Add YOLOv8 FastAPI application

Dockerfile

@@ -0,0 +1,24 @@
+FROM python:3.9
+
+# Install system dependencies as root for OpenCV support
+USER root
+RUN apt-get update && apt-get install -y \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set up a new user named "user" with UID 1000 to comply with Hugging Face security
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+# Copy dependencies first to leverage caching
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy application files
+COPY --chown=user . /app
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -0,0 +1,56 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import cv2
+import numpy as np
+import base64
+from ultralytics import YOLO
+
+app = FastAPI()
+
+# Enable CORS so your React frontend/Node backend can query this Space
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load lightweight YOLOv8 Nano model (pretrained on COCO dataset)
+model = YOLO("yolov8n.pt")
+
+class ImagePayload(BaseModel):
+    image: str
+
+@app.get("/")
+def home():
+    return {"status": "YOLOv8 Active", "model": "yolov8n"}
+
+@app.post("/predict")
+def predict(payload: ImagePayload):
+    try:
+        # Decode base64 image
+        encoded_data = payload.image.split(',')[1] if ',' in payload.image else payload.image
+        nparr = np.frombuffer(base64.b64decode(encoded_data), np.uint8)
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+        
+        if img is None:
+            return {"phoneDetected": False, "error": "Invalid image data"}
+
+        # Run inference
+        results = model(img)
+        phone_detected = False
+        
+        for r in results:
+            for box in r.boxes:
+                class_id = int(box.cls[0])
+                label = model.names[class_id]
+                # Label 'cell phone' or 'laptop' or 'remote' in COCO dataset
+                if label in ['cell phone', 'laptop', 'remote']:
+                    phone_detected = True
+                    break
+                    
+        return {"phoneDetected": phone_detected}
+    except Exception as e:
+        return {"phoneDetected": False, "error": str(e)}

requirements.txt

@@ -0,0 +1,6 @@
+fastapi
+uvicorn[standard]
+ultralytics
+opencv-python-headless
+pydantic
+numpy