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| # -*-coding:utf-8 -*- | |
| import os | |
| import gradio as gr | |
| from ape.instance import LoadFactory | |
| from ape.prompt import MyTemplate | |
| from ape.ape import * | |
| from self.generate import init_instance, generate_instruction | |
| from self.prompt import self_prompt | |
| with gr.Blocks(title="Automatic Prompt Engineer", theme=gr.themes.Glass()) as demo: | |
| gr.Markdown("# Automatic Prompt Engineer") | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(scale=2): | |
| gr.Markdown("## 第一步：输入参数") | |
| with gr.Row(): | |
| openai_key = gr.Textbox(type='password', label='输入 API key') | |
| with gr.Row(): | |
| n_train = gr.Slider(label="训练样本数", minimum=1, maximum=20, step=1, value=5) | |
| n_few_shot = gr.Slider(label="每组几个样例", minimum=1, maximum=20, step=1, value=5) | |
| with gr.Row(): | |
| n_eval = gr.Slider(label="评估样本数", minimum=1, maximum=30, step=5, value=20) | |
| with gr.Column(scale=3): | |
| gr.Markdown("## 第二步：加载数据(选任务或上传数据)") | |
| with gr.Tab("选择数据"): | |
| with gr.Row().style(equal_height=True): | |
| file = gr.File(label='上传txt文件，input[空格]output[换行]') | |
| with gr.Row().style(equal_height=True): | |
| task = gr.Dropdown(label="Chosse Existing Task", choices=list(LoadFactory.keys()), value=None) | |
| with gr.Row().style(equal_height=True): | |
| instance = gr.State() | |
| load_button = gr.Button("Load Task") | |
| load_flag = gr.Textbox() | |
| sample_button = gr.Button('sample Data') | |
| sample_flag = gr.Textbox() | |
| with gr.Tab("展示数据"): | |
| with gr.Row(): | |
| train_str = gr.Textbox(max_lines=100, lines=10, label="Data for prompt generation") | |
| eval_str = gr.Textbox(max_lines=100, lines=10, label="Data for scoring") | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(scale=2): | |
| gr.Markdown("## 第三步: Run APE(可替换默认指令)") | |
| gen_prompt = gr.Textbox(max_lines=100, lines=3, interative=True, | |
| placeholder=MyTemplate['gen_user_prompt'], | |
| value='', label="Prompt for generation") | |
| eval_prompt = gr.Textbox(max_lines=100, lines=3, interative=True, | |
| placeholder=MyTemplate['eval_prompt'], | |
| value='', label="Prompt for Evaluation") | |
| test_prompt = gr.Textbox(max_lines=100, lines=3, interative=True, | |
| placeholder=MyTemplate['test_prompt'], | |
| value='', label="Prompt for Single Test") | |
| with gr.Row().style(equal_height=True): | |
| cost = gr.Textbox(lines=1, value="", label="Estimated Cost ($)") | |
| cost_button = gr.Button("Estimate Cost") | |
| with gr.Row().style(equal_height=True): | |
| gen_button = gr.Button("Generate") | |
| eval_button = gr.Button("Eval") | |
| with gr.Column(scale=3): | |
| gr.Markdown("## 第四步：APE 结果") | |
| with gr.Tab("生成指令"): | |
| all_prompt = gr.Textbox(label='Generated Prompt') | |
| # Display all generated prompt with log probs | |
| output_df = gr.DataFrame(type='pandas', headers=['Prompt', 'Likelihood'], wrap=True, interactive=False) | |
| with gr.Tab("指令单测"): | |
| # Test the output of LLM using prompt | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(scale=1): | |
| test_instruction = gr.Textbox(lines=4, value="", label="Prompt to test") | |
| test_input = gr.Textbox(lines=4, value="", label="Inputs used to test prompt[多个输入以换行分割]") | |
| test_button = gr.Button("Test") | |
| with gr.Column(scale=1): | |
| test_output = gr.Textbox(lines=9, value="", label="Model Output") | |
| with gr.Tab("指令评估"): | |
| # By Default use the Evaluation Set in APE | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(scale=1): | |
| score_instruction = gr.Textbox(lines=3, value="", | |
| label="Prompt to Evaluate") | |
| score_button = gr.Button("Evaluate") | |
| with gr.Column(scale=1): | |
| test_score = gr.Textbox(lines=1, value="", label="Log(p)", disabled=True) | |
| gr.Markdown('\n\n') | |
| gr.Markdown('--------') | |
| gr.Markdown('\n\n') | |
| gr.Markdown("# SELF INSTRUCT") | |
| gr.Markdown('## 第一步：确认参数并上传种子指令') | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(): | |
| n_human = gr.Slider(label="人工指令数", minimum=1, maximum=5, step=1, value=2) | |
| n_machine = gr.Slider(label="机器指令数", minimum=1, maximum=5, step=1, value=1) | |
| n_instruct = gr.Slider(label="生成指令数", minimum=1, maximum=100, step=1, value=4, help="生成指令数>人工+机器") | |
| self_prompt_input = gr.Textbox(max_lines=100, lines=20, interative=True, | |
| placeholder=self_prompt, | |
| value='', label="Prompt for self-instruct") | |
| with gr.Column(): | |
| openai_key2 = gr.Textbox(type='password', label='输入 API key') | |
| seed_file = gr.File(label='上传json文件, 格式参考./self/data/seed_task.json') | |
| self_submit = gr.Button('上传') | |
| self_instance = gr.State() | |
| gr.Markdown('\n\n') | |
| gr.Markdown('## 第二步：采样并生成新指令，每点一次会重采样并生成，生成结果会累计') | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(scale=1): | |
| fewshot = gr.Textbox(label='采样few-shot') | |
| with gr.Column(scale=1): | |
| gen_data = gr.JSON(label='新生成指令样本') | |
| with gr.Row().style(equal_height=True): | |
| with gr.Column(scale=7): | |
| generate_instruct_button = gr.Button("指令生成") | |
| with gr.Column(scale=1): | |
| counter = gr.Textbox() | |
| """ | |
| APE Callback | |
| """ | |
| # 1. 选择已有任务/上传文件，实例化Instance | |
| load_button.click(load_task, [task, file], [instance, load_flag]) | |
| # 2. 按 Configuration Sample数据 得到训练样本和验证集, 并在前端展示。支持重采样 | |
| sample_button.click(sample_data, [instance, n_train, n_few_shot, n_eval], | |
| [train_str, eval_str, instance, sample_flag]) | |
| # 3. Estimate Cost for train + Eval | |
| cost_button.click(esttimate_cost, [instance], [cost]) | |
| # 4. Run APE -> 所有指令 | |
| gen_button.click(generate, [gen_prompt, instance, openai_key], [all_prompt]) | |
| # 5. Evaluate -> 得到所有指令的Log Prob | |
| eval_button.click(evaluate, [eval_prompt, all_prompt, instance, openai_key], [output_df]) | |
| # 6. 输入指令单测 | |
| test_button.click(single_test, [test_prompt, test_instruction, test_input, openai_key], [test_output]) | |
| # 7. 输入指令打分 | |
| score_button.click(score_single, [eval_prompt, instance, score_instruction, openai_key], [test_score]) | |
| """ | |
| SELF Callback | |
| """ | |
| # 1. 加载种子文件 | |
| self_submit.click(init_instance, inputs=[seed_file, openai_key2, n_human, n_machine, n_instruct, self_prompt_input], | |
| outputs=[self_instance]) | |
| # 2. 生成 | |
| generate_instruct_button.click(generate_instruction, inputs=[self_instance], outputs=[fewshot, gen_data, counter]) | |
| demo.launch(show_error=True) | |