README.md

metadata

title: >-
  Book of Souls - Search your name+day (journal) oracle with ELS over Torah,
  Bible, Quran, Rigveda, Tripitaka
emoji: 📊
colorFrom: green
colorTo: pink
sdk: gradio
sdk_version: 4.39.0
app_file: app.py
pinned: false

This application searches for equidistant letter sequences (ELS) in the Torah, Bible, Quran, and Rigveda. It also integrates a network search functionality to find related phrases based on gematria.

Inputs:

• Target Language for Translation: The language to translate the results into.
• Date to investigate (optional): A date to include in the gematria calculation.
• Language of the person/topic (optional) (Date Word Language): The language to use for converting the date to words.
• Name and/or Topic (required): The text to calculate the gematria for.
• Jump Width (Steps) (optional) for ELS: The step size for the ELS search.
• Round (1) / Round (2) (optional): The number of rounds for the ELS search (positive or negative).
• Include Torah / Include Bible / Include Quran / Include Rigveda: Checkboxes to select which texts to search.
• Strip Spaces from Books / Strip Text in Braces from Books / Strip Diacritics from Books: Options for text preprocessing.

Outputs:

• ELS Results: A dataframe containing the ELS search results.
• Most Frequent Phrase in Network Search: The most frequent phrase found in the network search.
• JSON Output: A JSON representation of the search results.

How to Use:

1. Enter the name or topic you want to investigate.
2. Optionally, select a date and the language for its representation.
3. Set the jump width (steps) and rounds for the ELS search.
4. Choose which texts to include in the search.
5. Configure text preprocessing options as needed.
6. Click "Search with ELS".
7. The results will be displayed in the output sections. You can copy the JSON output using the provided button.

Network Search:

The network search functionality uses the calculated gematria of the ELS results to search a database for phrases with the same gematria. It displays the most frequent matching phrase. If no exact match is found, it attempts to find the closest match based on similarity and word count difference.