| 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) | |