import sys,os

from community.graph_helper import GraphHelper
from model.response import StandardResponse

current_path = os.getcwd()
sys.path.append(current_path)
import time
from fastapi import APIRouter, Depends, Query
from typing import Optional, List

import sys
sys.path.append('..')
from utils.agent import call_chat_api,get_conversation_id
import json



router = APIRouter(prefix="/graph", tags=["Knowledge Graph"])
graph_helper = GraphHelper()



@router.get("/nodes/recommend", response_model=StandardResponse)
async def recommend(
    chief: str
):
    app_id = "256fd853-60b0-4357-b11b-8114b4e90ae0"
    conversation_id = get_conversation_id(app_id)
    result = call_chat_api(app_id, conversation_id, chief)
    json_data = json.loads(result)
    keyword = " ".join(json_data["chief_complaint"])
    return await neighbor_search(keyword=keyword, neighbor_type='Check',limit=10)


@router.get("/nodes/neighbor_search", response_model=StandardResponse)
async def neighbor_search(
    keyword: str = Query(..., min_length=2),
    limit: int = Query(10, ge=1, le=100),
    node_type: Optional[str] = Query(None),
    neighbor_type: Optional[str] = Query(None),
    min_degree: Optional[int] = Query(None)
):
    """
    根据关键词和属性过滤条件搜索图谱节点
    """
    try:
        start_time = time.time()
        print(f"开始执行neighbor_search，参数：keyword={keyword}, limit={limit}, node_type={node_type}, neighbor_type={neighbor_type}, min_degree={min_degree}")
        scores_factor = 1.7
        results = []
        diseases = {}

        has_good_result = False

        if not has_good_result:
            keywords = keyword.split(" ")
            new_results = []
            for item in keywords:
                if len(item) > 1:
                    results = graph_helper.node_search2(
                        item,
                        limit=limit,
                        node_type=node_type,
                        min_degree=min_degree
                    )

                    for result_item in results:
                        if result_item["score"] > scores_factor:
                            new_results.append(result_item)
                            if result_item["type"] == "Disease":
                                if result_item["id"] not in diseases:
                                    diseases[result_item["id"]] =  {
                                                        "id":result_item["id"],
                                                        "type":1,
                                                        "count":1
                                                    }
                                else:
                                    diseases[result_item["id"]]["count"] = diseases[result_item["id"]]["count"] + 1
                                has_good_result = True
            results = new_results
            print("扩展搜索的结果数量:",len(results))

        neighbors_data = {}

        for item in results:
            entities, relations = graph_helper.neighbor_search(item["id"], 1)
            max = 20 #因为类似发热这种疾病会有很多关联的疾病，所以需要防止检索范围过大，设置了上限
            for neighbor in entities:

                #有时候数据中会出现链接有的节点，但是节点数据中缺失的情况，所以这里要检查
                if "type" not in neighbor.keys():
                    continue
                if neighbor["type"] == neighbor_type:
                    #如果这里正好找到了要求检索的节点类型
                    if neighbor["id"] not in neighbors_data:
                        neighbors_data[neighbor["id"]] =  {
                                            "id":neighbor["id"],
                                            "type":neighbor["type"],
                                            "count":1
                                        }
                    else:
                         neighbors_data[neighbor["id"]]["count"] = neighbors_data[neighbor["id"]]["count"] + 1
                else:
                    #如果这里找到的节点是个疾病，那么就再检索一层，看看是否有符合要求的节点类型
                    if neighbor["type"] == "Disease":
                        if neighbor["id"] not in diseases:
                            diseases[neighbor["id"]] =  {
                                                "id":neighbor["id"],
                                                "type":"Disease",
                                                "count":1
                                            }
                        else:
                            diseases[neighbor["id"]]["count"] = diseases[neighbor["id"]]["count"] + 1
                        disease_entities, relations = graph_helper.neighbor_search(neighbor["id"], 1)
                        for disease_neighbor in disease_entities:
                            #有时候数据中会出现链接有的节点，但是节点数据中缺失的情况，所以这里要检查
                            if "type" in disease_neighbor.keys():
                                if disease_neighbor["type"] == neighbor_type:
                                    if disease_neighbor["id"] not in neighbors_data:
                                        neighbors_data[disease_neighbor["id"]] = {
                                            "id":disease_neighbor["id"],
                                            "type":disease_neighbor["type"],
                                            "count":1
                                        }
                                    else:
                                        neighbors_data[disease_neighbor["id"]]["count"] = neighbors_data[disease_neighbor["id"]]["count"] + 1
                        #最多搜索的范围是max个疾病
                        max = max - 1
                        if max == 0:
                            break
        disease_data = [diseases[k] for k in diseases]
        disease_data = sorted(disease_data, key=lambda x:x["count"],reverse=True)
        data = [neighbors_data[k] for k in neighbors_data if neighbors_data[k]["type"] == "Check"]

        data = sorted(data, key=lambda x:x["count"],reverse=True)

        if len(data) > 10:
            data = data[:10]
            factor = 1.0
            total = 0.0
            for item in data:
                total = item["count"] * factor + total
            for item in data:
                item["count"] = item["count"] / total
            factor = factor * 0.9

        if len(disease_data) > 10:
            disease_data = disease_data[:10]
            factor = 1.0
            total = 0.0
            for item in disease_data:
                total = item["count"] * factor + total
            for item in disease_data:
                item["count"] = item["count"] / total
            factor = factor * 0.9

        for item in data:
            item["type"] = 3
            item["name"] = item["id"]
            item["rate"] = round(item["count"] * 100, 2)
        for item in disease_data:
            item["type"] = 1
            item["name"] = item["id"]
            item["rate"] = round(item["count"] * 100, 2)
        end_time = time.time()
        print(f"neighbor_search执行完成，耗时：{end_time - start_time:.2f}秒")

        return StandardResponse(
            success=True,
            records={"可能诊断":disease_data,"推荐检验":data},
            error_code = 0,
            error_msg=f"Found {len(results)} nodes"
        )
    except Exception as e:
        print(e)
        raise e
        return StandardResponse(
            success=False,
            error_code=500,
            error_msg=str(e)
        )

graph_router = router