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()


    client, _models = connect_qumo_ollama(OLLAMA_LOCATION, print_models=False)

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

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

    model_select = mo.ui.dropdown(
        options=_models,
        value=_models[0],
        label="Select Ollama Model to use",
        searchable=True,
    )
    model_select
    return TAGUETTE_EXPORT_DIR, WORKING_DIR, client, mo, model_select, 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
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 _(all_tags_df, mo, tag_select):
    mo.stop(not tag_select.value, mo.md("Select tag to continue"))
    # filter all_tags_df to only the document = file_dropdown.value
    df = all_tags_df.loc[all_tags_df['tag'] == tag_select.value].copy()
    df
    return (df,)


@app.cell(hide_code=True)
def _(mo):
    mo.md(r"""
    # 4) Keyword extraction
    """)
    return


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

    # mdf = mpd.from_pandas(df)

    start_processing_btn = mo.ui.button(
        label="Start Keyword Extraction",
        kind="warn",
        on_click=lambda val: True
    )
    start_processing_btn
    return (start_processing_btn,)


@app.cell
def _(
    WORKING_DIR,
    client,
    df,
    mo,
    model_select,
    pd,
    start_processing_btn,
    tag_select,
):
    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")


    # 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
    )

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

    df[['id', 'tag', 'content', 'keywords_txt']].to_csv(
        WORKING_DIR / f'keywords_{tag_select.value.replace(" ", "-")}.csv',
        index=False
    )
    return


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


@app.cell
def _():
    # Start with loading 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

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


@app.cell
def _(df):
    MIN_FREQ = 2

    all_keywords_list = df['keywords'].tolist()
    all_keywords_flat = [item for sublist in all_keywords_list for item in sublist]


    keyword_freq = {}
    for kw in all_keywords_flat:
        if kw in keyword_freq:
            keyword_freq[kw] += 1
        else:
            keyword_freq[kw] = 1

    keyword_freq_filtered = {kw: freq for kw, freq in keyword_freq.items() if freq >= MIN_FREQ}

    # create list of keywords sorted by their frequencies. only store the keyword
    sorted_keywords = sorted(keyword_freq_filtered.items(), key=lambda x: x[1], reverse=True)
    sorted_keywords_list = [f"{kw}:{freq}" for kw, freq in sorted_keywords]
    sorted_keywords_list
    return (keyword_freq_filtered,)


@app.cell
def _():
    IGNORE_WORDS = {
        'chase as a brand': [
            "brand"
        ]
    }


    return (IGNORE_WORDS,)


@app.cell
def _(plt):
    import random
    import matplotlib.colors as mcolors

    def blue_color_func(word, font_size, position, orientation, random_state=None, **kwargs):
        # Use the provided random_state for reproducibility if available, else use random module
        r = random_state if random_state else random

        # Sample from the darker end of the 'Blues' colormap (e.g., 0.4 to 1.0)
        # 0.0 is white/light, 1.0 is dark blue
        min_val, max_val = 0.4, 1.0
        color_val = r.uniform(min_val, max_val)

        # Get color from matplotlib colormap
        rgba = plt.cm.Blues(color_val)
        return mcolors.to_hex(rgba)
    return (blue_color_func,)


@app.cell
def _():
    # chase_mask = np.array(Image.open("./data/assets/Chase-National-Bank-Logo.png"))

    # def transform_format(val):
    #     if val == 0:
    #         return 255
    #     else:
    #         return 1

    # transformed_chase_mask = np.ndarray((chase_mask.shape[0], chase_mask.shape[1]), np.int32)
    # for i in range(len(chase_mask)):
    #     transformed_chase_mask[i] = list(map(transform_format, chase_mask[i]))
    return


@app.cell
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)
    logo_switch

    return buffer, canvas_size, logo_switch


@app.cell(hide_code=True)
def _(
    IGNORE_WORDS,
    Image,
    ImageDraw,
    WordCloud,
    blue_color_func,
    buffer,
    canvas_size,
    keyword_freq_filtered,
    logo_switch,
    mo,
    np,
    plt,
    tag_select,
):
    # 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 = "./data/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=20,      # 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=100,  # Increased font size for larger canvas
            max_words=20,      # 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_button",
        kind="warn",
        on_click=lambda val: True
    )
    save_wordcloud_btn
    return save_wordcloud_btn, wc_image


@app.cell
def _(WORKING_DIR, mo, save_wordcloud_btn, tag_select, 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_select.value.replace(" ", "-")}.png'
    fpath = WORKING_DIR / filename

    # add a (increasing) number to the filename so we can save multiple. find the latest in the directory first
    existing_files = list(WORKING_DIR.glob(f'wordcloud_{tag_select.value.replace(" ", "-")}*.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 = WORKING_DIR / f'wordcloud_{tag_select.value.replace(" ", "-")}_{next_number}.png'

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


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