Initial commit: Dockerized SVM classifier with FastAPI and Gradio UI.

README.md

@@ -0,0 +1,61 @@
+---
+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"
+      ]
+    }