from pprint import pprint
import requests
import json
from hashlib import md5
import uvicorn
from fastapi import FastAPI
from typing import Optional

app = FastAPI()

host = "172.16.57.71"

# 请求头
headers = {
	"token": '369ca613c7a74a58b0f95be2cfd59257',
	"Content-Type": "application/json",
}

eduback_dict = {
	26:'博士后', 
	24:'博士', 
	22:'MBA/EMBA', 
	20:'硕士', 
	18:'本科', 
	16:'大专', 
	14:'中专/中技', 
	12:'高中', 
	10:'初中及以下'
}

def getQaAttachment(data):
    nodes = []
    ids = []
    links = []
    for d in data:
        if not d['target']['id']:
            d['target']['id'] = md5(str(d['target']['entity']).encode(encoding='UTF-8')).hexdigest()
        if not d['target']['id'] in ids:
            nodes.append({"id":d['target']['id'], "name": str(d['target']['entity']), "target":True, "source":False})
            ids.append(d['target']['id'])
        if not d['source']['id'] in ids:
            nodes.append({"id":d['source']['id'], "name": d['source']['entity'], "source":True, "target":False})
            ids.append(d['source']['id'])
        links.append({'source': d['source']['id'], 'name':d["relation"], 'target': d['target']['id']})
    return nodes, links

def post_json(json_obj, token="1664516531417"):
	""" 
	将json数据提交到push接口
	:param json_obj: 
	:return: 
	""" 
	# 推送
	url = 'http://{}:8284/data/api/access/push'.format(host)
	headers = {
		"token": token,
		# "Cookie": "JSESSIONID=40225388-b817-471b-8fc0-7afb72389712"
	}
	try:
		response = requests.post(url, json=json_obj, headers=headers)
		result = response.text
		logger.info(result)
	except:
		logger.error(e)
		return "ERROR: {} 数据推送 KG 失败!".format(json)
	else:
		return 0

@app.get('/person_to_person')
def search_person(query: str, less: Optional[bool] = False):
	"""
	# 人与人的相关关系查询
	# 必选参数:
	# query: 人名
	# 可选参数
	# less: 显示少量关系
	"""
	url = 'http://:8085/mpks/api/extra/gremlin'.format(host)
	if less:
		json_obj = {
			# "gremlin": "g.has('name.@value', MATCH, '"+ query +"').outE(['当前公司','最高学历学校']).inV.inE('相关机构').outV.with('*').graph"
			"gremlin": "g.has('name.@value', MATCH, '"+ query +"').outE('相关机构').limit(8).inV.inE('相关机构').outV.with('*').graph"
		}
	else:
		json_obj = {
			"gremlin": "g.has('name.@value', MATCH, '"+ query +"').outE('相关机构').inV.inE('相关机构').outV.with('*').graph"
		}
	# 请求查询
	try:
		r = requests.post(url, headers=headers, json=json_obj)
		rst = json.loads(r.text)
	except Exception as e:
		logger.error(e)
		return {"errno":555, "msg": "请求检索失败"}

	entity_array = []
	nodes = []
	links = []
	ids = dict([])
	
	# 查询成功
	if (rst['errno'] == 0) and rst['data']:
		# 所有实体
		for entity in rst['data']['vertices']:
			if entity["@id"] not in ids:
				ids[entity["@id"]] = entity["name"]
				nodes.append({
					"id": entity["@id"],
					"name": entity["name"],
					"target": False if (entity["@type"] == "人才特征") else True,
					"source": True if (entity["@type"] == "人才特征") else False,
					"itemStyle": {
            			"normal": {
              				"color": 'red' if (entity["@type"] == "人才特征") else 'blue'
            			}
            		},
            	})
		for edge in rst['data']['edges']:
			source = {'id': edge['@from'], 'entity': ids[edge['@from']], "refId": None, "entityIndex": None}
			target = {'id': edge['@to'], 'entity': ids[edge['@to']], "refId": None, "entityIndex": None}
			relation = edge['@label']
			entity_array.append({"source": source, "relation": relation, "target": target})
			links.append({'source': edge['@from'], 'name': edge['@label'], 'target': edge['@to']})
	else:
		pprint(rst['errno'])
		pprint(rst['msg'])
	return {"errno": 0, "msg": [], "graph": entity_array, "nodes": nodes, "links": links}

@app.get('/search_query')
def search_query(query: str):
	"""
	# 搜索接口,模糊搜索
	# 參數：
	# query: 查詢的關鍵詞
	"""
	entity_array = []
	base_url = 'http://{}:8085/mpks/api/search'.format(host)
	json_obj = {
		"query": query,
		"sort": "relevance",
		"needCorrect": True,
		"saveHistory": False
	}
	try:
		r = requests.post(base_url, headers=headers, json=json_obj)
		rst = json.loads(r.text)
	except Exception as e:
		logger.error(e)
		return {"errno":555, "msg": "请求检索失败"}

	# 查询结果
	if (rst['errno'] == 0) and rst['data']:
		# 当前实体
		source = {'id': rst['data']['results'][0]['entityList'][0]['@id'], 'entity': rst['data']['results'][0]['entityList'][0]['name'],'refId': None, "entityIndex": None}
		# 实体属性关系
		for dic in rst['data']['results'][0]['entityList'][0]['properties']:
			relation = dic['key']
			# 关系
			if isinstance(dic['value'], dict) and ("@id" in dic['value']):
				target = {"id": dic['value']['@id'], "entity":dic['value']['value'], 'refId': None, "entityIndex": None}
				entity_array.append({"source": source, "relation": relation, "target": target})
			# 属性
			elif (not isinstance(dic['value'], list)) or ("@id" not in dic['value'][0]):
				target = {"id": None, "entity":dic['value'], 'refId': None, "entityIndex": None}
				entity_array.append({"source": source, "relation": relation, "target": target})
			# 关系
			else:
				for vec in dic['value']:
					target = {"id": vec["@id"], "entity": vec["value"], 'refId': None, "entityIndex": None}
					entity_array.append({"source": source, "relation": relation, "target": target})
		# 边
		for edge in rst['data']['results'][0]['entityList'][0]['graphData']['vertices']:
			relation = edge['@type'].replace('demo', '')
			target = {"id": edge["@id"], "entity": edge["name"], 'refId': None, "entityIndex": None}
			entity_array.append({"source": source, "relation": relation, "target": target})
	else:
		pprint(rst['errno'])
		pprint(rst['msg'])
	nodes, links = getQaAttachment(entity_array)
	return {"errno": 0, "msg": [], "graph": entity_array, "nodes": nodes, "links": links}

@app.get('/search_gremlin')
def search_gremlin(query: Optional[str] = None, _id: Optional[str] = None):
	"""
	# 搜索接口,精确搜索
	# 參數：
	# query[可选]: 使用实体名称查询
	# _id  [可选]: 使用实体 id 查询
	# 同时填写时 _id 优先
	g.has("name.@value",MATCH,"于策").outE("相关机构").inV.inE("相关机构").outV.with("*").graph
	"""
	# 请求地址
	url = 'http://{}:8085/mpks/api/extra/gremlin'.format(host)
	# 查询语句
	if _id:
		json_obj = {
			"gremlin" : "g.key('"+ _id +"').both.with('*').graph"
		}
	elif query:
		json_obj = {
			"gremlin" : "g.has('name.@value', MATCH, '"+ query +"').both.with('*').graph"
		}
	else:
		return {"errno": 3001, "msg": "can not get query or id", "data": []}
	# 请求查询
	try:
		r = requests.post(url, headers=headers, json=json_obj)
		rst = json.loads(r.text)
	except Exception as e:
		logger.error(e)
		return {"errno":555, "msg": "请求检索失败"}

	entity_array = []
	
	# 查询成功
	if (rst['errno'] == 0) and rst['data']:
		# 当前实体
		source = {'id': rst['data']['vertices'][0]['@id'], 'entity': rst['data']['vertices'][0]['name'],'refId': None, "entityIndex": None}
		# 属性
		for relation in rst['data']['vertices'][0].keys():
			if relation not in ['@context', '@del', '@edge_number', '@formattype', '@fromtype', '@fromurl', '@id', '@kbid', '@nodeid', '@semiid', '@tags', '@type', '_id', '_type', 'alias', 'appId', 'name', 'nodeId', 'tags', '教育经历', '工作经历', '项目经历', '培训和海外经历', '最高学历学校', '当前公司']:
				target = {'id': None, 'entity': rst['data']['vertices'][0][relation], 'refId': None, "entityIndex": None}
				if rst['data']['vertices'][0][relation]:
					entity_array.append({"source": source, "relation": relation, "target": target})
		# 关系
		if len(rst['data']['vertices']) > 1:
			for index in range(1, len(rst['data']['vertices'])):
				target = {'id': rst['data']['vertices'][index]['@id'], 'entity': rst['data']['vertices'][index]['name'], 'refId': None, "entityIndex": None}
				for edge in rst['data']['edges']:
					if edge['@from'] == rst['data']['vertices'][index]['@id']:
						entity_array.append({"source": target, "relation": edge['@label'].replace('demo', ''), "target": source})
						break
				else:
					entity_array.append({"source": source, "relation": rst['data']['vertices'][index]['@type'].replace('demo', ''), "target": target})
		# 边
		# for edge in rst['data']['edges']:
		# 	target = {'id': edge['@to'], 'entity': None, "refId": edge['@from'], "entityIndex": source_index}
		# 	relation = edge['@label']
		# 	entity_array.append({"source": source, "relation": relation, "target": target})
	else:
		pprint(rst['errno'])
		pprint(rst['msg'])
	nodes, links = getQaAttachment(entity_array)
	return {"errno": 0, "msg": [], "graph": entity_array, "nodes": nodes, "links": links}

@app.post('/update_person')
def update_person(xdpost: dict):
	xdpost = xdpost['data']
	# 最高学历
	high_edu = {"schoolName": None, "major": None, "degree": None}
	# 机构列表
	org_list = []
	# 工作经历
	job_list = []
	for job in xdpost["hisJob"]:
		org_list.append(job['companyName'])
		job_list.append(job['companyName'] + job['industry'] + job['jobName'] + job['jobDesc'])
		post_json({
			"@id": md5(job['companyName'].encode(encoding="UTF-8")).hexdigest(),
			"id": md5(job['companyName'].encode(encoding="UTF-8")).hexdigest(),
			"@type": "相关机构",
			"@contentType": "struct",
			"@markdel": "0",
			"name": [{"@value": job['companyName']}]
		})
		post_json({
			"@id": md5(job['id'].encode(encoding="UTF-8")).hexdigest(),
			"id": job['id'],
			"@type": "工作经历",
			"name": job['companyName'] + job['industry'] + job['jobName'] + job['jobDesc'],
			"@markdel": "0",
			"@contentType": "struct",
			"时间": [{"@value": job['startTime']+job['endTime']}],
			"公司": [{"@value": job['companyName']}],
			"行业": [{"@value": job['industry']}],
			"职位": [{"@value": job['jobName']}],
			"工作内容": [{"@value": job['jobDesc']}]
		})
	# 教育经历
	edu_list = []
	for edu in xdpost["hisEdu"]:
		if (not high_edu.get("schoolName")) or (high_edu['degree'] < edu['degree']):
			high_edu['schoolName'] = edu['schoolName']
			high_edu['major'] = edu['major']
			high_edu['degree'] = edu['degree']
		org_list.append(edu['schoolName'])
		edu_list.append(edu['startTime'] + edu['schoolName'] + edu['major'] + eduback_dict[edu['degree']])
		post_json({
			"@id": md5(edu['schoolName'].encode(encoding="UTF-8")).hexdigest(),
			"id": md5(edu['schoolName'].encode(encoding="UTF-8")).hexdigest(),
			"@type": "相关机构",
			"@contentType": "struct",
			"@markdel": "0",
			"name": [{"@value": edu['schoolName']}]
		})
		post_json({
			"@id": md5(edu['id'].encode(encoding="UTF-8")).hexdigest(),
			"id": edu['id'],
			"@type": "教育经历",
			"name": edu['startTime'] + edu['schoolName'] + edu['major'] + eduback_dict[edu['degree']],
			"@markdel": "0",
			"@contentType": "struct",
			"时间": [{"@value": edu['startTime']+edu['endTime']}],
			"学校": [{"@value": edu['schoolName']}],
			"专业": [{"@value": edu['major']}],
			"学历": [{"@value": edu['degree']}]
		})
	# 项目经历
	pro_list = []
	for pro in xdpost["hisProject"]:
		pro_list.append(pro['companyName'] + pro['projectName'] + pro['projectOffice'] + pro['projectDuty'])
		post_json({
			"@id": md5(pro['id'].encode(encoding="UTF-8")).hexdigest(),
			"id": pro['id'],
			"@type": "项目经历",
			"name": pro['companyName'] + pro['projectName'] + pro['projectOffice'] + pro['projectDuty'],
			"@markdel": "0",
			"@contentType": "struct",
			"时间": [{"@value": pro['startTime']+pro['endTime']}],
			"公司": [{"@value": pro['companyName']}],
			"项目": [{"@value": pro['projectName']}],
			"职位": [{"@value": pro['projectDuty']}],
			"成果": [{"@value": pro['projectOffice']}]
		})
	# 海外培训经历
	tra_list = [{"@value": None}]
	# 基本信息
	json_obj = {
		"@id": md5(xdpost['tId'].encode(encoding='UTF-8')).hexdigest(),
		"id": xdpost['tId'],
		"@type": "人才特征",
		"name": xdpost['name'],
		"@markdel": '0',
		"@contentType": "struct",
		"姓名": [{"@value": xdpost['name']}],
		"年龄": [{"@value": xdpost['age']}],
		"性别": [{"@value": '男' if xdpost['gender'] == "0" else '女'}],
		"出生年月": [{"@value": xdpost['birthTime']}],
		"手机号码": [{"@value": xdpost['mobile']}],
		"电子邮箱": [{"@value": xdpost['email']}],
		"政治面貌": [{"@value": xdpost['politics']}],
		"参加工作时间": [{"@value": xdpost['workBeginTime']}],
		"当前职位": [{"@value": xdpost['currentJob']}],
		"意向职位": [{"@value": xdpost['intentJob']}],
		"当前年薪": [{"@value": xdpost['currentSalaryYearly']}],
		"当前所在城市": [{"@value": None}],
		"意向城市": [{"@value": None}],
		"意向年薪": [{"@value": xdpost['intentSalaryYearlyMax']}],
		"相关机构": org_list,
		"教育经历": edu_list,
		"工作经历": job_list,
		"项目经历": pro_list,
		"培训和海外经历": tra_list,
		"当前最高学历": [{"@value": eduback_dict[high_edu['degree']]}],
		"最高学历学校": [{"@value": high_edu['schoolName']}],
		"当前最高学历专业": [{"@value": high_edu['major']}],
		"语言能力": [{"@value": val['lanName']} for val in xdpost['language']],
		"技术职称": [{"@value": None}],
		"研究领域": [{"@value": val['researchName']} for val in xdpost['researchList']],
		"研究领域分类": [{"@value": val['researchName']} for val in xdpost['researchList']],
		"婚姻状况": [{"@value": None}],
		"特长爱好": [{"@value": None}],
		"人才标签": [{"@value": None}],
		"人才特点": [{"@value": None}],
		"当前行业": [{"@value": None}],
		"专业证书": [{"@value": None}],
		"入选人才": [{"@value": None}],
		"知识产权": [{"@value": None}],
		"获得荣誉及证明": [{"@value": None}],
		"备注信息": [{"@value": None}],
		"对报名岗位认识及工作设想": [{"@value": None}],
		"自我评价及主要工作业绩": [{"@value": None}],
		"当前公司": [{"@value": None}],
		"毕业院校分类": [{"@value": None}],
		"工作年限": [{"@value": None}],
		"专业方向大类": [{"@value": None}],
		"报名岗位": [{"@value": None}],
	}
	post_json(json_obj)
	return {"errno": 0, "msg": "Success"}

if __name__ == '__main__':
	uvicorn.run(app=app, host='0.0.0.0', port=9000)