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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("## 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")
with gr.Tab("选择数据"):
with gr.Row():
file = gr.File(label='上传txt文件,input[空格]output[换行]')
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("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():
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():
cost = gr.Textbox(lines=1, value="", label="Estimated Cost ($)")
cost_button = gr.Button("Estimate Cost")
with gr.Row():
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():
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():
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)
"""
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])
demo.launch(show_error=True)
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