Aiveo

AI-powered Video Analytics Platform
Upload, analyze, and visualize video data with deep learning, all in real time.

---

✨ Features

• 🎥 Video Upload & Streaming — Upload videos for analysis or stream live feeds.
• 🧠 AI-Powered Analytics — Detect, classify, and extract insights from video frames using deep learning.
• 📊 Interactive Dashboard — Visualize analytics results in real-time with beautiful charts and tables.
• ⚡ Fast & Responsive UI — Built with Next.js, Tailwind CSS, and modern React components.
• 🔌 Modular Python Pipeline — Easily extend detection/classification logic in Python.
• 🛡️ Production-Ready — Scalable, maintainable, and easy to deploy.

---

🏗️ Architecture

+---------------------------+         +-----------------------------+
|   Next.js Dashboard (UI)  | <-----> |   Python DL Streamer API    |
|  (ai-video-analytics-     |  REST   |  (dl_streamer_pipeline)     |
|   -dashboard)             |         |                             |
+---------------------------+         +-----------------------------+

• Frontend: Next.js app for user interaction, video upload, and analytics visualization.
• Backend: Python pipeline for decoding, detecting, and classifying video frames.

---

🛠️ Tech Stack

Layer	Technology
Frontend	Next.js · React · Tailwind CSS · Lucide Icons
Backend	Python 3 · OpenCV · Deep Learning frameworks
Tooling	pnpm · PostCSS · Webpack · SWC · Redux Thunk

---

📁 Folder Structure

Aiveo/
│
├── ai-video-analytics-dashboard/   # Next.js frontend
│   ├── app/                       # App directory (Next.js routing)
│   ├── components/                # Reusable React components
│   ├── hooks/                     # Custom React hooks
│   ├── lib/                       # Utility libraries
│   ├── public/                    # Static assets
│   ├── styles/                    # Tailwind/global CSS
│   ├── package.json               # Frontend dependencies
│   └── ...                        # Configs, build, etc.
│
├── dl_streamer_pipeline/          # Python backend
│   ├── main.py                    # Entry point for pipeline
│   ├── decoder.py                 # Video decoding logic
│   ├── detector.py                # Object detection logic
│   ├── classifier.py              # Classification logic
│   ├── utils.py                   # Helper functions
│   └── requirements.txt           # Python dependencies
│
└── README.md                      # (You are here!)

---

🚀 Getting Started

1️⃣ Clone the Repository

git clone https://github.com/yourusername/aiveo.git
cd aiveo

2️⃣ Setup the Frontend

cd ai-video-analytics-dashboard
pnpm install
pnpm dev

• The dashboard will be available at http://localhost:3000

3️⃣ Setup the Backend

cd ../dl_streamer_pipeline
python -m venv venv
venv\Scripts\activate  # On Windows
pip install -r requirements.txt
python main.py

• The backend API will start (see main.py for port/config).

---

🧑‍💻 Usage

1. Open the Dashboard: Go to http://localhost:3000
2. Upload a Video: Use the upload interface to select a video file.
3. View Analytics: Watch as the dashboard displays real-time analytics, charts, and detected objects.
4. Explore Results: Download reports or view frame-by-frame analysis.

---

🎥 Video Demo

Watch the full walkthrough of the project here:
📺 Aiveo - Intel Unnati Project Demo

---

🎨 Customization

• 🏷️ Add New Models: Extend detector.py or classifier.py in dl_streamer_pipeline to add new AI models.
• 🎨 UI Customization: Modify components or styles in ai-video-analytics-dashboard/components and styles.
• 🔗 API Integration: Update API endpoints in the frontend to match your backend configuration.

---

🤝 Contributing

Contributions are welcome! Please open issues or submit pull requests for new features, bug fixes, or improvements.

1. Fork the repo
2. Create your feature branch (git checkout -b feature/YourFeature)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/YourFeature)
5. Open a Pull Request

---

🙏 Acknowledgements

• Next.js
• Tailwind CSS
• Lucide Icons
• OpenCV
• All contributors and open-source libraries used.

---

Made with ❤️ for AI-powered video analytics.

---

For any questions or support, please open an issue on GitHub.