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: `var w = {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 - v3.13.3 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)