import gradio as gr
import numpy as np
from PIL import Image
import cv2
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from huggingface_hub import hf_hub_download
import torch
import re

# Download and load the GOT OCR model
got_model_path = hf_hub_download(repo_id="junyeopkim/got_2.0_torch_script", filename="got_2.0_tiny.torchscript")
got_model = torch.jit.load(got_model_path)

# Load the Surya-OCR model
surya_processor = TrOCRProcessor.from_pretrained("suryavarmaaddala/suryaocr")
surya_model = VisionEncoderDecoderModel.from_pretrained("suryavarmaaddala/suryaocr")

def preprocess_image(image):
    if isinstance(image, str):
        image = Image.open(image).convert("RGB")
    elif isinstance(image, np.ndarray):
        image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    return image

def got_ocr(image):
    image = preprocess_image(image)
    image = image.resize((224, 224))
    input_tensor = torch.from_numpy(np.array(image)).permute(2, 0, 1).float() / 255.0
    input_tensor = input_tensor.unsqueeze(0)
    
    with torch.no_grad():
        output = got_model(input_tensor)
    
    return output[0].item()

def surya_ocr(image):
    image = preprocess_image(image)
    pixel_values = surya_processor(image, return_tensors="pt").pixel_values
    
    generated_ids = surya_model.generate(pixel_values)
    generated_text = surya_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
    
    return generated_text

def post_process_text(text):
    # Simple post-processing to split into lines
    return '\n'.join(text.split('. '))

def search_text(text, query):
    try:
        pattern = re.compile(query, re.IGNORECASE)
        lines = text.split('\n')
        matching_lines = [line for line in lines if pattern.search(line)]
        return '\n'.join(matching_lines) if matching_lines else "No matches found."
    except re.error:
        return "Invalid regex pattern. Please try again."

def process_and_search(image, search_query):
    try:
        got_score = got_ocr(image)
        surya_text = surya_ocr(image)
        
        result = f"GOT OCR Score: {got_score:.4f}\n\nExtracted Text:\n{surya_text}"
        processed_text = post_process_text(result)
        
        search = None
        if search_query:
            search = search_text(processed_text, search_query)
        return image, processed_text, search
    except Exception as e:
        return None, f"An error occurred: {str(e)}", None

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=1):
            image_input = gr.Image(type="filepath", label="Upload your image")
            search_query_input = gr.Textbox(label="Enter search query")
            submit_button = gr.Button("Submit")
        
        with gr.Column(scale=2):
            displayed_image = gr.Image(label="Uploaded Image")
            ocr_result = gr.Textbox(label="OCR Result", lines=10)
            search_result = gr.Textbox(label="Search Result", lines=5)

    submit_button.click(
        fn=process_and_search,
        inputs=[image_input, search_query_input],
        outputs=[displayed_image, ocr_result, search_result]
    )

if __name__ == "__main__":
    demo.launch()