thanhnew2001/taipycode3 · Datasets at Hugging Face

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from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.document_loaders import TextLoader loader = TextLoader("data/data_2.0.txt") # Use this line if you only need data.txt text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=0) data = loader.load() texts = ...
from langchain.embeddings.openai import OpenAIEmbeddings from dotenv import load_dotenv import os load_dotenv() OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY") from langchain.chains import RetrievalQAWithSourcesChain from langchain import OpenAI from langchain.vectorstores import Chroma from langcha...
import os import sys import openai from langchain.chains import RetrievalQA from langchain.chat_models import ChatOpenAI from langchain.document_loaders import DirectoryLoader, TextLoader from langchain.embeddings import OpenAIEmbeddings from langchain.indexes import VectorstoreIndexCreator from langchain.ind...
s = open("data/data.txt", "r") s = s.read().replace("\n", "") with open("data/new_data.txt", "w") as x: x.write(s)
import requests from bs4 import BeautifulSoup from urllib.parse import urlparse, urljoin def scrape_domain_and_subdomains(base_url, file_path): visited_urls = set() def scrape(url): response = requests.get(url) if response.status_code == 200: soup = BeautifulSoup(respon...
# This is a sample Python script. # Press Maj+F10 to execute it or replace it with your code. # Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings. def print_hi(name): # Use a breakpoint in the code line below to debug your script. print(f'Hi, {name}') # ...
import taipy as tp from taipy.gui import Gui, notify from taipy.config import Config import numpy as np import pandas as pd BUSINESS_PATH = "data/yelp_business.csv" # Load the business data using pandas business_df = pd.read_csv(BUSINESS_PATH) # Remove quotation marks from the name business_df.name =...
import dask.dataframe as dd def get_data(path_to_csv: str, optional: str = None): """ Loads a csv file into a dask dataframe. Converts the date column to datetime. Args: - path_to_csv: path to the csv file - optional: optional argument (currently necessary to fix Core bug wi...
import pandas as pd import dask.dataframe as dd def get_id_from_name(name: str, business_dict: dict): """ Returns the business_id from the name of the business. Args: - name: name of the business - business_dict: dict with the name as key and the business_id as value Retu...
import time import pandas as pd import dask.dataframe as dd def task1(path_to_original_data: str): print("__________________________________________________________") print("1. TASK 1: DATA PREPROCESSING AND CUSTOMER SCORING ...") start_time = time.perf_counter() # Start the timer # Step...
from recsys.recsys import page_scenario_manager from taipy.gui import Gui gui = Gui(page_scenario_manager) gui.run()
import pandas as pd import numpy as np TEST_RATIO = 0.2 MOVIELENS_DATA_PATH = "u.data" MOVIE_DATA_PATH = "u.item" def convert_data_to_dataframe(data_path: str, movie_path: str): data = pd.read_table(data_path) data.columns = ["u_id", "i_id", "rating", "timestamp"] data_sort = data.sort_values(...
import pandas as pd from datetime import datetime data = pd.read_csv("dataset/rating_train.csv") timestamp = data.timestamp.to_list() date = [] for time in timestamp: date.append(datetime.fromtimestamp(time)) data["timestamp"] = date data.to_csv("data.csv", index=False)
import pandas as pd import numpy as np from scipy import sparse class DataLoader: def __init__(self): self.__train_data = None self.__val_data = None self.__test_data = None def __create_id_mapping(self): if self.__val_data: unique_uIds = pd.concat( ...
from taipy import Config, Scope from functions.funtions import preprocess_data, fit, predict from config.svd_config import ( n_epochs_cfg, n_factors_cfg, learning_rate_cfg, qi_cfg, bi_cfg, bu_cfg, pu_cfg, ) from config.kNN_config import ( x_id_cfg, n_k_neighboor_cfg, ...
from taipy import Config, Scope x_id_cfg = Config.configure_data_node(id="x_id", default_data=1) n_min_k_cfg = Config.configure_data_node(id="n_min_k", default_data=10) n_k_neighboor_cfg = Config.configure_data_node( id="n_k_neighboor", default_data=1) sim_measure_cfg = Config.configure_in_memory_data_...
from taipy import Config, Scope n_factors_cfg = Config.configure_data_node(id="n_factors", default_data=30) n_epochs_cfg = Config.configure_data_node(id="n_epochs", default_data=50) learning_rate_cfg = Config.configure_data_node(id="learning_rate", default_data=0.001) qi_cfg = Config.configure_data_node(id="q...
import taipy as tp from taipy.config import Config from taipy.gui import notify, Markdown import pandas as pd import numpy as np from helper.knn_helper import calculate_precision_recall from config.config import pipeline_cfg Config.configure_global_app(clean_entities_enabled=True) tp.clean_all_entities(...
import pandas as pd import numpy as np from ultis.dataloader import DataLoader from helper.svd_helper import sgd, predict_svd_pair from helper.knn_helper import predict_pair, compute_similarity_matrix VALID_ALGORITHM = ["kNN", "MF"] def preprocess_data( train_data: pd.DataFrame, test_data: pd.DataFrame...
import numpy as np from numba import njit def compute_similarity_matrix(train_set, sim_measure="pcc"): x_rated, _, n_x, _, x_list, y_list = list_ur_ir(train_set) if sim_measure == "pcc": print("Computing similarity matrix as pcc...") S = pcc(n_x, x_rated, min_support=1) elif sim_m...
import numpy as np from numba import njit def sgd( X, pu, qi, bu, bi, n_epochs, global_mean, n_factors, lr_pu, lr_qi, lr_bu, lr_bi, reg_pu, reg_qi, reg_bu, reg_bi, ): for epoch_ix in range(n_epochs): pu, qi, bu, bi, tra...
import pandas as pd from prophet import Prophet from taipy import Config def clean_data(initial_dataset: pd.DataFrame): print("Cleaning Data") initial_dataset = initial_dataset.rename(columns={"Date": "ds", "Close": "y"}) initial_dataset['ds'] = pd.to_datetime(initial_dataset['ds']).dt.tz_localize(N...
from taipy.gui import Gui, notify import pandas as pd import yfinance as yf from taipy.config import Config import taipy as tp import datetime as dt Config.load('config_model_train.toml') scenario_cfg = Config.scenarios['stock'] def get_stock_data(ticker, start): ticker_data = yf.download(ticker, star...
from taipy import Gui import pandas as pd # Interactive GUI and state, we maintain states for each individual client # hence we can have multiple clients with each client having its own state, # change of the state of one client will not affect the other client's state. # Each client has its own state and glob...
from taipy import Gui import pandas as pd # visual elements: Taipy adds visual elements on top of markdown # to give you the ability to add charts, tables... The format for # it is as follows: # <\|{variable}\|visual_element_name\|param_1=param_1\|param_2=param_2\| ... \|>. # variable: python variable eg dataframe ...
from taipy import Gui # not no empty spaces at the beginning of the markdown # markdown must start from the baseline page = """ ### Hello world """ # Gui(page=page).run(dark_mode=False) # you can specify the port number in the run(port=xxxx) # its by default 5000 Gui(page="Intro to Taipy").run(dark_mode=...
# 데이터를 처리하기 위한 파이썬의 기본 패키지 from login.login import * import pandas as pd # 타이피 코어 import import taipy as tp # 내 파이썬 코드의 백엔드 가져오기 \| 시나리오를 생성하려면 원본 파이프라인_cfg 및 시나리오_cfg가 필요합니다. # fixed_variables_default는 고정 변수의 기본값으로 사용됩니다. from config.config import fixed_variables_default, scenario_cfg, pipeline_cfg from tai...
import taipy as tp from taipy import Scope, Config from taipy.core import Frequency import json from algos.algos import * # 이 코드는 시나리오_cfg 및 파이프라인_csg를 생성하는 코드입니다. # 이 두 변수는 기본 코드에서 새 시나리오를 만드는 데 사용됩니다. # 이 코드는 실행 그래프를 구성할 것입니다. ##########################################################################...
import pandas as pd import numpy as np from pulp import * # 이 코드는 이러한 기능이 필요한 작업을 생성하는 config.py에서 사용됩니다. # 이 함수는 전형적인 파이썬 함수입니다(Taipy는 없습니다) ############################################################################### # 기능 ############################################################################### ...
import numpy as np import pandas as pd # 이 코드는 수요에 대한 csv 파일을 생성하는 데 사용되며 문제의 소스 데이터입니다. def create_time_series(nb_months=12,mean_A=840,mean_B=760,std_A=96,std_B=72, amplitude_A=108,amplitude_B=144): time_series_A = [] time_series_A.append(mean_A) time_series_B = [] time_series_B.append...
da_display_table_md = "<center>\n<\|{ch_results.round()}\|table\|columns={list(chart.columns)}\|width=fit-content\|height={height_table}\|></center>\n" d_chart_csv_path = None def da_create_display_table_md(str_to_select_chart): return "<center>\n<\|{" + str_to_select_chart + \ "}\|table\|width=fit-content\|hei...
import pandas as pd import json with open('data/fixed_variables_default.json', "r") as f: fixed_variables_default = json.load(f) # Taipy Core의 코드는 아직 실행되지 않았습니다. csv 파일을 이런 식으로 읽습니다. da_initial_demand = pd.read_csv('data/time_series_demand.csv') da_initial_demand = da_initial_demand[['Year', 'Month', 'Dem...
from pages.annex_scenario_manager.chart_md import ch_chart_md, ch_choice_chart, ch_show_pie, ch_layout_dict, ch_results from pages.annex_scenario_manager.parameters_md import pa_parameters_md, pa_param_selector, pa_param_selected, pa_choice_product_param, pa_product_param from taipy.gui.gui_actions import notify f...
from data.create_data import time_series_to_csv from config.config import scenario_cfg from taipy.core import taipy as tp import datetime as dt import pandas as pd cc_data = pd.DataFrame( { 'Date': [dt.datetime(2021, 1, 1)], 'Cycle': [dt.date(2021, 1, 1)], 'Cost of Back Order'...
import taipy as tp import pandas as pd cs_compare_scenario_md = """ # 시나리오 비교 <\|layout\|columns=3 3 1\|columns[mobile]=1\| <layout_scenario\| Scenario 1 <\|layout\|columns=1 1 3\|columns[mobile]=1\| <\| Year <\|{sm_selected_year}\|selector\|lov={sm_year_selector}\|dropdown\|width=100%\|on_change=change_sm_mo...
from .chart_md import ch_chart_md, ch_layout_dict, ch_results from config.config import fixed_variables_default from taipy.gui import Icon def create_sliders(fixed_variables): """" 이것은 매개변수에 대한 슬라이더를 자체적으로 생성하는 매우 복잡한 함수입니다. 손으로 할 수도 있었습니다. 그러나 이 방법은 장기적으로 더 유연합니다. """ # 반환될 문자열 ...
from taipy.gui import Icon import pandas as pd ch_layout_dict = {"margin":{"t":20}} # 차트 설정 토글 ch_choice_chart = [("pie", Icon("images/pie.png", "pie")), ("chart", Icon("images/chart.png", "chart"))] ch_show_pie = ch_choice_chart[1][0] ch_results = pd.DataFrame({"Monthly Production FPA"...
import taipy as tp from taipy.gui import Icon import datetime as dt import os import hashlib import json login = '' password = '' dialog_login = False dialog_new_account = False new_account = False all_scenarios = tp.get_scenarios() users = {} json.dump(users, open('login/login.json', 'w')) ...
from taipy import Gui page = """ # Hello World 🌍 with Taipy This is my first Taipy test app. And it is running fine! """ Gui(page).run(use_reloader=True) # use_reloader=True if you are in development
from taipy import Gui from page.dashboard_fossil_fuels_consumption import * if __name__ == "__main__": Gui(page).run( use_reloader=True, title="Test", dark_mode=False, ) # use_reloader=True if you are in development
import pandas as pd import taipy as tp from data.data import dataset_fossil_fuels_gdp country = "Spain" region = "Europe" lov_region = list(dataset_fossil_fuels_gdp.Entity.unique()) def load_dataset(_country): """Load dataset for a specific country. Args: _country (str): The name of t...
import pandas as pd dataset_fossil_fuels_gdp = pd.read_csv("data/per-capita-fossil-energy-vs-gdp.csv") country_codes = pd.read_csv("./data/country_codes.csv") dataset_fossil_fuels_gdp = dataset_fossil_fuels_gdp.merge( country_codes[["alpha-3", "region"]], how="left", left_on="Code", right_on="alpha-3" ) ...
from taipy.gui import Gui as tpGui from taipy.gui import notify as tpNotify import pandas as pd text = "Original text" col1 = "first col" col2 = "second col" col3 = "third col" ballon_img = "./img/Ballon_15_20.png" section_1 = """ <h1 align="center">Getting started with Taipy GUI</h1> <\|layout\|colum...
import os import logging from opentelemetry import metrics from opentelemetry import trace from opentelemetry.sdk.metrics import MeterProvider from opentelemetry.sdk.resources import Resource, SERVICE_NAME from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter from opentelemetry.ex...
import time from pathlib import Path from taipy import Config, Status import taipy as tp from taipy.gui import get_state_id, invoke_callback from metrics import init_metrics, tracer # Telemetry rec_svc_metrics = init_metrics() @tracer.start_as_current_span("function_double") def double(nb): """D...
from taipy.gui import Gui from tensorflow.keras import models from PIL import Image import numpy as np class_names = { 0: 'airplane', 1: 'automobile', 2: 'bird', 3: 'cat', 4: 'deer', 5: 'dog', 6: 'frog', 7: 'horse', 8: 'ship', 9: 'truck', } model = models.lo...
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import os import threading from flask import Flask from pyngrok import ngrok from hf_hub_ctranslate2 import GeneratorCT2fromHfHub from flask import request, jsonify model_name = "taipy5-ct2" # note this is local folder model, the model uploaded to huggingface did not response correctly #model_name = "micha...

#Imports from taipy.gui import Gui from tensorflow.keras import models from PIL import Image import numpy as np #Variables classes = { 0: 'airplane', 1: 'automobile', 2: 'bird', 3: 'cat', 4: 'deer', 5: 'dog', 6: 'frog', 7: 'horse', 8: 'ship', 9: 'truck' } ...
from taipy.gui import Gui, notify import pandas as pd import webbrowser import datetime import os DOWNLOAD_PATH = "data/download.csv" upload_file = None section_1 = """ <center> <\|navbar\|lov={[("page1", "This Page"), ("https://docs.taipy.io/en/latest/manuals/about/", "Taipy Docs"), ("https://docs.taipy.io/...
from taipy.gui import Gui from pages.all_regions import * from pages.by_region import * # Toggle theme: switch dark/light mode root_md = """ <\|toggle\|theme\|> <center>\n<\|navbar\|>\n</center> """ stylekit = { "color-primary": "#CC3333", "color-secondary": "#E0C095", "color-background-light": ...
import geopandas as gpd import pandas as pd def add_basic_stats(df_wine: pd.DataFrame) -> pd.DataFrame: """Add basic statistics to a DataFrame containing wine production data. This function calculates the minimum, maximum, and average wine production values for each row in the input DataFrame bas...
import taipy.core as tp from taipy import Config # Loading of the TOML Config.load("config/taipy-config.toml") tp.Core().run() # Get the scenario configuration scenario_cfg = Config.scenarios["SC_WINE"] sc_wine = tp.create_scenario(scenario_cfg) sc_wine.submit() df_wine_production = sc_wine.WINE_PRODUC...
from typing import Any import pandas as pd from config.config import df_wine_production, df_wine_with_geometry selected_year = "average" year_list = [ "average", "08/09", "09/10", "10/11", "11/12", "12/13", "13/14", "14/15", "15/16", "16/17", "17/18", ...
from typing import Any, Tuple import pandas as pd from config.config import df_wine_with_geometry list_of_regions = df_wine_with_geometry["Region"].unique().tolist() selected_region = "SUD-OUEST" def clean_df_region_color(df_region_color: pd.DataFrame) -> pd.DataFrame: """Clean and transform a DataF...
import numpy as np from taipy.gui import Gui from pages.country import country_md, on_change_country,\ selected_representation, data_country_date, pie_chart from pages.world import world_md from pages.map import map_md from pages.predictions import predictions_md, selected_scenario, ...
from taipy.config import Config, Scope import datetime as dt from algos.algos import add_features, create_train_data, preprocess,\ train_arima, train_linear_regression,\ forecast, forecast_linear_regression,\ result #Config.configure_job_...
import pandas as pd from pmdarima import auto_arima from sklearn.linear_model import LinearRegression import datetime as dt import numpy as np def add_features(data): dates = pd.to_datetime(data["Date"]) data["Months"] = dates.dt.month data["Days"] = dates.dt.isocalendar().day data["Week"] ...
import pandas as pd path_to_data = "data/covid-19-all.csv" data = pd.read_csv(path_to_data, low_memory=False)
from taipy.gui import Markdown import numpy as np import json from data.data import data type_selector = ['Absolute', 'Relative'] selected_type = type_selector[0] def initialize_world(data): data_world = data.groupby(["Country/Region", 'Date'])\ ...
from taipy.gui import Markdown, invoke_long_callback import taipy as tp import pandas as pd import datetime as dt from config.config import scenario_cfg scenario_selector = [(s.id, s.name) for s in tp.get_scenarios()] selected_scenario = None selected_date = dt.datetime(2020,10,1) scenario_country = "N...
import numpy as np from taipy.gui import Markdown from data.data import data marker_map = {"color":"Deaths", "size": "Size", "showscale":True, "colorscale":"Viridis"} layout_map = { "dragmode": "zoom", "mapbox": { "style": "open-street-map", "center": { "lat": 38, "lon": -90 }, "zoom...
import numpy as np import pandas as pd from taipy.gui import Markdown from data.data import data selected_country = 'France' data_country_date = None representation_selector = ['Cumulative', 'Density'] selected_representation = representation_selector[0] layout = {'barmode':'stack', "hovermode":"x"} ...
from taipy.gui import Gui, notify text = "" revealed = False page = """ <\|{text if text else 'Write something in the input'}\|> <\|{text}\|input\|on_change={lambda state: notify(state, 'i', s.text)}\|> <\|part\|render={len(text)>0}\| Part hidden and discovered after input \|> ----- <\|Push\|button\|on_action=...
# Write an app that calls a function every 5 seconds
from taipy.gui import Gui, Markdown, invoke_long_callback, notify import numpy as np status = 0 num_iterations = 10_000_000 pi_list = [] def pi_approx(num_iterations): k, s = 3.0, 1.0 pi_list = [] for i in range(num_iterations): s = s-((1/k) * (-1)**i) k += 2 if (i+1)%...
from taipy.gui import Gui, notify text = "" page_1 = """ # Page 1 <\|{text}\|> """ page_2 = """ # Page 2 <\|Raise error\|button\|on_action=raise_error\|> """ hidden_page_3 = """ # Hidden Page """ def raise_error(state): raise(ValueError("This is an error")) def on_init(state): print("Wh...
from taipy.gui import Gui selected = [] a = [1, 2] b = [2, 3] selector_lov = [a, b] page = """ <\|{selected}\|selector\|lov={selector_lov}\|adapter={lambda s: s.name}\|> """ gui = Gui(page) gui.run()
from taipy.gui import Gui from math import cos, exp value = 10 page = """ Markdown # Taipy Demo Value: <\|{value}\|text\|> <\|{value}\|slider\|on_change=on_slider\|> <\|{data}\|chart\|> """ def on_slider(state): state.data = compute_data(state.value) def compute_data(decay:int)->list: return ...
from taipy.gui import Gui from math import cos, exp value = 10 page = """ Markdown # Taipy Demo """ Gui(page).run(use_reloader=True, port=5002)
# Write an app that create simple charts by poviding inputs and diplay them
from taipy.gui import Gui title = 1 md = """ <\|{title}\|number\|on_change=change_partial\|> <\|part\|partial={p}\|> """ def change_partial(state): title_int = int(state.title) new_html = f'<h{title_int}>test{title_int}</h{title_int}>' print(new_html) state.p.update_content(state, new_html) ...
import yfinance as yf from taipy.gui import Gui from taipy.gui.data.decimator import MinMaxDecimator, RDP, LTTB df_AAPL = yf.Ticker("AAPL").history(interval="1d", period = "100Y") df_AAPL["DATE"] = df_AAPL.index.astype(int).astype(float) n_out = 500 decimator_instance = MinMaxDecimator(n_out=n_out) decim...
from taipy import Config import taipy as tp def read_text(path: str) -> str: with open(path, 'r') as text_reader: data = text_reader.read() return data def write_text(data: str, path: str) -> None: with open(path, 'w') as text_writer: text_writer.write(data) historical_dat...
import taipy as tp from config.config_toml import scenario_cfg if __name__=="__main__": tp.Core().run() scenario_1 = tp.create_scenario(scenario_cfg) scenario_2 = tp.create_scenario(scenario_cfg) scenario_1.submit() scenario_2.submit() scenario_1 = tp.create_scenario(scenario_cfg) ...
import taipy as tp from config.config import scenario_cfg if __name__=="__main__": tp.Core().run() scenario_1 = tp.create_scenario(scenario_cfg) scenario_2 = tp.create_scenario(scenario_cfg) scenario_1.submit() scenario_2.submit() scenario_1 = tp.create_scenario(scenario_cfg) ...
from taipy import Config from algos.algos import * Config.configure_job_executions(mode="standalone", max_nb_of_workers=2) # Configuration of Data Nodes input_cfg = Config.configure_data_node("input", default_data=21) intermediate_cfg = Config.configure_data_node("intermediate", default_data=21) output_cfg = ...
from taipy import Config from algos.algos import * Config.load('config/config_07.toml') Config.configure_job_executions(mode="standalone", max_nb_of_workers=2) scenario_cfg = Config.scenarios['my_scenario']
import time # Normal function used by Taipy def double(nb): return nb * 2 def add(nb): print("Wait 5 seconds in add function") time.sleep(5) return nb + 10
import taipy as tp from taipy.gui import Markdown from config.config import * scenario = None df_metrics = None data_node = None pages = {'/':'<\|navbar\|> <\|toggle\|theme\|> <br/>', 'Scenario': Markdown('pages/scenario.md'), 'Data-Node': Markdown('pages/data_node.md')} if __name__ ...
import taipy as tp import datetime as dt from config.config import * def create_and_run_scenario(date: dt.datetime): scenario = tp.create_scenario(config=scenario_cfg, name=f"scenario_{date.date()}", creation_date=date) scenario....
from taipy import Config, Scope, Frequency from taipy.core import Scenario import datetime as dt from algos.algos import clean_data, predict, evaluate ## Input Data Nodes initial_dataset_cfg = Config.configure_data_node(id="initial_dataset", storage_type=...
import datetime as dt import pandas as pd def clean_data(initial_dataset: pd.DataFrame): print(" Cleaning data") initial_dataset['Date'] = pd.to_datetime(initial_dataset['Date']) cleaned_dataset = initial_dataset[['Date', 'Value']] return cleaned_dataset def predict(cleaned_dataset: p...
from taipy.gui import Gui, notify import datetime as dt import yfinance as yf def get_stock_data(ticker): now = dt.date.today() past = now - dt.timedelta(days=365*2) return yf.download(ticker, past, now).reset_index() def update_ticker(state): # state.data = ... ticker = 'AAPL' dat...
from taipy.gui import Gui, notify import datetime as dt import yfinance as yf def get_stock_data(ticker): now = dt.date.today() past = now - dt.timedelta(days=365*2) return yf.download(ticker, past, now).reset_index() def update_ticker(state): state.data = get_stock_data(state.ticker) n...
from taipy.gui import Gui import taipy as tp from pages.data_viz import historical from pages.predictions_sol import predictions, scenario ticker = 'AAPL' pages = {"/":"<\|navbar\|>", "Historical":historical, "Prediction":predictions} tp.Core().run() Gui(pages=pages).run(port=5001)
from datetime import timedelta import yfinance as yf from prophet import Prophet def get_data(ticker, date): past = date - timedelta(days=365*7) return yf.download(ticker, past, date).reset_index() def predict(preprocessed_dataset): df_train = preprocessed_dataset[['Date', 'Close']] df_train...
import taipy as tp from taipy.gui import notify, Markdown import datetime as dt tp.Config.load("config/config.toml") ticker = 'AAPL' scenario = None data_node = None jobs = [] default_data = {"Date":[0], "Predictions":[0]} def on_submission_status_change(state, submittable, details): submission_sta...
import taipy as tp from taipy.gui import notify, Markdown import datetime as dt tp.Config.load("config/config.toml") ticker = 'AAPL' scenario = None data_node = None jobs = [] default_data = {"Date":[0], "Predictions":[0]} def on_submission_status_change(state, submittable, details): submission_sta...
from taipy.gui import notify, Markdown import datetime as dt import yfinance as yf def get_stock_data(ticker): now = dt.date.today() past = now - dt.timedelta(days=365*2) return yf.download(ticker, past, now).reset_index() def update_ticker(state): state.data = get_stock_data(state.ticker) ...
from taipy import Gui import pandas as pd import requests API_URL = "https://api-inference.huggingface.co/models/bigcode/starcoder" headers = {"Authorization": "Bearer [ENTER-YOUR-API-KEY-HERE]"} DATA_PATH = "data.csv" df = pd.read_csv(DATA_PATH, sep=";") data = pd.DataFrame( { "Date": pd.t...
import json def add_line(source, line, step): line = line.replace('Getting Started with Taipy Core', 'Getting Started with Taipy Core on Notebooks') line = line.replace('(../src/', '(https://docs.taipy.io/en/latest/getting_started/src/') line = line.replace('(time_series.csv)', '(https://docs.taip...
from taipy.core.config import Config, Frequency, Status import taipy as tp import datetime as dt import time # Normal function used by Taipy def double(nb): return nb * 2 def add(nb): print("Wait 10 seconds in add function") time.sleep(10) return nb + 10 # Configuration of Data Node...
from taipy.core.config import Config, Scope, Frequency import taipy as tp import datetime as dt import pandas as pd def filter_by_month(df, month): df['Date'] = pd.to_datetime(df['Date']) df = df[df['Date'].dt.month == month] return df def count_values(df): return len(df) Config.loa...
from taipy.core.config import Config, Frequency import taipy as tp # Normal function used by Taipy def double(nb): return nb * 2 def add(nb): return nb + 10 # Configuration of Data Nodes input_cfg = Config.configure_data_node("input", default_data=21) intermediate_cfg = Config.configure_data_...
from taipy.core.config import Config import taipy as tp import datetime as dt import pandas as pd import time # Normal function used by Taipy def double(nb): return nb * 2 def add(nb): print("Wait 10 seconds in add function") time.sleep(10) return nb + 10 Config.load('config_07.toml'...
from taipy.core.config import Config import taipy as tp import datetime as dt import pandas as pd def filter_current(df): current_month = dt.datetime.now().month df['Date'] = pd.to_datetime(df['Date']) df = df[df['Date'].dt.month == current_month] return df def count_values(df): ret...
from taipy.core.config import Config import taipy as tp import datetime as dt import pandas as pd import time # Normal function used by Taipy def double(nb): return nb * 2 def add(nb): print("Wait 10 seconds in add function") time.sleep(10) return nb + 10 Config.configure_job_execution...

from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.document_loaders import TextLoader loader = TextLoader("data/data_2.0.txt") # Use this line if you only need data.txt text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=0) data = loader.load() texts = ...

from langchain.embeddings.openai import OpenAIEmbeddings from dotenv import load_dotenv import os load_dotenv() OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY") from langchain.chains import RetrievalQAWithSourcesChain from langchain import OpenAI from langchain.vectorstores import Chroma from langcha...

import os import sys import openai from langchain.chains import RetrievalQA from langchain.chat_models import ChatOpenAI from langchain.document_loaders import DirectoryLoader, TextLoader from langchain.embeddings import OpenAIEmbeddings from langchain.indexes import VectorstoreIndexCreator from langchain.ind...

s = open("data/data.txt", "r") s = s.read().replace("\n", "") with open("data/new_data.txt", "w") as x: x.write(s)

import requests from bs4 import BeautifulSoup from urllib.parse import urlparse, urljoin def scrape_domain_and_subdomains(base_url, file_path): visited_urls = set() def scrape(url): response = requests.get(url) if response.status_code == 200: soup = BeautifulSoup(respon...

# This is a sample Python script. # Press Maj+F10 to execute it or replace it with your code. # Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings. def print_hi(name): # Use a breakpoint in the code line below to debug your script. print(f'Hi, {name}') # ...

import taipy as tp from taipy.gui import Gui, notify from taipy.config import Config import numpy as np import pandas as pd BUSINESS_PATH = "data/yelp_business.csv" # Load the business data using pandas business_df = pd.read_csv(BUSINESS_PATH) # Remove quotation marks from the name business_df.name =...

import dask.dataframe as dd def get_data(path_to_csv: str, optional: str = None): """ Loads a csv file into a dask dataframe. Converts the date column to datetime. Args: - path_to_csv: path to the csv file - optional: optional argument (currently necessary to fix Core bug wi...

import pandas as pd import dask.dataframe as dd def get_id_from_name(name: str, business_dict: dict): """ Returns the business_id from the name of the business. Args: - name: name of the business - business_dict: dict with the name as key and the business_id as value Retu...

import time import pandas as pd import dask.dataframe as dd def task1(path_to_original_data: str): print("__________________________________________________________") print("1. TASK 1: DATA PREPROCESSING AND CUSTOMER SCORING ...") start_time = time.perf_counter() # Start the timer # Step...

from recsys.recsys import page_scenario_manager from taipy.gui import Gui gui = Gui(page_scenario_manager) gui.run()

import pandas as pd import numpy as np TEST_RATIO = 0.2 MOVIELENS_DATA_PATH = "u.data" MOVIE_DATA_PATH = "u.item" def convert_data_to_dataframe(data_path: str, movie_path: str): data = pd.read_table(data_path) data.columns = ["u_id", "i_id", "rating", "timestamp"] data_sort = data.sort_values(...

import pandas as pd from datetime import datetime data = pd.read_csv("dataset/rating_train.csv") timestamp = data.timestamp.to_list() date = [] for time in timestamp: date.append(datetime.fromtimestamp(time)) data["timestamp"] = date data.to_csv("data.csv", index=False)

import pandas as pd import numpy as np from scipy import sparse class DataLoader: def __init__(self): self.__train_data = None self.__val_data = None self.__test_data = None def __create_id_mapping(self): if self.__val_data: unique_uIds = pd.concat( ...

from taipy import Config, Scope from functions.funtions import preprocess_data, fit, predict from config.svd_config import ( n_epochs_cfg, n_factors_cfg, learning_rate_cfg, qi_cfg, bi_cfg, bu_cfg, pu_cfg, ) from config.kNN_config import ( x_id_cfg, n_k_neighboor_cfg, ...

from taipy import Config, Scope x_id_cfg = Config.configure_data_node(id="x_id", default_data=1) n_min_k_cfg = Config.configure_data_node(id="n_min_k", default_data=10) n_k_neighboor_cfg = Config.configure_data_node( id="n_k_neighboor", default_data=1) sim_measure_cfg = Config.configure_in_memory_data_...

from taipy import Config, Scope n_factors_cfg = Config.configure_data_node(id="n_factors", default_data=30) n_epochs_cfg = Config.configure_data_node(id="n_epochs", default_data=50) learning_rate_cfg = Config.configure_data_node(id="learning_rate", default_data=0.001) qi_cfg = Config.configure_data_node(id="q...

import taipy as tp from taipy.config import Config from taipy.gui import notify, Markdown import pandas as pd import numpy as np from helper.knn_helper import calculate_precision_recall from config.config import pipeline_cfg Config.configure_global_app(clean_entities_enabled=True) tp.clean_all_entities(...

import pandas as pd import numpy as np from ultis.dataloader import DataLoader from helper.svd_helper import sgd, predict_svd_pair from helper.knn_helper import predict_pair, compute_similarity_matrix VALID_ALGORITHM = ["kNN", "MF"] def preprocess_data( train_data: pd.DataFrame, test_data: pd.DataFrame...

import numpy as np from numba import njit def compute_similarity_matrix(train_set, sim_measure="pcc"): x_rated, _, n_x, _, x_list, y_list = list_ur_ir(train_set) if sim_measure == "pcc": print("Computing similarity matrix as pcc...") S = pcc(n_x, x_rated, min_support=1) elif sim_m...

import numpy as np from numba import njit def sgd( X, pu, qi, bu, bi, n_epochs, global_mean, n_factors, lr_pu, lr_qi, lr_bu, lr_bi, reg_pu, reg_qi, reg_bu, reg_bi, ): for epoch_ix in range(n_epochs): pu, qi, bu, bi, tra...

import pandas as pd from prophet import Prophet from taipy import Config def clean_data(initial_dataset: pd.DataFrame): print("Cleaning Data") initial_dataset = initial_dataset.rename(columns={"Date": "ds", "Close": "y"}) initial_dataset['ds'] = pd.to_datetime(initial_dataset['ds']).dt.tz_localize(N...

from taipy.gui import Gui, notify import pandas as pd import yfinance as yf from taipy.config import Config import taipy as tp import datetime as dt Config.load('config_model_train.toml') scenario_cfg = Config.scenarios['stock'] def get_stock_data(ticker, start): ticker_data = yf.download(ticker, star...

from taipy import Gui import pandas as pd # Interactive GUI and state, we maintain states for each individual client # hence we can have multiple clients with each client having its own state, # change of the state of one client will not affect the other client's state. # Each client has its own state and glob...

from taipy import Gui import pandas as pd # visual elements: Taipy adds visual elements on top of markdown # to give you the ability to add charts, tables... The format for # it is as follows: # <|{variable}|visual_element_name|param_1=param_1|param_2=param_2| ... |>. # variable: python variable eg dataframe ...

from taipy import Gui # not no empty spaces at the beginning of the markdown # markdown must start from the baseline page = """ ### Hello world """ # Gui(page=page).run(dark_mode=False) # you can specify the port number in the run(port=xxxx) # its by default 5000 Gui(page="Intro to Taipy").run(dark_mode=...

# 데이터를 처리하기 위한 파이썬의 기본 패키지 from login.login import * import pandas as pd # 타이피 코어 import import taipy as tp # 내 파이썬 코드의 백엔드 가져오기 | 시나리오를 생성하려면 원본 파이프라인_cfg 및 시나리오_cfg가 필요합니다. # fixed_variables_default는 고정 변수의 기본값으로 사용됩니다. from config.config import fixed_variables_default, scenario_cfg, pipeline_cfg from tai...

import taipy as tp from taipy import Scope, Config from taipy.core import Frequency import json from algos.algos import * # 이 코드는 시나리오_cfg 및 파이프라인_csg를 생성하는 코드입니다. # 이 두 변수는 기본 코드에서 새 시나리오를 만드는 데 사용됩니다. # 이 코드는 실행 그래프를 구성할 것입니다. ##########################################################################...

import pandas as pd import numpy as np from pulp import * # 이 코드는 이러한 기능이 필요한 작업을 생성하는 config.py에서 사용됩니다. # 이 함수는 전형적인 파이썬 함수입니다(Taipy는 없습니다) ############################################################################### # 기능 ############################################################################### ...

import numpy as np import pandas as pd # 이 코드는 수요에 대한 csv 파일을 생성하는 데 사용되며 문제의 소스 데이터입니다. def create_time_series(nb_months=12,mean_A=840,mean_B=760,std_A=96,std_B=72, amplitude_A=108,amplitude_B=144): time_series_A = [] time_series_A.append(mean_A) time_series_B = [] time_series_B.append...

da_display_table_md = "<center>\n<|{ch_results.round()}|table|columns={list(chart.columns)}|width=fit-content|height={height_table}|></center>\n" d_chart_csv_path = None def da_create_display_table_md(str_to_select_chart): return "<center>\n<|{" + str_to_select_chart + \ "}|table|width=fit-content|hei...

import pandas as pd import json with open('data/fixed_variables_default.json', "r") as f: fixed_variables_default = json.load(f) # Taipy Core의 코드는 아직 실행되지 않았습니다. csv 파일을 이런 식으로 읽습니다. da_initial_demand = pd.read_csv('data/time_series_demand.csv') da_initial_demand = da_initial_demand[['Year', 'Month', 'Dem...

from pages.annex_scenario_manager.chart_md import ch_chart_md, ch_choice_chart, ch_show_pie, ch_layout_dict, ch_results from pages.annex_scenario_manager.parameters_md import pa_parameters_md, pa_param_selector, pa_param_selected, pa_choice_product_param, pa_product_param from taipy.gui.gui_actions import notify f...

from data.create_data import time_series_to_csv from config.config import scenario_cfg from taipy.core import taipy as tp import datetime as dt import pandas as pd cc_data = pd.DataFrame( { 'Date': [dt.datetime(2021, 1, 1)], 'Cycle': [dt.date(2021, 1, 1)], 'Cost of Back Order'...

import taipy as tp import pandas as pd cs_compare_scenario_md = """ # 시나리오 비교 <|layout|columns=3 3 1|columns[mobile]=1| <layout_scenario| **Scenario 1** <|layout|columns=1 1 3|columns[mobile]=1| <| Year <|{sm_selected_year}|selector|lov={sm_year_selector}|dropdown|width=100%|on_change=change_sm_mo...

from .chart_md import ch_chart_md, ch_layout_dict, ch_results from config.config import fixed_variables_default from taipy.gui import Icon def create_sliders(fixed_variables): """" 이것은 매개변수에 대한 슬라이더를 자체적으로 생성하는 매우 복잡한 함수입니다. 손으로 할 수도 있었습니다. 그러나 이 방법은 장기적으로 더 유연합니다. """ # 반환될 문자열 ...

from taipy.gui import Icon import pandas as pd ch_layout_dict = {"margin":{"t":20}} # 차트 설정 토글 ch_choice_chart = [("pie", Icon("images/pie.png", "pie")), ("chart", Icon("images/chart.png", "chart"))] ch_show_pie = ch_choice_chart[1][0] ch_results = pd.DataFrame({"Monthly Production FPA"...

import taipy as tp from taipy.gui import Icon import datetime as dt import os import hashlib import json login = '' password = '' dialog_login = False dialog_new_account = False new_account = False all_scenarios = tp.get_scenarios() users = {} json.dump(users, open('login/login.json', 'w')) ...

from taipy import Gui page = """ # Hello World 🌍 with *Taipy* This is my first Taipy test app. And it is running fine! """ Gui(page).run(use_reloader=True) # use_reloader=True if you are in development

from taipy import Gui from page.dashboard_fossil_fuels_consumption import * if __name__ == "__main__": Gui(page).run( use_reloader=True, title="Test", dark_mode=False, ) # use_reloader=True if you are in development

import pandas as pd import taipy as tp from data.data import dataset_fossil_fuels_gdp country = "Spain" region = "Europe" lov_region = list(dataset_fossil_fuels_gdp.Entity.unique()) def load_dataset(_country): """Load dataset for a specific country. Args: _country (str): The name of t...

import pandas as pd dataset_fossil_fuels_gdp = pd.read_csv("data/per-capita-fossil-energy-vs-gdp.csv") country_codes = pd.read_csv("./data/country_codes.csv") dataset_fossil_fuels_gdp = dataset_fossil_fuels_gdp.merge( country_codes[["alpha-3", "region"]], how="left", left_on="Code", right_on="alpha-3" ) ...

from taipy.gui import Gui as tpGui from taipy.gui import notify as tpNotify import pandas as pd text = "Original text" col1 = "first col" col2 = "second col" col3 = "third col" ballon_img = "./img/Ballon_15_20.png" section_1 = """ <h1 align="center">Getting started with Taipy GUI</h1> <|layout|colum...

import os import logging from opentelemetry import metrics from opentelemetry import trace from opentelemetry.sdk.metrics import MeterProvider from opentelemetry.sdk.resources import Resource, SERVICE_NAME from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter from opentelemetry.ex...

import time from pathlib import Path from taipy import Config, Status import taipy as tp from taipy.gui import get_state_id, invoke_callback from metrics import init_metrics, tracer # Telemetry rec_svc_metrics = init_metrics() @tracer.start_as_current_span("function_double") def double(nb): """D...

from taipy.gui import Gui from tensorflow.keras import models from PIL import Image import numpy as np class_names = { 0: 'airplane', 1: 'automobile', 2: 'bird', 3: 'cat', 4: 'deer', 5: 'dog', 6: 'frog', 7: 'horse', 8: 'ship', 9: 'truck', } model = models.lo...

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import os import threading from flask import Flask from pyngrok import ngrok from hf_hub_ctranslate2 import GeneratorCT2fromHfHub from flask import request, jsonify model_name = "taipy5-ct2" # note this is local folder model, the model uploaded to huggingface did not response correctly #model_name = "micha...

#Imports from taipy.gui import Gui from tensorflow.keras import models from PIL import Image import numpy as np #Variables classes = { 0: 'airplane', 1: 'automobile', 2: 'bird', 3: 'cat', 4: 'deer', 5: 'dog', 6: 'frog', 7: 'horse', 8: 'ship', 9: 'truck' } ...

from taipy.gui import Gui, notify import pandas as pd import webbrowser import datetime import os DOWNLOAD_PATH = "data/download.csv" upload_file = None section_1 = """ <center> <|navbar|lov={[("page1", "This Page"), ("https://docs.taipy.io/en/latest/manuals/about/", "Taipy Docs"), ("https://docs.taipy.io/...

from taipy.gui import Gui from pages.all_regions import * from pages.by_region import * # Toggle theme: switch dark/light mode root_md = """ <|toggle|theme|> <center>\n<|navbar|>\n</center> """ stylekit = { "color-primary": "#CC3333", "color-secondary": "#E0C095", "color-background-light": ...

import geopandas as gpd import pandas as pd def add_basic_stats(df_wine: pd.DataFrame) -> pd.DataFrame: """Add basic statistics to a DataFrame containing wine production data. This function calculates the minimum, maximum, and average wine production values for each row in the input DataFrame bas...

import taipy.core as tp from taipy import Config # Loading of the TOML Config.load("config/taipy-config.toml") tp.Core().run() # Get the scenario configuration scenario_cfg = Config.scenarios["SC_WINE"] sc_wine = tp.create_scenario(scenario_cfg) sc_wine.submit() df_wine_production = sc_wine.WINE_PRODUC...

from typing import Any import pandas as pd from config.config import df_wine_production, df_wine_with_geometry selected_year = "average" year_list = [ "average", "08/09", "09/10", "10/11", "11/12", "12/13", "13/14", "14/15", "15/16", "16/17", "17/18", ...

from typing import Any, Tuple import pandas as pd from config.config import df_wine_with_geometry list_of_regions = df_wine_with_geometry["Region"].unique().tolist() selected_region = "SUD-OUEST" def clean_df_region_color(df_region_color: pd.DataFrame) -> pd.DataFrame: """Clean and transform a DataF...

import numpy as np from taipy.gui import Gui from pages.country import country_md, on_change_country,\ selected_representation, data_country_date, pie_chart from pages.world import world_md from pages.map import map_md from pages.predictions import predictions_md, selected_scenario, ...

from taipy.config import Config, Scope import datetime as dt from algos.algos import add_features, create_train_data, preprocess,\ train_arima, train_linear_regression,\ forecast, forecast_linear_regression,\ result #Config.configure_job_...

import pandas as pd from pmdarima import auto_arima from sklearn.linear_model import LinearRegression import datetime as dt import numpy as np def add_features(data): dates = pd.to_datetime(data["Date"]) data["Months"] = dates.dt.month data["Days"] = dates.dt.isocalendar().day data["Week"] ...

import pandas as pd path_to_data = "data/covid-19-all.csv" data = pd.read_csv(path_to_data, low_memory=False)

from taipy.gui import Markdown import numpy as np import json from data.data import data type_selector = ['Absolute', 'Relative'] selected_type = type_selector[0] def initialize_world(data): data_world = data.groupby(["Country/Region", 'Date'])\ ...

from taipy.gui import Markdown, invoke_long_callback import taipy as tp import pandas as pd import datetime as dt from config.config import scenario_cfg scenario_selector = [(s.id, s.name) for s in tp.get_scenarios()] selected_scenario = None selected_date = dt.datetime(2020,10,1) scenario_country = "N...

import numpy as np from taipy.gui import Markdown from data.data import data marker_map = {"color":"Deaths", "size": "Size", "showscale":True, "colorscale":"Viridis"} layout_map = { "dragmode": "zoom", "mapbox": { "style": "open-street-map", "center": { "lat": 38, "lon": -90 }, "zoom...

import numpy as np import pandas as pd from taipy.gui import Markdown from data.data import data selected_country = 'France' data_country_date = None representation_selector = ['Cumulative', 'Density'] selected_representation = representation_selector[0] layout = {'barmode':'stack', "hovermode":"x"} ...

from taipy.gui import Gui, notify text = "" revealed = False page = """ <|{text if text else 'Write something in the input'}|> <|{text}|input|on_change={lambda state: notify(state, 'i', s.text)}|> <|part|render={len(text)>0}| Part hidden and discovered after input |> ----- <|Push|button|on_action=...

# Write an app that calls a function every 5 seconds

from taipy.gui import Gui, Markdown, invoke_long_callback, notify import numpy as np status = 0 num_iterations = 10_000_000 pi_list = [] def pi_approx(num_iterations): k, s = 3.0, 1.0 pi_list = [] for i in range(num_iterations): s = s-((1/k) * (-1)**i) k += 2 if (i+1)%...

from taipy.gui import Gui, notify text = "" page_1 = """ # Page 1 <|{text}|> """ page_2 = """ # Page 2 <|Raise error|button|on_action=raise_error|> """ hidden_page_3 = """ # Hidden Page """ def raise_error(state): raise(ValueError("This is an error")) def on_init(state): print("Wh...

from taipy.gui import Gui selected = [] a = [1, 2] b = [2, 3] selector_lov = [a, b] page = """ <|{selected}|selector|lov={selector_lov}|adapter={lambda s: s.name}|> """ gui = Gui(page) gui.run()

from taipy.gui import Gui from math import cos, exp value = 10 page = """ Markdown # Taipy *Demo* Value: <|{value}|text|> <|{value}|slider|on_change=on_slider|> <|{data}|chart|> """ def on_slider(state): state.data = compute_data(state.value) def compute_data(decay:int)->list: return ...

from taipy.gui import Gui from math import cos, exp value = 10 page = """ Markdown # Taipy *Demo* """ Gui(page).run(use_reloader=True, port=5002)

# Write an app that create simple charts by poviding inputs and diplay them

from taipy.gui import Gui title = 1 md = """ <|{title}|number|on_change=change_partial|> <|part|partial={p}|> """ def change_partial(state): title_int = int(state.title) new_html = f'<h{title_int}>test{title_int}</h{title_int}>' print(new_html) state.p.update_content(state, new_html) ...

import yfinance as yf from taipy.gui import Gui from taipy.gui.data.decimator import MinMaxDecimator, RDP, LTTB df_AAPL = yf.Ticker("AAPL").history(interval="1d", period = "100Y") df_AAPL["DATE"] = df_AAPL.index.astype(int).astype(float) n_out = 500 decimator_instance = MinMaxDecimator(n_out=n_out) decim...

from taipy import Config import taipy as tp def read_text(path: str) -> str: with open(path, 'r') as text_reader: data = text_reader.read() return data def write_text(data: str, path: str) -> None: with open(path, 'w') as text_writer: text_writer.write(data) historical_dat...

import taipy as tp from config.config_toml import scenario_cfg if __name__=="__main__": tp.Core().run() scenario_1 = tp.create_scenario(scenario_cfg) scenario_2 = tp.create_scenario(scenario_cfg) scenario_1.submit() scenario_2.submit() scenario_1 = tp.create_scenario(scenario_cfg) ...

import taipy as tp from config.config import scenario_cfg if __name__=="__main__": tp.Core().run() scenario_1 = tp.create_scenario(scenario_cfg) scenario_2 = tp.create_scenario(scenario_cfg) scenario_1.submit() scenario_2.submit() scenario_1 = tp.create_scenario(scenario_cfg) ...

from taipy import Config from algos.algos import * Config.configure_job_executions(mode="standalone", max_nb_of_workers=2) # Configuration of Data Nodes input_cfg = Config.configure_data_node("input", default_data=21) intermediate_cfg = Config.configure_data_node("intermediate", default_data=21) output_cfg = ...

from taipy import Config from algos.algos import * Config.load('config/config_07.toml') Config.configure_job_executions(mode="standalone", max_nb_of_workers=2) scenario_cfg = Config.scenarios['my_scenario']

import time # Normal function used by Taipy def double(nb): return nb * 2 def add(nb): print("Wait 5 seconds in add function") time.sleep(5) return nb + 10

import taipy as tp from taipy.gui import Markdown from config.config import * scenario = None df_metrics = None data_node = None pages = {'/':'<|navbar|> <|toggle|theme|> <br/>', 'Scenario': Markdown('pages/scenario.md'), 'Data-Node': Markdown('pages/data_node.md')} if __name__ ...

import taipy as tp import datetime as dt from config.config import * def create_and_run_scenario(date: dt.datetime): scenario = tp.create_scenario(config=scenario_cfg, name=f"scenario_{date.date()}", creation_date=date) scenario....

from taipy import Config, Scope, Frequency from taipy.core import Scenario import datetime as dt from algos.algos import clean_data, predict, evaluate ## Input Data Nodes initial_dataset_cfg = Config.configure_data_node(id="initial_dataset", storage_type=...

import datetime as dt import pandas as pd def clean_data(initial_dataset: pd.DataFrame): print(" Cleaning data") initial_dataset['Date'] = pd.to_datetime(initial_dataset['Date']) cleaned_dataset = initial_dataset[['Date', 'Value']] return cleaned_dataset def predict(cleaned_dataset: p...

from taipy.gui import Gui, notify import datetime as dt import yfinance as yf def get_stock_data(ticker): now = dt.date.today() past = now - dt.timedelta(days=365*2) return yf.download(ticker, past, now).reset_index() def update_ticker(state): # state.data = ... ticker = 'AAPL' dat...

from taipy.gui import Gui, notify import datetime as dt import yfinance as yf def get_stock_data(ticker): now = dt.date.today() past = now - dt.timedelta(days=365*2) return yf.download(ticker, past, now).reset_index() def update_ticker(state): state.data = get_stock_data(state.ticker) n...

from taipy.gui import Gui import taipy as tp from pages.data_viz import historical from pages.predictions_sol import predictions, scenario ticker = 'AAPL' pages = {"/":"<|navbar|>", "Historical":historical, "Prediction":predictions} tp.Core().run() Gui(pages=pages).run(port=5001)

from datetime import timedelta import yfinance as yf from prophet import Prophet def get_data(ticker, date): past = date - timedelta(days=365*7) return yf.download(ticker, past, date).reset_index() def predict(preprocessed_dataset): df_train = preprocessed_dataset[['Date', 'Close']] df_train...

import taipy as tp from taipy.gui import notify, Markdown import datetime as dt tp.Config.load("config/config.toml") ticker = 'AAPL' scenario = None data_node = None jobs = [] default_data = {"Date":[0], "Predictions":[0]} def on_submission_status_change(state, submittable, details): submission_sta...

from taipy.gui import notify, Markdown import datetime as dt import yfinance as yf def get_stock_data(ticker): now = dt.date.today() past = now - dt.timedelta(days=365*2) return yf.download(ticker, past, now).reset_index() def update_ticker(state): state.data = get_stock_data(state.ticker) ...

from taipy import Gui import pandas as pd import requests API_URL = "https://api-inference.huggingface.co/models/bigcode/starcoder" headers = {"Authorization": "Bearer [ENTER-YOUR-API-KEY-HERE]"} DATA_PATH = "data.csv" df = pd.read_csv(DATA_PATH, sep=";") data = pd.DataFrame( { "Date": pd.t...

import json def add_line(source, line, step): line = line.replace('Getting Started with Taipy Core', 'Getting Started with Taipy Core on Notebooks') line = line.replace('(../src/', '(https://docs.taipy.io/en/latest/getting_started/src/') line = line.replace('(time_series.csv)', '(https://docs.taip...

from taipy.core.config import Config, Frequency, Status import taipy as tp import datetime as dt import time # Normal function used by Taipy def double(nb): return nb * 2 def add(nb): print("Wait 10 seconds in add function") time.sleep(10) return nb + 10 # Configuration of Data Node...

from taipy.core.config import Config, Scope, Frequency import taipy as tp import datetime as dt import pandas as pd def filter_by_month(df, month): df['Date'] = pd.to_datetime(df['Date']) df = df[df['Date'].dt.month == month] return df def count_values(df): return len(df) Config.loa...

from taipy.core.config import Config, Frequency import taipy as tp # Normal function used by Taipy def double(nb): return nb * 2 def add(nb): return nb + 10 # Configuration of Data Nodes input_cfg = Config.configure_data_node("input", default_data=21) intermediate_cfg = Config.configure_data_...

from taipy.core.config import Config import taipy as tp import datetime as dt import pandas as pd import time # Normal function used by Taipy def double(nb): return nb * 2 def add(nb): print("Wait 10 seconds in add function") time.sleep(10) return nb + 10 Config.load('config_07.toml'...

from taipy.core.config import Config import taipy as tp import datetime as dt import pandas as pd def filter_current(df): current_month = dt.datetime.now().month df['Date'] = pd.to_datetime(df['Date']) df = df[df['Date'].dt.month == current_month] return df def count_values(df): ret...

from taipy.core.config import Config import taipy as tp import datetime as dt import pandas as pd import time # Normal function used by Taipy def double(nb): return nb * 2 def add(nb): print("Wait 10 seconds in add function") time.sleep(10) return nb + 10 Config.configure_job_execution...