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title: Testing AI Contain
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: docker
sdk_version: "latest"
app_file: app.py
pinned: false
---
# Testing AI Contain
This Hugging Face Space uses **Docker** to run a custom environment for AI content detection.
## How to run locally
---
title: Testing AI Contain
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: docker
sdk_version: "latest"
app_file: app.py
pinned: false
---
# AI-Contain-Checker
# AI-Content-Checker
A modular AI content detection system with support for **image classification**, **image edit detection**, **Nepali text classification**, and **general text classification**. Built for performance and extensibility, it is ideal for detecting AI-generated content in both visual and textual forms.
## 🌟 Features
### 🖼️ Image Classifier
* **Purpose**: Classifies whether an image is AI-generated or a real-life photo.
* **Model**: Fine-tuned **InceptionV3** CNN.
* **Dataset**: Custom curated dataset with **\~79,950 images** for binary classification.
* **Location**: [`features/image_classifier`](features/image_classifier)
* **Docs**: [`docs/features/image_classifier.md`](docs/features/image_classifier.md)
### 🖌️ Image Edit Detector
* **Purpose**: Detects image tampering or post-processing.
* **Techniques Used**:
* **Error Level Analysis (ELA)**: Visualizes compression artifacts.
* **Fast Fourier Transform (FFT)**: Detects unnatural frequency patterns.
* **Location**: [`features/image_edit_detector`](features/image_edit_detector)
* **Docs**:
* [ELA](docs/detector/ELA.md)
* [FFT](docs/detector/fft.md )
* [Metadata Analysis](docs/detector/meta.md)
* [Backend Notes](docs/detector/note-for-backend.md)
### 📝 Nepali Text Classifier
* **Purpose**: Determines if Nepali text content is AI-generated or written by a human.
* **Model**: Based on `XLMRClassifier` fine-tuned on Nepali language data.
* **Dataset**: Scraped dataset of **\~18,000** Nepali texts.
* **Location**: [`features/nepali_text_classifier`](features/nepali_text_classifier)
* **Docs**: [`docs/features/nepali_text_classifier.md`](docs/features/nepali_text_classifier.md)
### 🌐 English Text Classifier
* **Purpose**: Detects if English text is AI-generated or human-written.
* **Pipeline**:
* Uses **GPT2 tokenizer** for input preprocessing.
* Custom binary classifier to differentiate between AI and human-written content.
* **Location**: [`features/text_classifier`](features/text_classifier)
* **Docs**: [`docs/features/text_classifier.md`](docs/features/text_classifier.md)
---
## 🗂️ Project Structure
```bash
AI-Checker/
│
├── app.py # Main FastAPI entry point
├── config.py # Configuration settings
├── Dockerfile # Docker build script
├── Procfile # Deployment file for Heroku or similar
├── requirements.txt # Python dependencies
├── README.md # You are here 📘
│
├── features/ # Core detection modules
│ ├── image_classifier/
│ ├── image_edit_detector/
│ ├── nepali_text_classifier/
│ └── text_classifier/
│
├── docs/ # Internal and API documentation
│ ├── api_endpoints.md
│ ├── deployment.md
│ ├── detector/
│ │ ├── ELA.md
│ │ ├── fft.md
│ │ ├── meta.md
│ │ └── note-for-backend.md
│ ├── functions.md
│ ├── nestjs_integration.md
│ ├── security.md
│ ├── setup.md
│ └── structure.md
│
├── IMG_Models/ # Saved image classifier model(s)
│ └── latest-my_cnn_model.h5
│
├── notebooks/ # Experimental and debug notebooks
├── static/ # Static assets if needed
└── test.md # Test notes
````
---
## 📚 Documentation Links
* [API Endpoints](docs/api_endpoints.md)
* [Deployment Guide](docs/deployment.md)
* [Detector Documentation](docs/detector/)
* [Error Level Analysis (ELA)](docs/detector/ELA.md)
* [Fast Fourier Transform (FFT)](docs/detector/fft.md)
* [Metadata Analysis](docs/detector/meta.md)
* [Backend Notes](docs/detector/note-for-backend.md)
* [Functions Overview](docs/functions.md)
* [NestJS Integration Guide](docs/nestjs_integration.md)
* [Security Details](docs/security.md)
* [Setup Instructions](docs/setup.md)
* [Project Structure](docs/structure.md)
---
## 🚀 Usage
1. **Install dependencies**
```bash
docker build -t testing-ai-contain .
docker run -p 7860:7860 testing-ai-contain
```
```bash
pip install -r requirements.txt
```
2. **Run the API**
```bash
chroma run --path ./chroma_database ## to run chromadb locally
uvicorn app:app --reload --port 8001 ## fastapi (run after chromadb)
```
3. **Build Docker (optional)**
```bash
docker build -t ai-contain-checker .
docker run -p 8000:8000 ai-contain-checker
```
---
## 🔐 Security & Integration
* **Token Authentication** and **IP Whitelisting** supported.
* NestJS integration guide: [`docs/nestjs_integration.md`](docs/nestjs_integration.md)
* Rate limiting handled using `slowapi`.
---
## 🛡️ Future Plans
* Add **video classifier** module.
* Expand dataset for **multilingual** AI content detection.
* Add **fine-tuning UI** for models.
---
## 📄 License
See full license terms here: [`LICENSE.md`](license.md)
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