update

colorful.py

@@ -0,0 +1,91 @@
+import platform
+from sys import stdout
+
+if platform.system()=="Linux":
+    pass
+else: 
+    from colorama import init
+    init()
+
+# Do you like the elegance of Chinese characters?
+def print红(*kw,**kargs):
+    print("\033[0;31m",*kw,"\033[0m",**kargs)
+def print绿(*kw,**kargs):
+    print("\033[0;32m",*kw,"\033[0m",**kargs)
+def print黄(*kw,**kargs):
+    print("\033[0;33m",*kw,"\033[0m",**kargs)
+def print蓝(*kw,**kargs):
+    print("\033[0;34m",*kw,"\033[0m",**kargs)
+def print紫(*kw,**kargs):
+    print("\033[0;35m",*kw,"\033[0m",**kargs)
+def print靛(*kw,**kargs):
+    print("\033[0;36m",*kw,"\033[0m",**kargs)
+
+def print亮红(*kw,**kargs):
+    print("\033[1;31m",*kw,"\033[0m",**kargs)
+def print亮绿(*kw,**kargs):
+    print("\033[1;32m",*kw,"\033[0m",**kargs)
+def print亮黄(*kw,**kargs):
+    print("\033[1;33m",*kw,"\033[0m",**kargs)
+def print亮蓝(*kw,**kargs):
+    print("\033[1;34m",*kw,"\033[0m",**kargs)
+def print亮紫(*kw,**kargs):
+    print("\033[1;35m",*kw,"\033[0m",**kargs)
+def print亮靛(*kw,**kargs):
+    print("\033[1;36m",*kw,"\033[0m",**kargs)
+
+
+
+def print亮红(*kw,**kargs):
+    print("\033[1;31m",*kw,"\033[0m",**kargs)
+def print亮绿(*kw,**kargs):
+    print("\033[1;32m",*kw,"\033[0m",**kargs)
+def print亮黄(*kw,**kargs):
+    print("\033[1;33m",*kw,"\033[0m",**kargs)
+def print亮蓝(*kw,**kargs):
+    print("\033[1;34m",*kw,"\033[0m",**kargs)
+def print亮紫(*kw,**kargs):
+    print("\033[1;35m",*kw,"\033[0m",**kargs)
+def print亮靛(*kw,**kargs):
+    print("\033[1;36m",*kw,"\033[0m",**kargs)
+    
+print_red = print红
+print_green = print绿
+print_yellow = print黄
+print_blue = print蓝
+print_purple = print紫
+print_indigo = print靛
+
+print_bold_red = print亮红
+print_bold_green = print亮绿
+print_bold_yellow = print亮黄
+print_bold_blue = print亮蓝
+print_bold_purple = print亮紫
+print_bold_indigo = print亮靛
+    
+if not stdout.isatty():
+    # redirection, avoid a fucked up log file
+    print红 = print
+    print绿 = print
+    print黄 = print
+    print蓝 = print
+    print紫 = print
+    print靛 = print
+    print亮红 = print
+    print亮绿 = print
+    print亮黄 = print
+    print亮蓝 = print
+    print亮紫 = print
+    print亮靛 = print
+    print_red = print
+    print_green = print
+    print_yellow = print
+    print_blue = print
+    print_purple = print
+    print_indigo = print
+    print_bold_red = print
+    print_bold_green = print
+    print_bold_yellow = print
+    print_bold_blue = print
+    print_bold_purple = print
+    print_bold_indigo = print

config.py

@@ -21,6 +21,9 @@ WEB_PORT = -1
 # 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
 MAX_RETRY = 2
 
+# 选择的OpenAI模型是（gpt4现在只对申请成功的人开放）
+LLM_MODEL = "gpt-3.5-turbo"
+
 # 检查一下是不是忘了改config
 if API_KEY == "sk-此处填API秘钥":
     assert False, "请在config文件中修改API密钥, 添加海外代理之后再运行"

functional_crazy.py

@@ -1,26 +1,18 @@
-# """
+from predict import predict_no_ui
-# 'primary' for main call-to-action, 
-# 'secondary' for a more subdued style, 
-# 'stop' for a stop button.
-# """
-
 fast_debug = False
 
-def 自我程序解构简单案例(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
+def 高阶功能模板函数(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-    import time
-    from predict import predict_no_ui_no_history
     for i in range(5):
         i_say = f'我给出一个数字，你给出该数字的平方。我给出数字：{i}'
-        gpt_say = predict_no_ui_no_history(inputs=i_say, top_p=top_p, temperature=temperature)
+        gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature)
         chatbot.append((i_say, gpt_say))
         history.append(i_say)
         history.append(gpt_say)
         yield chatbot, history, '正常'
-        time.sleep(10)
+
 
 def 解析项目本身(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
     import time, glob, os
-    from predict import predict_no_ui
     file_manifest = [f for f in glob.glob('*.py')]
     
     for index, fp in enumerate(file_manifest):
@@ -30,7 +22,7 @@ def 解析项目本身(txt, top_p, temperature, chatbot, history, systemPromptTx
         前言 = "接下来请你分析自己的程序构成，别紧张，" if index==0 else ""
         i_say = 前言 + f'请对下面的程序文件做一个概述文件名是{fp}，文件代码是 ```{file_content}```'
         i_say_show_user = 前言 + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[local] waiting gpt response."))
+        chatbot.append((i_say_show_user, "[waiting gpt response]"))
         yield chatbot, history, '正常'
 
         if not fast_debug: 
@@ -43,7 +35,7 @@ def 解析项目本身(txt, top_p, temperature, chatbot, history, systemPromptTx
             time.sleep(2)
 
     i_say = f'根据以上你自己的分析，对程序的整体功能和构架做出概括。然后用一张markdown表格整理每个文件的功能（包括{file_manifest}）。'
-    chatbot.append((i_say, "[local] waiting gpt response."))
+    chatbot.append((i_say, "[waiting gpt response]"))
     yield chatbot, history, '正常'
 
     if not fast_debug: 
@@ -64,7 +56,6 @@ def report_execption(chatbot, history, a, b):
 
 def 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt):
     import time, glob, os
-    from predict import predict_no_ui
     print('begin analysis on:', file_manifest)
     for index, fp in enumerate(file_manifest):
         with open(fp, 'r', encoding='utf-8') as f:
@@ -73,7 +64,7 @@ def 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot,
         前言 = "接下来请你逐文件分析下面的工程" if index==0 else ""
         i_say = 前言 + f'请对下面的程序文件做一个概述文件名是{os.path.relpath(fp, project_folder)}，文件代码是 ```{file_content}```'
         i_say_show_user = 前言 + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[local] waiting gpt response."))
+        chatbot.append((i_say_show_user, "[waiting gpt response]"))
         print('[1] yield chatbot, history')
         yield chatbot, history, '正常'
 
@@ -98,7 +89,7 @@ def 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot,
 
     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，对程序的整体功能和构架做出概括。然后用一张markdown表格整理每个文件的功能（包括{all_file}）。'
-    chatbot.append((i_say, "[local] waiting gpt response."))
+    chatbot.append((i_say, "[waiting gpt response]"))
     yield chatbot, history, '正常'
 
     if not fast_debug: 
@@ -159,22 +150,22 @@ def 解析一个C项目的头文件(txt, top_p, temperature, chatbot, history, s
 
 def get_crazy_functionals():
     return {
-        "程序解构简单案例": {
+        "[实验功能] 请解析并解构此项目本身": {
-            "Color": "stop",    # 按钮颜色
-            "Function": 自我程序解构简单案例
-        },
-        "请解析并解构此项目本身": {
             "Color": "stop",    # 按钮颜色
             "Function": 解析项目本身
         },
-        "解析一整个Python项目（输入栏给定项目完整目录）": {
+        "[实验功能] 解析一整个Python项目（输入栏给定项目完整目录）": {
             "Color": "stop",    # 按钮颜色
             "Function": 解析一个Python项目
         },
-        "解析一整个C++项目的头文件（输入栏给定项目完整目录）": {
+        "[实验功能] 解析一整个C++项目的头文件（输入栏给定项目完整目录）": {
             "Color": "stop",    # 按钮颜色
             "Function": 解析一个C项目的头文件
         },
+        "[实验功能] 高阶功能模板函数": {
+            "Color": "stop",    # 按钮颜色
+            "Function": 高阶功能模板函数
+        },
 
     }
 

main.py

@@ -106,7 +106,7 @@ with gr.Blocks() as demo:
     # submitBtn.click(reset_textbox, [], [txt])
     for k in functional:
         functional[k]["Button"].click(predict, 
-            [txt, top_p, temperature, chatbot, history, systemPromptTxt, FALSE, TRUE, gr.State(k)], [chatbot, history, statusDisplay], show_progress=True)
+            [txt, top_p, temperature, chatbot, history, systemPromptTxt, TRUE, gr.State(k)], [chatbot, history, statusDisplay], show_progress=True)
     for k in crazy_functional:
         crazy_functional[k]["Button"].click(crazy_functional[k]["Function"], 
             [txt, top_p, temperature, chatbot, history, systemPromptTxt, gr.State(PORT)], [chatbot, history, statusDisplay])

predict.py

@@ -6,11 +6,12 @@ import logging
 import traceback
 import requests
 import importlib
+from colorful import *
 
 # config_private.py放自己的秘密如API和代理网址
 # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
-try: from config_private import proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY
+try: from config_private import proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL
-except: from config import proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY
+except: from config import proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL
 
 timeout_bot_msg = '[local] Request timeout, network error. please check proxy settings in config.py.'
 
@@ -23,51 +24,12 @@ def get_full_error(chunk, stream_response):
     return chunk
 
 def predict_no_ui(inputs, top_p, temperature, history=[]):
-    messages = [{"role": "system", "content": ""}]
+    headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt="", stream=False)
-
-    # 
-    chat_counter = len(history) // 2
-    if chat_counter > 0:
-        for index in range(0, 2*chat_counter, 2):
-            what_i_have_asked = {}
-            what_i_have_asked["role"] = "user"
-            what_i_have_asked["content"] = history[index]
-            what_gpt_answer = {}
-            what_gpt_answer["role"] = "assistant"
-            what_gpt_answer["content"] = history[index+1]
-            if what_i_have_asked["content"] != "":
-                messages.append(what_i_have_asked)
-                messages.append(what_gpt_answer)
-            else:
-                messages[-1]['content'] = what_gpt_answer['content']
-
-    what_i_ask_now = {}
-    what_i_ask_now["role"] = "user"
-    what_i_ask_now["content"] = inputs
-    messages.append(what_i_ask_now)
-
-    # messages
-    payload = {
-        "model": "gpt-3.5-turbo",
-        # "model": "gpt-4",
-        "messages": messages, 
-        "temperature": temperature,  # 1.0,
-        "top_p": top_p,  # 1.0,
-        "n": 1,
-        "stream": False,
-        "presence_penalty": 0,
-        "frequency_penalty": 0,
-    }
-    
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {API_KEY}"
-    }
 
     retry = 0
     while True:
         try:
-            # make a POST request to the API endpoint using the requests.post method, passing in stream=True
+            # make a POST request to the API endpoint, stream=False
             response = requests.post(API_URL, headers=headers, proxies=proxies,
                                     json=payload, stream=False, timeout=TIMEOUT_SECONDS*2); break
         except TimeoutError as e:
@@ -84,9 +46,7 @@ def predict_no_ui(inputs, top_p, temperature, history=[]):
         raise ConnectionAbortedError("Json解析不合常规，可能是文本过长" + response.text)
 
 
-
+def predict(inputs, top_p, temperature, chatbot=[], history=[], system_prompt='', 
-
-def predict(inputs, top_p, temperature, chatbot=[], history=[], system_prompt='', retry=False, 
             stream = True, additional_fn=None):
 
     if additional_fn is not None:
@@ -101,60 +61,13 @@ def predict(inputs, top_p, temperature, chatbot=[], history=[], system_prompt=''
         chatbot.append((inputs, ""))
         yield chatbot, history, "等待响应"
 
-    headers = {
+    headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt, stream)
-        "Content-Type": "application/json",
+    history.append(inputs); history.append(" ")
-        "Authorization": f"Bearer {API_KEY}"
-    }
-
-    chat_counter = len(history) // 2
-
-    print(f"chat_counter - {chat_counter}")
-
-    messages = [{"role": "system", "content": system_prompt}]
-    if chat_counter:
-        for index in range(0, 2*chat_counter, 2):
-            what_i_have_asked = {}
-            what_i_have_asked["role"] = "user"
-            what_i_have_asked["content"] = history[index]
-            what_gpt_answer = {}
-            what_gpt_answer["role"] = "assistant"
-            what_gpt_answer["content"] = history[index+1]
-            if what_i_have_asked["content"] != "":
-                if not (what_gpt_answer["content"] != "" or retry): 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']
-
-    if retry and chat_counter:
-        messages.pop()
-    else:
-        what_i_ask_now = {}
-        what_i_ask_now["role"] = "user"
-        what_i_ask_now["content"] = inputs
-        messages.append(what_i_ask_now)
-        chat_counter += 1
-
-    # messages
-    payload = {
-        "model": "gpt-3.5-turbo",
-        # "model": "gpt-4",
-        "messages": messages, 
-        "temperature": temperature,  # 1.0,
-        "top_p": top_p,  # 1.0,
-        "n": 1,
-        "stream": stream,
-        "presence_penalty": 0,
-        "frequency_penalty": 0,
-    }
-
-    history.append(inputs)
 
     retry = 0
     while True:
         try:
-            # make a POST request to the API endpoint using the requests.post method, passing in stream=True
+            # make a POST request to the API endpoint, stream=True
             response = requests.post(API_URL, headers=headers, proxies=proxies,
                                     json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
         except:
@@ -164,37 +77,30 @@ def predict(inputs, top_p, temperature, chatbot=[], history=[], system_prompt=''
             yield chatbot, history, "请求超时"+retry_msg
             if retry > MAX_RETRY: raise TimeoutError
 
-    token_counter = 0
+    gpt_replying_buffer = ""
-    partial_words = ""
+    
-
+    is_head_of_the_stream = True
-    counter = 0
     if stream:
         stream_response =  response.iter_lines()
         while True:
             chunk = next(stream_response)
-            if chunk == b'data: [DONE]':
+            # print(chunk.decode()[6:])
-                break
+            if is_head_of_the_stream:
-
+                is_head_of_the_stream = False; continue
-            if counter == 0:
+            
-                counter += 1
-                continue
-            counter += 1
-            # check whether each line is non-empty
             if chunk:
-                # decode each line as response data is in bytes
                 try:
                     if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
-                        logging.info(f'[response] {chatbot[-1][-1]}')
+                        # 判定为数据流的结束，gpt_replying_buffer也写完了
+                        logging.info(f'[response] {gpt_replying_buffer}')
                         break
+                    # 处理数据流的主体
                     chunkjson = json.loads(chunk.decode()[6:])
                     status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}"
-                    partial_words = partial_words + json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"]
+                    # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
-                    if token_counter == 0:
+                    gpt_replying_buffer = gpt_replying_buffer + json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"]
-                        history.append(" " + partial_words)
+                    history[-1] = gpt_replying_buffer
-                    else:
-                        history[-1] = partial_words
                     chatbot[-1] = (history[-2], history[-1])
-                    token_counter += 1
                     yield chatbot, history, status_text
 
                 except Exception as e:
@@ -207,4 +113,48 @@ def predict(inputs, top_p, temperature, chatbot=[], history=[], system_prompt=''
                     yield chatbot, history, "Json解析不合常规，很可能是文本过长" + error_msg
                     return
 
+def generate_payload(inputs, top_p, temperature, history, system_prompt, stream):
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {API_KEY}"
+    }
+
+    conversation_cnt = len(history) // 2
+
+    messages = [{"role": "system", "content": system_prompt}]
+    if conversation_cnt:
+        for index in range(0, 2*conversation_cnt, 2):
+            what_i_have_asked = {}
+            what_i_have_asked["role"] = "user"
+            what_i_have_asked["content"] = history[index]
+            what_gpt_answer = {}
+            what_gpt_answer["role"] = "assistant"
+            what_gpt_answer["content"] = history[index+1]
+            if what_i_have_asked["content"] != "":
+                if what_gpt_answer["content"] == "": continue
+                if what_gpt_answer["content"] == timeout_bot_msg: continue
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+            else:
+                messages[-1]['content'] = what_gpt_answer['content']
+
+    what_i_ask_now = {}
+    what_i_ask_now["role"] = "user"
+    what_i_ask_now["content"] = inputs
+    messages.append(what_i_ask_now)
+
+    payload = {
+        "model": LLM_MODEL,
+        "messages": messages, 
+        "temperature": temperature,  # 1.0,
+        "top_p": top_p,  # 1.0,
+        "n": 1,
+        "stream": stream,
+        "presence_penalty": 0,
+        "frequency_penalty": 0,
+    }
+    
+    print(f" {LLM_MODEL} : {conversation_cnt} : {inputs}")
+    return headers,payload
+