README.md

---
title: OJOS AI
emoji: 👁️
colorFrom: green
colorTo: blue
sdk: docker
app_port: 7860
pinned: false
---

# 👁️ OJOS AI — Advanced Eye Disease Detection Web App

A state-of-the-art deep learning web application that detects and classifies multiple eye diseases with **96.69% validation accuracy** using fine-tuned Convolutional Neural Networks (CNNs). Equipped with a premium glassmorphic dark-mode UI.

---

## 🎯 Key Features

- **Multi-Disease Classification**: Detects 5 distinct conditions:
  - 👁️ **Cataract**
  - ⚠️ **Diabetic Retinopathy**
  - 🔴 **Glaucoma**
  - 👓 **Myopia**
  - ✅ **Healthy Eye**
- **High Performance**: Achieves **96.69% accuracy** on clinical validation datasets.
- **Advanced Preprocessing**: Uses LAB color space **CLAHE (Contrast Limited Adaptive Histogram Equalization)** to enhance contrast in retinal/eye images, matching clinical standards.
- **Premium User Interface**: Dark glassmorphic theme with sliding login/signup panel, drag-and-drop upload, scanning progress animation, and multi-probability confidence charts.
- **Robust Authentication**: Fully integrated SQLite database system for secure user registration and login session management.
- **Keras 3 Runtime Compatibility**: Includes a custom monkeypatch to bypass quantization config parameters when loading legacy `.h5` model files.

---

## 🛠️ Tech Stack

- **Backend**: Python, Flask, SQLite3
- **Deep Learning**: TensorFlow / Keras (EfficientNetV2 backbone)
- **Computer Vision**: OpenCV, Pillow (PIL)
- **Frontend**: HTML5, Vanilla CSS3 (Glassmorphism, Neon Accents, CSS Keyframes)
- **Deployment**: Docker, Hugging Face Spaces (Git LFS)

---

## 🚀 How to Run Locally

### 1. Prerequisites
Ensure you have Python 3.9+ installed. Install the required libraries:
```bash
pip install tensorflow opencv-python numpy pillow flask
```

### 2. Clone the Repository
```bash
git clone https://github.com/Sharad9084/OJOS-AI.git
cd OJOS-AI
```

### 3. Run the Application
```bash
python app.py
```
Open [http://localhost:5000](http://localhost:5000) in your web browser.

---

## 📁 Project Structure

```
OJOS-AI/
├── best_eye_disease_model.h5   # Fine-tuned EfficientNetV2 multiclass model (Git LFS)
├── class_indices.json          # Mapping of class names to model indices
├── app.py                      # Core Flask server and prediction pipeline
├── database.py                 # SQLite database utility functions
├── requirements.txt            # Python dependencies
├── Dockerfile                  # Containerization config for Hugging Face Spaces
├── Procfile                    # Web process config
├── static/
│   ├── css/
│   │   └── style.css           # Premium glassmorphic styling sheet
│   ├── logo.png                # Custom circular eye logo
│   └── uploads/                # Directory for temporary preview images
└── templates/
    ├── home.html               # Main landing page
    ├── login.html              # Login & signup portal
    ├── prediction.html         # Real-time scan and multi-probability results
    ├── about.html              # Team / info page
    └── diseases_info.html      # Visual medical encyclopedia
```

---

## 💡 How It Works

### 1. Image Preprocessing & Contrast Enhancement
Retinal images can have inconsistent illumination. To normalize this:
- The input image is converted to the **LAB color space**.
- **CLAHE** is applied on the L-channel (lightness) to enhance local contrast without over-amplifying noise.
- The image is merged back, converted to RGB, and resized to **300x300**.
- EfficientNetV2 normalization (`preprocess_input`) is applied.

### 2. Inference & Classification
- The preprocessed image is passed into `best_eye_disease_model.h5`.
- The model outputs logits/probabilities for all 5 target categories.
- The front-end renders a interactive bar chart displaying the confidence percentage for every condition.

---

## ⚕️ Medical Compliance & Disclaimer

- **Screening Tool Only**: This application is designed as an assistive screening tool. It does not replace professional medical diagnosis, advice, or treatment by a qualified ophthalmologist.
- **Data Privacy**: Images uploaded are saved temporarily on the server for visualization and are not shared.

---

## 📞 Contact & Support

Developed by **Sharad Sharma** (sharmasharad9794@gmail.com).

⭐ **If you found this project helpful, please star the repository!**