🔍 AI Image Detector

An AI-powered web app that detects whether an uploaded image is AI-generated or real, with intelligent explanation of the verdict.

🌐 Live Demo

Run locally following setup instructions below

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🏗️ Architecture

User uploads image
      ↓
Streamlit Frontend (frontend/streamlit_app.py)
      ↓ HTTP POST /detect
FastAPI Backend (app/main.py)
      ↓
Sightengine API → AI detection score
      ↓
Groq LLaMA3 → human-readable explanation
      ↓
JSON Response → displayed on UI

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🛠️ Tech Stack

Layer	Technology
Backend API	FastAPI + Uvicorn
AI Detection Model	Sightengine API
AI Explanation Agent	Groq API (LLaMA3-8b)
Frontend UI	Streamlit
Containerization	Docker

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✨ Features

• Upload JPG, PNG, or WEBP images
• Instant AI vs Real verdict with confidence score
• AI-powered explanation of WHY the image was flagged
• Clean progress bar showing AI generation probability
• Error handling and file size validation
• REST API with auto-generated Swagger docs

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🚀 Setup & Run

1. Clone the repo

git clone https://github.com/Prajwal4581/ai-image-detector.git
cd ai-image-detector

2. Create virtual environment

python -m venv venv

# Windows
venv\Scripts\activate

# Mac/Linux
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Set up API keys

cp .env.example .env
# Edit .env and add your keys

Get your free API keys:

• Groq API key → https://console.groq.com
• Sightengine keys → https://sightengine.com

Your .env should look like:

GROQ_API_KEY=gsk_xxxxxxxxxxxxxxx
SIGHTENGINE_USER=your_user_id
SIGHTENGINE_SECRET=your_secret

5. Run FastAPI backend

uvicorn app.main:app --reload --port 8000

6. Run Streamlit frontend (new terminal)

venv\Scripts\activate
streamlit run frontend/streamlit_app.py

7. Open browser

• Frontend → http://localhost:8501
• API Docs → http://localhost:8000/docs

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🐳 Docker

docker build -t ai-image-detector .
docker run -p 8000:8000 --env-file .env ai-image-detector

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📁 Project Structure

ai-image-detector/
├── app/
│   ├── __init__.py
│   ├── main.py          # FastAPI routes + validation
│   ├── detector.py      # Sightengine API integration
│   └── agent.py         # Groq LLaMA3 explanation agent
├── frontend/
│   └── streamlit_app.py # Streamlit UI
├── requirements.txt
├── Dockerfile
├── .env.example
└── README.md

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📸 Sample Results

Image Type	Verdict	Confidence
Real photo	✅ Real / Authentic	99%
AI generated (Midjourney/DALL-E)	🤖 AI Generated	99%

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🔮 Future Improvements

• Video / deepfake detection
• Batch image processing
• Detection history with database
• Deploy on Render with live URL
• Add more detection models for ensemble voting