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A Django-based web application that uses machine learning to detect deepfake images and videos. Upload media files and get instant analysis results with confidence scores.

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# Deepfake Detector 🔍

A Django-based web application that uses machine learning to detect deepfake images and videos. Upload media files and get instant analysis results with confidence scores.

## 📁 Project Structure

```src/
├── deepfake_detector/     # Django project configuration
│   ├── settings.py       # Project settings
│   ├── urls.py          # Main URL routing
│   └── wsgi.py          # WSGI configuration
├── myapp/                # Main application
│   ├── models.py        # Database models
│   ├── views.py         # Application logic
│   ├── urls.py          # App URL routes
│   └── templates/       # HTML templates
├── media/uploads/        # User uploaded files storage
├── manage.py            # Django management script
├── db.sqlite3           # SQLite database (development)
├── requirements.txt     # Python dependencies
└── .gitignore          # Git ignore rules

🚀 Quick Start

Prerequisites

  • Python 3.8 or higher
  • pip (Python package manager)

Installation Steps

  1. Clone and setup
git clone https://github.com/Fritz-nvm/deepfake_detector/
cd src
  1. Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  1. Install dependencies
pip install -r requirements.txt
  1. Setup database
python manage.py migrate
  1. Create admin user (optional)
python manage.py createsuperuser
  1. Run development server
python manage.py runserver

Visit http://localhost:8000 to access the application.

🎯 Features

Core Functionality

  • Media Upload: Support for images and video files
  • Deepfake Analysis: Machine learning model inference
  • Results Display: Confidence scores and detection results
  • User Interface: Clean, responsive web interface

Technical Features

  • Django admin interface for management
  • File upload handling with validation
  • SQLite database for development
  • Modular application structure

🔧 Development

Common Management Commands

# Database operations
python manage.py makemigrations
python manage.py migrate

# Admin operations
python manage.py createsuperuser

# Testing
python manage.py test

# Run server
python manage.py runserver

Access Admin Interface

  1. Create a superuser account using python manage.py createsuperuser
  2. Start the development server
  3. Visit http://localhost:8000/admin
  4. Login with your superuser credentials

📦 Dependencies

Main dependencies listed in requirements.txt:

  • Django (web framework)
  • Machine learning libraries (TensorFlow/PyTorch)
  • Image processing libraries (OpenCV, Pillow)
  • Other Python utilities

🔒 File Storage

  • Uploaded files are stored in media/uploads/
  • Development uses SQLite database (db.sqlite3)
  • Add large files to .gitignore to avoid repository bloat

🚨 Important Notes

  • This is a development version
  • Machine learning model files are not included in this repository
  • For production, consider using PostgreSQL instead of SQLite
  • Add proper environment variables for security settings

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Submit a pull request

Built with Django & Python 🐍


This version includes:
- **Better project structure** with sub-file details
- **More detailed installation** with virtual environment
- **Expanded features** section
- **Development commands** section
- **File storage** explanation
- **Important notes** for developers
- **Clean, professional** formatting

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A Django-based web application that uses machine learning to detect deepfake images and videos. Upload media files and get instant analysis results with confidence scores.

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