System Component to Code Module and LLM Utility Mapping

UI Layer - Gradio UI: Handles user input, diagram type selection, preview, and download. - Code: GradioInterface (to be implemented in the main app, e.g., UMLBot_streamlit_app.md or a new UI module) - Interacts with: ChatBotService (service layer)

Service Layer - ChatBotService: Orchestrates the flow from user request to diagram generation. - Code: New module/class, e.g., UMLBot/service/chatbot_service.py - Uses: NLPParser, DiagramBuilder - NLPParser: Converts free text to UML constructs using LLM. - Code: New module/class, e.g., UMLBot/service/nlp_parser.py - Leverages: llm_utils/aiweb_common/generate/ChatServicer.py, PromptAssembler.py for LLM calls and prompt construction. - DiagramBuilder: Builds PlantUML code from parsed model, manages validation and repair. - Code: New module/class, e.g., UMLBot/service/diagram_builder.py - Uses: PlantUMLValidator, ThemeManager, PlantUmlClient - PlantUMLValidator & AutoRepairStrategy: Validate and repair PlantUML code. - Code: New module/class, e.g., UMLBot/service/plantuml_validator.py - Can leverage: LLM utilities for advanced repair (e.g., ChatServicer for LLM-based repair suggestions) - ThemeManager: Supplies PlantUML themes. - Code: New module/class, e.g., UMLBot/service/theme_manager.py

Infrastructure Layer - PlantUmlClient: Renders PlantUML code (remote or local). - Code: New module/class, e.g., UMLBot/infrastructure/plantuml_client.py - Implements: HTTP and local JAR rendering, with failover logic. - Database, Auth, Monitoring: Standard integrations for user/session/history, authentication, and logging. - Code: To be implemented as needed, e.g., UMLBot/infrastructure/db.py, auth.py, monitoring.py

Supporting Services - DownloadController: Serves images to the UI. - Code: Part of UI or a small API endpoint.

Chat-Based UML Revision Workflow

  • The chat workflow enables users to iteratively describe, revise, and correct UML diagrams through conversational interaction.
  • The UI (Gradio/Streamlit) relays user messages to the service layer, which orchestrates prompt construction, LLM invocation, and diagram updates.
  • Each chat turn can request new features, corrections, or explanations; the system updates the PlantUML code and diagram accordingly.
  • The workflow supports error correction by leveraging LLM-based auto-repair and surfacing actionable feedback to the user.

Generic Error Handler Integration

  • The generic error handler is integrated across all layers:
  • UI: Displays status and error messages, provides fallback diagrams.
  • Service: Captures errors from LLM, prompt, and rendering steps; attempts auto-correction.
  • Infrastructure: Handles network and rendering failures, reporting issues to the service layer.
  • All errors are routed through the error handler, ensuring transparency and robust recovery.

LLM Utility Integration - All LLM calls (NLP parsing, auto-repair, chat-based revision, error handling) should use the llm_utils/aiweb_common/generate/ classes: - ChatServicer for conversational LLM calls. - PromptAssembler for prompt construction. - QueryInterface for abstraction. - ObjectFactory for extensibility (e.g., plugin new diagram types). - WorkflowHandler for orchestrating multi-step flows and logging. - GenericErrorHandler for capturing, reporting, and auto-correcting errors.

This mapping ensures that all LLM-related logic and error handling are centralized and reusable, following the project's coding standards and leveraging the common codebase.

Next steps: Specify in detail how and where to leverage these LLM interface classes and error handler in the design.