# YOLOv5 🚀 by Ultralytics, GPL-3.0 license """ Run a Flask REST API exposing a YOLOv5s model """ import argparse import io import torch from flask import Flask, request from PIL import Image app = Flask(__name__) DETECTION_URL = "/v1/object-detection/yolov5s" @app.route(DETECTION_URL, methods=["POST"]) def predict(): if not request.method == "POST": return if request.files.get("image"): image_file = request.files["image"] image_bytes = image_file.read() img = Image.open(io.BytesIO(image_bytes)) results = model(img, size=640) # reduce size=320 for faster inference return results.pandas().xyxy[0].to_json(orient="records") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model") parser.add_argument("--port", default=5000, type=int, help="port number") opt = parser.parse_args() # Fix known issue urllib.error.HTTPError 403: rate limit exceeded https://github.com/ultralytics/yolov5/pull/7210 torch.hub._validate_not_a_forked_repo = lambda a, b, c: True model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True) # force_reload to recache app.run(host="0.0.0.0", port=opt.port) # debug=True causes Restarting with stat