Upload 4 files

DOCKER

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+FROM python:3.11
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, File, UploadFile, Form
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from ultralytics import YOLO
+import numpy as np
+import cv2
+
+app = FastAPI()
+
+# Allow your Vercel app to call this API
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # replace * with your vercel domain in production
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+model = YOLO("best.pt")
+
+
+@app.post("/detect")
+async def detect(
+    file: UploadFile = File(...),
+    confidence: float = Form(default=0.5),   # 0.0 - 1.0, matches Roboflow slider default
+    overlap: float = Form(default=0.5),      # NMS IoU threshold, same as Roboflow overlap slider
+):
+    # Clamp values to valid range
+    confidence = max(0.01, min(1.0, confidence))
+    overlap = max(0.01, min(1.0, overlap))
+
+    contents = await file.read()
+    nparr = np.frombuffer(contents, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+    if img is None:
+        return JSONResponse({"error": "Could not decode image"}, status_code=400)
+
+    results = model(img, conf=confidence, iou=overlap)[0]
+
+    detections = []
+    for box in results.boxes:
+        detections.append({
+            "class": model.names[int(box.cls)],
+            "confidence": round(float(box.conf), 3),
+            "bbox": {
+                # Normalized coords (0-1), easy to draw on any canvas size
+                "x": round(float(box.xywhn[0][0]), 4),
+                "y": round(float(box.xywhn[0][1]), 4),
+                "w": round(float(box.xywhn[0][2]), 4),
+                "h": round(float(box.xywhn[0][3]), 4),
+            },
+            "bbox_pixels": {
+                # Absolute pixel coords in the original image
+                "x1": int(box.xyxy[0][0]),
+                "y1": int(box.xyxy[0][1]),
+                "x2": int(box.xyxy[0][2]),
+                "y2": int(box.xyxy[0][3]),
+            }
+        })
+
+    return JSONResponse({
+        "detections": detections,
+        "count": len(detections),
+        "image_shape": {
+            "width": img.shape[1],
+            "height": img.shape[0],
+        },
+        "settings": {
+            "confidence": confidence,
+            "overlap": overlap,
+        }
+    })
+
+
+@app.get("/")
+def root():
+    return {
+        "status": "ok",
+        "model": "floor-jaaps YOLOv8s",
+        "classes": list(model.names.values()),
+        "usage": "POST /detect with form-data: file (image), confidence (0-1), overlap (0-1)"
+    }

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5b488a70f8c7b433589369e023e73e4fb10c80f0feb4970aa1b28e7493e5dcca
+size 67128820

requirements.txt

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+fastapi
+uvicorn
+ultralytics
+opencv-python-headless
+python-multipart