import json
import os

from kn_utils.logging import logger

from image_prediction.config import CONFIG
from image_prediction.pipeline import load_pipeline
from image_prediction.utils.pdf_annotation import annotate_pdf
import numpy as np
import random
import tensorflow as tf


def process_pdf(pipeline, pdf_path, page_range=None):
    with open(pdf_path, "rb") as f:
        logger.info(f"Processing {pdf_path}")
        predictions = list(pipeline(f.read(), page_range=page_range))

    return predictions

def ensure_seeds():
    seed = 42
    np.random.seed(seed)
    random.seed(seed)
    tf.random.set_seed(seed)

def debug_info():
    devices = tf.config.list_physical_devices()
    print("Available devices:", devices)
    current_path = os.path.dirname(os.path.abspath(__file__))
    print(current_path)


if __name__ == "__main__":
    # Change to path in docker container
    debug_file_path = "./src/debug.pdf"
    ensure_seeds()
    debug_info()

    pipeline = load_pipeline(verbose=True, batch_size=CONFIG.service.batch_size)
    predictions = process_pdf(pipeline, debug_file_path)
    # This is the image that has the wrong prediction mentioned in RED-9948. The predictions should inconclusive, and
    # the flag all passed should be false.
    predictions = [x for x in predictions if x["representation"] == "FA30F080F0C031CE17E8CF237"]
    print(json.dumps(predictions, indent=2))