Merge pull request #4 from scub-france/adapt-containerisation

Adapt documentation and fix containerisation

Dockerfile

@@ -1,4 +1,6 @@
 FROM python:3.12
+# Install poppler for PDF processing
+RUN apt-get update && apt-get install -y poppler-utils && rm -rf /var/lib/apt/lists/*
 WORKDIR /app
 COPY backend/requirements.txt ./backend/requirements.txt
 RUN pip install --no-cache-dir -r backend/requirements.txt

README.md

@@ -1,9 +1,108 @@
 # DocTags Analyzer and Visualizer
 
-Simple command to process PDF pages with DocTags:
+AI-powered document analysis and visualization tool for extracting structured content from PDFs.
+
+## 🚀 Quick Start with Docker
+
+### Prerequisites
+- Docker and Docker Compose installed
+- At least 4GB of free memory
+- ~500MB disk space for the AI model
+
+### Running with Docker
+
+1. **Clone the repository**
+   ```bash
+   git clone <repository-url>
+   cd doc-analyzer
+   ```
+
+2. **Place your PDF files in the project directory**
+   ```bash
+   cp /path/to/your/document.pdf ./
+   ```
+
+3. **Start the application**
+   ```bash
+   docker-compose up -d --build
+   ```
+
+4. **Access the web interface**
+   - Open http://localhost:8080 in your browser
+   - Select a PDF from the dropdown
+   - Process your documents through the three-step workflow
+
+### First Run Notice
+⚠️ **Important**: The first analysis will take 5-10 minutes as the AI model (SmolDocling-256M) needs to be downloaded (~500MB). Subsequent runs will be much faster (30-60 seconds).
+
+## 📋 Features
+
+- **Document Analysis**: Extract comprehensive document structure using AI
+- **Visualization**: Generate visual overlays showing document elements
+- **Image Extraction**: Automatically extract and catalog embedded images
+- **Web Interface**: User-friendly interface for document processing
+
+## 🛠️ Manual Usage
+
+Process PDF pages with DocTags:
 
 ```bash
 python analyzer.py --image document.pdf --page 8 && python visualizer.py --doctags results/output.doctags.txt --pdf document.pdf --page 8 --adjust && python picture_extractor.py --doctags results/output.doctags.txt --pdf document.pdf --page 8 --adjust
 ```
 
-All output files will be automatically stored in the `results` folder.
+## 🐛 Troubleshooting
+
+### Docker Issues
+
+1. **Container won't start**
+   - Check logs: `docker-compose logs analyser`
+   - Ensure ports aren't in use: `lsof -i :8080`
+
+2. **"No module named 'docling_core'" error**
+   - Rebuild the container: `docker-compose down && docker-compose up -d --build`
+
+3. **Analysis stuck on "Running..."**
+   - First run downloads the AI model (~500MB), this can take 5-10 minutes
+   - Check progress: `docker-compose exec analyser du -sh /root/.cache/huggingface/`
+   - Monitor CPU usage: `docker-compose exec analyser ps aux | grep analyzer`
+
+4. **PDF not loading**
+   - Ensure poppler is installed (already included in Dockerfile)
+   - Place PDFs in the project root directory
+   - PDFs must have `.pdf` extension
+
+### Performance Tips
+
+- First analysis is slow due to model download
+- Subsequent analyses are much faster (model is cached)
+- Processing time depends on PDF complexity and page size
+- Monitor memory usage: `docker-compose exec analyser free -h`
+
+## 📁 Project Structure
+
+```
+doc-analyzer/
+├── backend/
+│   ├── page_treatment/     # Core processing scripts
+│   │   ├── analyzer.py     # AI-powered document analysis
+│   │   ├── visualizer.py   # Visualization generator
+│   │   └── picture_extractor.py  # Image extraction
+│   ├── app.py             # Flask web application
+│   └── requirements.txt   # Python dependencies
+├── frontend/              # Web interface
+├── results/              # Output directory (auto-created)
+├── Dockerfile           # Docker configuration
+└── docker-compose.yml   # Docker Compose setup
+```
+
+## 🔧 Development
+
+To modify the application:
+
+1. Make changes to the code
+2. Rebuild the Docker image: `docker-compose up -d --build`
+3. Check logs for errors: `docker-compose logs -f analyser`
+
+## 📄 License
+
+This project is open source and available under the MIT License.

backend/config.py

@@ -12,7 +12,7 @@ APP_VERSION = "1.0.0"
 DEBUG = os.environ.get('DEBUG', 'False').lower() == 'true'
 
 # Server settings
-HOST = '127.0.0.1'
+HOST = '0.0.0.0'
 PORT = 5000
 MAX_CONTENT_LENGTH = 100 * 1024 * 1024  # 100MB
 

backend/requirements.txt

@@ -5,4 +5,5 @@ torchvision
 pdf2image
 pillow
 requests
-flask
+flask
+docling_core

docker-compose.yml

@@ -1,7 +1,7 @@
 services:
   analyser:
-    image: ddd
+    build: .
     ports:
       - "8080:5000"
     volumes:
-      - ./input:/
+      - ./:/app