src/pages/home.py

from src.configs import Languages
from src.utils import (
    encode,
    wordifier,
    download_button,
    TextPreprocessor,
    plot_labels_prop,
    plot_nchars,
    plot_score,
    read_file,
)
import streamlit as st


def write(session, uploaded_file):

    st.markdown(
        """
        Hi! Welcome to __Wordify__. Start by uploading a file - CSV, XLSX (avoid Strict Open XML Spreadsheet format [here](https://stackoverflow.com/questions/62800822/openpyxl-cannot-read-strict-open-xml-spreadsheet-format-userwarning-file-conta)), 
        or PARQUET are currently supported.

        Once you have uploaded the file, __Wordify__ will show an interactive UI through which
        you'll be able to interactively decide the text preprocessing steps, their order, and 
        proceed to Wordify your text.

        If you're ready, let's jump in:

        :point_left: upload a file via the upload widget in the sidebar!

        NOTE: whenever you want to reset everything, simply refresh the page
        """
    )
    if uploaded_file:

        # 1. READ FILE
        with st.spinner("Reading file"):
            # TODO: write parser function that automatically understands format
            data = read_file(uploaded_file)

        # 2. CREATE UI TO SELECT COLUMNS
        st.markdown("")
        st.markdown("")
        st.header("Process")

        col1, col2, col3 = st.beta_columns(3)
        with col1:
            language = st.selectbox("Select language", [i.name for i in Languages])
            with st.beta_expander("Description"):
                st.markdown(
                    f"Select a language of text amongst those supported: {', '.join([f'`{i.name}`' for i in Languages])}"
                )
        with col2:
            cols_options = [""] + data.columns.tolist()
            label_column = st.selectbox("Select label column name", cols_options, index=0)
            with st.beta_expander("Description"):
                st.markdown("Select the column containing the label")

            if label_column:
                plot = plot_labels_prop(data, label_column)
                if plot: st.altair_chart(plot, use_container_width=True)

        with col3:
            text_column = st.selectbox("Select text column name", cols_options, index=0)
            with st.beta_expander("Description"):
                st.markdown("Select the column containing the text")

            if text_column:
                st.altair_chart(plot_nchars(data, text_column), use_container_width=True)

        with st.beta_expander("Advanced options"):
            # Lemmatization option
            col1, col2 = st.beta_columns([1, 3])
            with col1:
                lemmatization_when_elem = st.empty()
            with col2:
                st.markdown("Choose lemmatization option")

            # stopwords option
            col1, col2 = st.beta_columns([1, 3])
            with col1:
                remove_stopwords_elem = st.empty()
            with col2:
                st.markdown("Choose stopword option")

            # cleaning steps
            col1, col2 = st.beta_columns([1, 3])
            with col1:
                cleaning_steps_elem = st.empty()
                reset_button = st.empty()
            with col2:
                st.markdown("Choose cleaning steps")

            # implement reset logic
            if reset_button.button("Reset steps"):
                session.run_id += 1

            steps_options = list(TextPreprocessor._cleaning_options().keys())
            cleaning_steps = cleaning_steps_elem.multiselect(
                "Select text processing steps (ordered)",
                options=steps_options,
                default=steps_options,
                format_func=lambda x: x.replace("_", " ").title(),
                key=session.run_id,
            )
            lemmatization_options = list(TextPreprocessor._lemmatization_options().keys())
            lemmatization_when = lemmatization_when_elem.selectbox(
                "Select when lemmatization happens",
                options=lemmatization_options,
                index=0,
                key=session.run_id,
            )
            remove_stopwords = remove_stopwords_elem.checkbox("Remove stopwords", value=True, key=session.run_id)

        # Show sample checkbox
        col1, col2 = st.beta_columns([1, 2])
        with col1:
            show_sample = st.checkbox("Show sample of preprocessed text")

        # initialize text preprocessor
        preprocessor = TextPreprocessor(
            language=language,
            cleaning_steps=cleaning_steps,
            lemmatizer_when=lemmatization_when,
            remove_stop=remove_stopwords,
        )

        # 3. PROVIDE FEEDBACK ON OPTIONS
        if show_sample and not (label_column and text_column):
            st.warning("Please select `label` and `text` columns")

        elif show_sample and (label_column and text_column):
            sample_data = data.sample(10)
            sample_data[f"preprocessed_{text_column}"] = preprocessor.fit_transform(sample_data[text_column]).values
            st.table(sample_data.loc[:, [label_column, text_column, f"preprocessed_{text_column}"]])

        # 4. RUN
        run_button = st.button("Wordify!")
        if run_button and not (label_column and text_column):
            st.warning("Please select `label` and `text` columns")

        elif run_button and (label_column and text_column) and not session.process:

            with st.spinner("Process started"):
                # data = data.head()
                data[f"preprocessed_{text_column}"] = preprocessor.fit_transform(data[text_column]).values

                inputs = encode(data[f"preprocessed_{text_column}"], data[label_column])
                session.posdf, session.negdf = wordifier(**inputs)
            st.success("Wordified!")

            # session.posdf, session.negdf = process(data, text_column, label_column)
            session.process = True

        # 5. RESULTS
        if session.process and (label_column and text_column):
            st.markdown("")
            st.markdown("")
            st.header("Results")

            # col1, col2, _ = st.beta_columns(3)
            col1, col2, col3 = st.beta_columns([2, 3, 3])

            with col1:
                label = st.selectbox("Select label", data[label_column].unique().tolist())
                # # with col2:
                # thres = st.slider(
                #     "Select threshold",
                #     min_value=0,
                #     max_value=100,
                #     step=1,
                #     format="%f",
                #     value=30,
                # )
                show_plots = st.checkbox("Show plots of top 100")

            with col2:
                st.subheader(f"Words __positively__ identifying label `{label}`")
                st.write(session.posdf[session.posdf[label_column] == label].sort_values("score", ascending=False))
                download_button(session.posdf, "positive_data")
                if show_plots:
                    st.altair_chart(plot_score(session.posdf, label_column, label), use_container_width=True)

            with col3:
                st.subheader(f"Words __negatively__ identifying label `{label}`")
                st.write(session.negdf[session.negdf[label_column] == label].sort_values("score", ascending=False))
                download_button(session.negdf, "negative_data")
                if show_plots:
                    st.altair_chart(plot_score(session.negdf, label_column, label), use_container_width=True)