import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Apply the default theme and activate color codes
sns.set_theme()
sns.set(color_codes=True)

# Import the dataset
tips = sns.load_dataset("tips")
tips["tip_percentage"] = tips["tip"] / tips["total_bill"] * 100

# Create the title and subtitle
st.title("How does the amount of tip / percentage of tips differ across different days of the week?")
st.subheader("This app shows which days of the week bring in higher tip percentages and tip amounts, helping restaurants and staff adapt to customer tipping behavior and optimize their business.")

# create filters/sidebars for our interactive plot
with st.sidebar:
    st.subheader("Filters")

    # Select the day
    all_days = sorted(tips["day"].unique())
    selected_days = st.multiselect(
        "Days to show",
        options=all_days,
        default=all_days,
    )

    # Select the x-axis
    feature_options = {
        "Tip": "tip",
        "Tip Percentage": "tip_percentage"
    }
    feature_label = st.selectbox("Feature (x-axis)", list(feature_options.keys()))
    x_col = feature_options[feature_label]

    # enable fill options
    fill = st.checkbox("Shade area", value=True)

if not selected_days:
    st.info("Select at least one day to display the plot.")
else:
    # Filter the data
    data = tips[tips["day"].isin(selected_days)].dropna(subset=[x_col])

    # Make/show the KPI
    avg_value = data[x_col].mean()
    unit = "$" if x_col == "tip" else "%"
    st.metric(
        label=f"Average {feature_label} for selected days",
        value=f"{avg_value:.2f} {unit}"
    )

    # The plot itself
    g = sns.displot(
        data=data,
        x=x_col,
        hue="day",
        kind="kde",
        fill=fill
    )

    fig = g.fig
    st.pyplot(fig)
    plt.close(fig)

    # Adding the dynamic text
    max_day = (
        data.groupby("day")[x_col].mean()
        .sort_values(ascending=False)
        .index[0]
    )
    st.success(
        f"💡 On average, **{max_day}** has the highest {feature_label.lower()} among the selected days."
    )