handler.py

from huggingface_hub import hf_hub_download
from typing import Dict, List, Any
from ultralytics import YOLO
import json
import urllib.request
import cv2
from io import BytesIO
import numpy as np
class EndpointHandler():
    def __init__(self, path=""):
        hf_hub_download(repo_id="Drazcat-AI/flejes", filename="flejes-16/runs/detect/train/weights/best.pt")
        self.model = YOLO(hf_hub_download(repo_id="Drazcat-AI/flejes", filename="flejes-16/runs/detect/train/weights/best.pt", local_files_only=True))
    
    def predict_objects(self, image_path, image_size_m):
        results = self.model(image_path, imgsz=[1280, 960])
        predictions = []
        for box in results[0].boxes:
            class_id = results[0].names[box.cls[0].item()]
            cords = box.xywh[0].tolist()
            #cords = [round(x) for x in cords]
            conf = box.conf[0].item()
            prediction = {
            "x": round(cords[0]*image_size_m[0]),
            "y": round(cords[1]*image_size_m[1]),
            "width": round(cords[2]*image_size_m[0]),
            "height": round(cords[3]*image_size_m[1]),
            "confidence": conf,
            "class": class_id
            }
            predictions.append(prediction)
        predictions_array = {"predictions": predictions}   

        return predictions_array

    def __call__(self, event):
        if "inputs" not in event:
            return {
                "statusCode": 400,
                "body": json.dumps("Error: Please provide an 'inputs' parameter."),
            }

        image_path = event["inputs"]

        try:
            with urllib.request.urlopen(image_path) as response:
                image_content = np.asarray(bytearray(response.read()), dtype=np.uint8)
            image = cv2.imdecode(image_content, cv2.IMREAD_COLOR)
            """
            image_size = image.shape
            if image.shape[0]>image.shape[0]:
                x, y = 1280, 960
            else:
                y, x = 1280, 960
            image = cv2.resize(image, (x, y))
            
            predictions = self.predict_objects(image, [image_size[0]/x,image_size[1]/y])
            """
            predictions = self.predict_objects(image, (1,1))
            return {
                "statusCode": 200,
                "body": json.dumps(predictions),
            }
        except Exception as e:
            return {
                "statusCode": 500,
                "body": json.dumps(f"Error: {str(e)}"),
            }