from flask import Flask, request, jsonify, send_file
from paddleocr import PaddleOCR
import base64
from PIL import Image
from io import BytesIO
import traceback
import numpy as np
import cv2
import logging
import os
import uuid
import datetime

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

app = Flask(__name__)

def get_dir_name():
    timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
    unique_id = str(uuid.uuid4())[:8]
    return f"{timestamp}_{unique_id}"

def create_debug_directory(dir_name):
    """Erstellt ein eindeutiges Verzeichnis für Debug-Bilder"""
    base_dir = 'images'
    full_path = os.path.join(base_dir, dir_name)
    
    if not os.path.exists(base_dir):
        os.makedirs(base_dir)
    
    os.makedirs(full_path)
    return full_path

def preprocess_image(image, debug_dir):
    """Bildverarbeitung mit optionalen Optimierungen"""
    try:
        # Graustufenkonvertierung
        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
        
        # Kontrastverbesserung mit CLAHE
        clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))  # Erhöhter Clip-Limit
        enhanced = clahe.apply(gray)
        
        # Rauschunterdrückung mit optimierten Parametern
        denoised = cv2.fastNlMeansDenoising(
            enhanced,
            h=15,                  # Stärkere Rauschreduzierung
            templateWindowSize=7,
            searchWindowSize=21
        )

        # Debug-Bilder speichern
        # cv2.imwrite(os.path.join(debug_dir, 'gray.png'), gray)
        # cv2.imwrite(os.path.join(debug_dir, 'enhanced.png'), enhanced)
        # cv2.imwrite(os.path.join(debug_dir, 'denoised.png'), denoised)

        # Thumbnail als WebP
        denoised_rgb = cv2.cvtColor(denoised, cv2.COLOR_GRAY2RGB)
        thumbnail = Image.fromarray(denoised_rgb)
        thumbnail.thumbnail((256, 256))
        thumbnail_path = os.path.join(debug_dir, 'thumbnail.webp')
        thumbnail.save(thumbnail_path, 'WEBP', quality=85)

        return denoised

    except Exception as e:
        logger.error(f"Preprocessing error: {str(e)}")
        raise

@app.route('/api/ocr', methods=['POST'])
def ocr_endpoint():
    debug_dir = None
    try:
        # Verzeichnis erstellen
        dir_name = get_dir_name()
        debug_dir = create_debug_directory(dir_name)
        
        # Bildverarbeitung
        data = request.get_json()
        image_data = base64.b64decode(data['image'])
        
        # Originalbild als WebP speichern
        original_image = Image.open(BytesIO(image_data)).convert('RGB')
        webp_path = os.path.join(debug_dir, 'original.webp')
        original_image.save(webp_path, 'WEBP', quality=50)

        # WebP-Bild für Verarbeitung laden
        with open(webp_path, 'rb') as f:
            webp_image = Image.open(BytesIO(f.read())).convert('RGB')
        
        # Vorverarbeitung
        processed_image = preprocess_image(np.array(webp_image), debug_dir)

        # OCR mit optimierter Konfiguration
        ocr = PaddleOCR(
            use_angle_cls=True,
            lang='en',
            det_model_dir='en_PP-OCRv3_det',
            rec_model_dir='en_PP-OCRv3_rec',
            det_limit_side_len=processed_image.shape[0] * 2,
            use_dilation=True,
            det_db_score_mode='fast'
        )

        # OCR durchführen
        try:
            result = ocr.ocr(processed_image, rec=True, cls=True)
            
            # Debug-Informationen in Datei speichern
            with open(os.path.join(debug_dir, 'ocr_results.txt'), 'w') as f:
                f.write(f"Raw OCR result:\n{result}\n\n")
            
            if not result:
                logger.warning("No results returned from OCR")
                return jsonify({
                    'warning': 'No text detected',
                    'debug_dir': debug_dir
                }), 200
                
            if not result[0]:
                logger.warning("Empty results list from OCR")
                return jsonify({
                    'warning': 'Empty results list',
                    'debug_dir': debug_dir
                }), 200

            # Ergebnisse verarbeiten
            extracted_results = []
            for idx, item in enumerate(result[0]):
                try:
                    box = item[0]
                    text = item[1][0] if item[1] else ''
                    confidence = float(item[1][1]) if item[1] and len(item[1]) > 1 else 0.0
                    
                    extracted_results.append({
                        'box': box,
                        'text': text,
                        'confidence': confidence,
                        'name': dir_name
                    })
                except Exception as proc_err:
                    logger.error(f"Error processing result {idx}: {str(proc_err)}")

            # Statistiken in Debug-Datei speichern
            with open(os.path.join(debug_dir, 'statistics.txt'), 'w') as f:
                f.write(f"Total results: {len(extracted_results)}\n")
                if extracted_results:
                    avg_confidence = np.mean([r['confidence'] for r in extracted_results])
                    f.write(f"Average confidence: {avg_confidence}\n")
                f.write("\nDetailed results:\n")
                for idx, result in enumerate(extracted_results):
                    f.write(f"Result {idx+1}:\n")
                    f.write(f"Text: {result['text']}\n")
                    f.write(f"Confidence: {result['confidence']}\n")
                    f.write(f"Name: {dir_name}\n")
                    f.write(f"Box coordinates: {result['box']}\n\n")

            return jsonify({
                'status': 'success',
                'results': extracted_results,
            })

        except Exception as ocr_err:
            logger.error(f"OCR processing error: {str(ocr_err)}")
            logger.error(traceback.format_exc())
            return jsonify({
                'error': 'OCR processing failed', 
                'details': str(ocr_err),
                'debug_dir': debug_dir
            }), 500

    except Exception as e:
        logger.error(f"Fehler: {str(e)}")
        return jsonify({
            'error': 'Verarbeitungsfehler',
            'details': str(e),
            'debug_dir': dir_name if debug_dir else None
        }), 500

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=False)