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# Create app to read and display data from Excel file import pandas as pd from taipy import Gui # ---- READ EXCEL ---- df = pd.read_excel( io="data/supermarkt_sales.xlsx", engine="openpyxl", sheet_name="Sales", skiprows=3, usecols="B:R", nrows=1000, ) # Add 'hour' column to datafra... |
# Create an app with slider and chart 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 compute_data(decay:int)->list: return [cos(i/6) * exp(-i*decay/600) ... |
# Create app to predict covid in the world from taipy.gui import Gui import taipy as tp from pages.country.country import country_md from pages.world.world import world_md from pages.map.map import map_md from pages.predictions.predictions import predictions_md, selected_scenario from pages.root import root, s... |
# Create app for finance data analysis 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("int64").astype(float) n_out = 500 decimator_instan... |
# Create an app to upload a csv and display it in a table from taipy.gui import Gui import pandas as pd data = [] data_path = "" def data_upload(state): state.data = pd.read_csv(state.data_path) page = """ <|{data_path}|file_selector|on_action=data_upload|> <|{data}|table|> """ Gui(page).run(... |
# Create an app to visualize sin and amp with slider and chart from taipy.gui import Gui from math import cos, exp state = {"amp": 1, "data":[]} def update(state): x = [i/10 for i in range(100)] y = [math.sin(i)*state.amp for i in x] state.data = [{"data": y}] page = """ Amplitude: <|{amp}|slider... |
# Create an app to visualize sin, cos with slider and chart from taipy.gui import Gui from math import sin, cos, pi state = { "frequency": 1, "decay": 0.01, "data": [] } page = """ # Sine and Cosine Functions Frequency: <|{frequency}|slider|min=0|max=10|step=0.1|on_change=update|> Decay: <|{de... |
# Create app to visualize country population 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] l... |
# Create Taipy app to generate mandelbrot fractals from taipy import Gui import numpy as np from PIL import Image import matplotlib.pyplot as plt WINDOW_SIZE = 500 cm = plt.cm.get_cmap("viridis") def generate_mandelbrot( center: int = WINDOW_SIZE / 2, dx_range: int = 1000, dx_start: fl... |
# Create app to auto generate Tweeter status import logging import random import re # Import from 3rd party libraries from taipy.gui import Gui, notify, state import taipy # Import modules import oai # Configure logger logging.basicConfig(format="\n%(asctime)s\n%(message)s", level=logging.INFO, force=Tr... |
# Create app for py2jsonl3.py py2jsonl3.py
import os
import json
EXCLUDED_FILES = ["CODE_OF_CONDUCT.md", "CONTRIBUTING.md", "INSTALLATION.md", "README.md"]
def find_files(directory, extensions):
for root, dirs, files in os.walk(directory):
for file in files:
if file.endswith(extensions) and fi... |
# Create app for demo-remove-background main.py
from taipy.gui import Gui, notify
from rembg import remove
from PIL import Image
from io import BytesIO
path_upload = ""
path_download = "fixed_img.png"
original_image = None
fixed_image = None
fixed = False
page = """<|toggle|theme|>
<page|layout|columns=300px 1fr|
... |
# Create app for demo-tweet-generation oai.py
"""OpenAI API connector."""
# Import from standard library
import os
import logging
# Import from 3rd party libraries
import openai
import os
# Assign credentials from environment variable or streamlit secrets dict
openai.api_key = "Enter your token here"
# Suppress op... |
# Create app for demo-tweet-generation main.py
# Import from standard library
import logging
import random
import re
# Import from 3rd party libraries
from taipy.gui import Gui, notify
# Import modules
import oai
# Configure logger
logging.basicConfig(format="\n%(asctime)s\n%(message)s", level=logging.INFO, force=Tr... |
# Create app for demo-realtime-pollution sender.py
# echo-client.py
import math
import time
import socket
import pickle
import numpy as np
HOST = "127.0.0.1"
PORT = 65432
init_lat = 49.247
init_long = 1.377
factory_lat = 49.246
factory_long = 1.369
diff_lat = abs(init_lat - factory_lat) * 15
diff_long = abs(init_l... |
# Create app for demo-realtime-pollution receiver.py
import socket
import pickle
import math
from threading import Thread
from taipy.gui import Gui, State, invoke_callback, get_state_id
import numpy as np
import pandas as pd
init_lat = 49.247
init_long = 1.377
factory_lat = 49.246
factory_long = 1.369
diff_lat = abs... |
# Create app for demo-pyspark-penguin-app config.py
### app/config.py
import datetime as dt
import os
import subprocess
import sys
from pathlib import Path
import pandas as pd
import taipy as tp
from taipy import Config
SCRIPT_DIR = Path(__file__).parent
SPARK_APP_PATH = SCRIPT_DIR / "penguin_spark_app.py"
input_cs... |
# Create app for demo-pyspark-penguin-app main.py
### app/main.py
from pathlib import Path
from typing import Optional
import taipy as tp
from config import scenario_cfg
from taipy.gui import Gui, notify
valid_features: dict[str, list[str]] = {
"species": ["Adelie", "Chinstrap", "Gentoo"],
"island": ["Torger... |
# Create app for demo-pyspark-penguin-app penguin_spark_app.py
### app/penguin_spark_app.py
import argparse
import os
import sys
parser = argparse.ArgumentParser()
parser.add_argument("--input-csv-path", required=True, help="Path to the input penguin CSV file.")
parser.add_argument("--output-csv-path", required=True, ... |
# Create app for demo-dask-customer-analysis config.py
from taipy import Config
from algos.algo import (
preprocess_and_score,
featurization_and_segmentation,
segment_analysis,
high_value_cust_summary_statistics,
)
# -------------------- Data Nodes --------------------
path_to_data_cfg = Config.confi... |
# Create app for demo-dask-customer-analysis algo.py
import time
import dask.dataframe as dd
import pandas as pd
def preprocess_and_score(path_to_original_data: str):
print("__________________________________________________________")
print("1. TASK 1: DATA PREPROCESSING AND CUSTOMER SCORING ...")
start_... |
# Create app for demo-taipy-gui-starter-1 main.py
from taipy.gui import Gui
from math import cos, exp
page = """
#This is *Taipy* GUI
A value: <|{decay}|>.
A slider: <br/>
<|{decay}|slider|>
My chart:
<|{data}|chart|>
"""
def compute_data(decay):
return [cos(i/16) * exp(-i*decay/6000) for i in range(720)]
... |
# Create app for demo-churn-classification main.py
import pandas as pd
import taipy as tp
from taipy.gui import Gui, Icon, navigate
from config.config import scenario_cfg
from taipy.config import Config
from pages.main_dialog import *
import warnings
with warnings.catch_warnings():
warnings.simplefilter(action='i... |
# Create app for demo-churn-classification config.py
from algos.algos import *
from taipy import Config, Scope
##############################################################################################################################
# Creation of the datanodes
######################################################... |
# Create app for demo-churn-classification algos.py
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
import datetime as dt
import pandas as pd
import numpy as np
#####... |
# Create app for demo-churn-classification main_dialog.py
from pages.compare_models_md import *
from pages.data_visualization_md import *
from pages.databases_md import *
from pages.model_manager_md import *
dr_show_roc = False
dialog_md = """
<|dialog|open={dr_show_roc}|title=ROC Curve|on_action={lambda s: s.assign(... |
# Create app for demo-churn-classification databases_md.py
import pathlib
# This path is used to create a temporary CSV file download the table
tempdir = pathlib.Path(".tmp")
tempdir.mkdir(exist_ok=True)
PATH_TO_TABLE = str(tempdir / "table.csv")
# Selector to select the table to show
db_table_selector = ['Training D... |
# Create app for demo-churn-classification data_visualization_md.py
import pandas as pd
import numpy as np
dv_graph_selector = ['Histogram','Scatter']
dv_graph_selected = dv_graph_selector[0]
# Histograms dialog
properties_histo_full = {}
properties_scatter_dataset = {}
def creation_scatter_dataset(test_dataset:pd.... |
# Create app for demo-churn-classification compare_models_md.py
import numpy as np
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... |
# Create app for demo-churn-classification model_manager_md.py
import pandas as pd
import numpy as np
mm_graph_selector_scenario = ['Metrics', 'Features', 'Histogram','Scatter']
mm_graph_selected_scenario = mm_graph_selector_scenario[0]
mm_algorithm_selector = ['Baseline', 'ML']
mm_algorithm_selected = 'ML'
mm_pie_... |
# Create app for demo-stock-visualization main.py
from taipy.gui import Gui, notify
from datetime import date
import yfinance as yf
from prophet import Prophet
import pandas as pd
# Parameters for retrieving the stock data
start_date = "2015-01-01"
end_date = date.today().strftime("%Y-%m-%d")
selected_stock = 'AAPL'
... |
# Create app for demo-movie-genre main.py
import taipy as tp
import pandas as pd
from taipy import Config, Scope, Gui
# Create a Taipy App that will output the 7 best movies for a genre
# Taipy Core - backend definition
# Filter function for Task
def filtering_genre(initial_dataset: pd.DataFrame, selected_genre):
... |
# Create app for demo-job-monitoring __init__.py
|
# Create app for demo-job-monitoring runtime.py
from taipy import run
class App:
"""A singleton class that provides the Taipy runtime objects."""
def __new__(cls):
if not hasattr(cls, "instance"):
cls.instance = super(App, cls).__new__(cls)
return cls.instance
@property
d... |
# Create app for demo-job-monitoring main.py
from runtime import App
from pages import root, monitoring
import taipy
from taipy.config.config import Config
from taipy.gui import Gui
import os
# Variables for bindings
all_jobs = [['','','','']]
show_dialog_run_pipeline = False
selected_pipeline = None
show_details_pane... |
# Create app for demo-job-monitoring __init__.py
|
# Create app for demo-job-monitoring ml.py
from sklearn.linear_model import LogisticRegression
import pandas as pd
import numpy as np
# Test prediction with a Female, 19 years old, earning 20000
fixed_value = [1, 19, 20000]
def preprocess(df: pd.DataFrame) -> pd.DataFrame:
def _gender_to_int(gender):
if... |
# Create app for demo-job-monitoring debug.py
import time
def long_running(anything):
print("Waiting 20 seconds...")
time.sleep(20)
print("Done!")
return anything
def raise_exception(anything):
print("Waiting 5 seconds before raising an exception...")
time.sleep(5)
raise Exception("A ver... |
# Create app for demo-job-monitoring monitoring.py
import taipy as tp
from taipy.gui import get_state_id, invoke_callback, Markdown
from taipy.config.config import Config
from taipy.core.job.job import Job
from runtime import App
def get_all_jobs():
"""Returns all the known jobs (as a array of fields)."""
de... |
# Create app for demo-job-monitoring __init__.py
|
# Create app for demo-job-monitoring root.py
from taipy.gui import Markdown
content = """
# Job Monitoring Demo
"""
page = Markdown(content)
|
# Create app for demo-job-monitoring monitoring.md
<|{all_jobs}|table|columns={columns}|width='100%'|on_action={on_table_click}|style=on_style|>
<|Refresh List|button|on_action={refresh_job_list}|>
<|Run Pipeline...|button|on_action={open_run_pipeline_dialog}|>
<|{show_dialog_run_pipeline}|dialog|title=Run pipeline...... |
# Create app for demo-fraud-detection charts.py
""" Prepare data for charts """
import pandas as pd
def gen_amt_data(transactions: pd.DataFrame) -> list:
"""
Create a list of amt values for fraudulent and non-fraudulent transactions
Args:
- transactions: the transactions dataframe
Returns:
... |
# Create app for demo-fraud-detection utils.py
""" Data Manipulation and Callbacks """
import datetime as dt
import numpy as np
import pandas as pd
from taipy.gui import State, navigate, notify
import xgboost as xgb
from shap import Explainer, Explanation
from sklearn.metrics import confusion_matrix
column_names = [
... |
# Create app for demo-fraud-detection main.py
""" Fraud Detection App """
import pickle
import numpy as np
import pandas as pd
from taipy.gui import Gui, Icon, State, navigate, notify
from utils import (
explain_pred,
generate_transactions,
update_threshold,
update_table,
)
from charts import *
DATA_... |
# Create app for dask_taipy_bigdata_DEMO algo.py
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_coun... |
# Create app for demo-image-classification-part-2 readme.md
# Image Classification Part 2 Using Taipy Core
## Usage
- [Usage](#usage)
- [Image Classification Part 2](#what-is-image-classification-part-2)
- [Directory Structure](#directory-structure)
- [License](#license)
- [Installation](#installation)
- [Contributing... |
# Create app for demo-image-classification-part-2 config_from_tp_studio.py
from main_functions import *
from taipy import Config
import taipy as tp
Config.load('built_with_tp_studio.toml')
scenario_cfg = Config.scenarios['testing_scenario']
tp.Core().run()
main_scenario = tp.create_scenario(scenario_cfg)
tp.submit(m... |
# Create app for demo-image-classification-part-2 main_functions.py
import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.utils import to_categorical
import pandas as pd
import numpy as np
class_names = ['... |
# Create app for demo-image-classification-part-2 main.py
from main_functions import *
from taipy import Config
import taipy as tp
#######################################################################################################
##############################################PIPELINE 1###########################... |
# Create app for demo-edit-log LICENSE.md
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the ter... |
# Create app for demo-edit-log main.py
from taipy.gui import Gui
import taipy as tp
from taipy.gui import notify
from config.config import *
# Variables for bindings
all_scenarios = [] # List of scenarios
all_scenarios_configs = [] # List of scenario configs
all_data_nodes = [] # List of node IDs
current_scenario... |
# Create app for demo-edit-log config.py
from algos.algos import task_function
from taipy import Config
Config.configure_job_executions(mode="standalone", max_nb_of_workers=1)
node_start_cfg = Config.configure_data_node(
id="node_start", default_data=[1, 2], description="This is the initial data node."
)
node_en... |
# Create app for demo-edit-log algos.py
def task_function(data):
"""A dummy task function"""
print(f"Executing function: {data}")
return data
|
# Create app for demo-face-recognition LICENSE.md
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean... |
# Create app for demo-face-recognition find_taipy_gui_dir.py
# This Python script tries to locate the taipy.gui package, and
# prints its absolute path if it finds it.
import importlib.util
import os
taipy_gui = importlib.util.find_spec("taipy.gui")
if taipy_gui is None:
print("Cannot find 'taipy.gui'\nPlease run ... |
# Create app for demo-face-recognition GETTING_STARTED.md
# Getting Started
## Installation
First you need to install the dependencies and build the front-end. Please refer to [INSTALLATION.md](INSTALLATION.md).
## How to use the demo
Once you started the application, your default Web browser should open automatica... |
# Create app for demo-face-recognition main.py
from taipy.gui import Gui
from webcam import Webcam
import cv2
import PIL.Image
import io
import logging
import uuid
from pathlib import Path
from demo.faces import detect_faces, recognize_face, train_face_recognizer
logging.basicConfig(level=logging.DEBUG)
training_d... |
# Create app for demo-face-recognition faces.py
import cv2
from pathlib import Path
import os
import numpy as np
import logging
from .image import crop_image
import pandas as pd
logging.basicConfig(level=logging.DEBUG)
# Create our face detector. Both HAAR and LBP classifiers are somehow equivelent and both give good... |
# Create app for demo-face-recognition __init__.py
|
# Create app for demo-face-recognition image.py
def crop_image(img, rect):
"""An utility function to crop an image to the given rect"""
x, y, w, h = rect
return img[y : y + h, x : x + w]
|
# Create app for demo-face-recognition __init__.py
from .webcam import Webcam
|
# Create app for demo-face-recognition webcam.py
from taipy.gui.extension import ElementLibrary, Element, ElementProperty, PropertyType
class Webcam(ElementLibrary):
def get_name(self) -> str:
return "webcam"
def get_elements(self) -> dict:
return {
"Webcam": Element(
... |
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