Landscape Classifier

An AI-powered image classifier that identifies landscape categories from uploaded photos. Classifies images into: Buildings, Forests, Mountains, Glacier, Street, Sea.

How It Works

• Backend: Flask API (app.py) loads a pre-trained Keras model (model.h5) and exposes a /predict endpoint
• Frontend: Static HTML/CSS/JS (web/) that sends images to the backend and displays the top 3 predictions with confidence scores

Requirements

• Python 3.11 (TensorFlow does not support Python 3.12+ yet)
• model.h5 is included in the repository

Setup

1. Clone the repository

git clone https://github.com/kenzitjandra/LandscapeClassifier.git
cd LandscapeClassifier

2. Create a virtual environment with Python 3.11

py -3.11 -m venv venv

3. Activate the virtual environment

Windows:

venv\Scripts\activate

Mac/Linux:

source venv/bin/activate

4. Install dependencies

pip install -r requirements.txt

Running the App

python app.py

Then open http://localhost:5000 in your browser. Upload an image and click Classify Image.

Project Structure

LandscapeClassifier/
├── app.py          # Flask backend + prediction logic
├── model.h5        # Trained Keras model (not in repo)
└── web/
    ├── index.html  # Frontend UI
    └── style.css   # Styles

API

POST /predict

Accepts a multipart form upload with a field named file.

{
  "top_predictions": [
    { "label": "Forests", "confidence": 92.4 },
    { "label": "Mountains", "confidence": 5.1 },
    { "label": "Sea", "confidence": 1.3 }
  ]
}