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

READMe.md

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