MAI/LabWork01/LoadDB.py
2023-10-26 16:05:43 +04:00

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import os
import secrets
from flask import Flask, redirect, url_for, request, render_template, session
from matplotlib import pyplot as plt
from LabWork01.AnalysCustomers import analysCustomersDataFrame
from LabWork01.AnalysSales import analysSalesDataFrame
from LabWork01.AnalysSalesCustomers import analysSalesCustomersDataFrame
from LabWork01.DataFrameAnalys import analysItemsDataFrame
from LabWork01.FuncLoad import createDataFrame
from LabWork01.LabWork3.AddData import addData
from LabWork01.LabWork3.CreateGraphics import createGraphics
from LabWork01.LabWork3.CustomGraphics import createCusGraphics
from LabWork01.LabWork3.DeletePng import deleteAllPng
from LabWork01.LabWork4.SiteSearch import SiteSearch
app = Flask(__name__)
# Для работы session
secret = secrets.token_urlsafe(32)
app.secret_key = secret
#сразу загружаем весь док, чтобы потом просто прыгать по нему
listShops = createDataFrame()
#список типов данных по столбцам
listTypes = listShops.dtypes.to_list()
#формируем записи о кол-ве пустых ячеек в каждом столбце
countNull = listShops.isnull().sum()
# для фильтра Блума
search_engine = SiteSearch()
search_engine.add("https://www.kaggle.com/datasets/ankanhore545/100-highest-valued-unicorns", ["Company", "Valuation", "Country", "State", "City", "Industries", "Founded Year", "Name of Founders", "Total Funding", "Number of Employees"])
search_engine.add("https://www.kaggle.com/datasets/ilyaryabov/tesla-insider-trading", ["Insider Trading", "Relationship", "Date", "Transaction", "Cost", "Shares", "Value", "Shares Total", "SEC Form 4"])
search_engine.add("https://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects", ["NASA", "est_diameter_min", "est_diameter_max", "relative_velocity", "miss_distance", "orbiting_body", "sentry_object", "absolute_magnitude", "hazardous"])
search_engine.add("https://www.kaggle.com/datasets/surajjha101/stores-area-and-sales-data", ["Store", "Area", "Available", "Daily", "Customer", "Sales"])
search_engine.add("https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database", ["Health", "Diabetes", "India"])
search_engine.add("https://www.kaggle.com/datasets/mirichoi0218/insurance", ["age", "sex", "bmi"])
search_engine.add("https://www.kaggle.com/datasets/muhammedtausif/world-population-by-countries", ["Country", "Population", "Continent", "Capital", "Yearly Change", "Land Area", "Fertility","Density"])
search_engine.add("https://www.kaggle.com/datasets/deepcontractor/car-price-prediction-challenge", ["car", "price", "manufacturer"])
search_engine.add("https://www.kaggle.com/datasets/surajjha101/forbes-billionaires-data-preprocessed", ["Name", "Networth", "Source"])
search_engine.add("https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset", [ "heart_disease" , "bmi", "stroke" ])
@app.route("/")
def home():
return render_template('main_page.html', context=[], main_img=[], image_names=[], tableAnalys=[], titles=[''], listTypes=listTypes, countNull=countNull, firstRow=1, secondRow=4, firstColumn=1, secondColumn=4)
@app.route("/showDiapason", methods=['GET','POST'])
def numtext():
data = request.args
#получаем срез и таблицы по введёным параметрам
newListShops = listShops.iloc[int(data['firstRow'])-1:int(data['secondRow']), int(data['firstColumn']):int(data['secondColumn'])+1]
_range = range(int(data['firstColumn']), int(data['secondColumn'])+1)
#список списков для шаблона
totalList = []
print(countNull[1])
#формирование 4-х списков для шаблонизатора
if 1 in _range:
listStoreArea = newListShops['Store_Area'].to_list()
totalList.append(listStoreArea)
if 2 in _range:
listItemsAvailable = newListShops['Items_Available'].to_list()
totalList.append(listItemsAvailable)
if 3 in _range:
listDailyCustomerCount = newListShops['Daily_Customer_Count'].to_list()
totalList.append(listDailyCustomerCount)
if 4 in _range:
listStoreSales = newListShops['Store_Sales'].to_list()
totalList.append(listStoreSales)
if int(data['firstRow']) and int(data['secondRow']) and int(data['firstColumn']) and int(data['secondColumn']):
return render_template('main_page.html', context=totalList, main_img=[], image_names=[], listTypes=listTypes, countNull=countNull,
firstColumn=int(data['firstColumn']), secondColumn=int(data['secondColumn']),
firstRow=int(data['firstRow']), secondRow=int(data['secondRow']))
return home()
#функция для проведения анализа данных
@app.route("/analysis", methods=['GET', 'POST'])
def analysis():
firstAnalys = analysItemsDataFrame(listShops)
secondAnalys = analysCustomersDataFrame(listShops)
thirdAnalys = analysSalesDataFrame(listShops)
fourthAnalys = analysSalesCustomersDataFrame(listShops)
# удаляем все текущие диаграммы
deleteAllPng()
# начинаем создавать диаграммы
createGraphics(firstAnalys, 'firstAn')
createGraphics(secondAnalys, 'secondAn')
createGraphics(thirdAnalys, 'thirdAn')
createGraphics(fourthAnalys, 'fourthAn')
# дополняем новыми значениями
additionListShops = addData(createDataFrame())
# получаем новые данные
newfirstAnalys = analysItemsDataFrame(additionListShops)
secondAnalys = analysCustomersDataFrame(additionListShops)
thirdAnalys = analysSalesDataFrame(additionListShops)
fourthAnalys = analysSalesCustomersDataFrame(additionListShops)
# создаём новые диаграммы
createGraphics(newfirstAnalys, 'addFirstAn')
createGraphics(secondAnalys, 'addSecondAn')
createGraphics(thirdAnalys, 'addThirdAn')
createGraphics(fourthAnalys, 'addFourthAn')
createCusGraphics(firstAnalys[0], newfirstAnalys[0])
image_names_start = ['firstAn0.jpg', 'firstAn1.jpg', 'firstAn2.jpg',
'secondAn0.jpg', 'secondAn1.jpg', 'secondAn2.jpg',
'thirdAn0.jpg', 'thirdAn1.jpg', 'thirdAn2.jpg',
'fourthAn0.jpg', 'fourthAn1.jpg', 'fourthAn2.jpg']
image_names_addition = ['addFirstAn0.jpg', 'addFirstAn1.jpg', 'addFirstAn2.jpg',
'addSecondAn0.jpg', 'addSecondAn1.jpg', 'addSecondAn2.jpg',
'addThirdAn0.jpg', 'addThirdAn1.jpg', 'addThirdAn2.jpg',
'addFourthAn0.jpg', 'addFourthAn1.jpg', 'addFourthAn2.jpg']
main_img = ['CustomJPG0.jpg']
result = listShops[['Store_Sales']]
# Строим boxplot с использованием Seaborn
plt.title('Valuation Boxplot by Store Sales')
plt.xlabel('Store Sales')
plt.boxplot(result['Store_Sales'])
script_dir = os.path.dirname(__file__)
results_dir = os.path.join(script_dir, 'static/')
plt.savefig(results_dir + 'NewCustomJPG' + str(0) + '.jpg')
newCustomJpg = ['NewCustomJPG0.jpg']
return render_template('main_page.html', context=[], image_names_start=image_names_start,
image_names_addition=image_names_addition,
tableAnalysOne=[],
tableAnalysTwo=[],
tableAnalysThree=[],
tableAnalysFour=[],
main_img=newCustomJpg,
titles=[''],
listTypes=listTypes, countNull=countNull, firstRow=1,
secondRow=4, firstColumn=1, secondColumn=4)
@app.route('/findURL', methods=['GET'])
def get_page_findURL():
return render_template('findURL.html', find=False, notFind=False)
@app.route('/findURL', methods=['POST'])
def findURL():
word = request.form["word"]
if (search_engine.contains(word)):
session["new_word"] = word
return render_template('findURL.html', find=True, notFind=False)
return render_template('findURL.html', find=False, notFind=True)
@app.route('/showFindLinks', methods=['POST'])
def get_page_showFindURL():
word = session.get("new_word", None)
links = search_engine.find_url(word)
word_links = []
for item in links:
word_links.append({item, word})
return render_template('showLinks.html', links=links)
if __name__=="__main__":
app.run(debug=True)