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from typing import Dict, Any
from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
from PIL import Image
import requests
from io import BytesIO
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]:
# Sprawdzamy, czy dane wejściowe zawierają wymagane pola
if "inputs" not in data:
return {"error": "Payload must contain 'inputs' key with 'image' and 'text'."}
inputs = data["inputs"]
if "image" not in inputs or "text" not in inputs:
return {"error": "Payload must contain 'image' (base64 or URL) and 'text' (queries)."}
# Pobieramy obraz (URL lub Base64)
image_data = inputs["image"]
if image_data.startswith("http"): # URL
response = requests.get(image_data)
image = Image.open(BytesIO(response.content))
else:
return {"error": "Handler currently supports only URL-based images."}
# Pobieramy tekst zapytań
text_queries = inputs["text"]
if isinstance(text_queries, list):
text_queries = ". ".join([t.lower().strip() + "." for t in text_queries])
# Przygotowujemy dane wejściowe
processed_inputs = self.processor(images=image, text=text_queries, return_tensors="pt").to(self.device)
# Przeprowadzamy inferencję
with torch.no_grad():
outputs = self.model(**processed_inputs)
# Post-process wyników
results = self.processor.post_process_grounded_object_detection(
outputs,
processed_inputs.input_ids,
box_threshold=0.4,
text_threshold=0.3,
target_sizes=[image.size[::-1]]
)
# Zwracamy wyniki
return {"detections": results}
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