https://github.com/brianlesko/epub_to_text

Extract text from an epub file for LLM training, similarity search embedding, textual data analysis, or text to speech. Created for learning purposes by a professional robotics engineer. Located in Cleveland and studying the world
https://github.com/brianlesko/epub_to_text

Last synced: about 20 hours ago
JSON representation

Extract text from an epub file for LLM training, similarity search embedding, textual data analysis, or text to speech. Created for learning purposes by a professional robotics engineer. Located in Cleveland and studying the world

• Host: GitHub
• URL: https://github.com/brianlesko/epub_to_text
• Owner: BrianLesko
• License: mit
• Created: 2023-11-25T17:13:29.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-01-17T23:15:58.000Z (10 months ago)
• Last Synced: 2024-01-18T06:06:48.309Z (10 months ago)
• Language: Python
• Size: 7.88 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

Awesome Lists containing this project

README

        

# Text Extraction from an Epub file

This code implements text parsing from an Electronic Publishing format. This implementation is written in [Pure Python](https://github.com/BrianLesko/epub_to_text/blob/main/app.py) - created for Learning Purposes.

 



 

## Dependencies

This code uses the following libraries:

- `streamlit`: for building the user interface.

- `os`: for interacting with the OS, used for file and directory operations.

- `io`: for handling the stream operations of files.

- `zipfile`: for reading and writing ZIP archive files.

- `bs4 (BeautifulSoup)`: for parsing HTML documents, used for extracting text.

 

## Usage

Run the following commands:

```

pip install --upgrade streamlit zipfile bs4

streamlit run https://raw.githubusercontent.com/BrianLesko/epub_to_text/main/app.py?token=GHSAT0AAAAAACEZSXSIC7IMS2W5EVZKLS7SZLCFV4Q

```

This will start the local Streamlit server, and you can access the chatbot by opening a web browser and navigating to `http://localhost:8501`.

 

## How it Works

The app as follows:

1. The user enters some text in the input field.

2. OpenAI is used to embed the text into a vector of numbers.

3. Scikit-learn is used to compare the embedding to an embedding created from a description of soccer and a description of math.

4. The app displays the similarity on a Plotly chart.

5. The user can enter more text to see more similarities.

 

## Repository Structure

```

repository/

├── app.py # the code and UI integrated together live here

├── customize_gui # class for adding gui elements

├── requirements.txt # the python packages needed to run locally

├── .gitignore # includes the local virtual environment named my_env

├── .streamlit/

│   └── config.toml # theme info for the UI

└── docs/

    └── preview.png # preview photo for Github

```

 

## Topics 

```

Python | Streamlit | Git | Low Code UI

text scraping | HTML parser | textual data | books

Self taught coding | Mechanical engineer | Robotics engineer

```

 



 



╭━━╮╭━━━┳━━┳━━━┳━╮╱╭╮        ╭╮╱╱╭━━━┳━━━┳╮╭━┳━━━╮

┃╭╮┃┃╭━╮┣┫┣┫╭━╮┃┃╰╮┃┃        ┃┃╱╱┃╭━━┫╭━╮┃┃┃╭┫╭━╮┃

┃╰╯╰┫╰━╯┃┃┃┃┃╱┃┃╭╮╰╯┃        ┃┃╱╱┃╰━━┫╰━━┫╰╯╯┃┃╱┃┃

┃╭━╮┃╭╮╭╯┃┃┃╰━╯┃┃╰╮┃┃        ┃┃╱╭┫╭━━┻━━╮┃╭╮┃┃┃╱┃┃

┃╰━╯┃┃┃╰┳┫┣┫╭━╮┃┃╱┃┃┃        ┃╰━╯┃╰━━┫╰━╯┃┃┃╰┫╰━╯┃

╰━━━┻╯╰━┻━━┻╯╱╰┻╯╱╰━╯        ╰━━━┻━━━┻━━━┻╯╰━┻━━━╯

  

 

                          

follow all of these or i will kick you