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from taipy.gui import Gui from math import cos, exp value = 10 page = """ Markdown # Taipy *Demo* Value: <|{value}|text|> <|{value}|slider|> <|{compute_data(value)}|chart|> """ def compute_data(decay: int) -> list: return [cos(i / 6) * exp(-i * decay / 600) for i in range(100)] Gui(page)... |
import pymongo from dotenv import load_dotenv import os from taipy.gui import notify import pandas as pd load_dotenv() client = pymongo.MongoClient(os.getenv("MONGO_URI")) db = client["GoShop"] collection_product = db["products"] def insert_one_collection(document): return collection_product.ins... |
from taipy.gui import Gui, Markdown, navigate from pages.addproduct import addproduct_md from pages.home import home_md from pages.developer import developer_md favicone = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcStQrffFMV3jCG2wB7o7Bs1VwUJ3Z0sWQhbzA&usqp=CAU" # root_md = "<|menu|label=Menu|lov={[('... |
from taipy.gui import Markdown, notify from database import all_prodcuts import pandas products = all_prodcuts() del products["_id"] products["isavailable"].replace({True: "Yes", False: "No"}) yes = 0 no = 0 for i in products["isavailable"]: if i == True: yes = yes+1 else: no... |
from taipy.gui import Markdown, notify, navigate from database import insert_one_collection, all_prodcuts image1 = "https://www.identixweb.com/wp-content/uploads/2022/01/Add-Customization-for-Custom-Products.png" image2 = "https://img.freepik.com/free-vector/online-wishes-list-concept-illustration_114360-3900.jp... |
from taipy.gui import Markdown img1 = "https://www.identixweb.com/wp-content/uploads/2022/01/Add-Customization-for-Custom-Products.png" developer_md = Markdown(""" <|toggle|theme|> ## Our Team **Mahi**{: .color-primary} <|container <|layout|columns= 1 1 1 |gap=30px| <| <|{img1}|image|width=100%|> Suruchi ... |
from pages.dialogs.dialog_roc_md import * from pages.compare_models_md import * from pages.data_visualization_md import * from pages.databases_md import * from pages.model_manager_md import * dialog_md = """ <|dialog|open={dr_show_roc}|title=ROC Curve|partial={dialog_partial_roc}|on_action=delete_dialog_roc|l... |
# νΌλ νλ ¬ λν μμ db_confusion_matrix_md = """ <|part|render={db_table_selected=='Confusion Matrix'}| <center> <|{score_table}|table|height=200px|width=400px|show_all=True|> </center> |> """ # νμ΅ λ°μ΄ν° μΈνΈμ λν ν
μ΄λΈ db_train_dataset_md = """ <|part|render={db_table_selected=='Training Dataset'}| <|{train_dataset}|t... |
from sklearn.metrics import f1_score import pandas as pd import numpy as np cm_height_histo = "100%" cm_dict_barmode = {"barmode": "stack","margin":{"t":30}} cm_options_md = "height={cm_height_histo}|width={cm_height_histo}|layout={cm_dict_barmode}" cm_compare_models_md = """ # λͺ¨λΈ λΉκ΅ <br/> <br/> <br/>... |
# Roc λ€μ΄μΌλ‘κ·Έ dr_show_roc = False def show_roc_fct(state): state.dr_show_roc = True def delete_dialog_roc(state): state.dr_show_roc = False dialog_roc = """ <center> <|{roc_dataset}|chart|x=False positive rate|y[1]=True positive rate|label[1]=True positive rate|height=500px|width=900px|type=scatter|... |
from taipy.gui import Gui from page.page import * if __name__ == "__main__": Gui(page).run( use_reloader=True, title="Wine π· production by Region and Year", dark_mode=False, ) |
from taipy.core.config import Config from config.config import df_wine_production page = """ # Wine production by Region and Year ## Data for all the regions: <|{df_wine_production}|table|height=400px|width=95%|> """ |
import pandas as pd def add_wine_colors(df_wine): """Adds 2 columns with Args: df_wine (DataFrame): Data from the csv file (input for the whole app) Returns: df_wine_with_colors: DataFrame with all the input columns plus 2 nexw ones, 'red_and_rose' and 'white', and drops 2 c... |
import taipy as tp from taipy.core.config import Config # Loading of the TOML Config.load("config/taipy-config.toml") # Get the scenario configuration scenario_cfg = Config.scenarios["SCENARIO_WINE"] tp.Core().run() scenario_wine = tp.create_scenario(scenario_cfg) scenario_wine.submit() df_wine_pro... |
from taipy.gui import Gui from tensorflow.keras import models from PIL import Image # to change img path to actual 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'... |
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 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=... |
import taipy as tp from taipy import Scope, Config from taipy.core import Frequency import json from algos.algos import * # μ΄ μ½λλ μλ리μ€_cfg λ° νμ΄νλΌμΈ_csgλ₯Ό μμ±νλ μ½λμ
λλ€. # μ΄ λ λ³μλ κΈ°λ³Έ μ½λμμ μ μλ리μ€λ₯Ό λ§λλ λ° μ¬μ©λ©λλ€. # μ΄ μ½λλ μ€ν κ·Έλνλ₯Ό ꡬμ±ν κ²μ
λλ€. ##########################################################################... |
import numpy as np import pandas as pd # μ΄ μ½λλ μμμ λν csv νμΌμ μμ±νλ λ° μ¬μ©λλ©° λ¬Έμ μ μμ€ λ°μ΄ν°μ
λλ€. 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 Coreμ μ½λλ μμ§ μ€νλμ§ μμμ΅λλ€. csv νμΌμ μ΄λ° μμΌλ‘ μ½μ΅λλ€. da_initial_demand = pd.read_csv('data/time_series_demand.csv') da_initial_demand = da_initial_demand[['Year', 'Month', 'Dem... |
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'... |
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): """" μ΄κ²μ 맀κ°λ³μμ λν μ¬λΌμ΄λλ₯Ό μ체μ μΌλ‘ μμ±νλ λ§€μ° λ³΅μ‘ν ν¨μμ
λλ€. μμΌλ‘ ν μλ μμμ΅λλ€. κ·Έλ¬λ μ΄ λ°©λ²μ μ₯κΈ°μ μΌλ‘ λ μ μ°ν©λλ€. """ # λ°νλ λ¬Έμμ΄ ... |
from taipy.gui import Icon import pandas as pd ch_layout_dict = {"margin":{"t":20}} # μ°¨νΈ μ€μ ν κΈ 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 π 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... |
def a |
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, 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... |
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