from transformers import AutoModel
import numpy as np
from PIL import Image
import torch
import os

current_dir = os.getcwd()
images = [
    os.path.join(current_dir, "test", "1.png"),
    os.path.join(current_dir, "test", "1.jpg"),
]


def read_image_as_np_array(image_path):
    with open(image_path, "rb") as file:
        image = Image.open(file).convert("L").convert("RGB")
        image = np.array(image)
    return image


images = [read_image_as_np_array(image) for image in images]

model = AutoModel.from_pretrained(
    "ragavsachdeva/magi", trust_remote_code=True).cuda()
# model = AutoModel.from_pretrained(
#     "./magi", trust_remote_code=True).cuda()
with torch.no_grad():
    results = model.predict_detections_and_associations(images)
    text_bboxes_for_all_images = [x["texts"] for x in results]
    ocr_results = model.predict_ocr(images, text_bboxes_for_all_images)

for i in range(len(images)):
    model.visualise_single_image_prediction(
        images[i], results[i], filename=f"image_{i}.png")
    model.generate_transcript_for_single_image(
        results[i], ocr_results[i], filename=f"transcript_{i}.txt")

print("Done")