import altair as alt
import pandas as pd
import streamlit as st
from stqdm import stqdm

stqdm.pandas()


def plot_labels_prop(data: pd.DataFrame, label_column: str):

    unique_value_limit = 100

    if data[label_column].nunique() > unique_value_limit:

        st.warning(
            f"""
            The column you selected has more than {unique_value_limit}.
            Are you sure it's the right column? If it is, please note that
            this will impact __Wordify__ performance.
            """
        )

        return

    source = data[label_column].value_counts().reset_index().rename(columns={"index": "Labels", label_column: "Counts"})
    source["Props"] = source["Counts"] / source["Counts"].sum()
    source["Proportions"] = (source["Props"].round(3) * 100).map("{:,.2f}".format) + "%"

    bars = (
        alt.Chart(source)
        .mark_bar()
        .encode(
            x=alt.X("Labels:O", sort="-y"),
            y="Counts:Q",
        )
    )

    text = bars.mark_text(align="center", baseline="middle", dy=15).encode(text="Proportions:O")

    return (bars + text).properties(height=300)


def plot_nchars(data: pd.DataFrame, text_column: str):
    source = data[text_column].str.len().to_frame()

    plot = (
        alt.Chart(source)
        .mark_bar()
        .encode(
            alt.X(f"{text_column}:Q", bin=True, axis=alt.Axis(title="# chars per text")),
            alt.Y("count()", axis=alt.Axis(title="")),
        )
    )

    return plot.properties(height=300)


def plot_score(data: pd.DataFrame, label_col: str, label: str):

    source = data.loc[data[label_col] == label].sort_values("score", ascending=False).head(100)

    plot = (
        alt.Chart(source)
        .mark_bar()
        .encode(
            y=alt.Y("word:O", sort="-x"),
            x="score:Q",
        )
    )

    return plot.properties(height=max(30 * source.shape[0], 50))