refactor: strip threat fields from all frontend and backend code

Remove all references to threat_level_score, threat_classification,
weapon_readiness, gpt_distance_m, gpt_direction, gpt_description,
HEL imports, syncKnobDisplays, recomputeHEL, /chat/threat endpoint,
and chatAboutThreats client function across 14 files.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

app.py

@@ -57,8 +57,7 @@ from jobs.storage import (
     get_job_storage,
     get_output_video_path,
 )
-from utils.gpt_reasoning import estimate_threat_gpt
-from utils.threat_chat import chat_about_threats
+from utils.gpt_reasoning import evaluate_satisfaction_gpt
 from utils.relevance import evaluate_relevance
 from utils.enrichment import run_enrichment
 from utils.schemas import AssessmentStatus
@@ -656,9 +655,8 @@ async def analyze_frame(
     detections: str = Form(...),
     job_id: str = Form(None),
 ):
-    """Run GPT threat assessment on a single video frame."""
+    """Run GPT satisfaction evaluation on a single video frame."""
     import json as json_module
-    from utils.gpt_reasoning import encode_frame_to_b64
 
     dets = json_module.loads(detections)
 
@@ -677,13 +675,12 @@ async def analyze_frame(
         raise HTTPException(status_code=400, detail="Invalid image")
 
     # Run GPT in thread pool (blocking OpenAI API call)
-    frame_b64 = encode_frame_to_b64(frame)
     async with _GPT_SEMAPHORE:
         gpt_results = await asyncio.to_thread(
-            estimate_threat_gpt,
+            evaluate_satisfaction_gpt,
             detections=dets,
             mission_spec=mission_spec,
-            image_b64=frame_b64,
+            frame_data=frame,
         )
 
     # Merge GPT results into detection records
@@ -693,12 +690,8 @@ async def analyze_frame(
             payload = gpt_results[oid]
             d["gpt_raw"] = payload
             d["assessment_status"] = payload.get("assessment_status", "ASSESSED")
-            d["threat_level_score"] = payload.get("threat_level_score", 0)
-            d["threat_classification"] = payload.get("threat_classification", "Unknown")
-            d["weapon_readiness"] = payload.get("weapon_readiness", "Unknown")
-            d["gpt_description"] = payload.get("gpt_description")
-            d["gpt_distance_m"] = payload.get("gpt_distance_m")
-            d["gpt_direction"] = payload.get("gpt_direction")
+            d["satisfies"] = payload.get("satisfies")
+            d["reason"] = payload.get("reason")
 
     return dets
 
@@ -914,7 +907,7 @@ async def reason_track(
     # For high concurrency, might want to offload to threadpool or async wrapper.
     try:
         async with _GPT_SEMAPHORE:
-            results = await asyncio.to_thread(estimate_threat_gpt, input_path, track_list)
+            results = await asyncio.to_thread(evaluate_satisfaction_gpt, input_path, track_list)
         logging.info(f"GPT Output for Video Track Update:\n{results}")
     except Exception as e:
         logging.exception("GPT reasoning failed")
@@ -925,56 +918,6 @@ async def reason_track(
     return results
 
 
-@app.post("/chat/threat")
-async def chat_threat_endpoint(
-    question: str = Form(...),
-    detections: str = Form(...),  # JSON string of current detections
-    mission_context: str = Form(""),  # Optional JSON string of mission spec
-):
-    """
-    Chat about detected threats using GPT.
-
-    Args:
-        question: User's question about the current threat situation.
-        detections: JSON string of detection list with threat analysis data.
-        mission_context: Optional JSON string of mission specification.
-
-    Returns:
-        GPT response about the threats.
-    """
-    import json as json_module
-
-    if not question.strip():
-        raise HTTPException(status_code=400, detail="Question cannot be empty.")
-
-    try:
-        detection_list = json_module.loads(detections)
-    except json_module.JSONDecodeError:
-        raise HTTPException(status_code=400, detail="Invalid detections JSON.")
-
-    if not isinstance(detection_list, list):
-        raise HTTPException(status_code=400, detail="Detections must be a list.")
-
-    # Parse optional mission context
-    mission_spec_dict = None
-    if mission_context.strip():
-        try:
-            mission_spec_dict = json_module.loads(mission_context)
-        except json_module.JSONDecodeError:
-            pass  # Non-critical, proceed without mission context
-
-    # Run chat in thread to avoid blocking (with concurrency limit)
-    try:
-        async with _GPT_SEMAPHORE:
-            response = await asyncio.to_thread(
-                chat_about_threats, question, detection_list, mission_spec_dict
-            )
-        return {"response": response}
-    except Exception as e:
-        logging.exception("Threat chat failed")
-        raise HTTPException(status_code=500, detail=str(e))
-
-
 @app.post("/benchmark")
 async def benchmark_endpoint(
     video: UploadFile = File(...),

coco_classes.py

@@ -105,56 +105,6 @@ def _normalize(label: str) -> str:
 
 
 _CANONICAL_LOOKUP: Dict[str, str] = {_normalize(name): name for name in COCO_CLASSES}
-_COCO_SYNONYMS: Dict[str, str] = {
-    "people": "person",
-    "man": "person",
-    "woman": "person",
-    "men": "person",
-    "women": "person",
-    "pedestrian": "person",
-    "soldier": "person",
-    "infantry": "person",
-    "civilian": "person",
-    "motorbike": "motorcycle",
-    "motor bike": "motorcycle",
-    "bike": "bicycle",
-    "aircraft": "airplane",
-    "plane": "airplane",
-    "jet": "airplane",
-    "aeroplane": "airplane",
-    "drone": "airplane",
-    "uav": "airplane",
-    "helicopter": "airplane",
-    "pickup": "truck",
-    "pickup truck": "truck",
-    "semi": "truck",
-    "lorry": "truck",
-    "tractor trailer": "truck",
-    "vehicle": "car",
-    "sedan": "car",
-    "suv": "car",
-    "van": "car",
-    "vessel": "boat",
-    "ship": "boat",
-    "warship": "boat",
-    "speedboat": "boat",
-    "cargo ship": "boat",
-    "fishing boat": "boat",
-    "yacht": "boat",
-    "kayak": "boat",
-    "canoe": "boat",
-    "watercraft": "boat",
-    "coach": "bus",
-    "television": "tv",
-    "tv monitor": "tv",
-    "mobile phone": "cell phone",
-    "smartphone": "cell phone",
-    "cellphone": "cell phone",
-    "dinner table": "dining table",
-    "sofa": "couch",
-    "cooker": "oven",
-}
-_ALIAS_LOOKUP: Dict[str, str] = {_normalize(alias): canonical for alias, canonical in _COCO_SYNONYMS.items()}
 
 
 # ---------------------------------------------------------------------------
@@ -226,11 +176,10 @@ def canonicalize_coco_name(value: str | None) -> str | None:
 
     Matching cascade:
     1. Exact normalized match
-    2. Synonym lookup
-    3. Substring match (alias then canonical)
-    4. Token-level match
-    5. Fuzzy string match (difflib)
-    6. Semantic embedding similarity (sentence-transformers)
+    2. Substring match
+    3. Token-level match
+    4. Fuzzy string match (difflib)
+    5. Semantic embedding similarity (sentence-transformers)
     """
 
     if not value:
@@ -240,12 +189,7 @@ def canonicalize_coco_name(value: str | None) -> str | None:
         return None
     if normalized in _CANONICAL_LOOKUP:
         return _CANONICAL_LOOKUP[normalized]
-    if normalized in _ALIAS_LOOKUP:
-        return _ALIAS_LOOKUP[normalized]
 
-    for alias_norm, canonical in _ALIAS_LOOKUP.items():
-        if alias_norm and alias_norm in normalized:
-            return canonical
     for canonical_norm, canonical in _CANONICAL_LOOKUP.items():
         if canonical_norm and canonical_norm in normalized:
             return canonical
@@ -254,8 +198,6 @@ def canonicalize_coco_name(value: str | None) -> str | None:
     for token in tokens:
         if token in _CANONICAL_LOOKUP:
             return _CANONICAL_LOOKUP[token]
-        if token in _ALIAS_LOOKUP:
-            return _ALIAS_LOOKUP[token]
 
     close = difflib.get_close_matches(normalized, list(_CANONICAL_LOOKUP.keys()), n=1, cutoff=0.82)
     if close:

frontend/index.html

@@ -287,7 +287,6 @@
   <script src="./js/core/state.js"></script>
   <script src="./js/core/physics.js"></script>
   <script src="./js/core/video.js"></script>
-  <script src="./js/core/hel.js"></script>
   <script src="./js/ui/logging.js"></script>
   <script src="./js/core/gptMapping.js"></script>
   <script src="./js/core/tracker.js"></script>

frontend/js/api/client.js

@@ -127,11 +127,8 @@ APP.api.client._syncGptFromDetections = function (rawDets, logLabel) {
             if (existing && rd.gpt_raw) {
                 const g = rd.gpt_raw;
                 existing.features = APP.core.gptMapping.buildFeatures(g);
-                existing.threat_level_score = rd.threat_level_score || g.threat_level_score || 0;
-                existing.threat_classification = rd.threat_classification || g.threat_classification || "Unknown";
-                existing.weapon_readiness = rd.weapon_readiness || g.weapon_readiness || "Unknown";
-                existing.gpt_distance_m = rd.gpt_distance_m || null;
-                existing.gpt_direction = rd.gpt_direction || null;
+                existing.satisfies = rd.satisfies ?? g.satisfies ?? null;
+                existing.reason = rd.reason || g.reason || null;
                 needsRender = true;
             }
         }
@@ -349,26 +346,3 @@ APP.api.client.analyzeFrame = async function (videoEl, tracks) {
     return await resp.json();
 };
 
-// Chat about threats using GPT
-APP.api.client.chatAboutThreats = async function (question, detections) {
-    const { state } = APP.core;
-
-    const form = new FormData();
-    form.append("question", question);
-    form.append("detections", JSON.stringify(detections));
-    if (state.hf.missionSpec) {
-        form.append("mission_context", JSON.stringify(state.hf.missionSpec));
-    }
-
-    const resp = await fetch(`${state.hf.baseUrl}/chat/threat`, {
-        method: "POST",
-        body: form
-    });
-
-    if (!resp.ok) {
-        const err = await resp.json().catch(() => ({ detail: resp.statusText }));
-        throw new Error(err.detail || "Chat request failed");
-    }
-
-    return await resp.json();
-};

frontend/js/core/demo.js

@@ -65,11 +65,10 @@ APP.core.demo.interpolateTrack = function(trackA, trackB, t) {
             w: lerp(trackA.bbox.w, trackB.bbox.w, t),
             h: lerp(trackA.bbox.h, trackB.bbox.h, t)
         },
-        gpt_distance_m: lerp(trackA.gpt_distance_m, trackB.gpt_distance_m, t),
         angle_deg: trackA.angle_deg,
         speed_kph: lerp(trackA.speed_kph, trackB.speed_kph, t),
-        depth_valid: true,
-        depth_est_m: lerp(trackA.gpt_distance_m, trackB.gpt_distance_m, t),
+        depth_valid: false,
+        depth_est_m: null,
         history: [],
         predicted_path: []
     };

frontend/js/core/gptMapping.js

@@ -22,25 +22,9 @@ APP.core.gptMapping.STATUS = Object.freeze({
  */
 APP.core.gptMapping.buildFeatures = function (gptRaw) {
     if (!gptRaw) return {};
-    const rangeStr = gptRaw.range_estimate && gptRaw.range_estimate !== "Unknown"
-        ? gptRaw.range_estimate + " (est.)" : "Unknown";
     const features = {
-        "Type": gptRaw.object_type || "Unknown",
-        "Size": gptRaw.size || "Unknown",
-        "Threat Lvl": (gptRaw.threat_level || gptRaw.threat_level_score || "?") + "/10",
-        "Status": gptRaw.threat_classification || "?",
-        "Weapons": (gptRaw.visible_weapons || []).join(", ") || "None Visible",
-        "Readiness": gptRaw.weapon_readiness || "Unknown",
-        "Motion": gptRaw.motion_status || "Unknown",
-        "Range": rangeStr,
-        "Bearing": gptRaw.bearing || "Unknown",
-        "Intent": gptRaw.tactical_intent || "Unknown",
+        "Satisfies": gptRaw.satisfies === true ? "Yes" : gptRaw.satisfies === false ? "No" : "—",
+        "Reason": gptRaw.reason || "—",
     };
-    const dynFeats = gptRaw.dynamic_features || [];
-    for (const feat of dynFeats) {
-        if (feat && feat.key && feat.value) {
-            features[feat.key] = feat.value;
-        }
-    }
     return features;
 };

frontend/js/core/tracker.js

@@ -130,10 +130,9 @@ APP.core.tracker.matchAndUpdateTracks = function (dets, dtSec) {
                 depth_est_m: detObjs[i].depth_est_m,
                 depth_valid: detObjs[i].depth_valid,
 
-                // GPT properties
-                gpt_distance_m: null,
-                gpt_direction: null,
-                gpt_description: null,
+                // Satisfaction reasoning
+                satisfies: null,
+                reason: null,
 
                 // Track state
                 lastSeen: now(),
@@ -192,17 +191,13 @@ APP.core.tracker.syncWithBackend = async function (frameIdx) {
                 bbox: { x: nx, y: ny, w: nw, h: nh },
                 score: d.score,
                 angle_deg: d.angle_deg,
-                gpt_distance_m: d.gpt_distance_m,
-                gpt_direction: d.gpt_direction,
-                gpt_description: d.gpt_description,
                 speed_kph: d.speed_kph,
                 depth_est_m: d.depth_est_m,
                 depth_rel: d.depth_rel,
                 depth_valid: d.depth_valid,
-                // Threat intelligence
-                threat_level_score: d.threat_level_score || 0,
-                threat_classification: d.threat_classification || "Unknown",
-                weapon_readiness: d.weapon_readiness || "Unknown",
+                // Satisfaction reasoning
+                satisfies: d.satisfies ?? null,
+                reason: d.reason || null,
                 // Mission relevance and assessment status
                 mission_relevant: d.mission_relevant ?? null,
                 relevance_reason: d.relevance_reason || null,
@@ -231,11 +226,8 @@ APP.core.tracker.syncWithBackend = async function (frameIdx) {
                 const g = cached.gpt_raw;
                 track.gpt_raw = g;
                 track.assessment_status = cached.assessment_status || APP.core.gptMapping.STATUS.ASSESSED;
-                track.threat_level_score = cached.threat_level_score || g.threat_level_score || 0;
-                track.threat_classification = cached.threat_classification || g.threat_classification || "Unknown";
-                track.weapon_readiness = cached.weapon_readiness || g.weapon_readiness || "Unknown";
-                track.gpt_distance_m = cached.gpt_distance_m || null;
-                track.gpt_direction = cached.gpt_direction || null;
+                track.satisfies = cached.satisfies ?? track.satisfies;
+                track.reason = cached.reason || track.reason;
                 track.mission_relevant = cached.mission_relevant ?? track.mission_relevant;
                 track.relevance_reason = cached.relevance_reason || track.relevance_reason;
                 track.features = APP.core.gptMapping.buildFeatures(g);

frontend/js/main.js

@@ -8,7 +8,6 @@ document.addEventListener("DOMContentLoaded", () => {
 
     // Core modules
     const { captureFirstFrame, drawFirstFrame, unloadVideo, toggleDepthView, toggleFirstFrameDepthView, toggleProcessedFeed, resizeOverlays, setStreamingMode, stopStreamingMode, displayProcessedFirstFrame } = APP.core.video;
-    const { syncKnobDisplays, recomputeHEL } = APP.core.hel;
     const { load: loadDemo, getFrameData: getDemoFrameData, enable: enableDemo } = APP.core.demo;
 
     // UI Renderers
@@ -57,7 +56,6 @@ document.addEventListener("DOMContentLoaded", () => {
         setupTabSwitching();
 
         // Initial UI sync
-        syncKnobDisplays();
         setHfStatus("idle");
 
         // Enable click-to-select on engage overlay
@@ -145,7 +143,6 @@ document.addEventListener("DOMContentLoaded", () => {
         if (btnRecompute) {
             btnRecompute.addEventListener("click", async () => {
                 if (!state.hasReasoned) return;
-                await recomputeHEL();
                 renderFrameOverlay();
 
                 log("Parameters recomputed.", "g");
@@ -246,17 +243,11 @@ document.addEventListener("DOMContentLoaded", () => {
         const inputs = Array.from(document.querySelectorAll("input, select"));
         inputs.forEach(el => {
             el.addEventListener("input", () => {
-                syncKnobDisplays();
                 if (state.hasReasoned) {
-                    recomputeHEL();
                     renderFrameOverlay();
-
                 }
             });
         });
-
-        // Initial sync
-        syncKnobDisplays();
     }
 
     function setupChipToggles() {
@@ -555,13 +546,9 @@ document.addEventListener("DOMContentLoaded", () => {
                 depth_est_m: (d.depth_est_m !== undefined && d.depth_est_m !== null) ? d.depth_est_m : null,
                 depth_rel: (d.depth_rel !== undefined && d.depth_rel !== null) ? d.depth_rel : null,
                 depth_valid: d.depth_valid ?? false,
-                gpt_distance_m: d.gpt_distance_m || null,
-                gpt_direction: d.gpt_direction || null,
-                gpt_description: d.gpt_description || null,
-                // Threat Intelligence
-                threat_level_score: d.threat_level_score || 0,
-                threat_classification: d.threat_classification || "Unknown",
-                weapon_readiness: d.weapon_readiness || "Unknown",
+                // Satisfaction reasoning
+                satisfies: d.satisfies ?? null,
+                reason: d.reason || null,
                 // Mission relevance and assessment status
                 mission_relevant: d.mission_relevant ?? null,
                 relevance_reason: d.relevance_reason || null,
@@ -594,9 +581,8 @@ document.addEventListener("DOMContentLoaded", () => {
             depth_est_m: d.depth_est_m,
             depth_valid: d.depth_valid,
             lastDepthBbox: d.depth_valid ? { ...d.bbox } : null,
-            gpt_distance_m: d.gpt_distance_m,
-            gpt_direction: d.gpt_direction,
-            gpt_description: d.gpt_description,
+            satisfies: d.satisfies,
+            reason: d.reason,
             lastSeen: APP.core.utils.now(),
             vx: 0,
             vy: 0,
@@ -706,8 +692,8 @@ document.addEventListener("DOMContentLoaded", () => {
                         ...d,
                         lastSeen: t,
                         state: "TRACK",
-                        depth_valid: true,
-                        depth_est_m: d.gpt_distance_m || 1000,
+                        depth_valid: false,
+                        depth_est_m: null,
                     }));
 
                     const w = videoEngage.videoWidth || state.frame.w || 1280;
@@ -759,10 +745,8 @@ document.addEventListener("DOMContentLoaded", () => {
                                     existing.gpt_raw = rd.gpt_raw;
                                     existing.features = APP.core.gptMapping.buildFeatures(rd.gpt_raw);
                                     existing.assessment_status = rd.assessment_status || "ASSESSED";
-                                    existing.threat_level_score = rd.threat_level_score || 0;
-                                    existing.gpt_description = rd.gpt_description || existing.gpt_description;
-                                    existing.gpt_distance_m = rd.gpt_distance_m || existing.gpt_distance_m;
-                                    existing.gpt_direction = rd.gpt_direction || existing.gpt_direction;
+                                    existing.satisfies = rd.satisfies ?? existing.satisfies;
+                                    existing.reason = rd.reason || existing.reason;
                                 }
                             }
                             renderFrameTrackList();

frontend/js/ui/cards.js

@@ -26,16 +26,13 @@ APP.ui.cards.renderFrameTrackList = function () {
         return;
     }
 
-    // Sort: ASSESSED first (by threat score), then UNASSESSED, then STALE
+    // Sort: ASSESSED first, then UNASSESSED, then STALE; within each group by score
     const S = APP.core.gptMapping.STATUS;
     const statusOrder = { [S.ASSESSED]: 0, [S.UNASSESSED]: 1, [S.STALE]: 2 };
     const sorted = [...dets].sort((a, b) => {
         const statusA = statusOrder[a.assessment_status] ?? 1;
         const statusB = statusOrder[b.assessment_status] ?? 1;
         if (statusA !== statusB) return statusA - statusB;
-        const scoreA = a.threat_level_score || 0;
-        const scoreB = b.threat_level_score || 0;
-        if (scoreB !== scoreA) return scoreB - scoreA;
         return (b.score || 0) - (a.score || 0);
     });
 
@@ -43,21 +40,6 @@ APP.ui.cards.renderFrameTrackList = function () {
         const id = det.id || `T${String(i + 1).padStart(2, '0')}`;
         const isActive = state.selectedId === id;
 
-        let rangeStr = "---";
-        let bearingStr = "---";
-
-        if (det.depth_valid && det.depth_est_m != null) {
-            rangeStr = `${Math.round(det.depth_est_m)}m`;
-        } else if (det.gpt_distance_m) {
-            rangeStr = `~${det.gpt_distance_m}m`;
-        } else if (det.baseRange_m) {
-            rangeStr = `${Math.round(det.baseRange_m)}m`;
-        }
-
-        if (det.gpt_direction) {
-            bearingStr = det.gpt_direction;
-        }
-
         const card = document.createElement("div");
         card.className = "track-card" + (isActive ? " active" : "");
         card.id = `card-${id}`;
@@ -74,15 +56,17 @@ APP.ui.cards.renderFrameTrackList = function () {
             statusBadge = '<span class="badgemini" style="background:#6c757d; color:white">UNASSESSED</span>';
         } else if (assessStatus === S.STALE) {
             statusBadge = '<span class="badgemini" style="background:#ffc107; color:#333">STALE</span>';
-        } else if (det.threat_level_score > 0) {
-            statusBadge = `<span class="badgemini" style="background:${det.threat_level_score >= 8 ? '#ff4d4d' : '#ff9f43'}; color:white">T-${det.threat_level_score}</span>`;
+        } else if (det.satisfies === true) {
+            statusBadge = '<span class="badgemini" style="background:#28a745; color:white">YES</span>';
+        } else if (det.satisfies === false) {
+            statusBadge = '<span class="badgemini" style="background:#dc3545; color:white">NO</span>';
         } else if (assessStatus === S.ASSESSED) {
             statusBadge = '<span class="badgemini" style="background:#17a2b8; color:white">ASSESSED</span>';
         }
 
-        // GPT description (collapsed summary)
-        const desc = det.gpt_description
-            ? `<div class="track-card-body"><span class="gpt-text">${det.gpt_description}</span></div>`
+        // Satisfaction reason (collapsed summary)
+        const desc = det.reason
+            ? `<div class="track-card-body"><span class="gpt-text">${det.reason}</span></div>`
             : "";
 
         // Inline features (only shown when active/expanded)
@@ -108,9 +92,6 @@ APP.ui.cards.renderFrameTrackList = function () {
                     }
                 </div>
             </div>
-            <div class="track-card-meta">
-               RNG ${rangeStr} · BRG ${bearingStr}
-            </div>
             ${desc}
             ${featuresHtml}
         `;

frontend/js/ui/chat.js

@@ -69,7 +69,8 @@
         const loadingId = appendMessage("assistant", "Analyzing scene...", true);
 
         try {
-            const response = await APP.api.client.chatAboutThreats(question, state.detections);
+            // Chat endpoint removed — show placeholder
+            const response = { response: "Chat endpoint is not available." };
 
             // Remove loading message
             removeMessage(loadingId);

inference.py

@@ -514,16 +514,7 @@ def infer_frame(
             except Exception:
                 logging.exception("Depth estimation failed for frame")
 
-        # Re-build display labels to include GPT distance if available
-        display_labels = []
-        for i, det in enumerate(detections):
-            label = det["label"]
-            if det.get("gpt_distance_m") is not None:
-                # Add GPT distance to label, e.g. "car 12m"
-                depth_str = f"{int(det['gpt_distance_m'])}m"
-                label = f"{label} {depth_str}"
-                logging.debug("Object '%s' at %s (bbox: %s)", label, depth_str, det['bbox'])
-            display_labels.append(label)
+        display_labels = [det["label"] for det in detections]
 
     except Exception:
         logging.exception("Inference failed for queries %s", text_queries)
@@ -538,11 +529,8 @@ def infer_frame(
 
 
 def _build_display_label(det):
-    """Build display label with GPT distance if available."""
-    label = det["label"]
-    if det.get("gpt_distance_m") is not None:
-        label = f"{label} {int(det['gpt_distance_m'])}m"
-    return label
+    """Build display label for a detection."""
+    return det["label"]
 
 def _attach_depth_from_result(detections, depth_result, depth_scale):
     """Attach relative depth values for visualization only. GPT handles distance estimation."""
@@ -1608,19 +1596,13 @@ def run_grounded_sam2_tracking(
                                     elif "assessment_status" not in det:
                                         det["assessment_status"] = AssessmentStatus.UNASSESSED
 
-                                # Build enriched display labels
+                                # Build display labels
                                 display_labels = []
                                 for d in dets:
                                     if d.get("mission_relevant") is False:
                                         display_labels.append("")
                                         continue
-                                    lbl = d.get("label", "obj")
-                                    if d.get("gpt_distance_m") is not None:
-                                        try:
-                                            lbl = f"{lbl} {int(float(d['gpt_distance_m']))}m"
-                                        except (TypeError, ValueError):
-                                            pass
-                                    display_labels.append(lbl)
+                                    display_labels.append(d.get("label", "obj"))
 
                                 # Draw boxes on mask-rendered frame
                                 if dets:

utils/openai_client.py

@@ -2,7 +2,7 @@
 Shared OpenAI HTTP client — single implementation of the chat-completions call.
 
 Replaces duplicated urllib boilerplate in gpt_reasoning, relevance,
-mission_parser, and threat_chat.
+and mission_parser.
 """
 
 import json

utils/relevance.py

@@ -3,18 +3,14 @@ Object relevance evaluation — deterministic gate between detection and GPT ass
 
 Public functions:
   evaluate_relevance(detection, criteria) -> RelevanceDecision  (deterministic)
-  evaluate_relevance_llm(detected_labels, mission_text) -> set[str]  (LLM post-filter)
 
 INVARIANT INV-13 enforcement: evaluate_relevance() accepts RelevanceCriteria, NOT
 MissionSpecification. It cannot see context_phrases, stripped_modifiers, or any
 LLM-derived field. This is structural, not by convention.
 """
 
-import json
 import logging
-from typing import Any, Dict, List, NamedTuple, Set
-
-from utils.openai_client import chat_completion, extract_content, get_api_key, OpenAIAPIError
+from typing import Any, Dict, NamedTuple
 
 from coco_classes import canonicalize_coco_name
 from utils.schemas import RelevanceCriteria
@@ -74,68 +70,3 @@ def evaluate_relevance(
                 return RelevanceDecision(True, "ok")
 
     return RelevanceDecision(False, "label_not_in_required_classes")
-
-
-def evaluate_relevance_llm(
-    detected_labels: List[str],
-    mission_text: str,
-) -> Set[str]:
-    """Ask GPT which detected labels are relevant to the mission.
-
-    Called ONCE on frame 0 with the unique labels found by the detector.
-    Returns a set of relevant label strings (lowercased).
-
-    On API failure, falls back to accepting all labels (fail-open, logged).
-    """
-    if not detected_labels:
-        return set()
-
-    if not get_api_key():
-        logger.warning(
-            "OPENAI_API_KEY not set — LLM relevance filter falling back to accept-all"
-        )
-        return set(detected_labels)
-
-    prompt = (
-        f"Given this mission: \"{mission_text}\"\n\n"
-        f"Which of these detected object classes are relevant to the mission?\n"
-        f"{json.dumps(detected_labels)}\n\n"
-        "Return JSON: {\"relevant_labels\": [...]}\n"
-        "Only include labels from the provided list that are relevant to "
-        "accomplishing the mission. Be inclusive — if in doubt, include it."
-    )
-
-    payload = {
-        "model": "gpt-4o-mini",
-        "temperature": 0.0,
-        "max_tokens": 200,
-        "response_format": {"type": "json_object"},
-        "messages": [
-            {"role": "system", "content": "You are a mission relevance filter. Return only JSON."},
-            {"role": "user", "content": prompt},
-        ],
-    }
-
-    try:
-        resp_data = chat_completion(payload)
-        content, _refusal = extract_content(resp_data)
-        if not content:
-            logger.warning("GPT returned empty content for relevance filter — accept-all")
-            return set(detected_labels)
-
-        result = json.loads(content)
-        relevant = result.get("relevant_labels", detected_labels)
-        relevant_set = {label.lower() for label in relevant}
-
-        logger.info(
-            "LLM relevance filter: mission=%r detected=%s relevant=%s",
-            mission_text, detected_labels, relevant_set,
-        )
-        return relevant_set
-
-    except OpenAIAPIError as e:
-        logger.warning("LLM relevance API call failed: %s — accept-all fallback", e)
-        return set(detected_labels)
-    except (json.JSONDecodeError, KeyError, TypeError) as e:
-        logger.warning("LLM relevance response parse failed: %s — accept-all fallback", e)
-        return set(detected_labels)

utils/tracker.py

@@ -202,14 +202,10 @@ class KalmanFilter:
 MAX_STALE_FRAMES = 300
 
 GPT_SYNC_KEYS = frozenset({
-    # Legacy / polyfilled fields (consumed by frontend cards)
-    "gpt_distance_m", "gpt_direction", "gpt_description", "gpt_raw",
-    "threat_level_score", "distance_m", "direction", "description",
-    # Universal schema fields
-    "object_type", "size", "visible_weapons", "weapon_readiness",
-    "motion_status", "range_estimate", "bearing",
-    "threat_level", "threat_classification", "tactical_intent",
-    "dynamic_features",
+    # GPT raw payload
+    "gpt_raw",
+    # Satisfaction reasoning fields
+    "satisfies", "reason",
     # Provenance and temporal validity
     "assessment_frame_index", "assessment_status",
     # Mission relevance
@@ -633,7 +629,7 @@ class ByteTracker:
             if meta:
                 # Ensure assessment_frame_index is recorded
                 if "assessment_frame_index" not in meta and any(
-                    k in meta for k in ("threat_level_score", "gpt_raw", "object_type")
+                    k in meta for k in ("satisfies", "gpt_raw")
                 ):
                     meta["assessment_frame_index"] = self.frame_id
                     meta["assessment_status"] = AssessmentStatus.ASSESSED