Spaces:
Runtime error
Runtime error
File size: 7,925 Bytes
019ee78 8c2caa0 66786b8 019ee78 15a824f 15fc122 7f271de 15fc122 7f271de 15fc122 7f271de 15fc122 7f271de 15fc122 7f271de 15fc122 7f271de 15fc122 7f271de 15fc122 7f271de 15fc122 7f271de 15fc122 019ee78 66786b8 769e5d5 7f271de 66786b8 54f0839 1820e5d 54f0839 1820e5d b13609f 4e7746a 548eced 66786b8 769e5d5 7f271de 66786b8 7f271de 66786b8 019ee78 66786b8 019ee78 15fc122 019ee78 66786b8 4e7746a 66786b8 8c2caa0 66786b8 3ff2f90 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 |
# -*-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)
|