A simple but powerful expert system that classifies music genres based on user-provided attributes. This project uses a Prolog knowledge base for its inference engine and a Python Streamlit application for an interactive web interface.

This application serves as a demonstration of a classic expert system. A user describes a song by providing characteristics such as its release year, tempo (BPM), vocal style, and instrumentation. This information is then asserted as temporary facts into a Prolog knowledge base. A set of predefined rules in the knowledge base evaluates these facts to infer the most likely music genre(s), complete with confidence scores and reasoning.

The core technologies used are:

• Prolog (SWI-Prolog): For the knowledge base and the rule-based inference engine.
• Python: As the backend server and for interfacing with Prolog.
• pyswip: A Python library that enables communication between Python and SWI-Prolog.
• Streamlit: To create a fast, interactive, and user-friendly web UI.

• Interactive UI: A clean web interface built with Streamlit for easy input of song attributes.
• Rule-Based Logic: Genre classification is handled by a declarative Prolog knowledge base, making the rules easy to read and modify.
• Ranked Results: Displays all matching genres, sorted by a confidence score.
• Clear Reasoning: Each recommendation comes with a simple explanation of why the rule was triggered.
• Dynamic Knowledge Base: User inputs are temporarily added to the Prolog database for each query and then retracted, ensuring a clean state for every classification.

Follow these steps to get the application running on your local machine.

You must install SWI-Prolog and Python 3 on your system first.

• SWI-Prolog: This is the engine that runs the .pl knowledge base.

  • Download SWI-Prolog here.
  • Important: During installation, make sure you check the option to add swipl to your system's PATH. This allows pyswip to find and communicate with it.

• Python: This project requires Python 3.8 or newer.

  • Download Python here.

Clone the repository:

git clone [https://github.com/your-username/your-repo-name.git](https://github.com/your-username/your-repo-name.git)
cd your-repo-name

Create a Python virtual environment (recommended):

# For Windows
python -m venv venv
venv\Scripts\activate

# For macOS/Linux
python3 -m venv venv
source venv/bin/activate

The required Python packages are listed in requirements.txt.

Create a file named requirements.txt in the root of your project and add the following lines:

streamlit
pyswip

Now, install these packages using pip:

pip install -r requirements.txt

Once the setup is complete, you can run the Streamlit app with a single command:

streamlit run app.py

Your web browser should automatically open a new tab with the running application. If not, the terminal will provide a local URL (usually http://localhost:8501) that you can visit.

.
├── app.py              # The main Python file containing the Streamlit UI and backend logic.
├── music_kb.pl         # The Prolog knowledge base with facts and classification rules.
├── requirements.txt    # A list of required Python packages for pip.
└── README.md