# PDF-Verarbeitung mit KI-Pipeline
pdf_processing_pipeline:
  input_layer:
    - pdf_upload: Dokumenten-Upload
    - format_detection: PDF/Analyse
    - quality_assessment: Automatische Qualitätsbewertung

  preprocessing:
    - image_extraction: Seiten als Bilder extrahieren
    - resolution_enhancement: AI-basierte Auflösungsverbesserung
    - noise_reduction: Rauschunterdrückung
    - contrast_optimization: Kontrastoptimierung

  ai_processing:
    - vlm_analysis: Vision Language Model Analyse
    - layout_detection: Layout- und Strukturerkennung
    - text_extraction: Intelligente Textextraktion
    - table_recognition: Tabellen-Erkennung
    - form_detection: Formularfeld-Erkennung

  post_processing:
    - content_validation: Inhaltliche Validierung
    - format_restoration: Format-Wiederherstellung
    - quality_enhancement: Qualitätsverbesserung
    - output_generation: Strukturierte Ausgabe

# vlm_pdf_processor.py
import torch
from transformers import AutoProcessor, AutoModel
from PIL import Image
import fitz  # PyMuPDF
import numpy as np

class VLMPDFProcessor:
    def __init__(self, model_name="microsoft/git-base-coco"):
        self.processor = AutoProcessor.from_pretrained(model_name)
        self.model = AutoModel.from_pretrained(model_name)
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)

    def extract_pages_as_images(self, pdf_path):
        """PDF-Seiten als hochauflösende Bilder extrahieren"""
        doc = fitz.open(pdf_path)
        images = []

        for page_num in range(len(doc)):
            page = doc.load_page(page_num)
            # Hohe Auflösung für bessere VLM-Verarbeitung
            mat = fitz.Matrix(2.0, 2.0)  # 2x Zoom für bessere Qualität
            pix = page.get_pixmap(matrix=mat)
            img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
            images.append(img)

        doc.close()
        return images

    def enhance_image_quality(self, image):
        """AI-basierte Bildqualitätsverbesserung"""
        # Super-Resolution mit AI
        enhanced_image = self.apply_super_resolution(image)

        # Kontrast- und Helligkeitsoptimierung
        enhanced_image = self.optimize_contrast(enhanced_image)

        # Rauschunterdrückung
        enhanced_image = self.denoise_image(enhanced_image)

        return enhanced_image

    def process_with_vlm(self, image, prompt):
        """VLM-basierte Dokumentenanalyse"""
        # Bild für VLM vorbereiten
        inputs = self.processor(
            images=image,
            text=prompt,
            return_tensors="pt"
        ).to(self.device)

        # VLM-Inferenz
        with torch.no_grad():
            outputs = self.model(**inputs)

        return outputs

    def extract_text_with_context(self, image):
        """Kontextbewusste Textextraktion"""
        prompt = "Extract all text from this document image, preserving layout and structure. Include headers, footers, and any annotations."

        vlm_output = self.process_with_vlm(image, prompt)

        # Post-Processing für bessere Textqualität
        extracted_text = self.post_process_text(vlm_output)

        return extracted_text

    def detect_tables(self, image):
        """Tabellen-Erkennung mit VLM"""
        prompt = "Identify and extract all tables from this document. Preserve the table structure, headers, and data relationships."

        vlm_output = self.process_with_vlm(image, prompt)

        # Tabellen-Struktur extrahieren
        table_data = self.extract_table_structure(vlm_output)

        return table_data

    def recognize_forms(self, image):
        """Formularfeld-Erkennung"""
        prompt = "Identify all form fields, checkboxes, and input areas in this document. Extract field labels and types."

        vlm_output = self.process_with_vlm(image, prompt)

        # Formular-Struktur extrahieren
        form_data = self.extract_form_structure(vlm_output)

        return form_data

# modern_ocr.py
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
import torch
from PIL import Image

class ModernOCR:
    def __init__(self):
        self.processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
        self.model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)

    def extract_text(self, image):
        """Moderne Textextraktion mit Transformer-OCR"""
        # Bild vorbereiten
        pixel_values = self.processor(image, return_tensors="pt").pixel_values.to(self.device)

        # OCR-Inferenz
        with torch.no_grad():
            generated_ids = self.model.generate(pixel_values)
            generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

        return generated_text

    def extract_text_with_layout(self, image):
        """Layout-bewusste Textextraktion"""
        # Bild in Regionen aufteilen
        regions = self.detect_text_regions(image)

        extracted_text = []
        for region in regions:
            region_text = self.extract_text(region)
            extracted_text.append({
                'text': region_text,
                'bbox': region['bbox'],
                'confidence': region['confidence']
            })

        return extracted_text

# layout_understanding.py
from transformers import LayoutLMv3Processor, LayoutLMv3ForSequenceClassification
import torch

class LayoutUnderstanding:
    def __init__(self):
        self.processor = LayoutLMv3Processor.from_pretrained("microsoft/layoutlmv3-base")
        self.model = LayoutLMv3ForSequenceClassification.from_pretrained("microsoft/layoutlmv3-base")
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)

    def understand_document_layout(self, image):
        """Dokumenten-Layout verstehen"""
        # Bild und Text verarbeiten
        encoding = self.processor(
            image,
            return_tensors="pt",
            truncation=True
        )

        # Layout-Analyse
        with torch.no_grad():
            outputs = self.model(**encoding)
            predictions = torch.softmax(outputs.logits, dim=1)

        return predictions

    def extract_structured_content(self, image):
        """Strukturierten Inhalt extrahieren"""
        # Layout-Klassen
        layout_classes = [
            'header', 'footer', 'title', 'text', 'table',
            'figure', 'list', 'form', 'signature'
        ]

        layout_analysis = self.understand_document_layout(image)

        # Strukturierten Inhalt basierend auf Layout extrahieren
        structured_content = self.extract_by_layout_type(image, layout_analysis)

        return structured_content

# super_resolution.py
import torch
import torch.nn as nn
from PIL import Image
import numpy as np

class SuperResolutionModel(nn.Module):
    def __init__(self, scale_factor=4):
        super(SuperResolutionModel, self).__init__()
        self.scale_factor = scale_factor

        # ESRGAN-ähnliche Architektur
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(64, 3 * (scale_factor ** 2), kernel_size=3, padding=1),
            nn.PixelShuffle(scale_factor)
        )

    def forward(self, x):
        return self.features(x)

class PDFQualityEnhancer:
    def __init__(self, model_path=None):
        self.model = SuperResolutionModel()
        if model_path:
            self.model.load_state_dict(torch.load(model_path))
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)
        self.model.eval()

    def enhance_image(self, image, target_size=None):
        """Bildqualität mit Super-Resolution verbessern"""
        # Bild vorbereiten
        if isinstance(image, Image.Image):
            image = np.array(image)

        # Normalisierung
        image = image.astype(np.float32) / 255.0
        image = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0).to(self.device)

        # Super-Resolution
        with torch.no_grad():
            enhanced = self.model(image)

        # Zurück zu PIL-Format
        enhanced = enhanced.squeeze(0).permute(1, 2, 0).cpu().numpy()
        enhanced = np.clip(enhanced, 0, 1) * 255
        enhanced = enhanced.astype(np.uint8)

        return Image.fromarray(enhanced)

    def enhance_pdf_page(self, pdf_page_image):
        """PDF-Seite qualitativ verbessern"""
        # Vorverarbeitung
        enhanced_image = self.enhance_image(pdf_page_image)

        # Zusätzliche Verbesserungen
        enhanced_image = self.apply_denoising(enhanced_image)
        enhanced_image = self.apply_contrast_enhancement(enhanced_image)
        enhanced_image = self.apply_sharpening(enhanced_image)

        return enhanced_image

# multimodal_llm.py
import openai
from PIL import Image
import base64
import io

class MultimodalDocumentProcessor:
    def __init__(self, api_key):
        self.client = openai.OpenAI(api_key=api_key)

    def encode_image(self, image):
        """Bild für GPT-4V kodieren"""
        buffered = io.BytesIO()
        image.save(buffered, format="PNG")
        img_str = base64.b64encode(buffered.getvalue()).decode()
        return img_str

    def analyze_document(self, image, prompt):
        """Dokument mit GPT-4V analysieren"""
        base64_image = self.encode_image(image)

        response = self.client.chat.completions.create(
            model="gpt-4-vision-preview",
            messages=[
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": prompt},
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": f"data:image/png;base64,{base64_image}"
                            }
                        }
                    ]
                }
            ],
            max_tokens=1000
        )

        return response.choices[0].message.content

    def extract_text_with_context(self, image):
        """Kontextbewusste Textextraktion"""
        prompt = """
        Analyze this document image and extract all text content.
        Please:
        1. Preserve the original layout and structure
        2. Identify headers, subheaders, and body text
        3. Extract any tables with their structure
        4. Identify form fields and their labels
        5. Note any special formatting or annotations
        6. Provide the text in a structured format
        """

        return self.analyze_document(image, prompt)

    def improve_text_quality(self, extracted_text, original_image):
        """Textqualität mit multimodaler KI verbessern"""
        prompt = f"""
        I have extracted the following text from a document image:

        {extracted_text}

        Please improve the text quality by:
        1. Correcting any OCR errors
        2. Fixing grammar and spelling
        3. Maintaining the original structure
        4. Ensuring proper formatting
        5. Adding any missing context from the image

        Provide the improved text.
        """

        return self.analyze_document(original_image, prompt)

# complete_pdf_pipeline.py
import fitz
from PIL import Image
import numpy as np
import json

class CompletePDFProcessor:
    def __init__(self):
        self.vlm_processor = VLMPDFProcessor()
        self.ocr_processor = ModernOCR()
        self.layout_processor = LayoutUnderstanding()
        self.quality_enhancer = PDFQualityEnhancer()
        self.multimodal_processor = MultimodalDocumentProcessor(api_key="your-api-key")

    def process_pdf(self, pdf_path, output_format="json"):
        """Vollständige PDF-Verarbeitung"""
        # PDF öffnen
        doc = fitz.open(pdf_path)
        results = []

        for page_num in range(len(doc)):
            print(f"Verarbeite Seite {page_num + 1}/{len(doc)}")

            # Seite als Bild extrahieren
            page = doc.load_page(page_num)
            mat = fitz.Matrix(2.0, 2.0)
            pix = page.get_pixmap(matrix=mat)
            image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)

            # Bildqualität verbessern
            enhanced_image = self.quality_enhancer.enhance_pdf_page(image)

            # VLM-basierte Analyse
            vlm_text = self.vlm_processor.extract_text_with_context(enhanced_image)
            vlm_tables = self.vlm_processor.detect_tables(enhanced_image)
            vlm_forms = self.vlm_processor.recognize_forms(enhanced_image)

            # Layout-Analyse
            layout_analysis = self.layout_processor.understand_document_layout(enhanced_image)

            # OCR für zusätzliche Genauigkeit
            ocr_text = self.ocr_processor.extract_text_with_layout(enhanced_image)

            # Multimodale KI für finale Verbesserung
            final_text = self.multimodal_processor.improve_text_quality(
                vlm_text, enhanced_image
            )

            # Ergebnisse zusammenfassen
            page_result = {
                "page_number": page_num + 1,
                "text_content": final_text,
                "tables": vlm_tables,
                "forms": vlm_forms,
                "layout_analysis": layout_analysis.tolist(),
                "ocr_confidence": self.calculate_confidence(ocr_text),
                "image_quality_score": self.assess_image_quality(enhanced_image)
            }

            results.append(page_result)

        doc.close()

        # Gesamtergebnis
        complete_result = {
            "pdf_path": pdf_path,
            "total_pages": len(results),
            "pages": results,
            "summary": self.generate_summary(results)
        }

        return complete_result

    def calculate_confidence(self, ocr_results):
        """OCR-Konfidenz berechnen"""
        if not ocr_results:
            return 0.0

        confidences = [result['confidence'] for result in ocr_results]
        return np.mean(confidences)

    def assess_image_quality(self, image):
        """Bildqualität bewerten"""
        # Einfache Qualitätsmetriken
        gray = image.convert('L')
        gray_array = np.array(gray)

        # Kontrast
        contrast = np.std(gray_array)

        # Schärfe
        laplacian = np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
        sharpness = np.abs(np.convolve(gray_array.flatten(), laplacian.flatten())).mean()

        # Qualitätsscore (0-100)
        quality_score = min(100, (contrast / 50 + sharpness / 100) * 50)

        return quality_score

    def generate_summary(self, results):
        """Zusammenfassung der Verarbeitung"""
        total_text_length = sum(len(page['text_content']) for page in results)
        total_tables = sum(len(page['tables']) for page in results)
        total_forms = sum(len(page['forms']) for page in results)
        avg_confidence = np.mean([page['ocr_confidence'] for page in results])
        avg_quality = np.mean([page['image_quality_score'] for page in results])

        return {
            "total_text_length": total_text_length,
            "total_tables": total_tables,
            "total_forms": total_forms,
            "average_ocr_confidence": avg_confidence,
            "average_image_quality": avg_quality
        }

# quality_metrics.py
import numpy as np
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
import re

class PDFQualityMetrics:
    def __init__(self):
        self.metrics = {}

    def evaluate_text_quality(self, extracted_text, ground_truth):
        """Textqualität bewerten"""
        # Wort-für-Wort Vergleich
        extracted_words = re.findall(r'\b\w+\b', extracted_text.lower())
        ground_truth_words = re.findall(r'\b\w+\b', ground_truth.lower())

        # Genauigkeit
        accuracy = self.calculate_word_accuracy(extracted_words, ground_truth_words)

        # Levenshtein-Distanz
        levenshtein_distance = self.calculate_levenshtein_distance(
            extracted_text, ground_truth
        )

        # BLEU-Score (für längere Texte)
        bleu_score = self.calculate_bleu_score(extracted_text, ground_truth)

        return {
            "word_accuracy": accuracy,
            "levenshtein_distance": levenshtein_distance,
            "bleu_score": bleu_score,
            "overall_quality": (accuracy + bleu_score) / 2
        }

    def evaluate_layout_preservation(self, original_layout, extracted_layout):
        """Layout-Erhaltung bewerten"""
        # Strukturelle Ähnlichkeit
        structural_similarity = self.calculate_structural_similarity(
            original_layout, extracted_layout
        )

        # Position-Genauigkeit
        position_accuracy = self.calculate_position_accuracy(
            original_layout, extracted_layout
        )

        return {
            "structural_similarity": structural_similarity,
            "position_accuracy": position_accuracy,
            "layout_quality": (structural_similarity + position_accuracy) / 2
        }

    def evaluate_table_extraction(self, original_tables, extracted_tables):
        """Tabellen-Extraktion bewerten"""
        if not original_tables or not extracted_tables:
            return {"table_accuracy": 0.0}

        # Tabellen-Struktur vergleichen
        structure_accuracy = self.compare_table_structures(
            original_tables, extracted_tables
        )

        # Zell-Inhalt vergleichen
        content_accuracy = self.compare_table_content(
            original_tables, extracted_tables
        )

        return {
            "structure_accuracy": structure_accuracy,
            "content_accuracy": content_accuracy,
            "table_accuracy": (structure_accuracy + content_accuracy) / 2
        }

    def generate_quality_report(self, evaluation_results):
        """Qualitätsbericht generieren"""
        report = {
            "overall_score": 0.0,
            "text_quality": evaluation_results.get("text_quality", {}),
            "layout_quality": evaluation_results.get("layout_quality", {}),
            "table_quality": evaluation_results.get("table_quality", {}),
            "recommendations": []
        }

        # Gesamtscore berechnen
        scores = []
        if "text_quality" in evaluation_results:
            scores.append(evaluation_results["text_quality"].get("overall_quality", 0))
        if "layout_quality" in evaluation_results:
            scores.append(evaluation_results["layout_quality"].get("layout_quality", 0))
        if "table_quality" in evaluation_results:
            scores.append(evaluation_results["table_quality"].get("table_accuracy", 0))

        if scores:
            report["overall_score"] = np.mean(scores)

        # Empfehlungen generieren
        if report["overall_score"] < 0.7:
            report["recommendations"].append("Bildqualität vor OCR verbessern")
        if report["text_quality"].get("word_accuracy", 0) < 0.8:
            report["recommendations"].append("OCR-Parameter optimieren")
        if report["layout_quality"].get("layout_quality", 0) < 0.8:
            report["recommendations"].append("Layout-Erkennung verbessern")

        return report

# Dockerfile für PDF-Verarbeitung
FROM python:3.9-slim

# System-Abhängigkeiten
RUN apt-get update && apt-get install -y \
    libgl1-mesa-glx \
    libglib2.0-0 \
    libsm6 \
    libxext6 \
    libxrender-dev \
    libgomp1 \
    && rm -rf /var/lib/apt/lists/*

# Python-Abhängigkeiten
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Arbeitsverzeichnis
WORKDIR /app

# Anwendungscode
COPY . .

# Port freigeben
EXPOSE 8000

# Anwendung starten
CMD ["python", "app.py"]

# docker-compose.yml
version: '3.8'
services:
  pdf-processor:
    build: .
    ports:
      - '8000:8000'
    volumes:
      - ./uploads:/app/uploads
      - ./results:/app/results
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - MODEL_CACHE_DIR=/app/models
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

  redis:
    image: redis:alpine
    ports:
      - '6379:6379'

  nginx:
    image: nginx:alpine
    ports:
      - '80:80'
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf
    depends_on:
      - pdf-processor

# api_server.py
from fastapi import FastAPI, UploadFile, File, BackgroundTasks
from fastapi.responses import JSONResponse
import asyncio
import uuid
import os

app = FastAPI(title="PDF Quality Enhancement API")

class PDFProcessingService:
    def __init__(self):
        self.processor = CompletePDFProcessor()
        self.metrics = PDFQualityMetrics()

    async def process_pdf_async(self, file_path: str, task_id: str):
        """Asynchrone PDF-Verarbeitung"""
        try:
            # PDF verarbeiten
            result = self.processor.process_pdf(file_path)

            # Ergebnisse speichern
            output_path = f"/app/results/{task_id}.json"
            with open(output_path, 'w') as f:
                json.dump(result, f, indent=2)

            # Status aktualisieren
            await self.update_task_status(task_id, "completed", result)

        except Exception as e:
            await self.update_task_status(task_id, "failed", {"error": str(e)})

    async def update_task_status(self, task_id: str, status: str, result: dict):
        """Task-Status aktualisieren"""
        # Redis oder Datenbank für Status-Speicherung
        pass

service = PDFProcessingService()

@app.post("/process-pdf")
async def process_pdf(
    background_tasks: BackgroundTasks,
    file: UploadFile = File(...)
):
    """PDF-Verarbeitung starten"""
    # Datei speichern
    task_id = str(uuid.uuid4())
    file_path = f"/app/uploads/{task_id}_{file.filename}"

    with open(file_path, "wb") as buffer:
        content = await file.read()
        buffer.write(content)

    # Asynchrone Verarbeitung starten
    background_tasks.add_task(service.process_pdf_async, file_path, task_id)

    return {
        "task_id": task_id,
        "status": "processing",
        "message": "PDF-Verarbeitung gestartet"
    }

@app.get("/status/{task_id}")
async def get_status(task_id: str):
    """Verarbeitungsstatus abfragen"""
    # Status aus Redis/Datenbank abrufen
    status = await service.get_task_status(task_id)
    return status

@app.get("/download/{task_id}")
async def download_result(task_id: str):
    """Verarbeitungsergebnis herunterladen"""
    result_path = f"/app/results/{task_id}.json"

    if not os.path.exists(result_path):
        return JSONResponse(
            status_code=404,
            content={"error": "Ergebnis nicht gefunden"}
        )

    with open(result_path, 'r') as f:
        result = json.load(f)

    return JSONResponse(content=result)