- Added backend.yaml.example with AI, DB, auth, and logging schema
- Added frontend.yaml.example with API and feature flag schema
- Added network.yaml.example with ports and SSL schema
- Added docker.yaml.example with resource and volume schema
- Added secrets.yaml.example as a template for sensitive data
- All files include comprehensive comments documenting every variable
- Created OPERATIONAL_RUNBOOK.md: comprehensive step-by-step procedures for both Docker and Standalone deployment modes covering deployment, daily ops, troubleshooting, backup/restore, disaster recovery, scaling, and updates
- Created HEALTH_MONITORING_CHECKLIST.md: daily/weekly/monthly health check procedures with alert thresholds and quick troubleshooting reference
- Created DISASTER_RECOVERY_PLAN.md: detailed procedures for 6 failure scenarios (database corruption, hardware failure, data center failure, app crash, disk full, network isolation) with RTO/RPO targets
- Created CONFIGURATION_REFERENCE.md: complete documentation of all inventory.env parameters for both deployment modes with common scenarios and troubleshooting
- Created EMERGENCY_PROCEDURES.md: quick-reference incident response playbook with 7 critical scenarios, decision tree, escalation path, and printable cheat sheet
- Created scripts/backup.sh: automated backup script supporting both Docker and Standalone with integrity verification and retention management
- Created scripts/restore.sh: restore script with triple confirmation, safety backups, and validation tests for both deployment modes
- Created config/backup-cron.sh: installer for daily/weekly automated backup cron jobs (2 AM daily, 3 AM Sunday)
All documentation covers dual-deployment modes with shared configuration files.
Documentation is operator-ready with copy-paste commands and clear expected outputs.
- Increase padding from 10-15px to 20-30px for context
- Emphasize ENTIRE item must be visible, not tight crop
- Add examples of correct vs wrong crop detection
- Address findings from debug panel testing: AI was cropping too tightly
Items often have multiple labels/zones at different orientations:
- Main label with part number, specs (PRIMARY)
- Vendor logos or small text (secondary)
- Barcodes at odd angles
Instruction: Always optimize rotation for the PRIMARY label (most text),
ignore secondary/vendor labels that conflict in orientation.
Examples added showing how to handle items with multiple label zones.
Real-world photos come at ANY angle - upside down, sideways, at weird
tilts. Stop assuming small angles. Instead:
- Measure CURRENT text orientation (horizontal/vertical/upside-down/tilted)
- Calculate rotation needed to make it READABLE in standard English
- Allow full -180° to +180° range
- No artificial limits or assumptions about how operator took the photo
Examples: vertical text → ±90°, upside-down → ±180°, tilted → measure tilt
This gives Gemini freedom to analyze real-world messy photos correctly.
Key changes:
- Explicitly state image has EXIF orientation stripped (raw/native state)
- Return ONLY the tilt angle, not orientation shifts
- Expect small angles (-45° to +45°), not large ones
- Add safety check: if angle > 45°, likely measurement error
- Simplify examples to show tilt-only measurement
This ensures Gemini returns consistent, sane rotation values regardless
of the item or how the photo was taken, without needing local offsets.
Previous prompt only measured text baseline angle from horizontal (-45° to +45°),
missing that text can be vertical/sideways, requiring larger rotations (up to ±180°).
New guidance:
- Measure TOTAL rotation needed to make text horizontal and readable
- Account for both chassis tilt AND text orientation (horizontal vs vertical)
- Example: 22° tilt + 90° vertical text = 112° total rotation
- Allows full -180° to +180° range instead of limiting to ±45°
No changes to item identification fields, OCR rules, or crop bounds analysis.
Gemini analyzes raw image and returns crop_bounds for that coordinate space.
Previous code applied EXIF rotation first, changing image dimensions, then
used crop_bounds on the rotated image (coordinate mismatch).
Now: crop on raw image (matches AI) → then apply EXIF + manual rotation.
This ensures cropped region contains the actual item, not background.
- Cap normal rotation to -45 to +45 degrees (flags errors beyond)
- Add explicit warning about 60°+ being measurement error
- Clarify 'safe default' is 0 if uncertain
- Prevent Gemini from wild angle guesses (like 110°)
- Help AI self-correct measurement errors
- Add rotation_degrees parameter to ImageProcessor.process_photo()
- Pass rotation through _auto_save_photo_from_extraction() to processor
- Allow no-crop fallback when crop_bounds is None
- Add buildPhotoUrl() helper to resolve backend URLs correctly
- Update frontend components to use backend URL for image sources
- Replace Use/Skip Photo buttons with checkbox in AI extraction UI
- Add images/ to .gitignore to prevent accidental commits
Addresses: rotation never applied, image 404s (relative to Next.js not backend), preview blank in edit form
- Added 11 comprehensive tests for image_processing parsing
- Tests validate crop_bounds structure: {x, y, width, height} all ints >= 0
- Tests validate rotation_degrees: int/float, -360 to +360
- Tests validate confidence: float, 0.0 to 1.0
- Tests graceful handling when image_processing field is missing
- Tests multiple items with image_processing data
- Tests partial data handling (optional fields)
- Tests with both Gemini and Claude providers
- Updated extract_label_info() to preserve and validate image_processing field
- All tests passing, no regressions
- Add SIZE CONVERSION RULES section (critical) with explicit thresholds
- Convert ≥1600GB to TB format (e.g., 1600GB → 1.6TB)
- Convert memory ≥1024MB to GB format
- Update Item field examples with human-readable sizes
- Update Size field definition to emphasize HUMAN-READABLE format
- Update OCR key definition with size conversion examples
- Apply rules consistently across all size-related fields
This ensures AI-extracted items display sizes in user-friendly format
and improves OCR matching accuracy with normalized size representations.
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
