model.py

import os
import logging
import torch
import datetime

from google.cloud import storage
from transformers import AutoImageProcessor, AutoModelForObjectDetection
from label_studio_ml.model import LabelStudioMLBase
from lxml import etree
from uuid import uuid4
from PIL import Image


import datetime

from google.cloud import storage

def generate_download_signed_url_v4(blob_name):
    """Generates a v4 signed URL for downloading a blob.

    Note that this method requires a service account key file. You can not use
    this if you are using Application Default Credentials from Google Compute
    Engine or from the Google Cloud SDK.
    """
    bucket_name = os.getenv("bucket")

    storage_client = storage.Client()
    bucket = storage_client.bucket(bucket_name)
    blob = bucket.blob(blob_name)

    url = blob.generate_signed_url(
        version="v4",
        # This URL is valid for 15 minutes
        expiration=datetime.timedelta(minutes=15),
        # Allow GET requests using this URL.
        method="GET",
    )

    print("Generated GET signed URL:")
    print(url)
    print("You can use this URL with any user agent, for example:")
    print(f"curl '{url}'")
    return url


class Model(LabelStudioMLBase):

    image_processor = AutoImageProcessor.from_pretrained("diegokauer/conditional-detr-coe-int")
    model = AutoModelForObjectDetection.from_pretrained("diegokauer/conditional-detr-coe-int")

    def predict(self, tasks, **kwargs):
        """ This is where inference happens: model returns 
            the list of predictions based on input list of tasks 
        """
        predictions = []
        for task in tasks:


            image_path = task["data"]["image"]
            image = Image.open(generate_download_signed_url_v4(image_path))
            original_width, original_height = image.size
            with torch.no_grad():
                
                inputs = image_processor(images=image, return_tensors="pt")
                outputs = model(**inputs)
                target_sizes = torch.tensor([image.size[::-1]])
                results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0]

            result_list = []
            for score, label, box in zip(results['scores'], results['labels'], results['boxes']):
                label_id = str(uuid4())[:4]
                x, y, x2, y2 = tuple(box)
                result_list.append({
                    'id': label_id,
                    'original_width': original_width,
                    'original_height': original_height,
                    'from_name': "label",
                    'to_name': "image",
                    'type': 'labels',
                    'score': score,  # per-region score, visible in the editor 
                    'value': {
                        'x': x,
                        'y': y,
                        'width': x2-x,
                        'height': y2-y,
                        'rotation': 0,
                        'labels': [self.id2label[label]]
                    }
                })
            
            predictions.append({
                'score': results['scores'].mean(),  # prediction overall score, visible in the data manager columns
                'model_version': 'diegokauer/conditional-detr-coe-int',  # all predictions will be differentiated by model version
                'result': result_list
            })
        return predictions

    def fit(self, annotations, **kwargs):
        """ This is where training happens: train your model given list of annotations, 
            then returns dict with created links and resources
        """
        return {'path/to/created/model': 'my/model.bin'}