增加每次对话统计tokens

func_box.py

@@ -21,7 +21,7 @@ from contextlib import ExitStack
 import logging
 import yaml
 import requests
-
+import tiktoken
 logger = logging
 from sklearn.feature_extraction.text import CountVectorizer
 import numpy as np
@@ -532,6 +532,14 @@ def reuse_chat(result, chatbot, history, pro_numb):
         i_say = pattern.sub('', chatbot[-1][0])
         return chatbot, history, i_say, gr.Tabs.update(selected='chatbot'), ''
 
+@timeStatistics
+def num_tokens_from_string(listing: list, encoding_name: str = 'cl100k_base') -> int:
+    """Returns the number of tokens in a text string."""
+    count_tokens = 0
+    for i in listing:
+        encoding = tiktoken.get_encoding(encoding_name)
+        count_tokens += len(encoding.encode(i))
+    return count_tokens
 
 class YamlHandle:
 
@@ -583,4 +591,69 @@ class JsonHandle:
 
 
 if __name__ == '__main__':
-    pass
+    num = num_tokens_from_string([
+        """
+        You are jvs, 帮我做一篇关于chatgpt的分享,大纲为ChatGPT 的基本原理和应用、Prompt 的作用和优化、ChatGPT 和 Prompt 的案例研究、ChatGPT 和 Prompt 的挑战和限制、ChatGPT 和 Prompt 的未来发展
+Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
+GOALS:
+1. 在文后，将市面上的AI应用作比较，对每个部分进行适当调整和拓展
+Constraints:    # 确定目标
+1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.
+2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
+3. No user assistance
+4. Exclusively use the commands listed below e.g. command_name
+Commands:   # 可执行的行动 
+1. analyze_code: Analyze Code, args: "code": "<full_code_string>"
+2. execute_python_file: Execute Python File, args: "filename": "<filename>"
+3. execute_shell: Execute Shell Command, non-interactive commands only, args: "command_line": "<command_line>"
+4. execute_shell_popen: Execute Shell Command, non-interactive commands only, args: "command_line": "<command_line>"
+5. append_to_file: Append to file, args: "filename": "<filename>", "text": "<text>"
+6. delete_file: Delete file, args: "filename": "<filename>"
+7. list_files: List Files in Directory, args: "directory": "<directory>"
+8. read_file: Read file, args: "filename": "<filename>"
+9. write_to_file: Write to file, args: "filename": "<filename>", "text": "<text>"
+10. google: Google Search, args: "query": "<query>"
+11. generate_image: Generate Image, args: "prompt": "<prompt>"
+12. improve_code: Get Improved Code, args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"
+13. send_tweet: Send Tweet, args: "tweet_text": "<tweet_text>"
+14. browse_website: Browse Website, args: "url": "<url>", "question": "<what_you_want_to_find_on_website>"
+15. write_tests: Write Tests, args: "code": "<full_code_string>", "focus": "<list_of_focus_areas>"
+16. delete_agent: Delete GPT Agent, args: "key": "<key>"
+17. get_hyperlinks: Get text summary, args: "url": "<url>"
+18. get_text_summary: Get text summary, args: "url": "<url>", "question": "<question>"
+19. list_agents: List GPT Agents, args: () -> str
+20. message_agent: Message GPT Agent, args: "key": "<key>", "message": "<message>"
+21. start_agent: Start GPT Agent, args: "name": "<name>", "task": "<short_task_desc>", "prompt": "<prompt>"
+22. task_complete: Task Complete (Shutdown), args: "reason": "<reason>"
+Resources:         # 可用资源
+1. Internet access for searches and information gathering.
+2. Long Term memory management.
+3. GPT-3.5 powered Agents for delegation of simple tasks.
+4. File output.
+Performance Evaluation:               # 自我评估
+1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
+2. Constructively self-criticize your big-picture behavior constantly.
+3. Reflect on past decisions and strategies to refine your approach.
+4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
+5. Write all code to a file.
+You should only respond in JSON format as described below  
+Response Format:             # 限制GPT的回答
+{
+    "thoughts": {
+        "text": "thought",
+        "reasoning": "reasoning",
+        "plan": "- short bulleted\n- list that conveys\n- long-term plan",
+        "criticism": "constructive self-criticism",
+        "speak": "thoughts summary to say to user"
+    },
+    "command": {
+        "name": "command name",
+        "args": {
+            "arg name": "value"
+        }
+    }
+} 
+Ensure the response can be parsed by Python json.loads
+        """
+    ])
+    print(num)

request_llm/bridge_chatgpt.py

@@ -200,7 +200,6 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}\t" \
                                   f"本次对话耗时: {func_box.html_tag_color(tag=f'{count_time}s')}"
                     yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
-
                 except Exception as e:
                     traceback.print_exc()
                     yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
@@ -229,6 +228,9 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                         chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
                     yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
                     return
+    count_tokens = func_box.num_tokens_from_string(listing=history)
+    status_text += f'\t 本次对话使用tokens: {func_box.html_tag_color(count_tokens)}'
+    yield from update_ui(chatbot=chatbot, history=history, msg=status_text)  # 刷新界面
 
 def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
     """