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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};`"""
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”
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
# 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
""")
with gr.Tab("Analysis"):
with gr.Row():
markdown_output = gr.Markdown()
df_output = gr.DataFrame()
with gr.Row():
plot_output = gr.Plot()
with gr.Row():
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)
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