REFACTORED_STRUCTURE.md

📁 Refactored Code Structure

The application has been refactored into modular components for better maintainability and understanding.

🗂️ File Structure

📦 Bahngleiserfassung/
├── 🎯 app.py                       # Main Streamlit application (refactored)
├── 📹 video_processing.py          # Video frame extraction and repair utilities
├── 🧠 ontology_integration.py      # Ontology-based scene analysis and risk assessment
├── 🤖 model_processing.py          # Local and remote AI model processing
├── 🖥️ ui_components.py             # Streamlit UI components and rendering
├── 🧮 ontology_eval.py             # Core ontology evaluation logic (unchanged)
├── 🔬 local_models.py              # Local AI models (ViT, BLIP) (unchanged)
└── 💾 app_original_backup.py       # Backup of original monolithic app.py

📋 Module Responsibilities

🎯 app.py - Main Application

• Purpose: Main entry point and orchestration
• Functions:
  • Application initialization and layout
  • Model setup and configuration
  • Main processing workflow coordination
  • Input validation and error handling

📹 video_processing.py - Video Processing

• Purpose: Video frame extraction and repair
• Functions:
  • extract_frames_from_video() - Extract frames at specified FPS
  • repair_video_with_ffmpeg() - Repair corrupted video files
  • Handle various video formats (MP4, AVI, MOV, MKV)

🧠 ontology_integration.py - Ontology Analysis

• Purpose: Scene analysis using ontology-based risk assessment
• Functions:
  • analyze_scene_with_ontology() - Main ontology analysis function
  • _extract_ontology_features() - Extract features from scene descriptions
  • _calculate_person_on_track_confidence() - Calculate specific risk confidence
  • extract_scene_description() - Extract text from model results

🤖 model_processing.py - Model Processing

• Purpose: Handle local and remote AI model processing
• Functions:
  • process_image_locally() - Process images using local models
  • query_huggingface_api() - Process images using remote HF API
  • process_frame() - Unified frame processing interface
  • image_to_base64() - Image conversion utilities

🖥️ ui_components.py - UI Components

• Purpose: Streamlit UI components and rendering
• Functions:
  • render_sidebar_config() - Configuration sidebar
  • render_input_section() - Video upload interface
  • render_frame_result() - Display frame analysis results
  • render_validation_errors() - Show validation messages
  • Various helper rendering functions

🔄 Data Flow

graph TD
    A[app.py] --> B[ui_components.py]
    A --> C[video_processing.py]
    A --> D[model_processing.py]
    A --> E[ontology_integration.py]
    
    C --> F[Extract Frames]
    D --> G[Process with AI Models]
    E --> H[Ontology Risk Assessment]
    
    F --> G
    G --> H
    H --> B
    
    I[local_models.py] --> D
    J[ontology_eval.py] --> E

✨ Benefits of Refactoring

1. 🧩 Modularity: Each module has a single responsibility
2. 🔧 Maintainability: Easier to update and debug individual components
3. 📚 Readability: Clear separation of concerns and smaller, focused files
4. 🧪 Testability: Each module can be tested independently
5. 🔄 Reusability: Components can be reused in other projects
6. 👥 Collaboration: Multiple developers can work on different modules

🚀 Usage

The refactored application works exactly the same as before:

streamlit run app.py

All functionality remains identical:

• ✅ NONE / 🟢 LOW / 🟠 MEDIUM / ⚠️ HIGH / 🚨 CRITICAL classification
• Toggle ontology analysis on/off
• Support for local and remote AI models
• Video processing with automatic repair

🔒 Backwards Compatibility

• Original functionality is preserved
• API and interface remain unchanged
• Configuration and settings work the same way
• The original monolithic code is backed up as app_original_backup.py