import streamlit as st
from transformers import LayoutLMv3ForTokenClassification, LayoutLMv3Processor
from PIL import Image
import torch
import easyocr
from PIL import Image
import re

# Load the model and processor from Hugging Face
model_name = "capitaletech/LayoutLMv3-v1"  # Replace with your model repository name
model = LayoutLMv3ForTokenClassification.from_pretrained(model_name)
processor = LayoutLMv3Processor.from_pretrained(model_name)

st.title("LayoutLMv3 Text Extraction")
st.write("Upload an image to get text predictions using the fine-tuned LayoutLMv3 model.")

uploaded_file = st.file_uploader("Choose an image...", type="png")

if uploaded_file is not None:
    image = Image.open(uploaded_file)
    st.image(image, caption='Uploaded Image.', use_column_width=True)
    st.write("")
    st.write("Classifying...")

    # Process the image
    words = uploaded_file["tokens"]
    boxes = uploaded_file["bboxes"]
    word_labels = uploaded_file["ner_tags"]

    encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**encoding)

    logits = outputs.logits
    predictions = logits.argmax(-1).squeeze().cpu.tolist()
    labels = encoding['labels'].squeeze().tolist()


    # Set up the EasyOCR reader for multiple languages
    languages = ["ru", "rs_cyrillic", "be", "bg", "uk", "mn", "en"]
    reader = easyocr.Reader(languages)

    # Load the image
    image_path = example["img_path"]
    image = Image.open(image_path)

    # Perform text detection
    ocr_results = reader.readtext(image_path, detail=1)

    # Extract text and bounding boxes, filtering non-alphabetic characters from text
    words = []
    boxes = []

    # Define a regular expression pattern for non-alphabetic characters
    non_alphabet_pattern = re.compile(r'[^a-zA-Z]+')

    for result in ocr_results:
        bbox, text, _ = result
        filtered_text = re.sub(non_alphabet_pattern, '', text)
        if filtered_text:  # Only append if there are alphabetic characters left
            words.append(filtered_text)
            boxes.append([
                bbox[0][0], bbox[0][1],
                bbox[2][0], bbox[2][1]
            ])

    words = words[1:]


    def unnormalize_box(bbox, width, height):
        return [
         width * (bbox[0] / 1000),
         height * (bbox[1] / 1000),
         width * (bbox[2] / 1000),
         height * (bbox[3] / 1000),
         ]

    token_boxes = encoding["bbox"].squeeze().tolist()
    width, height = image.size

    true_predictions = [model.config.id2label[pred] for pred, label in zip(predictions, labels) if label != - 100]
    true_labels = [model.config.id2label[label] for prediction, label in zip(predictions, labels) if label != -100]
    true_boxes = [unnormalize_box(box, width, height) for box, label in zip(token_boxes, labels) if label != -100]
    true_tokens = words

    # Associate languages with their levels
    languages_with_levels = {}
    current_language = None

    j=0

    for i in range(0, len(true_labels)):
        if true_labels[i] == 'language':
            current_language = words[j]
            j= j+1
            languages_with_levels[current_language] = true_labels[i+1]

    print(languages_with_levels)

    input_ids = encoding["input_ids"]
    bbox = encoding["bbox"]
    attention_mask = encoding["attention_mask"]

    st.write("Predicted labels:")
    # Print languages with their levels
    for language, level in languages_with_levels.items():
        st.write(f"{language}: {level}")