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

# Load the LayoutLMv3 model and processor
model_name = "your-username/your-model-name"  # Replace with your model repository name
model = LayoutLMv3ForTokenClassification.from_pretrained(model_name)
processor = LayoutLMv3Processor.from_pretrained(model_name)

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

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

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...")

    # Perform text detection with EasyOCR
    ocr_results = reader.readtext(uploaded_file, detail=1)

    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]
            ])

    # Convert to layoutlmv3 format
    encoding = processor(image, words=words, boxes=boxes, return_tensors="pt")

    # Perform inference with LayoutLMv3
    with torch.no_grad():
        outputs = model(**encoding)

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

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

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

    true_predictions = [model.config.id2label[pred] for pred, label in zip(predictions, labels) if label != -100]
    true_labels = [model.config.id2label[label] for label in 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

    # Display results
    st.write("Predicted labels:")
    for word, box, pred in zip(true_tokens, true_boxes, true_predictions):
        st.write(f"Word: {word}, Box: {box}, Prediction: {pred}")

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

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

    st.write("Languages and Levels:")
    for language, level in languages_with_levels.items():
        st.write(f"{language}: {level}")