* feat: 保存历史对话，json支持中文，和格式化json

* feat: 使用log代替print

---------

Co-authored-by: lihuacai <huacai2.li@tcl.com>

ChuanhuChatbot.py

@@ -1,11 +1,13 @@
 # -*- coding:utf-8 -*-
 import gradio as gr
 import os
+import logging
 import sys
 import argparse
 from utils import *
 from presets import *
 
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s")
 
 my_api_key = ""    # 在这里输入你的 API 密钥
 
@@ -20,7 +22,7 @@ authflag = False
 if dockerflag:
     my_api_key = os.environ.get('my_api_key')
     if my_api_key == "empty":
-        print("Please give a api key!")
+        logging.error("Please give a api key!")
         sys.exit(1)
     #auth
     username = os.environ.get('USERNAME')
@@ -140,7 +142,7 @@ with gr.Blocks(css=customCSS) as demo:
 
     templateApplyBtn.click(get_template_content, [promptTemplates, templateSelectDropdown, systemPromptTxt],  [systemPromptTxt], show_progress=True)
 
-print(colorama.Back.GREEN + "\n川虎的温馨提示：访问 http://localhost:7860 查看界面" + colorama.Style.RESET_ALL)
+logging.info(colorama.Back.GREEN + "\n川虎的温馨提示：访问 http://localhost:7860 查看界面" + colorama.Style.RESET_ALL)
 # 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接
 demo.title = "川虎ChatGPT 🚀"
 

utils.py

@@ -1,6 +1,7 @@
 # -*- coding:utf-8 -*-
 from __future__ import annotations
 from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
+import logging
 import json
 import gradio as gr
 # import openai
@@ -16,6 +17,8 @@ import tiktoken
 from tqdm import tqdm
 import colorama
 
+# logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s")
+
 if TYPE_CHECKING:
     from typing import TypedDict
 
@@ -128,7 +131,7 @@ def stream_predict(openai_api_key, system_prompt, history, inputs, chatbot, all_
     def get_return_value():
         return chatbot, history, status_text, all_token_counts
 
-    print("实时回答模式")
+    logging.info("实时回答模式")
     partial_words = ""
     counter = 0
     status_text = "开始实时传输回答……"
@@ -142,7 +145,7 @@ def stream_predict(openai_api_key, system_prompt, history, inputs, chatbot, all_
     else:
         user_token_count = count_token(inputs)
     all_token_counts.append(user_token_count)
-    print(f"输入token计数: {user_token_count}")
+    logging.info(f"输入token计数: {user_token_count}")
     yield get_return_value()
     try:
         response = get_response(openai_api_key, system_prompt, history, temperature, top_p, True, selected_model)
@@ -170,7 +173,7 @@ def stream_predict(openai_api_key, system_prompt, history, inputs, chatbot, all_
             try:
                 chunk = json.loads(chunk[6:])
             except json.JSONDecodeError:
-                print(chunk)
+                logging.info(chunk)
                 error_json_str += chunk
                 status_text = f"JSON解析错误。请重置对话。收到的内容: {error_json_str}"
                 yield get_return_value()
@@ -195,7 +198,7 @@ def stream_predict(openai_api_key, system_prompt, history, inputs, chatbot, all_
 
 
 def predict_all(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model):
-    print("一次性回答模式")
+    logging.info("一次性回答模式")
     history.append(construct_user(inputs))
     history.append(construct_assistant(""))
     chatbot.append((parse_text(inputs), ""))
@@ -222,10 +225,10 @@ def predict_all(openai_api_key, system_prompt, history, inputs, chatbot, all_tok
 
 
 def predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, stream=False, selected_model = MODELS[0], should_check_token_count = True):  # repetition_penalty, top_k
-    print("输入为：" +colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL)
+    logging.info("输入为：" +colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL)
     if len(openai_api_key) != 51:
         status_text = standard_error_msg + no_apikey_msg
-        print(status_text)
+        logging.info(status_text)
         chatbot.append((parse_text(inputs), ""))
         if len(history) == 0:
             history.append(construct_user(inputs))
@@ -237,23 +240,23 @@ def predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_c
         return
     yield chatbot, history, "开始生成回答……", all_token_counts
     if stream:
-        print("使用流式传输")
+        logging.info("使用流式传输")
         iter = stream_predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model)
         for chatbot, history, status_text, all_token_counts in iter:
             yield chatbot, history, status_text, all_token_counts
     else:
-        print("不使用流式传输")
+        logging.info("不使用流式传输")
         chatbot, history, status_text, all_token_counts = predict_all(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model)
         yield chatbot, history, status_text, all_token_counts
-    print(f"传输完毕。当前token计数为{all_token_counts}")
+    logging.info(f"传输完毕。当前token计数为{all_token_counts}")
     if len(history) > 1 and history[-1]['content'] != inputs:
-        print("回答为：" +colorama.Fore.BLUE + f"{history[-1]['content']}" + colorama.Style.RESET_ALL)
+        logging.info("回答为：" +colorama.Fore.BLUE + f"{history[-1]['content']}" + colorama.Style.RESET_ALL)
     if stream:
         max_token = max_token_streaming
     else:
         max_token = max_token_all
     if sum(all_token_counts) > max_token and should_check_token_count:
-        print(f"精简token中{all_token_counts}/{max_token}")
+        logging.info(f"精简token中{all_token_counts}/{max_token}")
         iter = reduce_token_size(openai_api_key, system_prompt, history, chatbot, all_token_counts, top_p, temperature, stream=False, hidden=True)
         for chatbot, history, status_text, all_token_counts in iter:
             status_text = f"Token 达到上限，已自动降低Token计数至 {status_text}"
@@ -261,7 +264,7 @@ def predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_c
 
 
 def retry(openai_api_key, system_prompt, history, chatbot, token_count, top_p, temperature, stream=False, selected_model = MODELS[0]):
-    print("重试中……")
+    logging.info("重试中……")
     if len(history) == 0:
         yield chatbot, history, f"{standard_error_msg}上下文是空的", token_count
         return
@@ -269,13 +272,13 @@ def retry(openai_api_key, system_prompt, history, chatbot, token_count, top_p, t
     inputs = history.pop()["content"]
     token_count.pop()
     iter = predict(openai_api_key, system_prompt, history, inputs, chatbot, token_count, top_p, temperature, stream=stream, selected_model=selected_model)
-    print("重试完毕")
+    logging.info("重试完毕")
     for x in iter:
         yield x
 
 
 def reduce_token_size(openai_api_key, system_prompt, history, chatbot, token_count, top_p, temperature, stream=False, hidden=False, selected_model = MODELS[0]):
-    print("开始减少token数量……")
+    logging.info("开始减少token数量……")
     iter = predict(openai_api_key, system_prompt, history, summarize_prompt, chatbot, token_count, top_p, temperature, stream=stream, selected_model = selected_model, should_check_token_count=False)
     for chatbot, history, status_text, previous_token_count in iter:
         history = history[-2:]
@@ -283,49 +286,49 @@ def reduce_token_size(openai_api_key, system_prompt, history, chatbot, token_cou
         if hidden:
             chatbot.pop()
         yield chatbot, history, construct_token_message(sum(token_count), stream=stream), token_count
-    print("减少token数量完毕")
+    logging.info("减少token数量完毕")
 
 
 def delete_last_conversation(chatbot, history, previous_token_count):
     if len(chatbot) > 0 and standard_error_msg in chatbot[-1][1]:
-        print("由于包含报错信息，只删除chatbot记录")
+        logging.info("由于包含报错信息，只删除chatbot记录")
         chatbot.pop()
         return chatbot, history
     if len(history) > 0:
-        print("删除了一组对话历史")
+        logging.info("删除了一组对话历史")
         history.pop()
         history.pop()
     if len(chatbot) > 0:
-        print("删除了一组chatbot对话")
+        logging.info("删除了一组chatbot对话")
         chatbot.pop()
     if len(previous_token_count) > 0:
-        print("删除了一组对话的token计数记录")
+        logging.info("删除了一组对话的token计数记录")
         previous_token_count.pop()
     return chatbot, history, previous_token_count, construct_token_message(sum(previous_token_count))
 
 
 def save_chat_history(filename, system, history, chatbot):
-    print("保存对话历史中……")
+    logging.info("保存对话历史中……")
     if filename == "":
         return
     if not filename.endswith(".json"):
         filename += ".json"
     os.makedirs(HISTORY_DIR, exist_ok=True)
     json_s = {"system": system, "history": history, "chatbot": chatbot}
-    print(json_s)
+    logging.info(json_s)
     with open(os.path.join(HISTORY_DIR, filename), "w") as f:
-        json.dump(json_s, f)
-    print("保存对话历史完毕")
+        json.dump(json_s, f, ensure_ascii=False, indent=4)
+    logging.info("保存对话历史完毕")
 
 
 def load_chat_history(filename, system, history, chatbot):
-    print("加载对话历史中……")
+    logging.info("加载对话历史中……")
     try:
         with open(os.path.join(HISTORY_DIR, filename), "r") as f:
             json_s = json.load(f)
         try:
             if type(json_s["history"][0]) == str:
-                print("历史记录格式为旧版，正在转换……")
+                logging.info("历史记录格式为旧版，正在转换……")
                 new_history = []
                 for index, item in enumerate(json_s["history"]):
                     if index % 2 == 0:
@@ -333,21 +336,21 @@ def load_chat_history(filename, system, history, chatbot):
                     else:
                         new_history.append(construct_assistant(item))
                 json_s["history"] = new_history
-                print(new_history)
+                logging.info(new_history)
         except:
             # 没有对话历史
             pass
-        print("加载对话历史完毕")
+        logging.info("加载对话历史完毕")
         return filename, json_s["system"], json_s["history"], json_s["chatbot"]
     except FileNotFoundError:
-        print("没有找到对话历史文件，不执行任何操作")
+        logging.info("没有找到对话历史文件，不执行任何操作")
         return filename, system, history, chatbot
 
 def sorted_by_pinyin(list):
     return sorted(list, key=lambda char: lazy_pinyin(char)[0][0])
 
 def get_file_names(dir, plain=False, filetypes=[".json"]):
-    print(f"获取文件名列表，目录为{dir}，文件类型为{filetypes}，是否为纯文本列表{plain}")
+    logging.info(f"获取文件名列表，目录为{dir}，文件类型为{filetypes}，是否为纯文本列表{plain}")
     files = []
     try:
         for type in filetypes:
@@ -363,13 +366,13 @@ def get_file_names(dir, plain=False, filetypes=[".json"]):
         return gr.Dropdown.update(choices=files)
 
 def get_history_names(plain=False):
-    print("获取历史记录文件名列表")
+    logging.info("获取历史记录文件名列表")
     return get_file_names(HISTORY_DIR, plain)
 
 def load_template(filename, mode=0):
-    print(f"加载模板文件{filename}，模式为{mode}（0为返回字典和下拉菜单，1为返回下拉菜单，2为返回字典）")
+    logging.info(f"加载模板文件{filename}，模式为{mode}（0为返回字典和下拉菜单，1为返回下拉菜单，2为返回字典）")
     lines = []
-    print("Loading template...")
+    logging.info("Loading template...")
     if filename.endswith(".json"):
         with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as f:
             lines = json.load(f)
@@ -388,18 +391,18 @@ def load_template(filename, mode=0):
         return {row[0]:row[1] for row in lines}, gr.Dropdown.update(choices=choices, value=choices[0])
 
 def get_template_names(plain=False):
-    print("获取模板文件名列表")
+    logging.info("获取模板文件名列表")
     return get_file_names(TEMPLATES_DIR, plain, filetypes=[".csv", "json"])
 
 def get_template_content(templates, selection, original_system_prompt):
-    print(f"应用模板中，选择为{selection}，原始系统提示为{original_system_prompt}")
+    logging.info(f"应用模板中，选择为{selection}，原始系统提示为{original_system_prompt}")
     try:
         return templates[selection]
     except:
         return original_system_prompt
 
 def reset_state():
-    print("重置状态")
+    logging.info("重置状态")
     return [], [], [], construct_token_message(0)
 
 def reset_textbox():