fclesio

from flask import Flask
from flask_restful import Api, Resource, reqparse
from sklearn.externals import joblib
import numpy as np

# Faz a carga do name space do Flask para isntanciar
# o endpoint
APP = Flask(__name__)

# Instancia o endpoint propriamente dito

fclesio

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

# Carga do conjunto de dados
df = \
    pd.read_csv("https://raw.githubusercontent.com/fclesio/metalbr/master/rebirth-remains.csv",
                index_col=False)

fclesio

## Ref: https://stackoverflow.com/questions/44785585/how-to-delete-all-docker-local-docker-images

docker container prune -f &&
docker stop $(docker ps -aq) &&
docker rm -vf $(docker ps -a -q) &&
docker rmi -f $(docker images -a -q)

fclesio

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from keras.layers import Dropout

# Arch #1: 3 Layers
model_arch_1 = keras.Sequential(
	[
        layers.Dense(2, activation="relu", name="layer1"),
        layers.Dense(3, activation="relu", name="layer2"),

fclesio

import sqlite3
import pandas as pd

conn = sqlite3.connect('prod.db')
c = conn.cursor()

query_create_table = '''
CREATE TABLE IF NOT EXISTS toplines(
	source_id TEXT,
	source_name TEXT,

fclesio

import requests
import json
import pandas as pd

URL = "http://newsapi.org/v2/everything?q=bitcoin&from=2020-04-18&sortBy=publishedAt&apiKey=1816423a00634e51839b61f8cfc624cf"

parsed = requests.get(URL).json()

print(f"type: {type(parsed)}")

fclesio

import json
import pandas as pd

print("Started Reading JSON file")
with open("citylots.json", "r") as read_file:
    print("Converting JSON encoded data into Python dictionary")
    developer = json.load(read_file)
    
points_coordinates = []

fclesio

# Source: https://stackoverflow.com/questions/44862712/td-idf-find-cosine-similarity-between-new-document-and-dataset/44863365#44863365
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Generate DF
df = \
    pd.DataFrame({'jobId' : [1, 2, 3, 4, 5],
                  'serviceId' : [99, 88, 77, 66, 55],
                  'text' : ['Ich hätte gerne ein Bild an meiner Wand.',

fclesio

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer

# Generate DF
df = \
    pd.DataFrame({'jobId' : [1,2,3,4,5],
                  'serviceId' : [99,88,77,66, 55],
                  'text' : ['Ich hätte gerne ein Bild an meiner Wand.',
                            'Ich will ein Bild auf meinem Auto.',
                            'Ich brauche ein Bild auf meinem Auto.',

fclesio

curl -X POST "http://127.0.0.1:8000/prediction?PAY_AMT6=1000&PAY_AMT5=2000&PAY_AMT4=300&PAY_AMT3=200&PAY_AMT2=450&PAY_AMT1=10000&BILL_AMT6=300&BILL_AMT5=23000&BILL_AMT4=24000&BILL_AMT3=1000&BILL_AMT2=1000&BILL_AMT1=1000&PAY_6=200&PAY_5=200&PAY_4=200&PAY_3=200&PAY_2=200&PAY_0=2000&AGE=35&MARRIAGE=1&EDUCATION=1&SEX=1&LIMIT_BAL=1000000" -H "accept: application/json"