程序自解析功能
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@ -134,3 +134,4 @@ dmypy.json
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history
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ssr_conf
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config_private.py
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gpt_log
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112
functional_crazy.py
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112
functional_crazy.py
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@ -0,0 +1,112 @@
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# """
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# 'primary' for main call-to-action,
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# 'secondary' for a more subdued style,
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# 'stop' for a stop button.
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# """
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def 自我程序解构简单案例(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
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import time
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from predict import predict_no_ui_no_history
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for i in range(5):
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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}'
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gpt_say = predict_no_ui_no_history(inputs=i_say, top_p=top_p, temperature=temperature)
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chatbot.append((i_say, gpt_say))
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history.append(i_say)
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history.append(gpt_say)
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yield chatbot, history, '正常'
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time.sleep(10)
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def 解析项目本身(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
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import time, glob, os
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from predict import predict_no_ui
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file_manifest = [f for f in glob.glob('*.py')]
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for index, fp in enumerate(file_manifest):
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with open(fp, 'r', encoding='utf-8') as f:
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file_content = f.read()
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前言 = "接下来请你分析自己的程序构成，别紧张，" if index==0 else ""
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i_say = f'请对下面的程序文件做一个概述: ```{file_content}```'
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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)}'
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chatbot.append((i_say_show_user, "[waiting gpt response]"))
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yield chatbot, history, '正常'
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# ** gpt request **
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gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature)
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chatbot[-1] = (i_say_show_user, gpt_say)
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history.append(i_say_show_user); history.append(gpt_say)
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yield chatbot, history, '正常'
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time.sleep(2)
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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}）。'
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chatbot.append((i_say, "[waiting gpt response]"))
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yield chatbot, history, '正常'
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# ** gpt request **
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gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature, history=history)
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chatbot[-1] = (i_say, gpt_say)
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history.append(i_say); history.append(gpt_say)
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yield chatbot, history, '正常'
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def 解析一个Python项目(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
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import time, glob, os
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from predict import predict_no_ui
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if os.path.exists(txt):
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project_folder = txt
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else:
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chatbot.append((f"解析项目: {txt}", "找不到本地项目: {txt}"))
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history.append(i_say_show_user); history.append(gpt_say)
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return chatbot, history, '正常'
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file_manifest = [f for f in glob.glob(f'{project_folder}/*.py')]
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print('begin analysis on:', file_manifest)
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for index, fp in enumerate(file_manifest):
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with open(fp, 'r', encoding='utf-8') as f:
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file_content = f.read()
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前言 = "接下来请你逐文件分析下面的Python工程" if index==0 else ""
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i_say = f'请对下面的程序文件做一个概述: ```{file_content}```'
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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)}'
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chatbot.append((i_say_show_user, "[waiting gpt response]"))
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print('[1] yield chatbot, history')
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yield chatbot, history, '正常'
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# ** gpt request **
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gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature)
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print('[2] end gpt req')
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chatbot[-1] = (i_say_show_user, gpt_say)
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history.append(i_say_show_user); history.append(gpt_say)
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print('[3] yield chatbot, history')
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yield chatbot, history, '正常'
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print('[4] next')
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time.sleep(2)
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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}）。'
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chatbot.append((i_say, "[waiting gpt response]"))
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yield chatbot, history, '正常'
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# ** gpt request **
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gpt_say = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature, history=history)
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chatbot[-1] = (i_say, gpt_say)
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history.append(i_say); history.append(gpt_say)
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yield chatbot, history, '正常'
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def get_crazy_functionals():
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return {
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"程序解构简单案例": {
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"Function": 自我程序解构简单案例
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},
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"请解析并解构此项目本身": {
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"Function": 解析项目本身
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},
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"解析一整个Python项目（输入栏给定项目完整目录）": {
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"Function": 解析一个Python项目
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},
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}
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16
main.py
16
main.py
@ -25,8 +25,14 @@ os.makedirs('gpt_log', exist_ok=True)
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logging.basicConfig(filename='gpt_log/chat_secrets.log', level=logging.INFO, encoding='utf-8')
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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！')
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# 一些普通功能
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from functional import get_functionals
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functional = get_functionals()
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# 对一些丧心病狂的实验性功能进行测试
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from functional_crazy import get_crazy_functionals
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crazy_functional = get_crazy_functionals()
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def reset_textbox(): return gr.update(value='')
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def text_divide_paragraph(text):
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@ -69,7 +75,7 @@ with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column(scale=2):
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chatbot = gr.Chatbot()
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chatbot.style(height=700)
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chatbot.style(height=1000)
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chatbot.style()
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history = gr.State([])
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TRUE = gr.State(True)
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@ -84,6 +90,9 @@ with gr.Blocks() as demo:
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for k in functional:
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variant = functional[k]["Color"] if "Color" in functional[k] else "secondary"
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functional[k]["Button"] = gr.Button(k, variant=variant)
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for k in crazy_functional:
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variant = crazy_functional[k]["Color"] if "Color" in crazy_functional[k] else "secondary"
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crazy_functional[k]["Button"] = gr.Button(k, variant=variant)
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from check_proxy import check_proxy
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statusDisplay = gr.Markdown(f"{check_proxy(proxies)}")
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systemPromptTxt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt).style(container=True)
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@ -97,7 +106,10 @@ with gr.Blocks() as demo:
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# submitBtn.click(reset_textbox, [], [txt])
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for k in functional:
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functional[k]["Button"].click(predict,
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[txt, top_p, temperature, chatbot,history, systemPromptTxt, FALSE, TRUE, gr.State(k)], [chatbot, history, statusDisplay], show_progress=True)
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[txt, top_p, temperature, chatbot, history, systemPromptTxt, FALSE, TRUE, gr.State(k)], [chatbot, history, statusDisplay], show_progress=True)
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for k in crazy_functional:
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crazy_functional[k]["Button"].click(crazy_functional[k]["Function"],
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[txt, top_p, temperature, chatbot, history, systemPromptTxt, gr.State(PORT)], [chatbot, history, statusDisplay])
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print(f"URL http://localhost:{PORT}")
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demo.title = "ChatGPT 学术优化"
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53
predict.py
53
predict.py
@ -14,6 +14,59 @@ except: from config import proxies, API_URL, API_KEY, TIMEOUT_SECONDS
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timeout_bot_msg = 'Request timeout, network error. please check proxy settings in config.py.'
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def predict_no_ui(inputs, top_p, temperature, history=[]):
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messages = [{"role": "system", "content": ""}]
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#
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chat_counter = len(history) // 2
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if chat_counter > 0:
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for index in range(0, 2*chat_counter, 2):
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what_i_have_asked = {}
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what_i_have_asked["role"] = "user"
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what_i_have_asked["content"] = history[index]
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what_gpt_answer = {}
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what_gpt_answer["role"] = "assistant"
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what_gpt_answer["content"] = history[index+1]
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if what_i_have_asked["content"] != "":
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messages.append(what_i_have_asked)
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messages.append(what_gpt_answer)
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else:
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messages[-1]['content'] = what_gpt_answer['content']
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what_i_ask_now = {}
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what_i_ask_now["role"] = "user"
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what_i_ask_now["content"] = inputs
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messages.append(what_i_ask_now)
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# messages
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payload = {
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"model": "gpt-3.5-turbo",
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# "model": "gpt-4",
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"messages": messages,
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"temperature": temperature, # 1.0,
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"top_p": top_p, # 1.0,
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"n": 1,
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"stream": False,
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"presence_penalty": 0,
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"frequency_penalty": 0,
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}
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {API_KEY}"
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}
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try:
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# make a POST request to the API endpoint using the requests.post method, passing in stream=True
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response = requests.post(API_URL, headers=headers, proxies=proxies,
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json=payload, stream=True, timeout=TIMEOUT_SECONDS*2)
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except:
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raise TimeoutError
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return json.loads(response.text)["choices"][0]["message"]["content"]
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def predict(inputs, top_p, temperature, chatbot=[], history=[], system_prompt='', retry=False,
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stream = True, additional_fn=None):
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