Project

This project implements 3 classifier structures:

• Local Classifier par Parent Node (LCPN) - each parent node receives one multiclass classifier.
• Local Classifier per Node (LCN) - training one multiclass classifier for each level.
• Flat Classifier ("flat" classifier) - training on concatenated data.

The implementation of each of them is in the app/model/analyze.ipynb folder.

Getting Started

Prerequisites

• Python 3.10 or higher
• pip for managing Python packages
• Docker (optional, if using Docker)

Installation

1. Clone the repository:

   git clone https://github.com/your-username/fastapi-text-classification.git
   cd fastapi-text-classification

2. Install the required Python packages:

   pip install -r requirements.txt

3. Make sure your model files (model.pkl, encoder.pkl, vectorizer.pkl) are located in the model/ directory.

Running the Service

Run the FastAPI application locally:

uvicorn main:app --reload

Docker

1. Building the Docker Image:

   docker build -t fastapi-app .

2. Running the Docker Container

   docker run -d -p 8000:8000 fastapi-app