# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""
Run a Flask REST API exposing one or more YOLOv5s models
"""

import argparse
import io

import torch
from flask import Flask, request
from PIL import Image

app = Flask(__name__)
models = {}

DETECTION_URL = '/v1/object-detection/<model>'


@app.route(DETECTION_URL, methods=['POST'])
def predict(model):
    if request.method != 'POST':
        return

    if request.files.get('image'):
        # Method 1
        # with request.files["image"] as f:
        #     im = Image.open(io.BytesIO(f.read()))

        # Method 2
        im_file = request.files['image']
        im_bytes = im_file.read()
        im = Image.open(io.BytesIO(im_bytes))

        if model in models:
            results = models[model](im, 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')
    parser.add_argument('--model', nargs='+', default=['yolov5s'], help='model(s) to run, i.e. --model yolov5n yolov5s')
    opt = parser.parse_args()

    for m in opt.model:
        models[m] = torch.hub.load('ultralytics/yolov5', m, force_reload=True, skip_validation=True)

    app.run(host='0.0.0.0', port=opt.port)  # debug=True causes Restarting with stat