# 🌸 IrisAI — Flask ML Web Application

A machine learning web app that classifies Iris flowers using a Random Forest model, built with Flask.

## 📁 Project Structure

```
ml_flask_app/
├── app.py              # Main Flask application
├── train_model.py      # Model training script
├── requirements.txt    # Python dependencies
├── users.db            # SQLite database (auto-created)
├── model/
│   ├── iris_model.pkl  # Trained Random Forest model
│   ├── scaler.pkl      # StandardScaler
│   └── class_names.pkl # Class label names
└── templates/
    ├── base.html       # Shared layout
    ├── login.html      # Login page
    ├── register.html   # Registration page
    └── predict.html    # Prediction form & results
```

## 🚀 Setup & Run

```bash
# 1. Install dependencies
pip install -r requirements.txt

# 2. (Optional) Retrain the model
python train_model.py

# 3. Run the app
python app.py
```

Then open http://127.0.0.1:5000 in your browser.

## 🔑 Default Login

- **Username:** `admin`
- **Password:** `password123`

Or register your own account at `/register`.

## 🤖 Model Details

| Item | Value |
|------|-------|
| Dataset | UCI Iris (150 samples, 4 features) |
| Algorithm | Random Forest (100 trees) |
| Accuracy | 90% on held-out test set |
| Classes | setosa, versicolor, virginica |
| Source | https://archive.ics.uci.edu/dataset/53/iris |

## ✨ Features

- 🔐 Login & registration with SHA-256 password hashing
- 📊 Real-time flower classification with confidence scores
- 📋 Prediction history per user (stored in SQLite)
- 💡 Quick-fill example inputs for each species
- 📱 Responsive design