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handler.py

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+from typing import Dict, Any
+from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
+from PIL import Image
+import torch
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # Ładowanie modelu i procesora
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.model = AutoModelForZeroShotObjectDetection.from_pretrained(path).to(self.device)
+        self.processor = AutoProcessor.from_pretrained(path)
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        # Sprawdź, czy dane wejściowe zawierają wymagane pola
+        if "image" not in data or "text" not in data:
+            return {"error": "Payload must contain 'image' (base64 or URL) and 'text' (queries)."}
+
+        # Załaduj obraz
+        image = Image.open(data["image"]) if isinstance(data["image"], str) else data["image"]
+
+        # Pobierz teksty zapytań
+        text_queries = data["text"]
+        if isinstance(text_queries, list):
+            text_queries = ". ".join([t.lower().strip() + "." for t in text_queries])
+
+        # Przygotuj dane wejściowe
+        inputs = self.processor(images=image, text=text_queries, return_tensors="pt").to(self.device)
+        
+        # Przeprowadź inferencję
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+
+        # Post-process detekcji
+        results = self.processor.post_process_grounded_object_detection(
+            outputs,
+            inputs.input_ids,
+            box_threshold=0.4,
+            text_threshold=0.3,
+            target_sizes=[image.size[::-1]]
+        )
+
+        # Przygotuj wynik
+        return {
+            "detections": results
+        }

requirements.txt

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+torch
+torchvision
+transformers
+addict
+yapf
+timm
+numpy
+opencv-python
+supervision>=0.22.0
+pillow