import gradio as gr
import pytz

from datetime import datetime

from utilities import extract, create_time_series_features, train_model, process_personalized_collection, my_loss, \
    cleanup
from markdown import instructions_markdown, faq_markdown
from memory_states import get_my_memory_states
from plot import make_plot


def get_w_markdown(w):
    return f"""
    # Updated Parameters
    Copy and paste these as shown in step 5 of the instructions:

    `{w}`
    
    Check out the Analysis tab for more detailed information."""


def anki_optimizer(file, timezone, next_day_starts_at, revlog_start_date, requestRetention, fast_mode,
                   progress=gr.Progress(track_tqdm=True)):
    now = datetime.now()
    files = ['prediction.tsv', 'revlog.csv', 'revlog_history.tsv', 'stability_for_analysis.tsv',
             'expected_repetitions.csv']
    prefix = now.strftime(f'%Y_%m_%d_%H_%M_%S')

    proj_dir = extract(file, prefix)

    type_sequence, time_sequence, df_out = create_time_series_features(revlog_start_date, timezone, next_day_starts_at, proj_dir)
    w, dataset = train_model(proj_dir)
    w_markdown = get_w_markdown(w)
    cleanup(proj_dir, files)
    if fast_mode:
        files_out = [proj_dir / file for file in files if (proj_dir / file).exists()]
        return w_markdown, None, None, "", files_out

    my_collection, rating_markdown = process_personalized_collection(requestRetention, w)
    difficulty_distribution_padding, difficulty_distribution = get_my_memory_states(proj_dir, dataset, my_collection)
    fig, suggested_retention_markdown = make_plot(proj_dir, type_sequence, time_sequence, w, difficulty_distribution_padding)
    loss_markdown = my_loss(dataset, w)
    difficulty_distribution = difficulty_distribution.to_string().replace("\n", "\n\n")
    markdown_out = f"""
{suggested_retention_markdown}

# Loss Information
{loss_markdown}

# Difficulty Distribution
{difficulty_distribution}

# Ratings
{rating_markdown}
"""
    files_out = [proj_dir / file for file in files if (proj_dir / file).exists()]
    return w_markdown, df_out, fig, markdown_out, files_out


description = """
# FSRS4Anki Optimizer App - v3.14.7
Based on the [tutorial](https://medium.com/@JarrettYe/how-to-use-the-next-generation-spaced-repetition-algorithm-fsrs-on-anki-5a591ca562e2) 
of [Jarrett Ye](https://github.com/L-M-Sherlock). This application can give you personalized anki parameters without having to code.

Read the `Instructions` if its your first time using the app.
"""

with gr.Blocks() as demo:
    with gr.Tab("FSRS4Anki Optimizer"):
        with gr.Box():
            gr.Markdown(description)
        with gr.Box():
            with gr.Row():
                with gr.Column():
                    file = gr.File(label='Review Logs (Step 1)')
                    fast_mode_in = gr.Checkbox(value=False, label="Fast Mode (Will just return the optimized weights)")
                with gr.Column():
                    next_day_starts_at = gr.Number(value=4,
                                                   label="Next Day Starts at (Step 2)",
                                                   precision=0)
                    timezone = gr.Dropdown(label="Timezone (Step 3.1)", choices=pytz.all_timezones)
                    with gr.Accordion(label="Advanced Settings (Step 3.2)", open=False):
                        requestRetention = gr.Number(value=.9, label="Desired Retention: Recommended to set between 0.8  0.9")
                        revlog_start_date = gr.Textbox(value="2006-10-05",
                                                       label="Revlog Start Date: Optimize review logs after this date.")
        with gr.Row():
            btn_plot = gr.Button('Optimize your Anki!')
        with gr.Row():
            w_output = gr.Markdown()
    with gr.Tab("Instructions"):
        with gr.Box():
            gr.Markdown(instructions_markdown)
    with gr.Tab("Analysis"):
        with gr.Row():
            markdown_output = gr.Markdown()
            with gr.Column():
                df_output = gr.DataFrame()
                plot_output = gr.Plot()
                files_output = gr.Files(label="Analysis Files")
    with gr.Tab("FAQ"):
        gr.Markdown(faq_markdown)

    btn_plot.click(anki_optimizer,
                   inputs=[file, timezone, next_day_starts_at, revlog_start_date, requestRetention, fast_mode_in],
                   outputs=[w_output, df_output, plot_output, markdown_output, files_output])

if __name__ == '__main__':
    demo.queue().launch(show_error=True)