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OpenCensor AI Service - My Final Year Project

This is my school project for final year in Software Engineering.

What is this?

OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.

What it can do

  • Runs on GPUs for quick checks.
  • Grows or shrinks as needed, no wait to start.
  • Checks many texts at the same time.
  • You can change the sure level and max text length.
  • Made just for Hebrew text.

How it works

This AI service (backend):

  • Python code with FastAPI that handles requests.
  • Runs on Modal servers with GPU help.
  • Uses OpenCensor-Hebrew model from Hugging Face.
  • Based on AlephBERT for Hebrew.
  • 95% right, trained on over 3,000 Hebrew texts.
  • Gives score from 0 (good) to 1 (bad).

Front-end API:

How to install

  1. Install Modal CLI: pip install modal
  2. Set up Modal account: modal setup

How to use the API

POST /predict

Check one text.

Ask:

{
"text": "זה טקסט כלשהו :)",
"threshold": 0.5,
"max_length": 256
}

Answer:

{
"prob": 0.05,
"label": 0
}

POST /batch

Check many texts (max 256).

Ask:

{
"texts": ["זה טקסט כלשהו 1", "זה טקסט כלשהו 2"],
"threshold": 0.5,
"max_length": 256
}

Answer:

[
{
"prob": 0.05,
"label": 0
},
{
"prob": 0.85,
"label": 1
}
]

Project files

├── main.py # Main code with FastAPI and model
├── README.md # This file

What you need

  • Modal: For running on servers with GPUs.
  • FastAPI: For the web part.
  • PyTorch: For the AI work.
  • Transformers: From Hugging Face for models.

How to put it online

Works on Modal servers with:

  • Python 3 or more.
  • Pip for needed files.

Steps:

  1. Upload files to your work space.
  2. Set settings if needed (like model ID).
  3. Run modal deploy main.py.

Other related stuff

License

This is a school project for final year in Software Engineering.

Give Credit: If you use this project or part of it in public, business, or school work, you must give credit to the maker. Include:

How to Use:

  • Free for school and research.
  • Free to change and share if you give credit.
  • Okay for business if you give credit.
  • No promise it works, no blame if problems.

Example Credit:

Based on OpenCensor AI Service by LikoKiko
Original repository: https://github.com/LikoKIko/OpenCensor-Modal

About

OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.

Resources

Readme
Activity

Stars

1 star

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, '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" + '

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OpenCensor AI Service - My Final Year Project
This is my school project for final year in Software Engineering.
What is this?
OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.
What it can do
  • Runs on GPUs for quick checks.
  • Grows or shrinks as needed, no wait to start.
  • Checks many texts at the same time.
  • You can change the sure level and max text length.
  • Made just for Hebrew text.
How it works
This AI service (backend):
  • Python code with FastAPI that handles requests.
  • Runs on Modal servers with GPU help.
  • Uses OpenCensor-Hebrew model from Hugging Face.
  • Based on AlephBERT for Hebrew.
  • 95% right, trained on over 3,000 Hebrew texts.
  • Gives score from 0 (good) to 1 (bad).
Front-end API:
How to install
  1. Install Modal CLI: pip install modal
  2. Set up Modal account: modal setup
How to use the API
POST /predict
Check one text.
Ask:
{
"text": "זה טקסט כלשהו :)",
"threshold": 0.5,
"max_length": 256
}
Answer:
{
"prob": 0.05,
"label": 0
}
POST /batch
Check many texts (max 256).
Ask:
{
"texts": ["זה טקסט כלשהו 1", "זה טקסט כלשהו 2"],
"threshold": 0.5,
"max_length": 256
}
Answer:
[
{
"prob": 0.05,
"label": 0
},
{
"prob": 0.85,
"label": 1
}
]
Project files
├── main.py # Main code with FastAPI and model
├── README.md # This file

What you need
  • Modal: For running on servers with GPUs.
  • FastAPI: For the web part.
  • PyTorch: For the AI work.
  • Transformers: From Hugging Face for models.
How to put it online
Works on Modal servers with:
  • Python 3 or more.
  • Pip for needed files.
Steps:
  1. Upload files to your work space.
  2. Set settings if needed (like model ID).
  3. Run modal deploy main.py.
Other related stuff
License
This is a school project for final year in Software Engineering.
Give Credit: If you use this project or part of it in public, business, or school work, you must give credit to the maker. Include:
How to Use:
  • Free for school and research.
  • Free to change and share if you give credit.
  • Okay for business if you give credit.
  • No promise it works, no blame if problems.
Example Credit:
Based on OpenCensor AI Service by LikoKiko
Original repository: https://github.com/LikoKIko/OpenCensor-Modal

About
OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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('^' + ".*" + '

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OpenCensor AI Service - My Final Year Project
This is my school project for final year in Software Engineering.
What is this?
OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.
What it can do
  • Runs on GPUs for quick checks.
  • Grows or shrinks as needed, no wait to start.
  • Checks many texts at the same time.
  • You can change the sure level and max text length.
  • Made just for Hebrew text.
How it works
This AI service (backend):
  • Python code with FastAPI that handles requests.
  • Runs on Modal servers with GPU help.
  • Uses OpenCensor-Hebrew model from Hugging Face.
  • Based on AlephBERT for Hebrew.
  • 95% right, trained on over 3,000 Hebrew texts.
  • Gives score from 0 (good) to 1 (bad).
Front-end API:
How to install
  1. Install Modal CLI: pip install modal
  2. Set up Modal account: modal setup
How to use the API
POST /predict
Check one text.
Ask:
{
"text": "זה טקסט כלשהו :)",
"threshold": 0.5,
"max_length": 256
}
Answer:
{
"prob": 0.05,
"label": 0
}
POST /batch
Check many texts (max 256).
Ask:
{
"texts": ["זה טקסט כלשהו 1", "זה טקסט כלשהו 2"],
"threshold": 0.5,
"max_length": 256
}
Answer:
[
{
"prob": 0.05,
"label": 0
},
{
"prob": 0.85,
"label": 1
}
]
Project files
├── main.py # Main code with FastAPI and model
├── README.md # This file

What you need
  • Modal: For running on servers with GPUs.
  • FastAPI: For the web part.
  • PyTorch: For the AI work.
  • Transformers: From Hugging Face for models.
How to put it online
Works on Modal servers with:
  • Python 3 or more.
  • Pip for needed files.
Steps:
  1. Upload files to your work space.
  2. Set settings if needed (like model ID).
  3. Run modal deploy main.py.
Other related stuff
License
This is a school project for final year in Software Engineering.
Give Credit: If you use this project or part of it in public, business, or school work, you must give credit to the maker. Include:
How to Use:
  • Free for school and research.
  • Free to change and share if you give credit.
  • Okay for business if you give credit.
  • No promise it works, no blame if problems.
Example Credit:
Based on OpenCensor AI Service by LikoKiko
Original repository: https://github.com/LikoKIko/OpenCensor-Modal

About
OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.
Resources
Readme
Activity
Stars
1 star
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('^' + ".*" + '

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OpenCensor AI Service - My Final Year Project
This is my school project for final year in Software Engineering.
What is this?
OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.
What it can do
  • Runs on GPUs for quick checks.
  • Grows or shrinks as needed, no wait to start.
  • Checks many texts at the same time.
  • You can change the sure level and max text length.
  • Made just for Hebrew text.
How it works
This AI service (backend):
  • Python code with FastAPI that handles requests.
  • Runs on Modal servers with GPU help.
  • Uses OpenCensor-Hebrew model from Hugging Face.
  • Based on AlephBERT for Hebrew.
  • 95% right, trained on over 3,000 Hebrew texts.
  • Gives score from 0 (good) to 1 (bad).
Front-end API:
How to install
  1. Install Modal CLI: pip install modal
  2. Set up Modal account: modal setup
How to use the API
POST /predict
Check one text.
Ask:
{
"text": "זה טקסט כלשהו :)",
"threshold": 0.5,
"max_length": 256
}
Answer:
{
"prob": 0.05,
"label": 0
}
POST /batch
Check many texts (max 256).
Ask:
{
"texts": ["זה טקסט כלשהו 1", "זה טקסט כלשהו 2"],
"threshold": 0.5,
"max_length": 256
}
Answer:
[
{
"prob": 0.05,
"label": 0
},
{
"prob": 0.85,
"label": 1
}
]
Project files
├── main.py # Main code with FastAPI and model
├── README.md # This file

What you need
  • Modal: For running on servers with GPUs.
  • FastAPI: For the web part.
  • PyTorch: For the AI work.
  • Transformers: From Hugging Face for models.
How to put it online
Works on Modal servers with:
  • Python 3 or more.
  • Pip for needed files.
Steps:
  1. Upload files to your work space.
  2. Set settings if needed (like model ID).
  3. Run modal deploy main.py.
Other related stuff
License
This is a school project for final year in Software Engineering.
Give Credit: If you use this project or part of it in public, business, or school work, you must give credit to the maker. Include:
How to Use:
  • Free for school and research.
  • Free to change and share if you give credit.
  • Okay for business if you give credit.
  • No promise it works, no blame if problems.
Example Credit:
Based on OpenCensor AI Service by LikoKiko
Original repository: https://github.com/LikoKIko/OpenCensor-Modal

About
OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.
Resources
Readme
Activity
Stars
1 star
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" + '

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OpenCensor AI Service - My Final Year Project
This is my school project for final year in Software Engineering.
What is this?
OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.
What it can do
  • Runs on GPUs for quick checks.
  • Grows or shrinks as needed, no wait to start.
  • Checks many texts at the same time.
  • You can change the sure level and max text length.
  • Made just for Hebrew text.
How it works
This AI service (backend):
  • Python code with FastAPI that handles requests.
  • Runs on Modal servers with GPU help.
  • Uses OpenCensor-Hebrew model from Hugging Face.
  • Based on AlephBERT for Hebrew.
  • 95% right, trained on over 3,000 Hebrew texts.
  • Gives score from 0 (good) to 1 (bad).
Front-end API:
How to install
  1. Install Modal CLI: pip install modal
  2. Set up Modal account: modal setup
How to use the API
POST /predict
Check one text.
Ask:
{
"text": "זה טקסט כלשהו :)",
"threshold": 0.5,
"max_length": 256
}
Answer:
{
"prob": 0.05,
"label": 0
}
POST /batch
Check many texts (max 256).
Ask:
{
"texts": ["זה טקסט כלשהו 1", "זה טקסט כלשהו 2"],
"threshold": 0.5,
"max_length": 256
}
Answer:
[
{
"prob": 0.05,
"label": 0
},
{
"prob": 0.85,
"label": 1
}
]
Project files
├── main.py # Main code with FastAPI and model
├── README.md # This file

What you need
  • Modal: For running on servers with GPUs.
  • FastAPI: For the web part.
  • PyTorch: For the AI work.
  • Transformers: From Hugging Face for models.
How to put it online
Works on Modal servers with:
  • Python 3 or more.
  • Pip for needed files.
Steps:
  1. Upload files to your work space.
  2. Set settings if needed (like model ID).
  3. Run modal deploy main.py.
Other related stuff
License
This is a school project for final year in Software Engineering.
Give Credit: If you use this project or part of it in public, business, or school work, you must give credit to the maker. Include:
How to Use:
  • Free for school and research.
  • Free to change and share if you give credit.
  • Okay for business if you give credit.
  • No promise it works, no blame if problems.
Example Credit:
Based on OpenCensor AI Service by LikoKiko
Original repository: https://github.com/LikoKIko/OpenCensor-Modal

About
OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.
Resources
Readme
Activity
Stars
1 star
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('^' + ".*" + '

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OpenCensor AI Service - My Final Year Project
This is my school project for final year in Software Engineering.
What is this?
OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.
What it can do
  • Runs on GPUs for quick checks.
  • Grows or shrinks as needed, no wait to start.
  • Checks many texts at the same time.
  • You can change the sure level and max text length.
  • Made just for Hebrew text.
How it works
This AI service (backend):
  • Python code with FastAPI that handles requests.
  • Runs on Modal servers with GPU help.
  • Uses OpenCensor-Hebrew model from Hugging Face.
  • Based on AlephBERT for Hebrew.
  • 95% right, trained on over 3,000 Hebrew texts.
  • Gives score from 0 (good) to 1 (bad).
Front-end API:
How to install
  1. Install Modal CLI: pip install modal
  2. Set up Modal account: modal setup
How to use the API
POST /predict
Check one text.
Ask:
{
"text": "זה טקסט כלשהו :)",
"threshold": 0.5,
"max_length": 256
}
Answer:
{
"prob": 0.05,
"label": 0
}
POST /batch
Check many texts (max 256).
Ask:
{
"texts": ["זה טקסט כלשהו 1", "זה טקסט כלשהו 2"],
"threshold": 0.5,
"max_length": 256
}
Answer:
[
{
"prob": 0.05,
"label": 0
},
{
"prob": 0.85,
"label": 1
}
]
Project files
├── main.py # Main code with FastAPI and model
├── README.md # This file

What you need
  • Modal: For running on servers with GPUs.
  • FastAPI: For the web part.
  • PyTorch: For the AI work.
  • Transformers: From Hugging Face for models.
How to put it online
Works on Modal servers with:
  • Python 3 or more.
  • Pip for needed files.
Steps:
  1. Upload files to your work space.
  2. Set settings if needed (like model ID).
  3. Run modal deploy main.py.
Other related stuff
License
This is a school project for final year in Software Engineering.
Give Credit: If you use this project or part of it in public, business, or school work, you must give credit to the maker. Include:
How to Use:
  • Free for school and research.
  • Free to change and share if you give credit.
  • Okay for business if you give credit.
  • No promise it works, no blame if problems.
Example Credit:
Based on OpenCensor AI Service by LikoKiko
Original repository: https://github.com/LikoKIko/OpenCensor-Modal

About
OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, '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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OpenCensor AI Service - My Final Year Project
This is my school project for final year in Software Engineering.
What is this?
OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.
What it can do
  • Runs on GPUs for quick checks.
  • Grows or shrinks as needed, no wait to start.
  • Checks many texts at the same time.
  • You can change the sure level and max text length.
  • Made just for Hebrew text.
How it works
This AI service (backend):
  • Python code with FastAPI that handles requests.
  • Runs on Modal servers with GPU help.
  • Uses OpenCensor-Hebrew model from Hugging Face.
  • Based on AlephBERT for Hebrew.
  • 95% right, trained on over 3,000 Hebrew texts.
  • Gives score from 0 (good) to 1 (bad).
Front-end API:
How to install
  1. Install Modal CLI: pip install modal
  2. Set up Modal account: modal setup
How to use the API
POST /predict
Check one text.
Ask:
{
"text": "זה טקסט כלשהו :)",
"threshold": 0.5,
"max_length": 256
}
Answer:
{
"prob": 0.05,
"label": 0
}
POST /batch
Check many texts (max 256).
Ask:
{
"texts": ["זה טקסט כלשהו 1", "זה טקסט כלשהו 2"],
"threshold": 0.5,
"max_length": 256
}
Answer:
[
{
"prob": 0.05,
"label": 0
},
{
"prob": 0.85,
"label": 1
}
]
Project files
├── main.py # Main code with FastAPI and model
├── README.md # This file

What you need
  • Modal: For running on servers with GPUs.
  • FastAPI: For the web part.
  • PyTorch: For the AI work.
  • Transformers: From Hugging Face for models.
How to put it online
Works on Modal servers with:
  • Python 3 or more.
  • Pip for needed files.
Steps:
  1. Upload files to your work space.
  2. Set settings if needed (like model ID).
  3. Run modal deploy main.py.
Other related stuff
License
This is a school project for final year in Software Engineering.
Give Credit: If you use this project or part of it in public, business, or school work, you must give credit to the maker. Include:
How to Use:
  • Free for school and research.
  • Free to change and share if you give credit.
  • Okay for business if you give credit.
  • No promise it works, no blame if problems.
Example Credit:
Based on OpenCensor AI Service by LikoKiko
Original repository: https://github.com/LikoKIko/OpenCensor-Modal

About
OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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
BranchesTags
Latest commit
History
2 Commits
Folders and files
NameName
Last commit message
Last commit date
README.md
README.md
main.py
main.py
Repository files navigation
OpenCensor AI Service - My Final Year Project
This is my school project for final year in Software Engineering.
What is this?
OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.
What it can do
  • Runs on GPUs for quick checks.
  • Grows or shrinks as needed, no wait to start.
  • Checks many texts at the same time.
  • You can change the sure level and max text length.
  • Made just for Hebrew text.
How it works
This AI service (backend):
  • Python code with FastAPI that handles requests.
  • Runs on Modal servers with GPU help.
  • Uses OpenCensor-Hebrew model from Hugging Face.
  • Based on AlephBERT for Hebrew.
  • 95% right, trained on over 3,000 Hebrew texts.
  • Gives score from 0 (good) to 1 (bad).
Front-end API:
How to install
  1. Install Modal CLI: pip install modal
  2. Set up Modal account: modal setup
How to use the API
POST /predict
Check one text.
Ask:
{
"text": "זה טקסט כלשהו :)",
"threshold": 0.5,
"max_length": 256
}
Answer:
{
"prob": 0.05,
"label": 0
}
POST /batch
Check many texts (max 256).
Ask:
{
"texts": ["זה טקסט כלשהו 1", "זה טקסט כלשהו 2"],
"threshold": 0.5,
"max_length": 256
}
Answer:
[
{
"prob": 0.05,
"label": 0
},
{
"prob": 0.85,
"label": 1
}
]
Project files
├── main.py # Main code with FastAPI and model
├── README.md # This file

What you need
  • Modal: For running on servers with GPUs.
  • FastAPI: For the web part.
  • PyTorch: For the AI work.
  • Transformers: From Hugging Face for models.
How to put it online
Works on Modal servers with:
  • Python 3 or more.
  • Pip for needed files.
Steps:
  1. Upload files to your work space.
  2. Set settings if needed (like model ID).
  3. Run modal deploy main.py.
Other related stuff
License
This is a school project for final year in Software Engineering.
Give Credit: If you use this project or part of it in public, business, or school work, you must give credit to the maker. Include:
How to Use:
  • Free for school and research.
  • Free to change and share if you give credit.
  • Okay for business if you give credit.
  • No promise it works, no blame if problems.
Example Credit:
Based on OpenCensor AI Service by LikoKiko
Original repository: https://github.com/LikoKIko/OpenCensor-Modal

About
OpenCensor AI Service is a fast AI service to find bad words in Hebrew text. It uses Modal, FastAPI, and Hugging Face tools. It acts as the backend AI that checks text for the OpenCensorAPI. This service runs on Modal servers with fast computer help and gives answers in real time.
Resources
Readme
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages