import os

from funcy import rcompose

from image_prediction.classifier.classifier import Classifier
from image_prediction.classifier.image_classifier import ImageClassifier
from image_prediction.config import CONFIG
from image_prediction.estimator.adapter.adapter import EstimatorAdapter
from image_prediction.extractor_classifier.extractor_classifier import ExtractorClassifier
from image_prediction.formatter.formatters.info_formatter import EnumFormatter
from image_prediction.image_extractor.extractors.parsable import ParsablePDFImageExtractor
from image_prediction.locations import MLRUNS_DIR
from image_prediction.model_loader.loader import ModelLoader
from image_prediction.model_loader.loaders.mlflow import MlflowConnector
from image_prediction.redai_adapter.mlflow import MlflowModelReader

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"


def get_image_classifier():
    model_loader = ModelLoader(MlflowConnector(MlflowModelReader(MLRUNS_DIR)))
    model = model_loader.load_model(CONFIG.service.run_id)
    classes = model_loader.load_classes(CONFIG.service.run_id)
    classifier = Classifier(EstimatorAdapter(model), classes)
    image_classifier = ImageClassifier(classifier)

    return image_classifier


def get_extractor():
    image_extractor = ParsablePDFImageExtractor(verbose=True)

    return image_extractor


def get_extractor_classifier():
    extractor_classifier = ExtractorClassifier(get_extractor(), get_image_classifier())

    return extractor_classifier


def get_formatter():
    formatter = EnumFormatter()

    return formatter


class Pipeline:

    def __init__(self):
        self.pipe = rcompose(get_extractor_classifier(), get_formatter())

    def __call__(self, pdf: bytes):
        return self.pipe(pdf)