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from typing import Dict, List, Any
from transformers import AutoModelForImageClassification, AutoFeatureExtractor
import torch
from PIL import Image
import io
class EndpointHandler:
def __init__(self):
# Initialize model and feature extractor
model_id = "alexionby/ainoai"
self.model = AutoModelForImageClassification.from_pretrained(model_id)
self.feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
# Convert bytes to PIL Image
image = data.pop('image', data)
image = Image.open(io.BytesIO(image))
# Preprocess the image
inputs = self.feature_extractor(images=image, return_tensors="pt")
# Run the model
with torch.no_grad():
outputs = self.model(**inputs)
# Post-process the model outputs as needed
logits = outputs.logits
probabilities = torch.nn.functional.softmax(logits, dim=-1)
predictions = probabilities.argmax(-1)
# Convert predictions to JSON-serializable format
return {"label": str(predictions.item())}
# import torch
# from PIL import Image
# import io
# class EndpointHandler():
# def __init__(self, path=""):
# # Preload all the elements you are going to need at inference.
# # pseudo:
# self.model= load_model(path)
# def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
# """
# data args:
# inputs (:obj: `str` | `PIL.Image` | `np.array`)
# kwargs
# Return:
# A :obj:`list` | `dict`: will be serialized and returned
# """
# # pseudo
# # self.model(input) |