VistAI: Visual Search, Personalized for You.

VistAI is an advanced visual search and recommendation system that leverages state-of-the-art AI technologies to find visually similar products and provide personalized recommendations. The system combines computer vision (CLIP) for visual understanding and reinforcement learning (DQN) for intelligent recommendations.

• Visual Search: Find visually similar products using CLIP-based image embeddings
• AI-Powered Recommendations: Get personalized recommendations using Deep Q-Learning
• Responsive Web Interface: User-friendly interface for seamless interaction
• Scalable Architecture: Modular design for easy extension and maintenance

• Framework: Flask
• Computer Vision: CLIP (Contrastive Language-Image Pretraining)
• Similarity Search: FAISS (Facebook AI Similarity Search)
• Reinforcement Learning: Stable Baselines3 (DQN)
• Data Processing: NumPy, Pandas, OpenCV
• Deep Learning: PyTorch

• HTML5, CSS3, JavaScript
• Responsive design for all devices

• Python 3.8+
• pip
• Git

1. Clone the repository:

   git clone https://github.com/Zeeshier/VistAI.git
   cd VistAI

2. Install dependencies:

   pip install -r requirements.txt

3. Download CLIP model:

   import clip
   clip.load("ViT-B/32", download_root="models/clip_model")

4. Run the application:

   python src/app.py

5. Open your browser and navigate to http://localhost:5000

VistAI/
├── data/                    # Dataset and processed images
├── models/                  # Pretrained models and embeddings
│   ├── clip_model/          # CLIP model weights
│   ├── dqn_model/           # Trained DQN models
│   └── embeddings/          # Image embeddings and FAISS index
├── src/                     # Source code
│   ├── app.py               # Flask application
│   ├── visual_search.py     # CLIP-based visual search
│   ├── rl_recommender.py    # DQN-based recommendation
│   ├── data_preprocessing.py# Image preprocessing utilities
│   └── utils.py             # Helper functions
├── static/                  # Static files (CSS, JS, uploads)
├── templates/               # HTML templates
├── tests/                   # Test files
├── requirements.txt         # Project dependencies
├── README.md                # Project Documentation

• Utilizes OpenAI's CLIP model to generate image embeddings
• Implements FAISS for efficient similarity search
• Returns visually similar products based on input image

• Implements Deep Q-Network (DQN) for personalized recommendations
• State space: CLIP embeddings of products
• Action space: Product indices
• Reward function: Positive for correct matches, negative for mismatches

1. Image Preprocessing: Resize and normalize input images
2. Feature Extraction: Generate embeddings using CLIP
3. Similarity Search: Find nearest neighbors using FAISS
4. Result Ranking: Sort results by similarity score

1. Environment Setup: Custom Gym environment for product recommendations
2. Model Training: Train DQN on product embeddings

1. Performance Optimization:

   • Implemented FAISS for efficient similarity search
   • Optimized CLIP model loading and inference

2. Data Processing:

   • Handled varying image sizes and formats
   • Implemented efficient batch processing

3. Model Training:

   • Addressed exploration-exploitation tradeoff in RL
   • Implemented reward shaping for better convergence

• Visual Search: High accuracy in finding visually similar products
• Recommendation Quality: Improved user engagement with personalized suggestions
• Performance: Fast response time for real-time recommendations

• AI Engineers: Zeeshan Ahmad(Team Lead), M Abeer(Team Member)
• Web Developer: Muhammad Hamza Sirang(Team Member)

This project is licensed under the MIT License - see the LICENSE file for details.

• OpenAI for the CLIP model
• Facebook Research for FAISS
• Stable Baselines3 team
• The open-source community for valuable libraries and tools