news_classification_model_monitor / read_predictions_from_db.py
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Create read_predictions_from_db.py
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import pymongo
import os
import pandas as pd
import logging
class PredictionDBRead:
def __init__(self):
self.url = os.getenv('PREDICTION_DB_URL')
self.database = "indian_news_predictions_db"
self.collection = "indian_news_predictions_coll"
self.__client = None
self.__error = 0
def __connect(self):
try:
self.__client = pymongo.MongoClient(self.url)
_ = self.__client.list_database_names()
except Exception as conn_exception:
self.__error = 1
self.__client = None
def __read(self):
try:
db = self.__client[self.database]
coll = db[self.collection]
docs = []
for doc in coll.find():
docs.append(doc)
rss_df = pd.DataFrame(docs)
except Exception as insert_err:
self.__error = 1
rss_df = None
return rss_df
def __close_connection(self):
if self.__client is not None:
self.__client.close()
self.__client = None
def read_predictions_from_db(self):
rss_df = None
if self.url is not None:
if self.__error == 0:
self.__connect()
if self.__error == 0:
rss_df = self.__read()
if self.__error == 0:
logging.info("Prediction Data Read Successful")
if self.__client is not None:
self.__close_connection()
return rss_df