🌸 Flower Detection & Classification

A deep learning-powered web application that classifies flower images using a Convolutional Neural Network (CNN). Upload an image and instantly identify whether it's a daisy, sunflower, tulip, dandelion, or rose with confidence scores.

🌟 Features

• Real-time Classification: Upload flower images and get instant predictions
• 5 Flower Types: Classifies daisy, sunflower, tulip, dandelion, and rose
• Confidence Scores: View probability distribution across all classes
• Modern UI: Clean, responsive web interface with dark mode design
• Fast API Backend: Built with FastAPI for high performance
• Deep Learning Model: Custom CNN architecture trained for flower recognition

🚀 Demo

Upload any flower image and the model will:

1. Process the image
2. Classify it into one of 5 categories
3. Display confidence percentages for each class

🛠️ Tech Stack

Backend

• FastAPI - Modern, fast web framework
• TensorFlow/Keras - Deep learning framework
• Pillow - Image processing
• Uvicorn - ASGI server
• NumPy - Numerical computations

Frontend

• HTML5 - Structure
• CSS3 - Modern styling with gradients and animations
• Vanilla JavaScript - Interactive functionality

Model

• Architecture: Custom CNN with 4 convolutional layers
• Input Size: 64x64 RGB images
• Layers: Conv2D, BatchNormalization, MaxPooling, Dropout, Dense
• Output: 5-class softmax classification

📦 Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Setup

1. Clone the repository

git clone https://github.com/yourusername/flower_detection_classification.git
cd flower_detection_classification-main

2. Install backend dependencies

cd backend
pip install -r requirements.txt

3. Verify model file exists Ensure models/my_model_exp2.h5 is present in the backend directory.

🎯 Usage

Running Locally

1. Start the Backend Server

cd backend
python app.py

Backend will run on http://localhost:8000

2. Start the Frontend Server

cd frontend
python -m http.server 5500

Frontend will run on http://localhost:5500

3. Open your browser Navigate to http://localhost:5500 and start classifying flowers!

API Endpoints

• GET /health - Health check endpoint
• POST /predict - Upload image for classification

Example API usage:

curl -X POST "http://localhost:8000/predict" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@flower.jpg"

Response:

{
  "prediction": "rose",
  "confidence": 0.9523,
  "probabilities": {
    "daisy": 0.0123,
    "sunflower": 0.0089,
    "tulip": 0.0156,
    "dandelion": 0.0109,
    "rose": 0.9523
  }
}

🧠 Model Architecture

Conv2D (32 filters, 5x5) → BatchNorm → MaxPool2D
    ↓
Conv2D (64 filters, 3x3) → BatchNorm → MaxPool2D
    ↓
Conv2D (96 filters, 3x3) → BatchNorm → MaxPool2D
    ↓
Conv2D (96 filters, 3x3) → BatchNorm → MaxPool2D
    ↓
Flatten → Dense(512) → BatchNorm → Dropout(0.5)
    ↓
Dense(5, softmax)

Input Shape: (64, 64, 3)
Output: 5 classes with probability distribution

📊 Supported Flower Classes

1. 🌼 Daisy
2. 🌻 Sunflower
3. 🌷 Tulip
4. 🌼 Dandelion
5. 🌹 Rose

🎨 Frontend Features

• Drag-and-drop image upload
• Image preview before prediction
• Real-time confidence visualization
• Responsive design for all devices
• Modern dark-mode UI with gradients
• Smooth animations and transitions

📁 Project Structure

flower_detection_classification-main/
├── backend/
│   ├── app.py                 # FastAPI application
│   ├── requirements.txt       # Python dependencies
│   └── models/
│       └── my_model_exp2.h5   # Trained CNN model
├── frontend/
│   └── index.html             # Web interface
├── Copy_of_EXP_FLOWER_CNN_F1.ipynb  # Model training notebook
└── README.md

🔧 Configuration

Environment Variables

• MODEL_PATH - Path to the model file (default: models/my_model_exp2.h5)

CORS Settings

The backend is configured to accept requests from http://localhost:5500. Update in app.py if needed:

allow_origins=["http://localhost:5500"]

🚀 Deployment

Option 1: Hugging Face Spaces (Recommended for ML)

Free hosting specifically for ML models with unlimited usage.

Option 2: Render.com

Free tier with auto-deployment from GitHub.

Option 3: Docker

Build and deploy as a container:

docker build -t flower-classifier .
docker run -p 8000:8000 flower-classifier

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

📝 License

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

👨‍💻 Author

Your Name - @mukesh-843

🙏 Acknowledgments

• TensorFlow and Keras teams for the amazing framework
• FastAPI for the excellent web framework
• Flower dataset contributors

📧 Contact

For questions or feedback, please open an issue or reach out via email.

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