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 memory_states import get_my_memory_states from plot import make_plot def anki_optimizer(file, timezone, next_day_starts_at, revlog_start_date, requestRetention, progress=gr.Progress(track_tqdm=True)): now = datetime.now() prefix = now.strftime(f'%Y_%m_%d_%H_%M_%S') proj_dir = extract(file, prefix) type_sequence, df_out = create_time_series_features(revlog_start_date, timezone, next_day_starts_at, proj_dir) w, dataset = train_model(proj_dir) 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, 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} """ w_markdown = f""" # These are the weights for step 5 `var w = {w};` Check out the Analysis tab for more detailed information.""" files = ['prediction.tsv', 'revlog.csv', 'revlog_history.tsv', 'stability_for_analysis.tsv', 'expected_repetitions.csv'] files_out = [proj_dir / file for file in files] cleanup(proj_dir, files) return w_markdown, df_out, fig, markdown_out, files_out with gr.Blocks() as demo: with gr.Tab("FSRS4Anki Optimizer"): with gr.Box(): gr.Markdown(""" 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) Check out the instructions on the next tab. """) with gr.Box(): with gr.Row(): file = gr.File(label='Review Logs') timezone = gr.Dropdown(label="Choose your timezone", choices=pytz.all_timezones) with gr.Row(): next_day_starts_at = gr.Number(value=4, label="Replace it with your Anki's setting in Preferences -> Scheduling.", precision=0) with gr.Accordion(label="Advanced Settings", open=False): requestRetention = gr.Number(value=.9, label="Recommended to set between 0.8 0.9") with gr.Row(): revlog_start_date = gr.Textbox(value="2006-10-05", label="Replace it if you don't want the optimizer to use the review logs before a specific 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(""" # How to get personalized Anki parameters If you have been using Anki for some time and have accumulated a lot of review logs, you can try this FSRS4Anki optimizer app to generate parameters for you. This is based on the amazing work of [Jarrett Ye](https://github.com/L-M-Sherlock) # Step 1 - Get the review logs to upload 1. Click the gear icon to the right of a deck’s name 2. Export 3. Check “Include scheduling information” and “Support older Anki versions” ![](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*W3Nnfarki2z7Ukyom4kMuw.png) 4. Export and upload that file to the app # Step 2 - Get the `next_day_starts_at` parameter 1. Open preferences 2. Copy the next day starts at value and paste it in the app ![](https://miro.medium.com/v2/resize:fit:1072/format:webp/1*qAUb6ry8UxFeCsjnKLXvsQ.png) # Step 3 - Fill in the rest of the settings # Step 4 - Click run # Step 5 - Replace the default parameters in FSRS4Anki with the optimized parameters ![](https://miro.medium.com/v2/resize:fit:1252/format:webp/1*NM4CR-n7nDk3nQN1Bi30EA.png) """) 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") btn_plot.click(anki_optimizer, inputs=[file, timezone, next_day_starts_at, revlog_start_date, requestRetention], outputs=[w_output, df_output, plot_output, markdown_output, files_output]) demo.queue().launch(debug=True, show_error=True) # demo.queue().launch(debug=True)