import json
from pilot.prompts.prompt_new import PromptTemplate
from pilot.configs.config import Config
from pilot.scene.base import ChatScene
from pilot.scene.chat_data.chat_excel.excel_analyze.out_parser import (
    ChatExcelOutputParser,
)
from pilot.common.schema import SeparatorStyle

CFG = Config()

_PROMPT_SCENE_DEFINE_EN = "You are a data analysis expert. "

_DEFAULT_TEMPLATE_EN = """
Please use the data structure information in the above historical dialogue and combine it with data analysis to answer the user's questions while satisfying the constraints.

Constraint:
    1.Please fully understand the user's problem and use duckdb sql for analysis. The analysis content is returned in the output format required below. Please output the sql in the corresponding sql parameter.
    2.Please choose the best one from the display methods given below for data rendering, and put the type name into the name parameter value that returns the required format. If you cannot find the most suitable one, use 'Table' as the display method. , the available data display methods are as follows: {disply_type}
    3.The table name that needs to be used in SQL is: {table_name}. Please check the sql you generated and do not use column names that are not in the data structure.
    4.Give priority to answering using data analysis. If the user's question does not involve data analysis, you can answer according to your understanding.
    5.The <api-call></api-call> part of the required output format needs to be parsed by the code. Please ensure that this part of the content is output as required.
    
Please respond in the following format:
    thoughts summary to say to user.<api-call><name>[Data display method]</name><args><sql>[Correct duckdb data analysis sql]</sql></args></api-call>
    
User Questions:
    {user_input}
"""

_PROMPT_SCENE_DEFINE_ZH = """你是一个数据分析专家！"""
_DEFAULT_TEMPLATE_ZH = """
请使用上述历史对话中的数据结构信息，在满足下面约束条件下通过数据分析回答用户的问题。
约束条件:
	1.请充分理解用户的问题，使用duckdb sql的方式进行分析， 分析内容按下面要求的输出格式返回，sql请输出在对应的sql参数中
	2.请从如下给出的展示方式种选择最优的一种用以进行数据渲染，将类型名称放入返回要求格式的name参数值种，如果找不到最合适的则使用'Table'作为展示方式，可用数据展示方式如下: {disply_type}
	3.SQL中需要使用的表名是: {table_name},请检查你生成的sql，不要使用没在数据结构中的列名，。
	4.优先使用数据分析的方式回答，如果用户问题不涉及数据分析内容，你可以按你的理解进行回答
	5.要求的输出格式中<api-call></api-call>部分需要被代码解析执行，请确保这部分内容按要求输出
请确保你的输出格式如下:
    对用户说的想法摘要.<api-call><name>[数据展示方式]</name><args><sql>[正确的duckdb数据分析sql]</sql></args></api-call>

用户问题：{user_input}
"""


_DEFAULT_TEMPLATE = (
    _DEFAULT_TEMPLATE_EN if CFG.LANGUAGE == "en" else _DEFAULT_TEMPLATE_ZH
)

_PROMPT_SCENE_DEFINE = (
    _PROMPT_SCENE_DEFINE_EN if CFG.LANGUAGE == "en" else _PROMPT_SCENE_DEFINE_ZH
)

PROMPT_SEP = SeparatorStyle.SINGLE.value

PROMPT_NEED_STREAM_OUT = True

# Temperature is a configuration hyperparameter that controls the randomness of language model output.
# A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output.
# For example, if you adjust the temperature to 0.5, the model will usually generate text that is more predictable and less creative than if you set the temperature to 1.0.
PROMPT_TEMPERATURE = 0.8

prompt = PromptTemplate(
    template_scene=ChatScene.ChatExcel.value(),
    input_variables=["user_input", "table_name", "disply_type"],
    template_define=_PROMPT_SCENE_DEFINE,
    template=_DEFAULT_TEMPLATE,
    stream_out=PROMPT_NEED_STREAM_OUT,
    output_parser=ChatExcelOutputParser(
        sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT
    ),
    need_historical_messages=True,
    # example_selector=sql_data_example,
    temperature=PROMPT_TEMPERATURE,
)
CFG.prompt_template_registry.register(prompt, is_default=True)