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import gradio as gr |
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import pytz |
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from datetime import datetime |
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from utilities import extract, create_time_series_features, train_model, process_personalized_collection, my_loss, \ |
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cleanup |
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from markdown import instructions_markdown, faq_markdown |
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from memory_states import get_my_memory_states |
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from plot import make_plot |
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def get_w_markdown(w): |
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return f""" |
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# Updated Parameters |
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Copy and paste these as shown in step 5 of the instructions: |
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`var w = {w};` |
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Check out the Analysis tab for more detailed information.""" |
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def anki_optimizer(file, timezone, next_day_starts_at, revlog_start_date, requestRetention, fast_mode, |
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progress=gr.Progress(track_tqdm=True)): |
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now = datetime.now() |
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files = ['prediction.tsv', 'revlog.csv', 'revlog_history.tsv', 'stability_for_analysis.tsv', |
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'expected_repetitions.csv'] |
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prefix = now.strftime(f'%Y_%m_%d_%H_%M_%S') |
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proj_dir = extract(file, prefix) |
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type_sequence, time_sequence, df_out = create_time_series_features(revlog_start_date, timezone, next_day_starts_at, proj_dir) |
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w, dataset = train_model(proj_dir) |
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w_markdown = get_w_markdown(w) |
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cleanup(proj_dir, files) |
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if fast_mode: |
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files_out = [proj_dir / file for file in files if (proj_dir / file).exists()] |
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return w_markdown, None, None, "", files_out |
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my_collection, rating_markdown = process_personalized_collection(requestRetention, w) |
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difficulty_distribution_padding, difficulty_distribution = get_my_memory_states(proj_dir, dataset, my_collection) |
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fig, suggested_retention_markdown = make_plot(proj_dir, type_sequence, time_sequence, w, difficulty_distribution_padding) |
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loss_markdown = my_loss(dataset, w) |
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difficulty_distribution = difficulty_distribution.to_string().replace("\n", "\n\n") |
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markdown_out = f""" |
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{suggested_retention_markdown} |
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# Loss Information |
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{loss_markdown} |
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# Difficulty Distribution |
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{difficulty_distribution} |
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# Ratings |
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{rating_markdown} |
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""" |
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files_out = [proj_dir / file for file in files if (proj_dir / file).exists()] |
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return w_markdown, df_out, fig, markdown_out, files_out |
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description = """ |
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# FSRS4Anki Optimizer App - v3.13.0 |
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Based on the [tutorial](https://medium.com/@JarrettYe/how-to-use-the-next-generation-spaced-repetition-algorithm-fsrs-on-anki-5a591ca562e2) |
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of [Jarrett Ye](https://github.com/L-M-Sherlock). This application can give you personalized anki parameters without having to code. |
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Read the `Instructions` if its your first time using the app. |
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""" |
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with gr.Blocks() as demo: |
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with gr.Tab("FSRS4Anki Optimizer"): |
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with gr.Box(): |
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gr.Markdown(description) |
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with gr.Box(): |
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with gr.Row(): |
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with gr.Column(): |
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file = gr.File(label='Review Logs (Step 1)') |
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fast_mode_in = gr.Checkbox(value=False, label="Fast Mode (Will just return the optimized weights)") |
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with gr.Column(): |
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next_day_starts_at = gr.Number(value=4, |
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label="Next Day Starts at (Step 2)", |
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precision=0) |
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timezone = gr.Dropdown(label="Timezone (Step 3.1)", choices=pytz.all_timezones) |
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with gr.Accordion(label="Advanced Settings (Step 3.2)", open=False): |
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requestRetention = gr.Number(value=.9, label="Desired Retention: Recommended to set between 0.8 0.9") |
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revlog_start_date = gr.Textbox(value="2006-10-05", |
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label="Revlog Start Date: Optimize review logs after this date.") |
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with gr.Row(): |
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btn_plot = gr.Button('Optimize your Anki!') |
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with gr.Row(): |
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w_output = gr.Markdown() |
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with gr.Tab("Instructions"): |
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with gr.Box(): |
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gr.Markdown(instructions_markdown) |
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with gr.Tab("Analysis"): |
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with gr.Row(): |
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markdown_output = gr.Markdown() |
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with gr.Column(): |
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df_output = gr.DataFrame() |
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plot_output = gr.Plot() |
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files_output = gr.Files(label="Analysis Files") |
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with gr.Tab("FAQ"): |
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gr.Markdown(faq_markdown) |
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btn_plot.click(anki_optimizer, |
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inputs=[file, timezone, next_day_starts_at, revlog_start_date, requestRetention, fast_mode_in], |
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outputs=[w_output, df_output, plot_output, markdown_output, files_output]) |
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if __name__ == '__main__': |
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demo.queue().launch(show_error=True) |
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