Quickstart —your first API call.

import os, requests
from pathlib import Path

resp = requests.post(
    "https://api.msightflow.ai/v1/detect",
    headers={"Authorization": f"Bearer {os.environ['MSF_API_KEY']}"},
    files={"image": Path("your_image.jpg").read_bytes()},
    data={"model": "yolov8m", "return_overlay": "true"},
)

result = resp.json()
print(f"Found {len(result['detections'])} objects")
for d in result["detections"]:
    print(f"  {d['label']:>20}  {d['confidence']:.2f}  {d['box']}")

import fetch from "node-fetch";
import FormData from "form-data";
import fs from "fs";

const form = new FormData();
form.append("image", fs.createReadStream("your_image.jpg"));
form.append("model", "yolov8m");

const resp = await fetch("https://api.msightflow.ai/v1/detect", {
  method: "POST",
  headers: { Authorization: `Bearer ${process.env.MSF_API_KEY}` },
  body: form,
});

const { detections } = await resp.json();
console.log(`Found ${detections.length} objects`);
detections.forEach((d) =>
  console.log(`\t${d.label}  ${d.confidence.toFixed(2)}  [${d.box.join(", ")}]`),
);

curl -X POST https://api.msightflow.ai/v1/detect \
  -H "Authorization: Bearer $MSF_API_KEY" \
  -F "image=@your_image.jpg" \
  -F "model=yolov8m" \
  -F "return_overlay=true"

{
  "detections": [
    {
      "label": "person",
      "confidence": 0.94,
      "box": [102, 88, 412, 670]
    },
    {
      "label": "hard_hat",
      "confidence": 0.81,
      "box": [156, 92, 280, 168]
    }
  ],
  "overlay_png": "<base64 image with boxes drawn>",
  "model": "yolov8m",
  "latency_ms": 142
}