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from typing import Any, Dict, List
import numpy as np
from huggingface_hub import from_pretrained_fastai
from PIL import Image
class ImageClassificationPipeline():
def __init__(self, model_id: str):
self.model = from_pretrained_fastai(model_id)
# Obtain labels
self.id2label = self.model.dls.vocab
# Return at most the top 5 predicted classes
self.top_k = 5
def __call__(self, inputs: "Image.Image") -> List[Dict[str, Any]]:
"""
Args:
inputs (:obj:`PIL.Image`):
The raw image representation as PIL.
No transformation made whatsoever from the input. Make all necessary transformations here.
Return:
A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
It is preferred if the returned list is in decreasing `score` order
"""
# FastAI expects a np array, not a PIL Image.
_, _, preds = self.model.predict(np.array(inputs))
preds = preds.tolist()
labels = [
{"label": str(self.id2label[i]), "score": float(preds[i])}
for i in range(len(preds))
]
return labels
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