Add Hugging Face Space config in README

README.md

@@ -1,115 +1,23 @@
-# 🦄 FastAPI Inference Server
-
-A blazing-fast inference server for computer vision tasks using FastAPI, YOLO, DINOv2, and FAISS! 🚀
-
-## Features
-- **YOLOv8 Segmentation**: Detect and segment objects in images
-- **DINOv2 Embeddings**: Extract powerful image features
-- **FAISS Vector Search**: Find similar items using vector search
-- **Easy REST API**: Simple endpoints for integration with any frontend
-
----
-
-## 🛠️ Setup
-
-### 1. Clone the repository
-```bash
-git clone <your-repo-url>
-cd <your-project-directory>
-```
-
-### 2. Install dependencies
-```bash
-pip install -r requirements.txt
-```
-
-### 3. Download/Place Model Files
-- Place your YOLO model at `models/deepfashion2_yolov8s-seg.pt`
-- Place your FAISS index and metadata at `index/jersey_index.faiss` and `index/jersey_metadata.npy`
-
-### 4. Start the server
-```bash
-uvicorn inference_server:app --host 0.0.0.0 --port 8000 --reload
-```
-
----
-
-## 🔥 API Endpoints
-
-### `POST /yolo`
-- **Description:** Run YOLOv8 segmentation on an image
-- **Request:** Multipart/form-data with an image file
-- **Response:** JSON with detected polygons
-
-### `POST /dino`
-- **Description:** Extract DINOv2 features from an image
-- **Request:** Multipart/form-data with an image file
-- **Response:** JSON with feature vector
-
-### `POST /faiss`
-- **Description:** Search for similar items using FAISS
-- **Request:** JSON with `features` (list of floats)
-- **Response:** JSON with ranked search results
-
 ---
-
-## 🧑‍💻 Example Usage
-
-### YOLO Inference (Python)
-```python
-import requests
-
-with open('your_image.jpg', 'rb') as f:
-    response = requests.post('http://localhost:8000/yolo', files={'file': f})
-print(response.json())
-```
-
-### DINOv2 Inference (Python)
-```python
-import requests
-
-with open('your_image.jpg', 'rb') as f:
-    response = requests.post('http://localhost:8000/dino', files={'file': f})
-print(response.json())
-```
-
-### FAISS Search (Python)
-```python
-import requests
-features = [0.1, 0.2, ...]  # Replace with your feature vector
-response = requests.post('http://localhost:8000/faiss', json={'features': features})
-print(response.json())
-```
-
+title: Devam Jersey Server
+emoji: 🏀
+colorFrom: blue
+colorTo: green
+sdk: docker
+app_file: inference_server.py
+pinned: false
 ---
 
-## 🌐 CORS
-If using a frontend on a different port, make sure to enable CORS in `inference_server.py`:
-```python
-from fastapi.middleware.cors import CORSMiddleware
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["https://localhost:8081"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-```
+# Devam Jersey Server
 
----
-
-## 📂 Project Structure
-```
-├── inference_server.py      # FastAPI app and endpoints
-├── requirements.txt         # Python dependencies
-├── models/
-│   └── deepfashion2_yolov8s-seg.pt
-├── index/
-│   ├── jersey_index.faiss
-│   └── jersey_metadata.npy
-```
+This Space runs my custom inference server for **jersey similarity and detection**.
 
----
+- **Model**: YOLOv8s-seg (fine-tuned on DeepFashion2)
+- **Indexing**: FAISS index for jersey embeddings
+- **Backend**: `inference_server.py` served with Docker
+- **Data**: Jersey metadata stored in `.npy` and FAISS index file
 
-## 📝 License
-MIT License 
+The server can:
+- Detect jersey regions
+- Extract embeddings
+- Search most visually similar designs