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Text Simplification Application

This project is a text simplification application that takes eBooks from the Gutenberg library as input, processes them to simplify complex sentences and vocabulary, and outputs the simplified text.

Overview

The Text Simplification Application aims to make reading complex texts more accessible by simplifying sentence structure and vocabulary. The app fetches eBooks from the Gutenberg library, cleans the text, segments it, applies AI-based text simplification, and generates simplified output.

Key Modules:

  1. Data Ingestion: Fetch and parse eBooks from Gutenberg API.
  2. Preprocessing: Clean and segment raw text.
  3. Text Simplification: Apply AI models to simplify text.
  4. Post-Processing: Format the simplified text for readability.
  5. Output Generation: Export simplified eBooks in different formats.

Installation

  1. Clone the repository: 
    git clone https://gitlab.rz.hft-stuttgart.de/vector-software-project/text-simplification.git
  2. Set up a Python virtual environment: 
    python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  3. Install the required dependencies: 
    pip install -r requirements.txt

Usage

  1. Run the Application: To run the text simplification pipeline on a Gutenberg eBook: 
    python main.py

Access it on http://127.0.0.1:5000

  1. Database Setup

  • Create .env file in the same directory as the main.py

    # .env file
# Database configuration
DB_USERNAME=<<YOUR_USERNAME>>
DB_PASSWORD=<<YOUR_PASSWORD>>
DB_HOST=localhost
DB_NAME=gutenberg_db

    .env file for running in docker

    DB_USERNAME=root DB_PASSWORD=root DB_HOST='host.docker.internal' # 'docker.for.mac.host.internal' for mac DB_NAME=test

  • Windows

  • Creating the local db

    create database gutenberg_db;
show databases;

  • Docker Command for creating Image and Running the container

    • Pre-requirements: Must have Docker Installed and Open Docker since it starts Docker Demon
    1. docker build -t gpt-neo-flask-api . (In place of gpt-neo-flask-api you can keep any <IMAGE_NAME>)
    2. docker run --add-host host.docker.internal:host-gateway -p 5000:5000 <IMAGE_NAME>
  1. Running Application using Docker Compose

Start and Stop the Application

You do NOT need to install Python or setup any databases on your local machine. Make sure that Docker have been installed and started

  • Run the following command to start the application:

    docker-compose up

    This command builds the services and starts the containers.

  • To stop the application and remove the containers, run:

    docker-compose down

Contributing

We welcome contributions! Please follow these steps:

  1. Clone the repository.
  2. Create a new feature branch (git checkout -b feature-name).
  3. Commit your changes (git commit -m "Add feature").
  4. Push to the branch (git push origin feature-name).
  5. Open a Pull Request.

About

Simplifies texts from open public libraries like Gutenberg

Topics

gutenberghuggingllmrest-apitext-simplificationtransformer

Resources

Readme
Activity

Stars

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Watchers

0 watching

Forks

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Text Simplification Application
This project is a text simplification application that takes eBooks from the Gutenberg library as input, processes them to simplify complex sentences and vocabulary, and outputs the simplified text.
Overview
The Text Simplification Application aims to make reading complex texts more accessible by simplifying sentence structure and vocabulary. The app fetches eBooks from the Gutenberg library, cleans the text, segments it, applies AI-based text simplification, and generates simplified output.
Key Modules:
  1. Data Ingestion: Fetch and parse eBooks from Gutenberg API.
  2. Preprocessing: Clean and segment raw text.
  3. Text Simplification: Apply AI models to simplify text.
  4. Post-Processing: Format the simplified text for readability.
  5. Output Generation: Export simplified eBooks in different formats.
Installation
  1. Clone the repository:

    git clone https://gitlab.rz.hft-stuttgart.de/vector-software-project/text-simplification.git
  2. Set up a Python virtual environment:

    python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  3. Install the required dependencies:

    pip install -r requirements.txt
Usage
  1. Run the Application:
To run the text simplification pipeline on a Gutenberg eBook:

    python main.py
Access it on http://127.0.0.1:5000
  1. Database Setup
  • Create .env file in the same directory as the main.py
    # .env file
# Database configuration
DB_USERNAME=<<YOUR_USERNAME>>
DB_PASSWORD=<<YOUR_PASSWORD>>
DB_HOST=localhost
DB_NAME=gutenberg_db

    .env file for running in docker
    DB_USERNAME=root
DB_PASSWORD=root
DB_HOST='host.docker.internal' # 'docker.for.mac.host.internal' for mac
DB_NAME=test
  • Windows
  • Creating the local db
    create database gutenberg_db;
show databases;

  • Docker Command for creating Image and Running the container
    • Pre-requirements: Must have Docker Installed and Open Docker since it starts Docker Demon
    1. docker build -t gpt-neo-flask-api . (In place of gpt-neo-flask-api you can keep any <IMAGE_NAME>)
    2. docker run --add-host host.docker.internal:host-gateway -p 5000:5000 <IMAGE_NAME>
  1. Running Application using Docker Compose
Start and Stop the Application
You do NOT need to install Python or setup any databases on your local machine. Make sure that Docker have been installed and started
  • Run the following command to start the application:
    docker-compose up
    This command builds the services and starts the containers.
  • To stop the application and remove the containers, run:
    docker-compose down
Contributing
We welcome contributions! Please follow these steps:
  1. Clone the repository.
  2. Create a new feature branch (git checkout -b feature-name).
  3. Commit your changes (git commit -m "Add feature").
  4. Push to the branch (git push origin feature-name).
  5. Open a Pull Request.
About
Simplifies texts from open public libraries like Gutenberg
Topics
gutenberghuggingllmrest-apitext-simplificationtransformer
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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Packages
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Text Simplification Application
This project is a text simplification application that takes eBooks from the Gutenberg library as input, processes them to simplify complex sentences and vocabulary, and outputs the simplified text.
Overview
The Text Simplification Application aims to make reading complex texts more accessible by simplifying sentence structure and vocabulary. The app fetches eBooks from the Gutenberg library, cleans the text, segments it, applies AI-based text simplification, and generates simplified output.
Key Modules:
  1. Data Ingestion: Fetch and parse eBooks from Gutenberg API.
  2. Preprocessing: Clean and segment raw text.
  3. Text Simplification: Apply AI models to simplify text.
  4. Post-Processing: Format the simplified text for readability.
  5. Output Generation: Export simplified eBooks in different formats.
Installation
  1. Clone the repository:

    git clone https://gitlab.rz.hft-stuttgart.de/vector-software-project/text-simplification.git
  2. Set up a Python virtual environment:

    python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  3. Install the required dependencies:

    pip install -r requirements.txt
Usage
  1. Run the Application:
To run the text simplification pipeline on a Gutenberg eBook:

    python main.py
Access it on http://127.0.0.1:5000
  1. Database Setup
  • Create .env file in the same directory as the main.py
    # .env file
# Database configuration
DB_USERNAME=<<YOUR_USERNAME>>
DB_PASSWORD=<<YOUR_PASSWORD>>
DB_HOST=localhost
DB_NAME=gutenberg_db

    .env file for running in docker
    DB_USERNAME=root
DB_PASSWORD=root
DB_HOST='host.docker.internal' # 'docker.for.mac.host.internal' for mac
DB_NAME=test
  • Windows
  • Creating the local db
    create database gutenberg_db;
show databases;

  • Docker Command for creating Image and Running the container
    • Pre-requirements: Must have Docker Installed and Open Docker since it starts Docker Demon
    1. docker build -t gpt-neo-flask-api . (In place of gpt-neo-flask-api you can keep any <IMAGE_NAME>)
    2. docker run --add-host host.docker.internal:host-gateway -p 5000:5000 <IMAGE_NAME>
  1. Running Application using Docker Compose
Start and Stop the Application
You do NOT need to install Python or setup any databases on your local machine. Make sure that Docker have been installed and started
  • Run the following command to start the application:
    docker-compose up
    This command builds the services and starts the containers.
  • To stop the application and remove the containers, run:
    docker-compose down
Contributing
We welcome contributions! Please follow these steps:
  1. Clone the repository.
  2. Create a new feature branch (git checkout -b feature-name).
  3. Commit your changes (git commit -m "Add feature").
  4. Push to the branch (git push origin feature-name).
  5. Open a Pull Request.
About
Simplifies texts from open public libraries like Gutenberg
Topics
gutenberghuggingllmrest-apitext-simplificationtransformer
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Text Simplification Application
This project is a text simplification application that takes eBooks from the Gutenberg library as input, processes them to simplify complex sentences and vocabulary, and outputs the simplified text.
Overview
The Text Simplification Application aims to make reading complex texts more accessible by simplifying sentence structure and vocabulary. The app fetches eBooks from the Gutenberg library, cleans the text, segments it, applies AI-based text simplification, and generates simplified output.
Key Modules:
  1. Data Ingestion: Fetch and parse eBooks from Gutenberg API.
  2. Preprocessing: Clean and segment raw text.
  3. Text Simplification: Apply AI models to simplify text.
  4. Post-Processing: Format the simplified text for readability.
  5. Output Generation: Export simplified eBooks in different formats.
Installation
  1. Clone the repository:

    git clone https://gitlab.rz.hft-stuttgart.de/vector-software-project/text-simplification.git
  2. Set up a Python virtual environment:

    python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  3. Install the required dependencies:

    pip install -r requirements.txt
Usage
  1. Run the Application:
To run the text simplification pipeline on a Gutenberg eBook:

    python main.py
Access it on http://127.0.0.1:5000
  1. Database Setup
  • Create .env file in the same directory as the main.py
    # .env file
# Database configuration
DB_USERNAME=<<YOUR_USERNAME>>
DB_PASSWORD=<<YOUR_PASSWORD>>
DB_HOST=localhost
DB_NAME=gutenberg_db

    .env file for running in docker
    DB_USERNAME=root
DB_PASSWORD=root
DB_HOST='host.docker.internal' # 'docker.for.mac.host.internal' for mac
DB_NAME=test
  • Windows
  • Creating the local db
    create database gutenberg_db;
show databases;

  • Docker Command for creating Image and Running the container
    • Pre-requirements: Must have Docker Installed and Open Docker since it starts Docker Demon
    1. docker build -t gpt-neo-flask-api . (In place of gpt-neo-flask-api you can keep any <IMAGE_NAME>)
    2. docker run --add-host host.docker.internal:host-gateway -p 5000:5000 <IMAGE_NAME>
  1. Running Application using Docker Compose
Start and Stop the Application
You do NOT need to install Python or setup any databases on your local machine. Make sure that Docker have been installed and started
  • Run the following command to start the application:
    docker-compose up
    This command builds the services and starts the containers.
  • To stop the application and remove the containers, run:
    docker-compose down
Contributing
We welcome contributions! Please follow these steps:
  1. Clone the repository.
  2. Create a new feature branch (git checkout -b feature-name).
  3. Commit your changes (git commit -m "Add feature").
  4. Push to the branch (git push origin feature-name).
  5. Open a Pull Request.
About
Simplifies texts from open public libraries like Gutenberg
Topics
gutenberghuggingllmrest-apitext-simplificationtransformer
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Text Simplification Application
This project is a text simplification application that takes eBooks from the Gutenberg library as input, processes them to simplify complex sentences and vocabulary, and outputs the simplified text.
Overview
The Text Simplification Application aims to make reading complex texts more accessible by simplifying sentence structure and vocabulary. The app fetches eBooks from the Gutenberg library, cleans the text, segments it, applies AI-based text simplification, and generates simplified output.
Key Modules:
  1. Data Ingestion: Fetch and parse eBooks from Gutenberg API.
  2. Preprocessing: Clean and segment raw text.
  3. Text Simplification: Apply AI models to simplify text.
  4. Post-Processing: Format the simplified text for readability.
  5. Output Generation: Export simplified eBooks in different formats.
Installation
  1. Clone the repository:

    git clone https://gitlab.rz.hft-stuttgart.de/vector-software-project/text-simplification.git
  2. Set up a Python virtual environment:

    python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  3. Install the required dependencies:

    pip install -r requirements.txt
Usage
  1. Run the Application:
To run the text simplification pipeline on a Gutenberg eBook:

    python main.py
Access it on http://127.0.0.1:5000
  1. Database Setup
  • Create .env file in the same directory as the main.py
    # .env file
# Database configuration
DB_USERNAME=<<YOUR_USERNAME>>
DB_PASSWORD=<<YOUR_PASSWORD>>
DB_HOST=localhost
DB_NAME=gutenberg_db

    .env file for running in docker
    DB_USERNAME=root
DB_PASSWORD=root
DB_HOST='host.docker.internal' # 'docker.for.mac.host.internal' for mac
DB_NAME=test
  • Windows
  • Creating the local db
    create database gutenberg_db;
show databases;

  • Docker Command for creating Image and Running the container
    • Pre-requirements: Must have Docker Installed and Open Docker since it starts Docker Demon
    1. docker build -t gpt-neo-flask-api . (In place of gpt-neo-flask-api you can keep any <IMAGE_NAME>)
    2. docker run --add-host host.docker.internal:host-gateway -p 5000:5000 <IMAGE_NAME>
  1. Running Application using Docker Compose
Start and Stop the Application
You do NOT need to install Python or setup any databases on your local machine. Make sure that Docker have been installed and started
  • Run the following command to start the application:
    docker-compose up
    This command builds the services and starts the containers.
  • To stop the application and remove the containers, run:
    docker-compose down
Contributing
We welcome contributions! Please follow these steps:
  1. Clone the repository.
  2. Create a new feature branch (git checkout -b feature-name).
  3. Commit your changes (git commit -m "Add feature").
  4. Push to the branch (git push origin feature-name).
  5. Open a Pull Request.
About
Simplifies texts from open public libraries like Gutenberg
Topics
gutenberghuggingllmrest-apitext-simplificationtransformer
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Text Simplification Application
This project is a text simplification application that takes eBooks from the Gutenberg library as input, processes them to simplify complex sentences and vocabulary, and outputs the simplified text.
Overview
The Text Simplification Application aims to make reading complex texts more accessible by simplifying sentence structure and vocabulary. The app fetches eBooks from the Gutenberg library, cleans the text, segments it, applies AI-based text simplification, and generates simplified output.
Key Modules:
  1. Data Ingestion: Fetch and parse eBooks from Gutenberg API.
  2. Preprocessing: Clean and segment raw text.
  3. Text Simplification: Apply AI models to simplify text.
  4. Post-Processing: Format the simplified text for readability.
  5. Output Generation: Export simplified eBooks in different formats.
Installation
  1. Clone the repository:

    git clone https://gitlab.rz.hft-stuttgart.de/vector-software-project/text-simplification.git
  2. Set up a Python virtual environment:

    python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  3. Install the required dependencies:

    pip install -r requirements.txt
Usage
  1. Run the Application:
To run the text simplification pipeline on a Gutenberg eBook:

    python main.py
Access it on http://127.0.0.1:5000
  1. Database Setup
  • Create .env file in the same directory as the main.py
    # .env file
# Database configuration
DB_USERNAME=<<YOUR_USERNAME>>
DB_PASSWORD=<<YOUR_PASSWORD>>
DB_HOST=localhost
DB_NAME=gutenberg_db

    .env file for running in docker
    DB_USERNAME=root
DB_PASSWORD=root
DB_HOST='host.docker.internal' # 'docker.for.mac.host.internal' for mac
DB_NAME=test
  • Windows
  • Creating the local db
    create database gutenberg_db;
show databases;

  • Docker Command for creating Image and Running the container
    • Pre-requirements: Must have Docker Installed and Open Docker since it starts Docker Demon
    1. docker build -t gpt-neo-flask-api . (In place of gpt-neo-flask-api you can keep any <IMAGE_NAME>)
    2. docker run --add-host host.docker.internal:host-gateway -p 5000:5000 <IMAGE_NAME>
  1. Running Application using Docker Compose
Start and Stop the Application
You do NOT need to install Python or setup any databases on your local machine. Make sure that Docker have been installed and started
  • Run the following command to start the application:
    docker-compose up
    This command builds the services and starts the containers.
  • To stop the application and remove the containers, run:
    docker-compose down
Contributing
We welcome contributions! Please follow these steps:
  1. Clone the repository.
  2. Create a new feature branch (git checkout -b feature-name).
  3. Commit your changes (git commit -m "Add feature").
  4. Push to the branch (git push origin feature-name).
  5. Open a Pull Request.
About
Simplifies texts from open public libraries like Gutenberg
Topics
gutenberghuggingllmrest-apitext-simplificationtransformer
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Text Simplification Application
This project is a text simplification application that takes eBooks from the Gutenberg library as input, processes them to simplify complex sentences and vocabulary, and outputs the simplified text.
Overview
The Text Simplification Application aims to make reading complex texts more accessible by simplifying sentence structure and vocabulary. The app fetches eBooks from the Gutenberg library, cleans the text, segments it, applies AI-based text simplification, and generates simplified output.
Key Modules:
  1. Data Ingestion: Fetch and parse eBooks from Gutenberg API.
  2. Preprocessing: Clean and segment raw text.
  3. Text Simplification: Apply AI models to simplify text.
  4. Post-Processing: Format the simplified text for readability.
  5. Output Generation: Export simplified eBooks in different formats.
Installation
  1. Clone the repository:

    git clone https://gitlab.rz.hft-stuttgart.de/vector-software-project/text-simplification.git
  2. Set up a Python virtual environment:

    python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  3. Install the required dependencies:

    pip install -r requirements.txt
Usage
  1. Run the Application:
To run the text simplification pipeline on a Gutenberg eBook:

    python main.py
Access it on http://127.0.0.1:5000
  1. Database Setup
  • Create .env file in the same directory as the main.py
    # .env file
# Database configuration
DB_USERNAME=<<YOUR_USERNAME>>
DB_PASSWORD=<<YOUR_PASSWORD>>
DB_HOST=localhost
DB_NAME=gutenberg_db

    .env file for running in docker
    DB_USERNAME=root
DB_PASSWORD=root
DB_HOST='host.docker.internal' # 'docker.for.mac.host.internal' for mac
DB_NAME=test
  • Windows
  • Creating the local db
    create database gutenberg_db;
show databases;

  • Docker Command for creating Image and Running the container
    • Pre-requirements: Must have Docker Installed and Open Docker since it starts Docker Demon
    1. docker build -t gpt-neo-flask-api . (In place of gpt-neo-flask-api you can keep any <IMAGE_NAME>)
    2. docker run --add-host host.docker.internal:host-gateway -p 5000:5000 <IMAGE_NAME>
  1. Running Application using Docker Compose
Start and Stop the Application
You do NOT need to install Python or setup any databases on your local machine. Make sure that Docker have been installed and started
  • Run the following command to start the application:
    docker-compose up
    This command builds the services and starts the containers.
  • To stop the application and remove the containers, run:
    docker-compose down
Contributing
We welcome contributions! Please follow these steps:
  1. Clone the repository.
  2. Create a new feature branch (git checkout -b feature-name).
  3. Commit your changes (git commit -m "Add feature").
  4. Push to the branch (git push origin feature-name).
  5. Open a Pull Request.
About
Simplifies texts from open public libraries like Gutenberg
Topics
gutenberghuggingllmrest-apitext-simplificationtransformer
Resources
Readme
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0 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
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Text Simplification Application
This project is a text simplification application that takes eBooks from the Gutenberg library as input, processes them to simplify complex sentences and vocabulary, and outputs the simplified text.
Overview
The Text Simplification Application aims to make reading complex texts more accessible by simplifying sentence structure and vocabulary. The app fetches eBooks from the Gutenberg library, cleans the text, segments it, applies AI-based text simplification, and generates simplified output.
Key Modules:
  1. Data Ingestion: Fetch and parse eBooks from Gutenberg API.
  2. Preprocessing: Clean and segment raw text.
  3. Text Simplification: Apply AI models to simplify text.
  4. Post-Processing: Format the simplified text for readability.
  5. Output Generation: Export simplified eBooks in different formats.
Installation
  1. Clone the repository:

    git clone https://gitlab.rz.hft-stuttgart.de/vector-software-project/text-simplification.git
  2. Set up a Python virtual environment:

    python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  3. Install the required dependencies:

    pip install -r requirements.txt
Usage
  1. Run the Application:
To run the text simplification pipeline on a Gutenberg eBook:

    python main.py
Access it on http://127.0.0.1:5000
  1. Database Setup
  • Create .env file in the same directory as the main.py
    # .env file
# Database configuration
DB_USERNAME=<<YOUR_USERNAME>>
DB_PASSWORD=<<YOUR_PASSWORD>>
DB_HOST=localhost
DB_NAME=gutenberg_db

    .env file for running in docker
    DB_USERNAME=root
DB_PASSWORD=root
DB_HOST='host.docker.internal' # 'docker.for.mac.host.internal' for mac
DB_NAME=test
  • Windows
  • Creating the local db
    create database gutenberg_db;
show databases;

  • Docker Command for creating Image and Running the container
    • Pre-requirements: Must have Docker Installed and Open Docker since it starts Docker Demon
    1. docker build -t gpt-neo-flask-api . (In place of gpt-neo-flask-api you can keep any <IMAGE_NAME>)
    2. docker run --add-host host.docker.internal:host-gateway -p 5000:5000 <IMAGE_NAME>
  1. Running Application using Docker Compose
Start and Stop the Application
You do NOT need to install Python or setup any databases on your local machine. Make sure that Docker have been installed and started
  • Run the following command to start the application:
    docker-compose up
    This command builds the services and starts the containers.
  • To stop the application and remove the containers, run:
    docker-compose down
Contributing
We welcome contributions! Please follow these steps:
  1. Clone the repository.
  2. Create a new feature branch (git checkout -b feature-name).
  3. Commit your changes (git commit -m "Add feature").
  4. Push to the branch (git push origin feature-name).
  5. Open a Pull Request.
About
Simplifies texts from open public libraries like Gutenberg
Topics
gutenberghuggingllmrest-apitext-simplificationtransformer
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages