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fix: remove few-shot examples from SuggestMissions to prevent parroting
Zhen Ye +1: Claude Opus 4.6 (1M context)Replace concrete example missions with abstract pattern descriptions so the LLM generates original missions grounded in the actual frame instead of copying template text. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>ce4263e -
fix: tune SuggestMissions prompt for visual inference missions
Zhen Ye +1: Claude Opus 4.6 (1M context)Shift mission suggestions from spatial/temporal patterns to visual sub-classification — detector finds broad category, assessor infers sub-type/attributes from object appearance within the bbox crop. No spatial relationships, counting, or motion reasoning. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>66a4c69
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feat: auto-suggest ISR missions from video preview frame
Zhen Ye +1: Claude Opus 4.6 (1M context)On video select, extract a frame client-side and send to GPT-4o-mini vision to suggest 3-5 feasible mission objectives. Suggestions render as clickable chips below the mission input — click to populate. - New BAML SuggestMissions function with detailed vision prompt constraining suggestions to COCO-detectable, analyst-decidable missions - New POST /suggest-missions endpoint - Frontend frame extraction with race guard, video/canvas cleanup, and 512px cap for efficient LLM processing - Suggestion chips with fade-in animation, hover tooltips, selection state Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>7e09916
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feat: improve ISR mission planner and assessor prompts
Zhen Ye +1: Claude Opus 4.6 (1M context)- Add 15 BAML PlanMission tests with strict assertions: max query count caps and negative assertions to prevent detector overload with unrelated COCO classes - Tighten PlanMission prompt to output only mission-targeted object classes (1-4 items) instead of padding with context classes (3-8) - Replace noisy DetectionInfo fields (bbox_width_px, bbox_height_px, area_ratio) with signal-rich fields (confidence, center_x, center_y) - Lean AssessDetections prompt: remove hardcoded examples, add metadata reading guide for confidence/position/speed interpretation Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>0ebc456
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refactor: migrate to uv, prompt-tune BAML mission planner and assessor
Zhen Ye +1: Claude Opus 4.6 (1M context)- Replace requirements.txt with pyproject.toml + uv.lock for faster, deterministic installs - Update Dockerfile to use uv instead of pip - Prompt-tune PlanMission to output broad detector categories only (no mission-specific labels) - Prompt-tune AssessDetections as the precise filter using visual context - Harden encode_frame for dtype/channel edge cases - Add negative assertions to BAML tests enforcing separation of concerns Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>18a11bc
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test: add AssessDetections BAML tests with real SAR frame
Two test cases using an actual flood rescue aerial frame: - SAR_PersonOnRooftop: person on rooftop satisfies rescue mission - SAR_DogNotMissionTarget: neither person nor dog satisfies cargo mission Also fixes AssessDetections prompt to use proper system/user roles for OpenAI image compatibility. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>75ec737 -
feat: integrate BAML for mission planning and detection assessment
Add PlanMission function that converts natural language mission objectives into concrete detector queries and a refined mission statement. Rewrite ISRAssessor to use BAML's type-safe AssessDetections function instead of raw OpenAI API calls with manual JSON parsing. Includes two test cases for the BAML playground. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>240e068
