add monitor

utils/message.py

+import base64
+import hashlib
+import hmac
+import json
+import time
+import urllib
+
+import requests
+
+
+def send_msg_to_dingtalk(msg, mobiles=[]):
+    try:
+        secret = "SECffcdd05c1bb9ca2e6268b2593e4891872c5d0a8adf6751915bc77583076ed0b7"
+        timestamp = str(round(time.time() * 1000))
+        secret_enc = secret.encode("utf-8")
+        string_to_sign = "{}\n{}".format(timestamp, secret)
+        string_to_sign_enc = string_to_sign.encode("utf-8")
+        hmac_code = hmac.new(secret_enc, string_to_sign_enc, digestmod=hashlib.sha256).digest()
+        sign = urllib.parse.quote_plus(base64.b64encode(hmac_code))
+
+        headers = {"Content-Type": "application/json"}
+        data = {"msgtype": "text", "text": {"content": msg}}
+        if mobiles:
+            data = {"msgtype": "text", "text": {"content": msg}, "at": {"atMobiles": mobiles, "isAtAll": False}}
+        json_data = json.dumps(data)
+        url = "https://oapi.dingtalk.com/robot/send?access_token=661cf4d772916d54597d5cd1636fbc6890ef220908dd1f81fe074f44896b6aa2&sign={}&timestamp={}".format(
+            sign, timestamp)
+        response = requests.post(url=url, data=json_data, headers=headers)
+        return str(response.status_code) + " " + str(response.content.decode("utf-8"))
+    except Exception as e:
+        print(e)
+        return str(e)

word_vector/tests.py

@@ -9,7 +9,7 @@ model_dir = os.path.join(base_dir, "_models")
 data_dir = os.path.join(base_dir, "_data")
 
 if __name__ == "__main__":
-    id1 = ["202647", "386735"]  # 下颚角 5/1252645
+    id1 = ["202647", "386735"]  # 下颚角 9/1252645
     id2 = ["87874", "84198"]  # 双眼皮 95
 
     with open(os.path.join(data_dir, "click_tractate_ids.csv"), "r") as f:

word_vector/word_to_vec.py

 import multiprocessing
 import os
 import time
+import traceback
 
 from gensim.models import Word2Vec, word2vec
 from gm_rpcd.all import bind
 from utils.es import es_scan
+from utils.message import send_msg_to_dingtalk
 
 base_dir = os.getcwd()
 print("base_dir: " + base_dir)
@@ -48,7 +50,12 @@ def w2v_train(f_name, model_output_name):
 
 @bind("strategy_embedding/word_vector/word_similarity")
 def word_similarity(word):
+    try:
         return WORD2VEC_MODEL.wv.most_similar(word)
+    except Exception as e:
+        send_msg_to_dingtalk(str(e) + "\n" + str(traceback.format_exc()))
+        print(traceback.format_exc())
+        return []
 
 
 def get_user_portrait_projects(score_limit=5):
@@ -106,6 +113,8 @@ def clicked_tractate_ids_item2vec_model(id, n=5):
     try:
         return TRACTATE_CLICK_IDS_MODEL.wv.most_similar(id, topn=n)
     except Exception as e:
+        send_msg_to_dingtalk(str(e) + "\n" + str(traceback.format_exc()))
+        print(traceback.format_exc())
         return []