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340 lines
15 KiB
340 lines
15 KiB
# coding:utf8
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import os, sys
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import io
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf8')
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cur_dir = os.path.dirname(os.path.abspath(__file__)) or os.getcwd()
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par_dir = os.path.abspath(os.path.join(cur_dir, os.path.pardir))
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sys.path.append(cur_dir)
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sys.path.append(par_dir)
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import json
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from django.http import HttpResponse
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from text_analysis.tools import to_kafka
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from django.views.decorators.csrf import csrf_exempt
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from log_util.set_logger import set_logger
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from text_analysis.tools.tool import parse_data
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logging = set_logger('logs/results.log')
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import traceback
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import queue
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import requests
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import time
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from datetime import datetime
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import os
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import joblib
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from text_analysis.cusException import userFile_Exception, postFile_Exception
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# 任务队列
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global task_queue
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task_queue = queue.Queue()
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#mysql连接池
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from text_analysis.tools.db_pool import get_conn_pool
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@csrf_exempt
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def robotIdentification(request):
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if request.method == 'POST':
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try:
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raw_data = json.loads(request.body)
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task_queue.put(raw_data)
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return HttpResponse(json.dumps({"code": 1, "msg": "请求正常！"}, ensure_ascii=False))
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except:
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logging.error(traceback.format_exc())
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return HttpResponse(json.dumps({"code": 0, "msg": "请求json格式不正确！"}, ensure_ascii=False))
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else:
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return HttpResponse(json.dumps({"code": 0, "msg": "请求方式错误，改为post请求"}, ensure_ascii=False))
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def predict(user_file_result,post_file_result,task):
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try:
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# raw_data = {"user_file": {"accountId": "39234393", "accountName": "hello", "nickName": "Johnson Leung",
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# "fansCount": 308, "likeCount": 92707, "postCount": 14237,
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# "otherInfo": "{\"\"otherInfo\"\":\"\"{\"\"bio\"\": \"\"Huge}",
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# "authentication": 0},
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# "post_file": {"count": 1, "LikeCount": 12, "CommentsCount": 1, "ShareCount": 1,
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# "length": 150, "tags": 0, "https": 0, "at": 0, "diffdate": 1}}
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# 用户数据
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res = {"successCode": "1", "errorLog": "", "results": {}}
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user_data = []
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# 返回值需要的三个字段
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accountId = ""
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nickName = ""
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accountName = ""
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data = {}
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if user_file_result:
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data['user_file'] = user_file_result
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logging.info('用户数据：{}'.format(data['user_file']))
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accountId = data["user_file"]["accountId"]
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nickName = data["user_file"]["nickName"]
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accountName = data["user_file"]["accountName"]
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else:
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data['user_file'] = {}
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raise userFile_Exception
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if post_file_result:
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data['post_file'] = post_file_result
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logging.info('帖子数据：{}'.format(data['post_file']))
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else:
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data['post_file'] = {}
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raise postFile_Exception
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# 识别结果返回值
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recognition_code = "0"
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try:
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user_data_otherInfo_1 = 0 if data["user_file"]["otherInfo"].strip() == "" else 1
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except:
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user_data_otherInfo_1 = 0
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try:
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user_data_nickName_2 = 0 if data["user_file"]["nickName"].strip() == "" else 1
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except:
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user_data_nickName_2 = 0
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try:
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user_data_fansCount_3 = int(data["user_file"]["fansCount"])
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except:
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user_data_fansCount_3 = 0
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try:
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user_data_likeCount_4 = int(data["user_file"]["likeCount"])
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except:
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user_data_likeCount_4 = 0
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try:
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user_data_postCount_5 = int(data["user_file"]["postCount"])
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except:
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user_data_postCount_5 = 0
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try:
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user_data_authentication_6 = int(data["user_file"]["authentication"])
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except:
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user_data_authentication_6 = 0
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user_data.extend(
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[user_data_otherInfo_1, user_data_nickName_2, user_data_fansCount_3, user_data_likeCount_4,
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user_data_postCount_5, user_data_authentication_6])
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# 帖子数据
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if data["post_file"] == {}:
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recognition_code = "-1"
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else:
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post_data = []
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try:
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post_data_count_1 = int(data["post_file"]["count"])
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except:
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post_data_count_1 = 0
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try:
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post_data_LikeCount_2 = int(data["post_file"]["LikeCount"])
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except:
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post_data_LikeCount_2 = 0
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try:
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post_data_CommentsCount_3 = int(data["post_file"]["CommentsCount"])
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except:
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post_data_CommentsCount_3 = 0
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try:
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post_data_ShareCount_4 = int(data["post_file"]["ShareCount"])
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except:
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post_data_ShareCount_4 = 0
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try:
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post_data_length_5 = int(data["post_file"]["length"])
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except:
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post_data_length_5 = 0
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try:
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post_data_tags_6 = int(data["post_file"]["tags"])
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except:
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post_data_tags_6 = 0
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try:
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post_data_https_7 = int(data["post_file"]["https"])
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except:
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post_data_https_7 = 0
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try:
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post_data_at_8 = int(data["post_file"]["at"])
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except:
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post_data_at_8 = 0
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try:
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post_data_diffdate_9 = int(data["post_file"]["diffdate"])
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except:
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post_data_diffdate_9 = 0
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post_data.extend(
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[post_data_count_1, post_data_LikeCount_2, post_data_CommentsCount_3, post_data_ShareCount_4,
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post_data_length_5, post_data_tags_6, post_data_https_7, post_data_at_8, post_data_diffdate_9])
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features = [user_data + post_data]
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bot_user = joblib.load(cur_dir + "/model/bot_user.pkl") # 加载训练好的模型
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result = bot_user.predict(features)
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recognition_code = str(result[0])
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# logging.info("预测模型结果为{}".format(result))
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results = {}
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# 用户id
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results['authorId'] = accountId
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# 用户昵称
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results['nickName'] = nickName
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# 用户账号
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results['accountName'] = accountName
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#结束标识
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res['isLast'] = True
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#数据类型 --目前只提供给图谱使用
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results['pageType'] = 'userAuthenPage'
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if recognition_code == '0':
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results['recognitionResult'] = '非机器人'
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results['recognitionCode'] = recognition_code
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elif recognition_code == '1':
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results['recognitionResult'] = '机器人'
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results['recognitionCode'] = recognition_code
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else:
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results['recognitionResult'] = '未知识别结果'
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results['recognitionCode'] = recognition_code
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results["isLast"]=1
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res['results'] = json.dumps(results)
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res["status"]=1
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res["message"]="成功"
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task["result"] = res
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logging.info("增加预测数据-{}".format(task))
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to_kafka.send_kafka(task, logging)
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except userFile_Exception:
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res = {"successCode": "0", "errorLog": "用户数据为空！", "results": {}}
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results = {}
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results['authorId'] = ""
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results['nickName'] = ""
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results['accountName'] = ""
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results['recognitionResult'] = '用户数据为空'
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results["isLast"]=1
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res['results'] = json.dumps(results)
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res["status"]=2
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res["message"]="用户数据为空"
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task["result"] = res
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logging.info("该条请求用户数据为空-{}".format(task))
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to_kafka.send_kafka(task, logging)
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except postFile_Exception:
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res = {"successCode": "0", "errorLog": "帖子数据为空！", "results": {}}
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results = {}
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results['authorId'] = accountId
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results['nickName'] = nickName
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results['accountName'] = accountName
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results['recognitionResult'] = '帖子数据为空'
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results["isLast"]=1
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res['results'] = json.dumps(results)
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res["status"]=2
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res["message"]="帖子数据为空"
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task["result"] = res
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logging.info("该条请求帖子数据为空-{}".format(task))
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to_kafka.send_kafka(task, logging)
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except:
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res = {"successCode": "0", "errorLog": "", "results": {}}
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results = {}
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results['authorId'] = accountId
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results['nickName'] = nickName
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results['accountName'] = accountName
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results['recognitionResult'] = ""
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results["isLast"]=1
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res["results"] = json.dumps(results)
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res["status"]=2
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res["message"]="异常"
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task["result"] = res
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task["result"]["error"] = traceback.format_exc()
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logging.info(traceback.format_exc())
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to_kafka.send_kafka(task, logging)
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def data_structure(dbConfig):
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'''
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水军识别数据构造
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主要是写入用户表、主贴表
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'''
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#获取数据库连接
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sqlhelper = get_conn_pool(dbConfig['host'],dbConfig['port'],dbConfig['username'],dbConfig['password'],dbConfig['db'])
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#用户任务作为响应体
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send_task = None
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while True:
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if task_queue.qsize() > 0:
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try:
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logging.info("队列长度：{}".format(task_queue.qsize()))
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task = task_queue.get()
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input = task['input']
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account = input['account']
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post = input['post']
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#判断数据类型
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data = task['data']
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page_type = None
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taskId = None
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app_data = None
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for data_str in data:
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try:
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app_data = json.loads(data[data_str])
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taskId = app_data['taskId']
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if "pageType" in app_data:
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page_type = app_data['pageType']
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break
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except:
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logging.error("正常判断，异常请忽略")
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logging.info("数据类型：{}".format(page_type))
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if page_type == 'userInfoPage':
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# 用户任务作为响应体
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send_task = task
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#用户类型数据写入
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sql = "INSERT INTO `user_account`(`taskId`, `accountId`, `accountName`, `nickName`, `fansCount`, `likeCount`, `postCount`, `otherInfo`, `authentication`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)"
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# 构造参数元组
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values = (
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parse_data(task, account['taskId']),
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parse_data(task, account['accountId']),
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parse_data(task, account['accountName']),
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parse_data(task, account['nickName']),
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parse_data(task, account['fansCount']),
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parse_data(task, account['likeCount']),
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parse_data(task, account['postCount']),
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parse_data(task, account['otherInfo']),
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parse_data(task, account['authentication'])
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)
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sqlhelper.insert(sql,values)
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elif page_type == 'storyDetailPage':
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#帖子类型数据写入
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# 定义 SQL 语句
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sql = "INSERT INTO `user_post`(`taskId`, `postId`, `accountId`, `accountName`, `likeCount`, `emotionCount`, `commentsCount`, `shareCount`, `content`, `pubTime`, `crawlTime`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
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# 构造参数元组
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values = (
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parse_data(task, post['taskId']),
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parse_data(task, post['postId']),
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parse_data(task, post['accountId']),
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parse_data(task, post['accountName']),
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parse_data(task, post['likeCount']),
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parse_data(task, post['emotionCount']),
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parse_data(task, post['commentsCount']),
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parse_data(task, post['shareCount']),
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parse_data(task, post['content']),
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parse_data(task, post['pubTime']),
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parse_data(task, post['crawlTime'])
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)
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sqlhelper.insert(sql,values)
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#判断是否是此次数据流的最后一条，最后一条直接触发用户的水军识别算法
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if 'isLast'in app_data:
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#获取用户相关的数据
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sql = "select accountId,accountName,nickName,fansCount,likeCount,postCount,otherInfo,authentication from user_account where taskId ='{}'".format(taskId)
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user_file_result = sqlhelper.queryOne(sql)
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# 获取帖子相关的数据
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sql = "SELECT CONVERT(COUNT(postId), CHAR(255)) AS count, CONVERT(AVG(likeCount), CHAR(255)) AS LikeCount, CONVERT(AVG(commentsCount), CHAR(255)) AS CommentsCount, CONVERT(AVG(shareCount), CHAR(255)) AS ShareCount, CONVERT(AVG(LENGTH(content)), CHAR(255)) AS length, CONVERT(AVG((LENGTH(content) - LENGTH(REPLACE(content, '#', ''))) / LENGTH('#')), CHAR(255)) AS tags, CONVERT(AVG((LENGTH(content) - LENGTH(REPLACE(content, 'https', ''))) / LENGTH('https')), CHAR(255)) AS https, CONVERT(AVG((LENGTH(content) - LENGTH(REPLACE(content, '@', ''))) / LENGTH('@')), CHAR(255)) AS at, CONVERT(MIN(TIMESTAMPDIFF(SECOND, pubTime, GREATEST(pubTime, crawlTime))), CHAR(255)) AS diffdate FROM user_post where taskId ='{}'".format(taskId)
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post_file_result = sqlhelper.queryOne(sql)
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if send_task == None:
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send_task = task
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predict(user_file_result,post_file_result,send_task)
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#结束置空
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send_task = None
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except Exception as e:
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traceback.print_exc()
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else:
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# 暂无任务，进入休眠
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time.sleep(10)
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if __name__ == '__main__':
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all_result = {
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"9_获取用户发帖信息": "{\"resultList\": [{\"count\": \"10\", \"LikeCount\": \"1\", \"CommentsCount\": \"0.1\", \"ShareCount\": \"0.4\", \"length\": \"241.8000\", \"tags\": \"5.80000000\", \"https\": \"1.20000000\", \"at\": \"0.40000000\", \"diffdate\": \"170269\"}]}",
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"8_获取用户信息": "{\"resultList\": [{\"accountId\": \"1368232444323799043\", \"accountName\": \"Ujjal best Tech@UjjalKumarGho19\", \"nickName\": \"UjjalKumarGho19\", \"fansCount\": \"660\", \"likeCount\": \"2096\", \"postCount\": \"579\", \"otherInfo\": \"\", \"authentication\": 1}]}"}
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data = {}
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# {"user_file": "9_获取用户信息", "post_file": "10_获取用户发帖信息"}
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user_file_result = json.loads(all_result[data['user_file']])
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post_file_result = json.loads(all_result[data['post_file']])
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if user_file_result['resultList']:
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resultList = user_file_result['resultList']
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data['user_file'] = resultList[0]
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else:
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data['user_file'] = {}
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if post_file_result['resultList']:
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data['post_file'] = post_file_result['resultList'][0]
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else:
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data['post_file'] = {}
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print(data)
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