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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 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, 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 - v3.10.1
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)
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