yassineAlouini

import os 
from flask import Flask, redirect, render_template, request, url_for 
from werkzeug.utils import secure_filename 


DATA_FOLDER_PATH = os.path.join( os.path.dirname(os.path.realpath(__file__)), 'static') 
ALLOWED_EXTENSIONS = set(['.png', '.jpg', '.jpeg']) 
IMG_MAX_SIZE = 16 * 1024 * 1024 

app = Flask(__name__) 

yassineAlouini

import numpy as np 
import keras.preprocessing.image as image_utils 
from keras.applications.imagenet_utils import decode_predictions, preprocess_input
# You can import any other model if you would like to :)
from keras.applications.resnet50 import ResNet50 

IMG_SIZE = (224, 224) 

# Loading the model only once
CLASSIFICATION_MODEL = ResNet50()

yassineAlouini

SELECT t1.datname                                   AS db_name, 
       Pg_size_pretty(Pg_database_size(t1.datname)) AS db_size 
FROM   pg_database t1 
ORDER  BY Pg_database_size(t1.datname) DESC 

yassineAlouini

# This function could be made generic to almost any loaded CSV file with
# pandas. Can you see how to do it?

import pandas as pd

# Some constants
PARQUET_ENGINE = "pyarrow"
DATE_COL = "purchase_date"
CATEGORICAL_COLS = ["card_id", "category_3", "merchant_id", "month_lag", 
                    "installments", "state_id", "subsector_id", 

yassineAlouini

# To get the segmentation_models library, run: 
# pip install segmentation-models


from segmentation_models import Unet


def build_pretrained_unet_model():
    """Build a pre-trained Unet model. """
    return Unet(backbone_name='resnet34', encoder_weights='imagenet')

yassineAlouini

# Extracted from this blog post: https://tech.marksblogg.com/install-and-configure-apache-airflow.html. 

import airflow
from airflow import models, settings
from airflow.contrib.auth.backends.password_auth import PasswordUser


user = PasswordUser(models.User())
user.username = 'username'
user.email    = '[email protected]'

yassineAlouini

from hyperopt import tpe, fmin, Trials
from hyperopt.hp import normal
from hyperopt.plotting import main_plot_history, main_plot_histogram
import pandas as pd
import matplotlib.pylab as plt


def rosenbrock(suggestion):
    """ 
    A test function to minimize using hyperopt. The

yassineAlouini

import pandas as pd
import matplotlib.pylab as plt
import seaborn as sns


# In the clipboard
# piece,price
# gpu,869
# ssd,140
# power,113

yassineAlouini

import geopandas as gpd
import requests
def get_data_from_url(url):
    data = requets.get(url).json()
    return gpd.GeoDataFrame.from_features(data)

yassineAlouini

import pandas as pd

INPUT_PATH = "your/input/path.csv"
OUPUT_PATH = "your/output/path_{}.csv"


df = pd.read_csv(INPUT_PATH, parse_dates=['tms_gmt'])
df['year'] = df.tms_gmt.dt.year

for year in df['year'].unique():