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# Dashboard from Vizro-AI calls and using Vizro-AI generated chart code
from vizro import Vizro
import vizro.models as vm
import vizro.plotly.express as px
from vizro.models.types import capture
import plotly.graph_objects as go
import pandas as pd

@capture('graph')
def compare_versions_custom_chart(data_frame=None):

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# Dashboard using Vizro-AI calls only
from vizro import Vizro
import vizro.models as vm
import vizro.plotly.express as px

first_page = vm.Page(
title="Documentation traffic dashboard",
layout=vm.Layout(
    grid=[
        [0, 2],

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from vizro import Vizro
import vizro.models as vm
import vizro.plotly.express as px

first_page = vm.Page(
title="Documentation traffic dashboard",
layout=vm.Layout(
    grid=[
        [0, 2],

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# Total views of the top performing stable pages
top_performing_pages_fig = vizro_ai.plot(df, "Collate rows of data for Path where Version==stable. Create a horizontal bar chart describing the total Views for the top 5 pages. Add the numbers to each bar. Decrease font size of marks", explain=True)
top_performing_pages_fig.show()

# Top 5 performing pages on stable by date
top_pages_by_date_fig = vizro_ai.plot(df, "Collate rows of data for Path where Version==stable. Create a line graph for the total Views per Date for the top 5 Paths", explain=True)
top_pages_by_date_fig.show()

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import vizro_ai
from vizro_ai import VizroAI
from dotenv import load_dotenv
load_dotenv()

vizro_ai = VizroAI(model="gpt-4–0613")
compare_versions_fig = vizro_ai.plot(df, "Combine rows of Views for each Date for latest and stable Version. Draw a line graph comparing Views per Date for latest and stable", explain=True)
compare_versions_fig.show()

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from kedro.config import OmegaConfigLoader
from kedro.io import DataCatalog

conf_loader = OmegaConfigLoader(".", base_env="", default_run_env="")
conf_catalog = conf_loader["catalog"]

# Create the DataCatalog instance from the configuration
catalog = DataCatalog.from_config(conf_catalog)

# Load the datasets

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def create_pipeline(**kwargs):
    return Pipeline(
        [
            node(
                func=split_data,
                inputs=["master_table", "parameters"],
                outputs=["X_train", "X_test", "y_train", "y_test"],
                name="split_data_node",
            ),
            node(

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def split_data(data: pd.DataFrame, parameters: Dict) -> Tuple:
    """Splits data into features and targets training and test sets.

    Args:
        data: Data containing features and target.
        parameters: Parameters defined in parameters.yml.
    Returns:
        Split data.
    """
    X = data[parameters["features"]]