import re
from PIL import Image
import cv2
import numpy as np


def inference(image_path):
    from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration

    image = Image.open(image_path)
    question = "How many living rooms are displayed on this floor plan?"  # not sure if it even has an effect
    processor = Pix2StructProcessor.from_pretrained("google/deplot")
    model = Pix2StructForConditionalGeneration.from_pretrained("google/deplot")

    inputs = processor(images=image, text=question, return_tensors="pt")
    predictions = model.generate(**inputs, max_new_tokens=512)
    output = processor.decode(predictions[0], skip_special_tokens=True)

    return output, predictions


def extract_total_sqm(input_str: str):
    sqmregex = r"(\d+\.?\d*) ?(sq ?m|sq. ?m)"
    matches = re.findall(sqmregex, input_str.lower())
    sqms = [float(m[0]) for m in matches]
    filtered = [sqm for sqm in sqms if 30 < sqm < 160]
    if len(filtered) == 0:
        return None
    return max(filtered)


def calculate_model(image_path):
    output, predictions_tensor = inference(image_path)
    estimated_sqm = extract_total_sqm(output)
    return estimated_sqm, output, predictions_tensor


def improve_img_for_ocr(img: Image):
    img2 = np.array(img.convert("L"))
    cv2.resize(img2, None, fx=1.2, fy=1.2, interpolation=cv2.INTER_CUBIC)
    thresh = cv2.adaptiveThreshold(
        img2, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2
    )
    return Image.fromarray(thresh)


def calculate_ocr(image_path):
    import pytesseract

    img = Image.open(image_path)
    text = pytesseract.image_to_string(img)
    estimated_sqm = extract_total_sqm(text)
    if estimated_sqm is None:
        improved_img = improve_img_for_ocr(img)
        text2 = pytesseract.image_to_string(improved_img)
        estimated_sqm2 = extract_total_sqm(text2)
        with open("recalculating.log", "a") as f:
            f.write(f"before: {estimated_sqm} after: {estimated_sqm2} - {image_path}\n")
        return estimated_sqm2, text2

    return estimated_sqm, text