anthonyli358

from flask import request
from jsonschema import validate, FormatChecker, exceptions

data = request.json

# Define the schema to check 
check_schema = {
  "type": "object",
  "properties": {
    "name": {"type": "string"},

anthonyli358

import random

def esimate_pi(n: int) -> float:
  n_points_in_circle = 0
  for _ in range(n):
    x = random.uniform(0, 1)
    y = random.uniform(0, 1)
    # don't need to square root:
    # if r < 1 then sqrt(r) < 1
    # if r > 1 then sqrt(r) > 1

anthonyli358

FROM python:3.8-slim-buster

WORKDIR /app

COPY requirements.txt requirements.txt
RUN pip3 install -r requirements.txt

COPY src/ .

CMD ["python3", "app.py"]

anthonyli358

from flask import Flask, jsonify, request

# This code is typically imported from a separate python module
# e.g. from ml_model.predict import predict_result (from folder.pythonfile import function)
# ============
import pickle

def predict_result(transaction_id):
  with open("model.pkl", "rb") as file:
    model = pickle.load(file)

anthonyli358

{%- macro dbt_greatest(cols, dummy_val="'1/1/1900'") -%}

-- Returns null if all arguments are null

{%- if cols is not iterable and (cols is not string and cols is not mapping) -%}
    {{ exceptions.raise_compiler_error(
        "Argument invalid... dbt_greatest() expects argument format (['col1', 'col2'], 'dummy_val')"
        )
    }}
{% endif %}

anthonyli358

select
    product_id,
    {{ dbt_utils.pivot(
        'month', 
        dbt_utils.get_column_values(ref('product_sales'), 'month'),
        then_value='amount'
    ) }}
from {{ ref('product_sales') }}
group by product_id

anthonyli358

{% snapshot product_sales_snapshot %}

{{ config(
    tags = ['hourly', 'product'],  -- we can use these for airflow orchestration
    target_schema = 'product',
    unique_key = 'product_sale_id',
    strategy = 'check',
    check_cols = 'all',
  )
}}

anthonyli358

select
  coalesce(
    try_to_date(freeflow_sale_date),
    try_to_date(freeflow_sale_date, 'DD Mon YYYY'),  -- 19 September 2020
    try_to_date(freeflow_sale_date, 'DD/MM/YYYY'),  -- 14/12/2020
    try_to_date(freeflow_sale_date, 'YYYY/MM/DD') -- 2020/12/08
  ) as sale_date
from product_sales

anthonyli358

with to_pivot as (
  select
    product_id,
    month,
    amount
  from product_sales
)

select *
from to_pivot

anthonyli358

select
  iff(p.status in ('open', 'active'), 'active', i.status) as clean_status,
  iff(clean_status = 'active', true, false) as is_active
from product_sales p