- Add qRegisterMetaType(QTextCursor) to prevent Qt threading warning
- Import QTextCursor and qRegisterMetaType from PyQt5
- Resolves "Cannot queue arguments of type 'QTextCursor'" warning
- Ensures thread-safe GUI updates for terminal display
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Re-enable kp.core.set_timeout() which is required for proper device communication
- Fix GUI terminal truncation issue by using append() instead of setPlainText()
- Remove aggressive line limiting that was causing log display to stop midway
- Implement gentler memory management (trim only after 1000+ lines)
- This should resolve pipeline timeout issues and complete log display
The previous USB timeout disable was causing stage timeouts without inference results.
The terminal display issue was due to frequent text replacement causing display corruption.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add time-window based FPS calculation using output queue timestamps
- Replace misleading "Theoretical FPS" (based on processing time) with real "Pipeline FPS"
- Track actual inference output generation rate over 10-second sliding window
- Add thread-safe FPS calculation with proper timestamp management
- Display realistic FPS values (4-9 FPS) instead of inflated values (90+ FPS)
Key improvements:
- _record_output_timestamp(): Records when each output is generated
- get_current_fps(): Calculates FPS based on actual throughput over time window
- Thread-safe implementation with fps_lock for concurrent access
- Automatic cleanup of old timestamps outside the time window
- Integration with GUI display to show meaningful FPS metrics
This provides users with accurate inference throughput measurements that reflect
real-world performance, especially important for multi-dongle setups where
understanding actual scaling is crucial.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add StdoutCapture context manager to capture all print() statements
- Connect captured output to GUI terminal display via stdout_captured signal
- Fix logging issue where pipeline initialization and operation logs were not shown in app
- Prevent infinite recursion with _emitting flag in TeeWriter
- Ensure both console and GUI receive all log messages during deployment
- Comment out USB timeout setting that was causing device timeout issues
This resolves the issue where logs would stop showing partially in the app,
ensuring complete visibility of MultiDongle and InferencePipeline operations.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Remove all debug print statements from deployment dialog
- Remove debug output from workflow orchestrator and inference pipeline
- Remove test signal emissions and unused imports
- Code is now clean and production-ready
- Results are successfully flowing from inference to GUI display
- Add time import for test result generation
- Add test signal emissions to verify GUI connection works
- Add debug prints for signal establishment
- Test both result_updated and terminal_output signals
- This will help identify if the issue is signal connection or data flow
- Add debug prints in combined_result_callback to see received data
- Add debug prints in update_inference_results to track GUI updates
- Fix tuple order in terminal formatting to match actual (probability, result) format
- This will help identify why results show in terminal but not in GUI
- Add upload_fw property to ExactModelNode for firmware upload control
- Display all model node properties in right panel (model_path, scpu_fw_path, ncpu_fw_path, dongle_series, num_dongles, port_id, upload_fw)
- Replace console terminal output with GUI terminal display in deployment dialog
- Add Terminal Output section to deployment tab with proper formatting
- Terminal results now appear in app view instead of console for packaged apps
- Maintain backward compatibility with existing pipeline configurations
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add result callback mechanism to WorkflowOrchestrator
- Implement result_updated signal in DeploymentWorker
- Create detailed inference results display with timestamps and formatted output
- Support both tuple and dict result formats
- Add auto-scrolling results panel with history management
- Connect pipeline results to Live View tab for real-time monitoring
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fix remaining array comparison error in inference result validation
- Update PyQt signal signature for proper numpy array handling
- Improve DeploymentWorker to keep running after deployment
- Enhance stop button with non-blocking UI updates and better error handling
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fix ambiguous truth value error in InferencePipeline result handling
- Add stop inference button to deployment dialog with proper UI state management
- Improve error handling for tuple vs dict result types
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Major Features:
• Complete deployment dialog system with validation and dongle management
• Enhanced dashboard with deploy button and validation checks
• Comprehensive deployment test suite and demo scripts
• Pipeline validation for model paths, firmware, and port configurations
• Real-time deployment status tracking and error handling
Technical Improvements:
• Node property validation for deployment readiness
• File existence checks for models and firmware files
• Port ID validation and format checking
• Integration between UI components and core deployment functions
• Comprehensive error messaging and user feedback
New Components:
• DeploymentDialog with advanced configuration options
• Pipeline deployment validation system
• Test deployment scripts with various scenarios
• Enhanced dashboard UI with deployment workflow
• Screenshot updates reflecting new deployment features
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
