File size: 5,981 Bytes
651b002 4246b36 651b002 4246b36 651b002 4246b36 651b002 4246b36 651b002 4246b36 651b002 4246b36 651b002 4246b36 7405c46 1f530a5 7405c46 651b002 7405c46 651b002 4246b36 7405c46 651b002 61d242b 651b002 61d242b 7405c46 651b002 7405c46 651b002 7405c46 651b002 7405c46 651b002 7405c46 651b002 a79f7cd 651b002 4246b36 651b002 7405c46 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
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 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, 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, 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
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 (No analysis)")
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="Recommended to set between 0.8 0.9")
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 FSRS 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). My goal is to further
democratize this technology so anyone can use it!
# 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
1. Your `Time Zone`
2. `Advanced settings` if you know what you are doing
# Step 4 - Click `Optimize your Anki!`
1. After it runs copy `var w = [...]`
2. Check out the analysis tab for more info
# Step 5 - Update FSRS4Anki with the optimized parameters
""")
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, fast_mode_in],
outputs=[w_output, df_output, plot_output, markdown_output, files_output])
if __name__ == '__main__':
demo.queue().launch(show_error=True)
|