import numpy as np
from PIL import Image, ImageDraw
from pdf2image import convert_from_path
import os
import shutil
import re
import fitz
import base64


def draw_boxes(image_path, boxes):
    image = Image.open(image_path)
    draw = ImageDraw.Draw(image)

    for box in boxes:
        draw.rectangle(box, outline="red", width=2)

    return image


def pdf_to_images(pdf_path):
    images = convert_from_path(pdf_path)

    image_paths = []
    for idx, image in enumerate(images):
        image_file_path = f"extract_tables/table_outputs/pdf-image-{idx + 1}.png"
        image.save(image_file_path, format="PNG")
        image_paths.append(image_file_path)

    return image_paths


def parse_bboxs_gemini_flash(input_string):
    lines = [line for line in input_string.strip().split("\n") if line]
    bounding_boxes = [list(map(int, re.findall(r"\d+", line))) for line in lines]
    return bounding_boxes


def convert_pdf_to_images(pdf_path):
    images = []
    with fitz.open(pdf_path) as doc:
        for page_num in range(len(doc)):
            page = doc.load_page(page_num)
            pix = page.get_pixmap()
            images.append(pix)
    return images


def encode_image_to_base64(image):
    image_bytes = image.tobytes()
    base64_encoded = base64.b64encode(image_bytes)
    base64_string = base64_encoded.decode("utf-8")
    return base64_string


def calculate_scaling_factors(groundtruth_boxes, extracted_boxes):
    assert len(groundtruth_boxes) == len(
        extracted_boxes
    ), "Mismatch in the number of bounding boxes."

    x_factors = []
    y_factors = []

    for gt_box, ext_box in zip(groundtruth_boxes, extracted_boxes):
        gt_xmin, gt_ymin, gt_xmax, gt_ymax = gt_box
        ext_xmin, ext_ymin, ext_xmax, ext_ymax = ext_box

        gt_width = gt_xmax - gt_xmin
        gt_height = gt_ymax - gt_ymin
        ext_width = ext_xmax - ext_xmin
        ext_height = ext_ymax - ext_ymin

        x_factors.append(ext_width / gt_width)
        y_factors.append(ext_height / gt_height)

    x_scale = np.mean(x_factors)
    y_scale = np.mean(y_factors)

    return x_scale, y_scale


def scale_bounding_boxes(extracted_boxes, scaling_factors):
    scaled_boxes = []
    for page_boxes in extracted_boxes:
        scaled_page_boxes = []
        for box in page_boxes:
            scaled_box = [
                box[0] / scaling_factors[0],
                box[1] / scaling_factors[1],
                box[2] / scaling_factors[0],
                box[3] / scaling_factors[1],
            ]
            scaled_page_boxes.append(scaled_box)
        scaled_boxes.append(scaled_page_boxes)
    return scaled_boxes


def clear_directory(directory_path):
    if os.path.exists(directory_path):
        for filename in os.listdir(directory_path):
            file_path = os.path.join(directory_path, filename)
            try:
                if os.path.isfile(file_path) or os.path.islink(file_path):
                    os.unlink(file_path)
                elif os.path.isdir(file_path):
                    shutil.rmtree(file_path)
            except Exception as e:
                print(f"Failed to delete {file_path}. Reason: {e}")