import json
import time
import re
from traceback import print_exc
from appPublic.dictObject import DictObject
from appPublic.httpclient import *
from appPublic.jsonConfig import getConfig
from appPublic.log import info, debug, warning, error, exception, critical

def search(request, env):
	params_kw = env.get(params_kw)
	prompt = params_kw.prompt
	f = params_kw.search_func

def get_json_text(json_str):
    pattern = r'json(.*?)```'
    match = re.search(pattern, json_str, re.DOTALL)
    if match:
        return match.group(1).strip()
    else:
        return json_str

def clear_dict(dic):
	d = { k:v for k,v in dic.items() if v not in [ 'undefined', '' ] }
	ret = {}
	for k ,v in d.items():
		if isinstance(v, dict):
			v = clear_dict(v)
			if v != {}:
				ret.update({k:v})
		else:
			ret.update({k:v})
	return ret

async def _llmchat(prompt):
	hc = HttpClient()
	config = getConfig()
	model = config.definitions.search_model
	url = config.definitions.search_url
	syscontent = config.definitions.search_syscontent
	headers = {
		"Content-Type":"application/json"
	}

	messages = [
		{
			"role":"system",
			"content":syscontent
		},
		{
			"role":"user",
			"content":prompt
		}
	]
	body = {
		"model":model,
		"stream":False,
		"messages":messages
	}
	data = json.dumps(body)
	t1 = time.time()
	resp = await hc.request(url, 'POST', headers=headers, 
						data=data, response_type=RESPONSE_JSON)
	t2 = time.time()
	resp = DictObject(resp)
	info(f'{resp.message.content=}, cost={t2 - t1}')
	text = resp.message.content
	jtxt = get_json_text(text)
	if jtxt is None:
		return None
	j = DictObject(**json.loads(jtxt))
	j.time_cost = t2 - t1
	j = clear_dict(j)
	info(f'return data={j}========')
	
	return j

async def llmchat2json(prompt):
	"""
	用大语言模型将用户输入转化为查询条件的json对象， 需要在项目的config.json文件中的definitions属性中
	定义以下变量
	        "search_model":"qwen2:7b",
        "search_url":"http://localhost:11434/api/chat",
        "search_syscontent":"将用户输入转换成算力和云服务器查询的json数据json格式为{ \"requestType\": \"resourceQuery\", \"queryParameters\": { \"resourceType\": \"undefined\", \"region\": \"undefined\", \"gpuModel\": \"undefined\", \"gpuCount\": 0, \"cpuModel\": \"undefined\", \"cpuCount\": \"undefined\", \"memorySizeGB\": \"undefined\", \"storageType\": \"undefined\", \"storageCapacityGB\": \"undefined\",\"bandwidthMbps\": \"undefined\",\"priceRange\": { \"min\": \"undefined\", \"max\": \"undefined\"}, \"availabilityZone\": \"undefined\" }}, 保持数据中原有语言，只需返回json数据内容
,输入中没有的属性省略"
	其中：search_model定义了用于转化查询的大模型名字
		search_url是提供大模型对话服务的url， 我们使用本地部署的开源大模型，所以不用定义apikey
		search_syscontent是一段给大模型的约束文本，要求大模型按要求回复信息
	输入参数：prompt 用户输入的查询条件
	返回：查询条件的json, 如果出错，返回None
	"""
	try:
		x = await _llmchat(prompt)
		return x
	except Exception as e:
		exception(f'{e=}')
		return None

if __name__ == '__main__':
	import sys
	if len(sys.argv) < 2:
		print(f'Usage:\n{sys.argv[0]} search_text')
		sys.exit(1)
	prompt= sys.argv[1]
	config = getConfig('.')
	loop = asyncio.get_event_loop().run_until_complete(llmchat(prompt))
	print('here')