import gradio as gr
import logging
import os
import json
from PIL import Image, ImageDraw
import torch
from surya.ocr import run_ocr
from surya.detection import batch_text_detection
from surya.layout import batch_layout_detection
from surya.ordering import batch_ordering
from surya.model.detection.model import load_model as load_det_model, load_processor as load_det_processor
from surya.model.recognition.model import load_model as load_rec_model
from surya.model.recognition.processor import load_processor as load_rec_processor
from surya.settings import settings
from surya.model.ordering.processor import load_processor as load_order_processor
from surya.model.ordering.model import load_model as load_order_model

# Configuração do TorchDynamo
torch._dynamo.config.capture_scalar_outputs = True

# Configuração de logging
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Configuração de variáveis de ambiente
logger.info("Configurando variáveis de ambiente para otimização de performance")
os.environ["RECOGNITION_BATCH_SIZE"] = "512"
os.environ["DETECTOR_BATCH_SIZE"] = "36"
os.environ["ORDER_BATCH_SIZE"] = "32"
os.environ["RECOGNITION_STATIC_CACHE"] = "true"

# Carregamento de modelos
logger.info("Iniciando carregamento dos modelos...")

try:
    logger.debug("Carregando modelo e processador de detecção...")
    det_processor, det_model = load_det_processor(), load_det_model()
    logger.debug("Modelo e processador de detecção carregados com sucesso")
except Exception as e:
    logger.error(f"Erro ao carregar modelo de detecção: {e}")
    raise

try:
    logger.debug("Carregando modelo e processador de reconhecimento...")
    rec_model, rec_processor = load_rec_model(), load_rec_processor()
    logger.debug("Modelo e processador de reconhecimento carregados com sucesso")
except Exception as e:
    logger.error(f"Erro ao carregar modelo de reconhecimento: {e}")
    raise

try:
    logger.debug("Carregando modelo e processador de layout...")
    layout_model = load_det_model(checkpoint=settings.LAYOUT_MODEL_CHECKPOINT)
    layout_processor = load_det_processor(checkpoint=settings.LAYOUT_MODEL_CHECKPOINT)
    logger.debug("Modelo e processador de layout carregados com sucesso")
except Exception as e:
    logger.error(f"Erro ao carregar modelo de layout: {e}")
    raise

try:
    logger.debug("Carregando modelo e processador de ordenação...")
    order_model = load_order_model()
    order_processor = load_order_processor()
    logger.debug("Modelo e processador de ordenação carregados com sucesso")
except Exception as e:
    logger.error(f"Erro ao carregar modelo de ordenação: {e}")
    raise

logger.info("Todos os modelos foram carregados com sucesso")

# Compilação do modelo de reconhecimento
logger.info("Iniciando compilação do modelo de reconhecimento...")
try:
    rec_model.decoder.model = torch.compile(rec_model.decoder.model)
    logger.info("Compilação do modelo de reconhecimento concluída com sucesso")
except Exception as e:
    logger.error(f"Erro durante a compilação do modelo de reconhecimento: {e}")
    logger.warning("Continuando sem compilação do modelo")

class CustomJSONEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, Image.Image):
            return "Image object (not serializable)"
        if hasattr(obj, '__dict__'):
            return {k: self.default(v) for k, v in obj.__dict__.items()}
        return str(obj)

def serialize_result(result):
    return json.dumps(result, cls=CustomJSONEncoder, indent=2)

def draw_boxes(image, predictions, color=(255, 0, 0)):
    draw = ImageDraw.Draw(image)
    if isinstance(predictions, list):
        for pred in predictions:
            if hasattr(pred, 'bboxes'):
                for bbox in pred.bboxes:
                    draw.rectangle(bbox, outline=color, width=2)
            elif hasattr(pred, 'bbox'):
                draw.rectangle(pred.bbox, outline=color, width=2)
            elif hasattr(pred, 'polygon'):
                draw.polygon(pred.polygon, outline=color, width=2)
    elif hasattr(predictions, 'bboxes'):
        for bbox in predictions.bboxes:
            draw.rectangle(bbox, outline=color, width=2)
    return image

def ocr_workflow(image, langs):
    logger.info(f"Iniciando workflow OCR com idiomas: {langs}")
    try:
        image = Image.open(image.name)
        logger.debug(f"Imagem carregada: {image.size}")
        predictions = run_ocr([image], [langs.split(',')], det_model, det_processor, rec_model, rec_processor)
        
        # Draw bounding boxes on the image
        image_with_boxes = draw_boxes(image.copy(), predictions[0].text_lines)
        
        # Format the OCR results
        formatted_text = "\n".join([line.text for line in predictions[0].text_lines])
        
        logger.info("Workflow OCR concluído com sucesso")
        return serialize_result(predictions), image_with_boxes, formatted_text
    except Exception as e:
        logger.error(f"Erro durante o workflow OCR: {e}")
        return serialize_result({"error": str(e)}), None, ""

def text_detection_workflow(image):
    logger.info("Iniciando workflow de detecção de texto")
    try:
        image = Image.open(image.name)
        logger.debug(f"Imagem carregada: {image.size}")
        predictions = batch_text_detection([image], det_model, det_processor)
        
        # Draw bounding boxes on the image
        image_with_boxes = draw_boxes(image.copy(), predictions)
        
        # Convert predictions to a serializable format
        serializable_predictions = []
        for pred in predictions:
            serializable_pred = {
                'bboxes': [bbox.tolist() if hasattr(bbox, 'tolist') else bbox for bbox in pred.bboxes],
                'polygons': [poly.tolist() if hasattr(poly, 'tolist') else poly for poly in pred.polygons],
                'confidences': pred.confidences,
                'vertical_lines': [line.tolist() if hasattr(line, 'tolist') else line for line in pred.vertical_lines],
                'image_bbox': pred.image_bbox.tolist() if hasattr(pred.image_bbox, 'tolist') else pred.image_bbox
            }
            serializable_predictions.append(serializable_pred)
        
        logger.info("Workflow de detecção de texto concluído com sucesso")
        return serialize_result(serializable_predictions), image_with_boxes
    except Exception as e:
        logger.error(f"Erro durante o workflow de detecção de texto: {e}")
        return serialize_result({"error": str(e)}), None

def layout_analysis_workflow(image):
    logger.info("Iniciando workflow de análise de layout")
    try:
        image = Image.open(image.name)
        logger.debug(f"Imagem carregada: {image.size}")
        line_predictions = batch_text_detection([image], det_model, det_processor)
        logger.debug(f"Detecção de linhas concluída. Número de linhas detectadas: {len(line_predictions[0].bboxes)}")
        layout_predictions = batch_layout_detection([image], layout_model, layout_processor, line_predictions)
        
        # Draw bounding boxes on the image
        image_with_boxes = draw_boxes(image.copy(), layout_predictions[0], color=(0, 255, 0))
        
        # Convert predictions to a serializable format
        serializable_predictions = []
        for pred in layout_predictions:
            serializable_pred = {
                'bboxes': [
                    {
                        'bbox': bbox.bbox.tolist() if hasattr(bbox.bbox, 'tolist') else bbox.bbox,
                        'polygon': bbox.polygon.tolist() if hasattr(bbox.polygon, 'tolist') else bbox.polygon,
                        'confidence': bbox.confidence,
                        'label': bbox.label
                    } for bbox in pred.bboxes
                ],
                'image_bbox': pred.image_bbox.tolist() if hasattr(pred.image_bbox, 'tolist') else pred.image_bbox
            }
            serializable_predictions.append(serializable_pred)
        
        logger.info("Workflow de análise de layout concluído com sucesso")
        return serialize_result(serializable_predictions), image_with_boxes
    except Exception as e:
        logger.error(f"Erro durante o workflow de análise de layout: {e}")
        return serialize_result({"error": str(e)}), None

def reading_order_workflow(image):
    logger.info("Iniciando workflow de ordem de leitura")
    try:
        image = Image.open(image.name)
        logger.debug(f"Imagem carregada: {image.size}")
        line_predictions = batch_text_detection([image], det_model, det_processor)
        logger.debug(f"Detecção de linhas concluída. Número de linhas detectadas: {len(line_predictions[0].bboxes)}")
        layout_predictions = batch_layout_detection([image], layout_model, layout_processor, line_predictions)
        logger.debug(f"Análise de layout concluída. Número de elementos de layout: {len(layout_predictions[0].bboxes)}")
        bboxes = [pred.bbox for pred in layout_predictions[0].bboxes]
        order_predictions = batch_ordering([image], [bboxes], order_model, order_processor)
        
        # Draw bounding boxes on the image
        image_with_boxes = image.copy()
        draw = ImageDraw.Draw(image_with_boxes)
        for i, bbox in enumerate(order_predictions[0].bboxes):
            draw.rectangle(bbox.bbox, outline=(0, 0, 255), width=2)
            draw.text((bbox.bbox[0], bbox.bbox[1]), str(bbox.position), fill=(255, 0, 0))
        
        logger.info("Workflow de ordem de leitura concluído com sucesso")
        return serialize_result(order_predictions), image_with_boxes
    except Exception as e:
        logger.error(f"Erro durante o workflow de ordem de leitura: {e}")
        return serialize_result({"error": str(e)}), None

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# dr rosheta")
    
    with gr.Tab("OCR"):
        gr.Markdown("## Optical Character Recognition")
        with gr.Row():
            ocr_input = gr.File(label="Carregar Imagem ou PDF")
            ocr_langs = gr.Textbox(label="Idiomas (separados por vírgula)", value="en")
        ocr_button = gr.Button("Executar OCR")
        ocr_output = gr.JSON(label="Resultados OCR")
        ocr_image = gr.Image(label="Imagem com Bounding Boxes")
        ocr_text = gr.Textbox(label="Texto Extraído", lines=10)
        ocr_button.click(ocr_workflow, inputs=[ocr_input, ocr_langs], outputs=[ocr_output, ocr_image, ocr_text])

    

    

if __name__ == "__main__":
    logger.info("Iniciando aplicativo Gradio...")
    demo.launch()