src/utils.py

import base64
from typing import List, Tuple

import streamlit as st
from pandas.core.frame import DataFrame
from PIL import Image

from .configs import ColumnNames, SupportedFiles

# import altair as alt


def get_col_indices(cols: List) -> Tuple[int, int]:
    """Ugly but works"""
    cols = [i.lower() for i in cols]
    try:
        label_index = cols.index(ColumnNames.LABEL.value)
    except:
        label_index = 0

    try:
        text_index = cols.index(ColumnNames.TEXT.value)
    except:
        text_index = 0

    return text_index, label_index


@st.cache
def get_logo(path: str) -> Image:
    return Image.open(path)


@st.experimental_memo
def read_file(uploaded_file) -> DataFrame:
    file_type = uploaded_file.name.split(".")[-1]
    read_fn = SupportedFiles[file_type].value[0]
    df = read_fn(uploaded_file)
    df = df.dropna()
    return df


@st.cache
def convert_df(df: DataFrame) -> bytes:
    # IMPORTANT: Cache the conversion to prevent computation on every rerun
    return df.to_csv(index=False, sep=";").encode("utf-8")


def download_button(dataframe: DataFrame, name: str) -> None:
    csv = dataframe.to_csv(index=False)
    # some strings <-> bytes conversions necessary here
    b64 = base64.b64encode(csv.encode()).decode()
    href = f'<a href="data:file/csv;base64,{b64}" download="{name}.csv">Download</a>'
    st.write(href, unsafe_allow_html=True)


# def plot_labels_prop(data: 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: 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: 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))