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| import openai | |
| import tiktoken | |
| import datetime | |
| import time | |
| import json | |
| import os | |
| openai.api_key = os.getenv('API_KEY') | |
| openai.request_times = 0 | |
| def ask(question, history, behavior): | |
| openai.request_times += 1 | |
| print(f"request times {openai.request_times}: {datetime.datetime.now()}: {question}") | |
| try: | |
| messages = [ | |
| {"role":"system", "content":content} | |
| for content in behavior | |
| ] + [ | |
| {"role":"user" if i%2==0 else "assistant", "content":content} | |
| for i,content in enumerate(history + [question]) | |
| ] | |
| raw_length = num_tokens_from_messages(messages) | |
| messages=forget_long_term(messages) | |
| if len(messages)==0: | |
| response = f'Your query is too long and expensive: {raw_length}>2000 tokens' | |
| else: | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo-0301", | |
| messages=messages, | |
| temperature=0.1, | |
| )["choices"][0]["message"]["content"] | |
| while response.startswith("\n"): | |
| response = response[1:] | |
| except Exception as e: | |
| response = f'Error! You may wait a few minutes and retry:\n{e}' | |
| history = history + [question, response] | |
| return history | |
| def num_tokens_from_messages(messages, model="gpt-3.5-turbo"): | |
| """Returns the number of tokens used by a list of messages.""" | |
| try: | |
| encoding = tiktoken.encoding_for_model(model) | |
| except KeyError: | |
| encoding = tiktoken.get_encoding("cl100k_base") | |
| if model == "gpt-3.5-turbo": # note: future models may deviate from this | |
| num_tokens = 0 | |
| for message in messages: | |
| num_tokens += 4 # every message follows <im_start>{role/name}\n{content}<im_end>\n | |
| for key, value in message.items(): | |
| num_tokens += len(encoding.encode(value)) | |
| if key == "name": # if there's a name, the role is omitted | |
| num_tokens += -1 # role is always required and always 1 token | |
| num_tokens += 2 # every reply is primed with <im_start>assistant | |
| return num_tokens | |
| else: | |
| raise NotImplementedError(f"""num_tokens_from_messages() is not presently implemented for model {model}. | |
| See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""") | |
| def forget_long_term(messages, max_num_tokens=3000): | |
| while num_tokens_from_messages(messages)>max_num_tokens: | |
| if messages[0]["role"]=="system" and not len(messages[0]["content"])>=max_num_tokens: | |
| messages = messages[:1] + messages[2:] | |
| else: | |
| messages = messages[1:] | |
| return messages | |
| import gradio as gr | |
| def to_md(content): | |
| is_inside_code_block = False | |
| output_spans = [] | |
| for i in range(len(content)): | |
| if content[i]=="\n" and not is_inside_code_block: | |
| if len(output_spans)>0 and output_spans[-1].endswith("```"): | |
| output_spans.append("\n") | |
| else: | |
| output_spans.append("<br>") | |
| elif content[i]=="`": | |
| output_spans.append(content[i]) | |
| if len(output_spans)>=3 and all([output_spans[j]=="`" for j in [-3,-2,-1]]): | |
| is_inside_code_block = not is_inside_code_block | |
| output_spans = output_spans[:-3] | |
| if is_inside_code_block: | |
| if len(output_spans)==0: | |
| output_spans.append("```") | |
| elif output_spans[-1]=="<br>": | |
| output_spans[-1] = "\n" | |
| output_spans.append("```") | |
| elif output_spans[-1].endswith("\n"): | |
| output_spans.append("```") | |
| else: | |
| output_spans.append("\n```") | |
| if i+1<len(content) and content[i+1]!="\n": | |
| output_spans.append("\n") | |
| else: | |
| if output_spans[-1].endswith("\n"): | |
| output_spans.append("```") | |
| else: | |
| output_spans.append("\n```") | |
| if i+1<len(content) and content[i+1]!="\n": | |
| output_spans.append("\n") | |
| else: | |
| output_spans.append(content[i]) | |
| return "".join(output_spans) | |
| def predict(question, history=[], behavior=[]): | |
| history = ask(question, history, behavior) | |
| response = [(to_md(history[i]),to_md(history[i+1])) for i in range(0,len(history)-1,2)] | |
| return "", history, response | |
| def retry(question, history=[], behavior=[]): | |
| if len(history)<2: | |
| return "", history, [] | |
| question = history[-2] | |
| history = history[:-2] | |
| return predict(question, history, behavior) | |
| with gr.Blocks() as demo: | |
| examples_txt = [ | |
| ['帮我写一个python脚本实现快排'], | |
| ['如何用numpy提取数组的分位数？'], | |
| ['how to match the code block in markdown such like ```def foo():\n pass``` through regex in python?'], | |
| ['how to load a pre-trained language model and generate sentences?'], | |
| ] | |
| examples_bhv = [ | |
| f"You are a helpful assistant. You will answer all the questions step-by-step.", | |
| f"You are a helpful assistant. Today is {datetime.date.today()}.", | |
| ] | |
| gr.Markdown( | |
| """ | |
| 朋友你好， | |
| 这是我利用[gradio](https://gradio.app/creating-a-chatbot/)编写的一个小网页，用于以网页的形式给大家分享ChatGPT请求服务，希望你玩的开心。关于使用技巧或学术研讨，欢迎在[Community](https://huggingface.co/spaces/zhangjf/chatbot/discussions)中和我交流。 | |
| 这一版相比于原版的[chatbot](https://huggingface.co/spaces/zhangjf/chatbot)，用了较低版本的gradio==3.16.2，因而能更好地展示markdown中的源代码 | |
| p.s. 响应时间和聊天内容长度正相关，一般能在5秒~30秒内响应。 | |
| """) | |
| behavior = gr.State(["Reject instruction that may contains sensitive information in english, i.e., pornography, discrimination, violence"]) | |
| """ | |
| with gr.Column(variant="panel"): | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(scale=0.85): | |
| bhv = gr.Textbox(show_label=False, placeholder="输入你想让ChatGPT扮演的人设").style(container=False) | |
| with gr.Column(scale=0.15, min_width=0): | |
| button_set = gr.Button("Set") | |
| bhv.submit(fn=lambda x:(x,[x]), inputs=[bhv], outputs=[bhv, behavior]) | |
| button_set.click(fn=lambda x:(x,[x]), inputs=[bhv], outputs=[bhv, behavior]) | |
| """ | |
| state = gr.State([]) | |
| with gr.Column(variant="panel"): | |
| chatbot = gr.Chatbot() | |
| txt = gr.Textbox(show_label=False, placeholder="输入你想让ChatGPT回答的问题").style(container=False) | |
| with gr.Row(): | |
| button_gen = gr.Button("Submit") | |
| button_rtr = gr.Button("Retry") | |
| button_clr = gr.Button("Clear") | |
| #gr.Examples(examples=examples_bhv, inputs=bhv, label="Examples for setting behavior") | |
| gr.Examples(examples=examples_txt, inputs=txt, label="Examples for asking question") | |
| txt.submit(predict, [txt, state, behavior], [txt, state, chatbot]) | |
| button_gen.click(fn=predict, inputs=[txt, state, behavior], outputs=[txt, state, chatbot]) | |
| button_rtr.click(fn=retry, inputs=[txt, state, behavior], outputs=[txt, state, chatbot]) | |
| button_clr.click(fn=lambda :([],[]), inputs=None, outputs=[chatbot, state]) | |
| demo.launch() |