程序自解析功能

.gitignore

@@ -134,3 +134,4 @@ dmypy.json
 history
 ssr_conf
 config_private.py
+gpt_log

functional_crazy.py

@@ -0,0 +1,112 @@
+# """
+# 'primary' for main call-to-action, 
+# 'secondary' for a more subdued style, 
+# 'stop' for a stop button.
+# """
+def 自我程序解构简单案例(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)
+        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):
+        with open(fp, 'r', encoding='utf-8') as f:
+            file_content = f.read()
+
+        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 = f'请对下面的程序文件做一个概述: ```{file_content}```'
+        i_say_show_user = 前言 + f'请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
+        chatbot.append((i_say_show_user, "[waiting gpt response]"))
+        yield chatbot, history, '正常'
+
+        # ** gpt request **
+        gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature)
+
+        chatbot[-1] = (i_say_show_user, gpt_say)
+        history.append(i_say_show_user); history.append(gpt_say)
+        yield chatbot, history, '正常'
+        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, "[waiting gpt response]"))
+    yield chatbot, history, '正常'
+
+    # ** gpt request **
+    gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature, history=history)
+
+    chatbot[-1] = (i_say, gpt_say)
+    history.append(i_say); history.append(gpt_say)
+    yield chatbot, history, '正常'
+
+
+def 解析一个Python项目(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
+    import time, glob, os
+    from predict import predict_no_ui
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        chatbot.append((f"解析项目: {txt}", "找不到本地项目: {txt}"))
+        history.append(i_say_show_user); history.append(gpt_say)
+        return chatbot, history, '正常'
+
+    file_manifest = [f for f in glob.glob(f'{project_folder}/*.py')]
+    print('begin analysis on:', file_manifest)
+    for index, fp in enumerate(file_manifest):
+        with open(fp, 'r', encoding='utf-8') as f:
+            file_content = f.read()
+
+        前言 = "接下来请你逐文件分析下面的Python工程" if index==0 else ""
+        i_say = f'请对下面的程序文件做一个概述: ```{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, "[waiting gpt response]"))
+        print('[1] yield chatbot, history')
+        yield chatbot, history, '正常'
+
+        # ** gpt request **
+        gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature)
+
+        print('[2] end gpt req')
+        chatbot[-1] = (i_say_show_user, gpt_say)
+        history.append(i_say_show_user); history.append(gpt_say)
+        print('[3] yield chatbot, history')
+        yield chatbot, history, '正常'
+        print('[4] next')
+        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, "[waiting gpt response]"))
+    yield chatbot, history, '正常'
+
+    # ** gpt request **
+    gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature, history=history)
+
+    chatbot[-1] = (i_say, gpt_say)
+    history.append(i_say); history.append(gpt_say)
+    yield chatbot, history, '正常'
+
+
+def get_crazy_functionals():
+    return {
+        "程序解构简单案例": {
+            "Function": 自我程序解构简单案例
+        },
+        "请解析并解构此项目本身": {
+            "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）": {
+            "Function": 解析一个Python项目
+        },
+    }
+
+

main.py

@@ -25,8 +25,14 @@ os.makedirs('gpt_log', exist_ok=True)
 logging.basicConfig(filename='gpt_log/chat_secrets.log', level=logging.INFO, encoding='utf-8')
 print('所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log，请注意自我隐私保护哦！')
 
+# 一些普通功能
 from functional import get_functionals
 functional = get_functionals()
+
+# 对一些丧心病狂的实验性功能进行测试
+from functional_crazy import get_crazy_functionals
+crazy_functional = get_crazy_functionals()
+
 def reset_textbox(): return gr.update(value='')
 
 def text_divide_paragraph(text):
@@ -69,7 +75,7 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column(scale=2):
             chatbot = gr.Chatbot()
-            chatbot.style(height=700)
+            chatbot.style(height=1000)
             chatbot.style()
             history = gr.State([])
             TRUE = gr.State(True)
@@ -84,6 +90,9 @@ with gr.Blocks() as demo:
                 for k in functional:
                     variant = functional[k]["Color"] if "Color" in functional[k] else "secondary"
                     functional[k]["Button"] = gr.Button(k, variant=variant)
+                for k in crazy_functional:
+                    variant = crazy_functional[k]["Color"] if "Color" in crazy_functional[k] else "secondary"
+                    crazy_functional[k]["Button"] = gr.Button(k, variant=variant)
             from check_proxy import check_proxy
             statusDisplay = gr.Markdown(f"{check_proxy(proxies)}")
             systemPromptTxt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt).style(container=True)
@@ -97,7 +106,10 @@ 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, FALSE, 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])
 
 print(f"URL http://localhost:{PORT}")
 demo.title = "ChatGPT 学术优化"

predict.py

@@ -14,6 +14,59 @@ except: from config import proxies, API_URL, API_KEY, TIMEOUT_SECONDS
 
 timeout_bot_msg = 'Request timeout, network error. please check proxy settings in config.py.'
 
+
+def predict_no_ui(inputs, top_p, temperature, history=[]):
+    messages = [{"role": "system", "content": ""}]
+
+    # 
+    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}"
+    }
+    try:
+        # make a POST request to the API endpoint using the requests.post method, passing in stream=True
+        response = requests.post(API_URL, headers=headers, proxies=proxies,
+                                json=payload, stream=True, timeout=TIMEOUT_SECONDS*2)
+    except:
+        raise TimeoutError
+        
+    return json.loads(response.text)["choices"][0]["message"]["content"]
+
+
+
 def predict(inputs, top_p, temperature, chatbot=[], history=[], system_prompt='', retry=False, 
             stream = True, additional_fn=None):