# -*-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_inputs = gr.Textbox(lines=1, value="", label="Input used to test prompt")
                        answer_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")
                        compute_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

    # 7. 输入指令打分

    demo.launch(show_error=True)