nithyadurai87

from datetime import datetime
from airflow import DAG
import os
from airflow.operators.python_operator import PythonOperator
from airflow.models import Variable

def backup():
    backup_cmd = 'mysqldump -u root -ppassword sample > "/home/shrini/backups/"sample_"$(date +"%Y%m%d_%H%M%S").sql"'
    os.system(backup_cmd)

nithyadurai87

from pykafka import KafkaClient
from pymongo import MongoClient
import json
import sys

K_client = KafkaClient(hosts='localhost:9092')
topic = K_client.topics['dataets']	 
consumer = topic.get_simple_consumer(consumer_timeout_ms=5000)
	 
M_client = MongoClient('localhost',27017)

nithyadurai87

from pykafka import KafkaClient
import json
import time

client = KafkaClient(hosts='localhost:9092')
topic = client.topics['dataets']
producer = topic.get_sync_producer()
producer.produce(b'test message')

for e in range(1000):

nithyadurai87

version: '3'

services:
  app:
    build: .
    image: flaskapp:v10
    environment:
      - FLASK_ENV=development
    ports:
      - 5000:5000

nithyadurai87

from flask import Flask, request, jsonify
from pymongo import MongoClient

app = Flask(__name__)
client = MongoClient('mongodb',27017)
db = client.forest
collection = db.flowers

@app.route('/', methods=['POST', 'GET'])

nithyadurai87

FROM ubuntu:20.04
COPY . /app
WORKDIR /app
RUN apt-get update
RUN apt-get install python3-pip -y
RUN pip3 install -r requirements.txt
ENTRYPOINT ["python3"]
CMD ["prediction_api.py"]

nithyadurai87

FROM ubuntu:20.04
COPY . /app
WORKDIR /app
RUN apt-get update
RUN apt-get install python3-pip -y
ENTRYPOINT ["python3"]
CMD ["sample_api.py"]

nithyadurai87

import os 
import json
import pandas as pd
import numpy
from flask import Flask, render_template, request, jsonify
from pandas.io.json import json_normalize
#from sklearn.externals import joblib
import joblib

app = Flask(__name__)

nithyadurai87

import os 
import json
import pandas as pd
import numpy
#from sklearn.externals import joblib
import joblib

s = pd.read_json('./parameters.json')
p = joblib.load("./model.pkl")
r = p.predict(s)

nithyadurai87

import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split,cross_val_score
#from sklearn.externals import joblib
import joblib
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
from math import sqrt
import os