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from typing import Dict, List, Any
from PIL import Image
import os
from fastai.learner import load_learner
from helpers import is_cat
def is_cat(x): return x[0].isupper()
class PreTrainedPipeline():
def __init__(self, path=""):
# IMPLEMENT_THIS
# Preload all the elements you are going to need at inference.
# For instance your model, processors, tokenizer that might be needed.
# This function is only called once, so do all the heavy processing I/O here"""
def is_cat(x): return x[0].isupper()
self.model = load_learner(os.path.join(path, "model.pkl"))
with open(os.path.join(path, "config.json")) as config:
config = json.load(config)
self.id2label = config["id2label"]
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
"""
def is_cat(x): return x[0].isupper()
# IMPLEMENT_THIS
# FastAI expects a np array, not a PIL Image.
_, _, preds = self.model.predict(np.array(inputs))
labels = [
{"label": str(self.id2label["0"]), "score": preds[0]},
{"label": str(self.id2label["1"]), "score": preds[1]},
]
return labels |