feat: add /chat/mission endpoint and rewire chat UI

Replace deleted /chat/threat with generic /chat/mission endpoint for
mission analyst Q&A. Add chatAboutMission() to client.js. Update
chat.js to use new endpoint with AI icon label.

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

app.py

@@ -174,18 +174,15 @@ async def add_no_cache_header(request: Request, call_next):
     """Ensure frontend assets are not cached by the browser (important for HF Spaces updates)."""
     response = await call_next(request)
     # Apply to all static files and the root page
-    if request.url.path.startswith("/laser") or request.url.path == "/":
+    if request.url.path.startswith("/app") or request.url.path == "/":
         response.headers["Cache-Control"] = "no-cache, no-store, must-revalidate"
         response.headers["Pragma"] = "no-cache"
         response.headers["Expires"] = "0"
     return response
 
-# Optional: serve the LaserPerception frontend from this backend.
-# The frontend files are now located in the 'frontend' directory.
 _FRONTEND_DIR = Path(__file__).with_name("frontend")
 if _FRONTEND_DIR.exists():
-    # Mount the entire frontend directory at /laser (legacy path) or /frontend
-    app.mount("/laser", StaticFiles(directory=_FRONTEND_DIR, html=True), name="laser")
+    app.mount("/app", StaticFiles(directory=_FRONTEND_DIR, html=True), name="frontend")
 
 # Valid detection modes
 VALID_MODES = {"object_detection", "segmentation", "drone_detection"}
@@ -237,9 +234,8 @@ def _default_queries_for_mode(mode: str) -> list[str]:
 
 @app.get("/", response_class=HTMLResponse)
 async def demo_page():
-    """Redirect to LaserPerception app."""
-    # The main entry point is now index.html in the mounted directory
-    return RedirectResponse(url="/laser/index.html")
+    """Redirect to Mission Console app."""
+    return RedirectResponse(url="/app/index.html")
 
 
 @app.post("/detect")
@@ -918,6 +914,98 @@ async def reason_track(
     return results
 
 
+@app.post("/chat/mission")
+async def chat_mission_endpoint(
+    question: str = Form(...),
+    detections: str = Form(...),
+    mission_context: str = Form(""),
+):
+    """Chat about detected objects using GPT.
+
+    Answers operator questions about the current detection results,
+    satisfaction reasoning, and mission context.
+    """
+    import json as json_module
+    from utils.openai_client import chat_completion, extract_content, get_api_key, OpenAIAPIError
+
+    if not question.strip():
+        raise HTTPException(status_code=400, detail="Question cannot be empty.")
+
+    if not get_api_key():
+        raise HTTPException(status_code=503, detail="OpenAI API key not configured.")
+
+    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.")
+
+    # Build detection context summary
+    det_lines = []
+    for det in detection_list:
+        obj_id = det.get("id", "?")
+        label = det.get("label", "object")
+        entry = f"[{obj_id}] {label}"
+        if det.get("satisfies") is True:
+            entry += " — satisfies condition"
+        elif det.get("satisfies") is False:
+            entry += " — does NOT satisfy condition"
+        if det.get("reason"):
+            entry += f" ({det['reason']})"
+        if det.get("score"):
+            entry += f" (conf: {det['score']:.0%})"
+        det_lines.append(entry)
+    det_context = "\n".join(det_lines) if det_lines else "No detections."
+
+    # Parse optional mission context
+    mission_block = ""
+    if mission_context.strip():
+        try:
+            spec = json_module.loads(mission_context)
+            mission_block = "\nMISSION CONTEXT:\n"
+            if spec.get("operator_text"):
+                mission_block += f"- Query: {spec['operator_text']}\n"
+            if spec.get("object_classes"):
+                mission_block += f"- Target Classes: {', '.join(spec['object_classes'])}\n"
+            if spec.get("satisfaction_condition"):
+                mission_block += f"- Condition: {spec['satisfaction_condition']}\n"
+        except json_module.JSONDecodeError:
+            pass
+
+    system_prompt = (
+        "You are a mission analyst assistant for a video surveillance system. "
+        "You have access to the current detection and analysis results. "
+        "Answer questions concisely based on the detection data provided.\n\n"
+        f"{mission_block}\n"
+        f"CURRENT DETECTIONS:\n{det_context}\n\n"
+        "Respond to the operator's question based on this data."
+    )
+
+    payload = {
+        "model": "gpt-4o",
+        "messages": [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": question},
+        ],
+        "max_tokens": 500,
+        "temperature": 0.3,
+    }
+
+    try:
+        async with _GPT_SEMAPHORE:
+            resp_data = await asyncio.to_thread(chat_completion, payload)
+        content, _refusal = extract_content(resp_data)
+        return {"response": content.strip() if content else "No response generated."}
+    except OpenAIAPIError as e:
+        logging.error("Chat API error: %s", e)
+        raise HTTPException(status_code=502, detail=f"API Error: {e}")
+    except Exception as e:
+        logging.exception("Mission chat failed")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
 @app.post("/benchmark")
 async def benchmark_endpoint(
     video: UploadFile = File(...),

frontend/js/api/client.js

@@ -241,6 +241,27 @@ APP.api.client.pollAsyncJob = async function () {
     });
 };
 
+// Chat about mission detections
+APP.api.client.chatAboutMission = async function (question, detections) {
+    const { state } = APP.core;
+    const form = new FormData();
+    form.append("question", question);
+    form.append("detections", JSON.stringify(detections));
+    form.append("mission_context", state.missionText || "");
+
+    const resp = await fetch(`${state.hf.baseUrl}/chat/mission`, {
+        method: "POST",
+        body: form,
+    });
+
+    if (!resp.ok) {
+        const err = await resp.json().catch(() => ({ detail: resp.statusText }));
+        return { error: err.detail || "Chat request failed" };
+    }
+
+    return await resp.json();
+};
+
 // External detection hook (can be replaced by user)
 APP.api.client.externalDetect = async function (input) {
     console.log("externalDetect called", input);

frontend/js/ui/chat.js

@@ -1,4 +1,4 @@
-// Chat UI Module - Threat-aware chat with GPT
+// Chat UI Module - Mission analyst chat with GPT
 (function () {
     const { state } = APP.core;
     const { $ } = APP.core.utils;
@@ -40,7 +40,7 @@
     }
 
     /**
-     * Send a chat message about current threats.
+     * Send a chat message about current detections.
      */
     async function sendMessage() {
         const chatInput = $("#chatInput");
@@ -69,8 +69,7 @@
         const loadingId = appendMessage("assistant", "Analyzing scene...", true);
 
         try {
-            // Chat endpoint removed — show placeholder
-            const response = { response: "Chat endpoint is not available." };
+            const response = await APP.api.client.chatAboutMission(question, state.detections);
 
             // Remove loading message
             removeMessage(loadingId);
@@ -111,7 +110,7 @@
         // Format content with line breaks
         const formatted = content.replace(/\n/g, "<br>");
 
-        const icon = role === "user" ? "YOU" : role === "assistant" ? "TAC" : "SYS";
+        const icon = role === "user" ? "YOU" : role === "assistant" ? "AI" : "SYS";
         msgDiv.innerHTML = `<span class="chat-icon">${icon}</span><span class="chat-content">${formatted}</span>`;
 
         chatMessages.appendChild(msgDiv);