import re
from PIL import Image
from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration
import pytesseract


def inference(image_path):
    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 calculate_ocr(image_path):
    img = Image.open(image_path)
    text = pytesseract.image_to_string(img)
    estimated_sqm = extract_total_sqm(text)
    return estimated_sqm, text