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from step_11 import * from sklearn.metrics import mean_absolute_error, mean_squared_error # Initial dataset for comparison comparison_scenario = pd.DataFrame({"Scenario Name": [], "RMSE baseline": [], "MAE baseline": [], "RMSE ML": [], "MA... |
import numpy as np import pandas as pd from step_04 import tp, baseline_pipeline_cfg, dt from step_02 import * # Initialize the "predictions" dataset predictions_dataset = pd.DataFrame( {"Date": [dt.datetime(2021, 6, 1)], "Historical values": [np.NaN], "Predicted values": [np.NaN]}) # Add a button and ... |
from taipy import Gui import pandas as pd def get_data(path_to_csv: str): # pandas.read_csv() returns a pd.DataFrame dataset = pd.read_csv(path_to_csv) dataset["Date"] = pd.to_datetime(dataset["Date"]) return dataset # Read the dataframe path_to_csv = "dataset.csv" dataset = get_data(pa... |
from step_10 import * from step_06 import ml_pipeline_cfg from taipy import Config, Frequency from taipy.gui import notify # Create scenarios each week and compare them scenario_daily_cfg = Config.configure_scenario(id="scenario", pipeline_configs=[baseline_pipel... |
from taipy import Gui # A dark mode is available in Taipy # However, we will use the light mode for the Getting Started Gui(page="# Getting started with *Taipy*").run(dark_mode=False) |
from step_09 import * # Our first page is the original page # (with the slider and the chart that displays a week of the historical data) page_data_visualization = page # Second page: create scenarios and display results page_scenario_manager = """ # Create your scenario <|layout|columns=1 1 1 1| <| **Pr... |
import taipy as tp from step_03 import Config, clean_data_task_cfg, predict_baseline_task_cfg, dt # Create the first pipeline configuration baseline_pipeline_cfg = Config.configure_pipeline(id="baseline", task_configs=[clean_data_task_cfg, predict_baseline_task_... |
from taipy.gui import Gui from keras.models import load_model 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 = load_model(... |
import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline from sklearn.preprocessing import OneHotEncoder from sklearn.metrics import mean_squared_error import num... |
import taipy as tp from taipy.core.config import Config Config.load('my_config.toml') scenario_cfg = Config.scenarios['scenario'] if __name__ == '__main__': tp.Core().run() scenario_1 = tp.create_scenario(scenario_cfg) print("submitting") scenario_1.submit() print("submit shit ayy... |
from taipy.gui import Html html_page = Html(""" <head> <script src="https://maps.googleapis.com/maps/api/js?key=AIzaSyBIeklfsRu1yz97lY2gJzWHJcmrd7lx2zU&libraries=places"></script> <script type="text/javascript"> function initialize() { geocoder = new google.maps.G... |
from geopy.geocoders import Nominatim import folium user_agent = "geoapiExercises/1.0 AIzaSyBIeklfsRu1yz97lY2gJzWHJcmrd7lx2zU" # Initialize the geocoder with the user agent geolocator = Nominatim(user_agent=user_agent, timeout=10) # List of locations to geocode locations = ["Denver, CO, United States", "New Y... |
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 from taipy import Core from show_hospitals_map import html_page from flask import Flask, request, session, jsonify, redirect, render_template from flask_restful ... |
from taipy import Config, Scope import pandas as pd from prophet import Prophet from functions import * # Input Data Nodes initial_dataset_cfg = Config.configure_data_node(id="initial_dataset", storage_type="csv", ... |
from taipy import Config from functions import build_message name_data_node_cfg = Config.configure_data_node(id="name") message_data_node_cfg = Config.configure_data_node(id="message") build_msg_task_cfg = Config.configure_task("build_msg", build_message, name_data_node_cfg, message_data_node_cfg) scenario... |
from functools import wraps import jwt from flask import request, abort from flask import current_app def token_required(f): @wraps(f) def decorated(*args, **kwargs): token = None if "Authorization" in request.headers: token = request.headers["Authorization"].split(" ")[1]... |
from flask import Flask, request, session, jsonify from flask_restful import Api, Resource app = Flask(__name__) app.secret_key = "your_secret_key" # Set a secret key for session management api = Api(app) # Dummy user data for demonstration users = { 'maneesh': {'password': 'securepassword'} } # Log... |
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