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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 * | |
| with gr.Blocks(title="Automatic Prompt Engineer", theme=gr.themes.Glass()) as demo: | |
| gr.Markdown("# Automatic Prompt Engineer") | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| gr.Markdown("## Configuration") | |
| with gr.Row(): | |
| openai_key = gr.Textbox(type='password', label='输入 API key') | |
| with gr.Row(): | |
| n_train = gr.Slider(label="Number of Train", minimum=1, maximum=20, step=1, value=5) | |
| n_few_shot = gr.Slider(label="Number of FewShot", minimum=1, maximum=20, step=1, value=5) | |
| with gr.Row(): | |
| n_eval = gr.Slider(label="Number of Eval", minimum=5, maximum=30, step=5, value=20) | |
| n_instruct = gr.Slider(label="Number of Prompt", minimum=1, maximum=5, step=1, value=2) | |
| with gr.Column(scale=3): | |
| gr.Markdown("## Load Data") | |
| with gr.Tab("Choose Dataset"): | |
| with gr.Row(): | |
| file = gr.File(label='上传txt文件，input\toutput\n', file_types=['txt']) | |
| with gr.Row(): | |
| task = gr.Dropdown(label="Chosse Existing Task", choices=list(LoadFactory.keys()), value=None) | |
| with gr.Row(): | |
| 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("Display Sampled Dataset"): | |
| 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(): | |
| with gr.Column(scale=2): | |
| gr.Markdown("## Run APE") | |
| gen_prompt = gr.Textbox(max_lines=100, lines=10, | |
| value=MyTemplate['gen_sys_prompt'], label="Prompt for generation") | |
| eval_prompt = gr.Textbox(max_lines=100, lines=10, | |
| value=MyTemplate['eval_prompt'], label="Prompt for Evaluation") | |
| with gr.Row(): | |
| cost = gr.Textbox(lines=1, value="", label="Estimated Cost ($)") | |
| cost_button = gr.Button("Estimate Cost") | |
| ape_button = gr.Button("Run APE") | |
| with gr.Column(scale=3): | |
| gr.Markdown("## Get Result") | |
| with gr.Tab("APE Results"): | |
| 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("Test Prompt"): | |
| # Test the output of LLM using prompt | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| test_prompt = gr.Textbox(lines=4, value="", label="Prompt to test") | |
| test_input = gr.Textbox(lines=1, value="", label="Input used to test prompt") | |
| test_button = gr.Button("Test") | |
| with gr.Column(scale=1): | |
| test_output = gr.Textbox(lines=9, value="", label="Model Output") | |
| with gr.Tab("Eval Prompt"): | |
| # By Default use the Evaluation Set in APE | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| score_prompt = 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) | |
| """ | |
| 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 | |
| ape_button.click(generate, [instance, openai_key, n_instruct], [all_prompt]) | |
| # 5. Evaluate -> 得到所有指令的Log Prob | |
| # 6. 输入指令单测 | |
| test_button.click(single_test, [test_prompt, test_input, openai_key, n_instruct], [test_output]) | |
| # 7. 输入指令打分 | |
| score_button.click(score_single, [score_prompt, openai_key, n_instruct], [test_score]) | |
| demo.launch(show_error=True) |