Interview-Analysis/02-B_WordClouds.py

import marimo

__generated_with = "0.18.3"
app = marimo.App(width="medium")


@app.cell
def _():
    import marimo as mo
    import pandas as pd
    import modin.pandas as mpd
    from tqdm import tqdm
    from pathlib import Path
    from datetime import datetime

    from utils import connect_qumo_ollama

    OLLAMA_LOCATION= 'localhost'
    # VM_NAME = 'ollama-lite'

    # initialize tqdm for pandas
    tqdm.pandas()



    TAGUETTE_EXPORT_DIR = Path('./data/processing/02_taguette_export')
    WORKING_DIR = Path('./data/processing/02-b_WordClouds')

    if not WORKING_DIR.exists():
        WORKING_DIR.mkdir(parents=True)
    if not TAGUETTE_EXPORT_DIR.exists():
        TAGUETTE_EXPORT_DIR.mkdir(parents=True)

    return (
        OLLAMA_LOCATION,
        TAGUETTE_EXPORT_DIR,
        WORKING_DIR,
        connect_qumo_ollama,
        mo,
        pd,
    )


@app.cell(hide_code=True)
def _(mo):
    mo.md(r"""
    # 1) Export Data out of Taguette

    **Highlights**
    1. Go to: https://taguette.qumo.io/project/1
    2. Select 'Highlights' (left side) > 'See all hightlights' > 'Export this view' (top right) > 'CSV'
    3. Save to '{TAGUETTE_EXPORT_DIR}/all_tags.csv'

    **Tags Codebook**
    1. Select 'Project Info' (left side) > 'Export codebook' > 'CSV'
    2. Save to '{TAGUETTE_EXPORT_DIR}/codebook.csv'

    _NOTE: Sometimes you need to explicitly allow 'Unsafe Download' in the browser's download manager_
    """)
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md(r"""
    # 2) Import Data
    """)
    return


@app.cell
def _(TAGUETTE_EXPORT_DIR, pd):
    all_tags_df = pd.read_csv(f'{TAGUETTE_EXPORT_DIR}/all_tags.csv')
    all_tags_df['_seq_id'] = range(len(all_tags_df))
    # all_tags_df
    return (all_tags_df,)


@app.cell
def _(all_tags_df):
    # get count of rows per tag
    tag_counts = all_tags_df['tag'].value_counts().reset_index()
    # tag_counts
    return


@app.cell
def _(TAGUETTE_EXPORT_DIR, pd):
    codebook_df = pd.read_csv(f'{TAGUETTE_EXPORT_DIR}/codebook.csv')
    codebook_df.rename(columns={'description': 'theme_description'}, inplace=True)
    # codebook_df
    return


@app.cell(hide_code=True)
def _(mo):
    mo.md(r"""
    # 3) Select Tag for processing
    """)
    return


@app.cell(hide_code=True)
def _(all_tags_df, mo):



    tag_select = mo.ui.dropdown(
        options=all_tags_df['tag'].unique().tolist(),
        label="Select Tag to Process",
        # value="Chase as a brand",
        full_width=True,
    )
    tag_select
    return (tag_select,)


@app.cell
def _(WORKING_DIR, all_tags_df, mo, tag_select):
    mo.stop(not tag_select.value, mo.md("Select tag to continue"))

    start_processing_btn = None
    start_processing_btn = mo.ui.button(
        label="Start Keyword Extraction",
        kind="warn",
        on_click=lambda val: True
    )

    tag_fname = tag_select.value.replace(" ", "-").replace('/','-')

    SAVE_DIR = WORKING_DIR / tag_fname

    if not SAVE_DIR.exists():
        SAVE_DIR.mkdir(parents=True)

    KEYWORDS_FPATH = SAVE_DIR / f'keywords_per-highlight_{tag_fname}.xlsx'
    KEYWORD_FREQ_FPATH = SAVE_DIR / f'keyword_frequencies_{tag_fname}.xlsx'

    # filter all_tags_df to only the document = file_dropdown.value
    tags_df = all_tags_df.loc[all_tags_df['tag'] == tag_select.value].copy()
    tags_df.head()
    return (
        KEYWORDS_FPATH,
        KEYWORD_FREQ_FPATH,
        SAVE_DIR,
        start_processing_btn,
        tag_fname,
        tags_df,
    )


@app.cell(hide_code=True)
def _(KEYWORD_FREQ_FPATH, mo):
    mo.md(rf"""
    # 4) Keyword extraction {'(skippable, see 4b)' if KEYWORD_FREQ_FPATH.exists() else ''}
    """)
    return


@app.cell
def _(OLLAMA_LOCATION, connect_qumo_ollama, mo):
    try:
        client, _models = connect_qumo_ollama(OLLAMA_LOCATION, print_models=False)
        model_select = mo.ui.dropdown(
            options=_models,
            value=_models[0],
            label="Select Ollama Model to use",
            searchable=True,
        )
    except Exception as e:
        mo.md(f"Error connecting to Ollama server at `{OLLAMA_LOCATION}`: {e}")
        model_select = None
        client = None

    model_select
    return client, model_select


@app.cell
def _(mo, model_select, start_processing_btn, tag_select):
    mo.stop(not tag_select.value or model_select is None, mo.md("Select tag to continue"))

    start_processing_btn
    return


@app.cell
def _(client, mo, model_select, pd, start_processing_btn, tags_df):
    from utils import ollama_keyword_extraction, worker_extraction
    # Wait for start processing button
    mo.stop(not start_processing_btn.value, "Click button above to start processing")

    if client is not None:
        df = tags_df
        # Run keyword extraction

        df['keywords'] = df.progress_apply(
            lambda row: pd.Series(ollama_keyword_extraction(
                content=row['content'], 
                tag=row['tag'], 
                client=client, 
                model=model_select.value
            )),
            axis=1
        )
    else:
        mo.md("Ollama client not available, See 4b) for loading data from xlsx.")
    return (df,)


@app.cell(hide_code=True)
def _(KEYWORDS_FPATH, KEYWORD_FREQ_FPATH, df, mo, pd, start_processing_btn):
    mo.stop(not start_processing_btn.value, "Click button above to process first")

    df['keywords_txt'] = df['keywords'].apply(lambda kws: ', '.join(kws))

    all_keywords_list = df['keywords'].tolist()

    all_keywords_flat = [item for sublist in all_keywords_list for item in sublist]

    # Calculate frequencies per keyword
    keyword_freq = {}
    for kw in all_keywords_flat:
        if kw in keyword_freq:
            keyword_freq[kw] += 1
        else:
            keyword_freq[kw] = 1

    freq_df = pd.DataFrame.from_dict(keyword_freq, orient='index', columns=['frequency'])
    freq_df.index.name = 'keyword'
    freq_df.reset_index(inplace=True)
    freq_df.sort_values(by='frequency', ascending=False, inplace=True)



    # Save to Excel files

    df[['id', 'tag', 'content', 'keywords_txt']].to_excel(
        KEYWORDS_FPATH,
        index=False
    )

    freq_df.to_excel(
        KEYWORD_FREQ_FPATH,
        index=False
    )
    mo.vstack([
        mo.md(f"Keywords per-highlight saved to: `{KEYWORDS_FPATH}`"),
        mo.md(f"Keyword frequencies saved to: `{KEYWORD_FREQ_FPATH}`")
    ])
    return (freq_df,)


@app.cell(hide_code=True)
def _(KEYWORD_FREQ_FPATH, mo):
    mo.md(rf"""
    # 4b) [optional] Load data from `keyword_frequencies_{KEYWORD_FREQ_FPATH.name}`
    """)
    return


@app.cell(hide_code=True)
def _(KEYWORD_FREQ_FPATH, mo, start_processing_btn):
    if start_processing_btn is not None: # Triggers re-execution of this cell when keyword extraction completes
        pass

    
    load_existing_btn = None
    if KEYWORD_FREQ_FPATH.exists():
        load_existing_btn = mo.ui.run_button(label=f"Load keywords from `{KEYWORD_FREQ_FPATH.name}`", kind='warn')

    load_existing_btn
    return (load_existing_btn,)


@app.cell(hide_code=True)
def _(KEYWORD_FREQ_FPATH, freq_df, load_existing_btn, pd):
    if load_existing_btn is not None and load_existing_btn.value:
        _fdf = pd.read_excel(KEYWORD_FREQ_FPATH, engine='openpyxl')

        # Drop nan rows if any
        _fdf.dropna(subset=['keyword', 'frequency'], inplace=True)
        _fdf.sort_values(by='frequency', ascending=False, inplace=True)
        _fdf.reset_index(drop=True, inplace=True)
        print(f"Loaded `{KEYWORD_FREQ_FPATH}` successfully.")

        frequency_df = _fdf

    else:
        frequency_df = freq_df
    return (frequency_df,)


@app.cell(hide_code=True)
def _(mo):
    mo.md(r"""
    # 5) Wordcloud generation
    """)
    return


@app.cell(hide_code=True)
def _():
    # Import all necessary libraries
    import numpy as np
    from os import path
    from PIL import Image, ImageDraw
    from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
    import matplotlib.pyplot as plt
    from utils import blue_color_func

    import warnings
    warnings.filterwarnings("ignore")
    return Image, ImageDraw, WordCloud, blue_color_func, np, plt


@app.cell(hide_code=True)
def _(mo):
    mo.md(r"""
    ## 5.1) Select threshold frequency
    """)
    return


@app.cell(hide_code=True)
def _(mo):
    min_freq_select = mo.ui.number(start=1, stop=20, label="Threshold Minimum Keyword Frequency: ", value=2)
    min_freq_select
    return (min_freq_select,)


@app.cell(hide_code=True)
def _(mo, tag_select):
    mo.md(rf"""
    ## 5.2) Inspect Keyword Dataset

    1. Check the threshold is set correctly. If not, adjust accordingly
    2. Read all the keywords and verify they are good. If not
       - Add explicit exclusions if necessary below
       - OR Rerun the keyword extraction above



    Add words to this dict that should be ignored in the WordCloud for specific tags. 
    Make sure to create the correct key that matches the active selected tag: 

    Active selected tag = '`{tag_select.value.lower()}`'
    """)
    return


@app.cell(hide_code=True)
def _(frequency_df, min_freq_select, mo):
    mo.stop('keyword' not in frequency_df.columns, "Waiting for keyword extraction to finish")

    MIN_FREQ = min_freq_select.value

    _freq_df_filtered = frequency_df.loc[frequency_df['frequency'] >= MIN_FREQ].copy()

    table_selection = mo.ui.table(_freq_df_filtered, page_size=50)
    table_selection

    return MIN_FREQ, table_selection


@app.cell(hide_code=True)
def _(mo, table_selection):
    remove_rows_btn = None
    if len(table_selection.value) >0 :
        remove_rows_btn = mo.ui.run_button(label="Click to remove selected keywords and update xlsx")

    remove_rows_btn
    return (remove_rows_btn,)


@app.cell(hide_code=True)
def _(KEYWORD_FREQ_FPATH, frequency_df, mo, remove_rows_btn, table_selection):
    _s = None
    if remove_rows_btn is not None and remove_rows_btn.value:
        # get selected rows
        selected_rows = table_selection.value
        if len(selected_rows) >0 :
            rows_to_drop = table_selection.value.index.tolist()
            try:
                frequency_df.drop(index=rows_to_drop, inplace=True, axis=0)
            except KeyError:
                _s = mo.callout("GO BACK TO STEP 4b) and reload data to continue refining the dataset.", kind='warn')
            else:
                # Save updated frequencies back to xlsx
                frequency_df.to_excel(
                    KEYWORD_FREQ_FPATH,
                    index=False
                )
            
                print(f"Updated keyword frequencies saved to: `{KEYWORD_FREQ_FPATH}`")

            # mo.callout(f"Updated keyword frequencies saved to: `{KEYWORD_FREQ_FPATH}`", kind="success")
            _s = mo.callout("GO BACK TO STEP 4b) and reload data to continue refining the dataset.", kind='warn')

    _s
    return


@app.cell(hide_code=True)
def _():
    IGNORE_WORDS = {
        'chase as a brand': [
            "brand",
            "banking experience",
            "banking",
            "chase",
            "jpmorgan",
            "youthful",
            "customer service",
            "customer service focused",
            "great brand",
        ],
        'why customer chase': [
            "customer service",
            "customer loyalty",
            "chase",
            "chase customer",
            "banking experience",
        ],
        'chase as a person (personification)': [
            "CPC1"
        ]
        # <active-selected-tag>: [list, of, words, to, ignore]
    }
    return (IGNORE_WORDS,)


@app.cell(hide_code=True)
def _(mo):
    buffer = -100 # Adjust this to increase/decrease space between logo and words
    canvas_size = (1200, 800)

    logo_switch = mo.ui.switch(label="Include Chase Logo", value=False)

    n_words = mo.ui.slider(start=10, stop=200, step=1, value=40, debounce=True, show_value=True, label="Max number of words in WordCloud")
    return buffer, canvas_size, logo_switch, n_words


@app.cell(hide_code=True)
def _(logo_switch, mo, n_words):
    run_wordcloud_btn = mo.ui.run_button(label="Generate WordCloud")

    mo.vstack([
        mo.md("## 5.4) Generate WordCloud with/without Logo"),
        mo.md("""Use these buttons to iteratively (re)generate the WordCloud until it looks nice. 

        Placement and color of words is randomized, size is proportional to frequency.

        When satisfied with the result, click 'Save WordCloud to File' to save the image."""),
        mo.md('---'),
        mo.hstack([logo_switch, n_words, run_wordcloud_btn], align='center', justify='space-around')]
    )
    return (run_wordcloud_btn,)


@app.cell(hide_code=True)
def _(
    IGNORE_WORDS,
    Image,
    ImageDraw,
    MIN_FREQ,
    WordCloud,
    blue_color_func,
    buffer,
    canvas_size,
    frequency_df,
    logo_switch,
    mo,
    n_words,
    np,
    plt,
    run_wordcloud_btn,
    tag_select,
):
    if run_wordcloud_btn.value:
        pass

    freq_df_filtered = frequency_df.loc[frequency_df['frequency'] >= MIN_FREQ].copy()

    # freq_df_filtered.reset_index(drop=True, inplace=True)

    keyword_freq_filtered = freq_df_filtered.set_index('keyword')['frequency'].to_dict()

    # remove specific keywords depending on selected tag
    if IGNORE_WORDS.get(tag_select.value.lower()):
        for word in IGNORE_WORDS[tag_select.value.lower()]:
            if word in keyword_freq_filtered:
                del keyword_freq_filtered[word]

    if logo_switch.value:
        # 1. Load the logo
        # Make sure this path points to your uploaded file
        logo_path = "./assets/JP-Morgan-Chase-Symbol.png" 
        logo = Image.open(logo_path).convert("RGBA")

        # Optional: Resize logo if it's too large or small for the canvas
        # target_width = 600
        # ratio = target_width / logo.width
        # logo = logo.resize((target_width, int(logo.height * ratio)), Image.Resampling.LANCZOS)
        target_width = 600  # Set a reasonable size for the logo
        if logo.width > target_width:
            ratio = target_width / logo.width
            new_height = int(logo.height * ratio)
            # Use Image.Resampling.LANCZOS for high-quality downsampling
            # If you get an error, try Image.LANCZOS or Image.ANTIALIAS
            logo = logo.resize((target_width, new_height), Image.Resampling.LANCZOS)

        # 3. Create the mask (0 = draw here, 255 = don't draw here)
        # Initialize with 0 (black/draw everywhere)
        mask_image = Image.new("L", canvas_size, 0)
        draw = ImageDraw.Draw(mask_image)

        # 4. Draw a protected circular area in the center
        center = (canvas_size[0] // 2, canvas_size[1] // 2)

        # Calculate radius: half of logo max dimension + buffer
        radius = (max(logo.size) // 2) + buffer

        # Draw the white circle (255) which the WordCloud will avoid
        draw.ellipse(
            (center[0] - radius, center[1] - radius, center[0] + radius, center[1] + radius),
            fill=255
        )

        chase_mask = np.array(mask_image)

        # Generate the WordCloud
        wordcloud = WordCloud(
            background_color='white',
            width=canvas_size[0],
            height=canvas_size[1],
            max_font_size=100,  # Increased font size for larger canvas
            max_words=n_words.value,      # Increased word count to fill space
            color_func=blue_color_func,
            mask=chase_mask,    # Apply the circular mask
            contour_width=0,
            contour_color='steelblue'
        ).generate_from_frequencies(keyword_freq_filtered)

    else:
        # Generate the WordCloud
        wordcloud = WordCloud(
            background_color='white',
            width=canvas_size[0],
            height=canvas_size[1],
            max_font_size=150,  # Increased font size for larger canvas
            max_words=n_words.value,      # Increased word count to fill space
            color_func=blue_color_func,
            # mask=chase_mask,    # Apply the circular mask
            # contour_width=0,
            # contour_color='steelblue'
        ).generate_from_frequencies(keyword_freq_filtered)

    # Convert WordCloud to Image to composite the logo
    wc_image = wordcloud.to_image()

    if logo_switch.value:

        # Calculate position to center the logo
        logo_pos = (
            (canvas_size[0] - logo.width) // 2,
            (canvas_size[1] - logo.height) // 2
        )

        # Paste logo (using alpha channel as mask to keep transparency)
        wc_image.paste(logo, logo_pos, logo)

        # Display the generated image
        fig = plt.figure(figsize=(7,7))

    # Display the generated image:
    plt.imshow(wc_image, interpolation='bilinear')
    plt.axis("off")
    plt.show()

    save_wordcloud_btn = None
    save_wordcloud_btn = mo.ui.button(
        label="Save WordCloud to File",
        kind="warn",
        on_click=lambda val: True
    )
    save_wordcloud_btn
    return save_wordcloud_btn, wc_image


@app.cell(hide_code=True)
def _(SAVE_DIR, mo, save_wordcloud_btn, tag_fname, wc_image):
    # Wait for start processing button
    mo.stop(not save_wordcloud_btn.value, "Click button above to save wordcloud image")


    filename = f'wordcloud_{tag_fname}.png'


    fpath = SAVE_DIR / filename

    # add a (increasing) number to the filename so we can save multiple. find the latest in the directory first
    existing_files = list(SAVE_DIR.glob(f'wordcloud_{tag_fname}*.png'))
    if existing_files:
        existing_numbers = []
        for ef in existing_files:
            parts = ef.stem.split('_')
            if len(parts) > 2 and parts[-1].isdigit():
                existing_numbers.append(int(parts[-1]))
        if existing_numbers:
            next_number = max(existing_numbers) + 1
        else:
            next_number = 1
        fpath = SAVE_DIR / f'wordcloud_{tag_fname}_{next_number}.png'

    wc_image.save(fpath)
    mo.md(f"Wordcloud saved to: {fpath}")
    return


if __name__ == "__main__":
    app.run()