Added my server

.gitignore

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+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+
+# Uploads and temporary files
+uploads/
+*.tmp
+*.temp
+
+
+
+# Profiles (if they contain sensitive data)
+profiles/*.json
+
+# Environment variables
+.env
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local

Dockerfile

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+# Base image
+FROM python:3.9
+
+# Create non-root user
+RUN useradd -m -u 1000 user
+USER user
+
+
+# Set working directory
+WORKDIR /app
+
+# Copy and install dependencies
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy project files
+COPY --chown=user . /app
+
+# Expose Hugging Face Space port
+EXPOSE 7860
+
+# Start FastAPI server
+CMD ["uvicorn", "inference_server:app", "--host", "0.0.0.0", "--port", "7860"]

Procfile

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+web: uvicorn inference_server:app --host 0.0.0.0 --port $PORT

README.md

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+# 🦄 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
+
 ---
-title: Devam
-emoji: ⚡
-colorFrom: purple
-colorTo: purple
-sdk: docker
-pinned: false
+
+## 🛠️ 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())
+```
+
+---
+
+## 🌐 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=["*"],
+)
+```
+
+---
+
+## 📂 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
+```
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 📝 License
+MIT License 

index/jersey_metadata.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5d9c7554b9a4ee1afa74586d50ffdde0b0e66b5899b4755fe2a06d1dc089049b
+size 5027

inference_server.py

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+from fastapi import FastAPI, File, UploadFile
+from pydantic import BaseModel
+from typing import List
+import torch
+from transformers import AutoImageProcessor, AutoModel
+from ultralytics import YOLO
+import faiss
+import numpy as np
+from PIL import Image
+import io
+
+app = FastAPI()
+
+# Load models and index ONCE at startup
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+processor = AutoImageProcessor.from_pretrained('facebook/dinov2-base')
+dino_model = AutoModel.from_pretrained('facebook/dinov2-base').to(device)
+yolo_model = YOLO("models/deepfashion2_yolov8s-seg.pt")
+faiss_index = faiss.read_index("index/jersey_index.faiss")
+
+loaded_data = np.load("index/jersey_metadata.npy", allow_pickle=True)
+if isinstance(loaded_data, dict):
+    index_to_path = {int(k): v for k, v in loaded_data.items()}
+elif isinstance(loaded_data, np.ndarray):
+    index_to_path = {i: str(item) for i, item in enumerate(loaded_data)}
+else:
+    index_to_path = {}
+
+class FeaturesRequest(BaseModel):
+    features: List[float] | List[List[float]]
+
+@app.post("/dino")
+async def dino_inference(file: UploadFile = File(...)):
+    image_bytes = await file.read()
+    image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
+    with torch.no_grad():
+        inputs = processor(images=image, return_tensors="pt").to(device)
+        outputs = dino_model(**inputs)
+    features = outputs.last_hidden_state.mean(dim=1).detach().cpu().numpy()[0]
+    return {"features": features.tolist()}
+
+@app.post("/faiss")
+async def faiss_search(request: FeaturesRequest):
+    features = request.features
+    if isinstance(features[0], list):
+        vector = np.array(features, dtype=np.float32)
+    else:
+        vector = np.array([features], dtype=np.float32)
+    if vector.shape[1] != faiss_index.d:
+        error_msg = f"Feature vector length {vector.shape[1]} does not match FAISS index dimension {faiss_index.d}"
+        return {"error": error_msg}
+    faiss.normalize_L2(vector)
+    distances, indices = faiss_index.search(vector, 15)
+    results = []
+    for i, (distance, idx) in enumerate(zip(distances[0], indices[0])):
+        if idx in index_to_path:
+            key = idx
+            results.append({
+                "rank": i + 1,
+                "distance": float(distance),
+                "file_path": index_to_path[key],
+                "full_path": f"catalogue/{index_to_path[key]}"
+            })
+    return {"results": results}
+
+@app.post("/yolo")
+async def yolo_inference(file: UploadFile = File(...)):
+    image_bytes = await file.read()
+    image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
+    results = yolo_model(image, device=0 if torch.cuda.is_available() else 'cpu', verbose=False)[0]
+    polygons = []
+    if hasattr(results, 'masks') and results.masks is not None and hasattr(results.masks, 'xy'):
+        for mask in results.masks.xy:
+            polygons.append(mask.tolist())
+    return {"polygons": polygons}

models/deepfashion2_yolov8s-seg.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c319c15e86443ea61f70b40620909d737c47ff2a39381536503ac5c54772f8ac
+size 23852321

requirements.txt

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+# Core ML libraries
+torch>=2.0.0
+torchvision>=0.15.0
+ultralytics>=8.0.0
+transformers>=4.30.0
+Pillow>=9.0.0
+
+# Computer Vision
+opencv-python>=4.8.0
+
+# Vector search
+faiss-cpu>=1.7.0
+# For GPU support, use: faiss-gpu>=1.7.0
+
+# Utilities
+numpy>=1.24.0
+scikit-image>=0.20.0
+tqdm>=4.65.0
+
+# Optional: For better performance
+# tensorrt  # Only if you have NVIDIA GPU
+# onnxruntime-gpu  # Only if you have NVIDIA GPU 
+
+fastapi
+uvicorn[standard]