README.md

---
title: STOP
sdk: docker
app_port: 7860
colorFrom: red
colorTo: indigo
description: STOP/NOT_STOP text classification using Linear SVM deployed with FastAPI and Docker.
---

# STOP Classifier API

This Hugging Face Space hosts a low-latency text classification service deployed with Docker and FastAPI.

The service uses a highly efficient Linear Support Vector Machine (SVM) model trained on text features extracted via TF-IDF to classify messages as either intending to end communication (`STOP`) or not (`NOT_STOP`). As confirmed by the training script, the SVM model provides millisecond-level inference, which is ideal for the required low-latency API.

## Project Structure

The deployment uses the following structure:

```
.
├── app.py           
├── Dockerfile       
├── requirements.txt 
├── README.md        
└── checkpoint/
    ├── tfidf_vectorizer.pkl
    └── svm_stop_classifier.pkl
```

## API Endpoints

The FastAPI application provides two primary endpoints for prediction:

### 1. Health Check (GET)

* **Path:** `/`
* **Method:** `GET`
* **Description:** A simple endpoint to confirm the service is running and the models are loaded.

### 2. Single Prediction (GET)

* **Path:** `/predict?text=<your_text>`
* **Method:** `GET`
* **Description:** Classifies a single text string passed as a query parameter. This is suitable for quick, individual queries.
* **Example Query:** `/predict?text=please%20discontinue%20all%20contact`

### 3. Batch Prediction (POST)

* **Path:** `/predict`
* **Method:** `POST`
* **Description:** Classifies a list of text strings in a single request. This is the recommended approach for high-throughput, low-latency production use cases due to reduced overhead.
* **Request Body (JSON):**

    ```json
    {
      "texts": [
        "do not ever text me again",
        "I will stop by your office tomorrow"
      ]
    }