update: added yolo v8 defense

demo.html

@@ -123,8 +123,9 @@ button:hover {
 
     <label class="label">OBJECT DETECTOR</label>
     <select id="detectorSelect">
-        <option value="owlv2" selected>OWL-V2 (text-conditioned)</option>
-        <option value="hf_yolov8">YOLOv8 (COCO classes)</option>
+        <option value="owlv2" selected>OWL-V2</option>
+        <option value="hf_yolov8">YOLOv8</option>
+        <option value="hf_yolov8_defence">YOLOv8m Defence</option>
     </select>
 
     <button onclick="executeMission()">EXECUTE MISSION</button>

mission_planner.py

@@ -5,6 +5,7 @@ import logging
 from dataclasses import asdict, dataclass
 from typing import Dict, List, Tuple
 
+from prompt import mission_planner_system_prompt, mission_planner_user_prompt
 from utils.openai_client import get_openai_client
 
 
@@ -140,18 +141,8 @@ class MissionReasoner:
 
     def _query_llm(self, mission: str) -> Dict[str, object]:
         client = get_openai_client()
-        system_prompt = (
-            "You are a mission-planning assistant helping a vision system select which YOLO object classes to detect. "
-            "You must only reference the provided list of YOLO classes."
-        )
-        classes_blob = ", ".join(YOLO_CLASSES)
-        user_prompt = (
-            f"Mission: {mission}\n"
-            f"Available YOLO classes: {classes_blob}\n"
-            f"Return JSON with: mission (string) and classes (array). "
-            f"Each entry needs name, score (0-1 float), rationale. "
-            f"Limit to at most {self._top_k} classes. Only choose names from the list."
-        )
+        system_prompt = mission_planner_system_prompt()
+        user_prompt = mission_planner_user_prompt(mission, YOLO_CLASSES, self._top_k)
         completion = client.chat.completions.create(
             model=self._model_name,
             temperature=0.2,

mission_summarizer.py

@@ -5,6 +5,7 @@ import logging
 from typing import Any, Dict, List
 
 from mission_planner import MissionPlan
+from prompt import mission_summarizer_system_prompt, mission_summarizer_user_prompt
 from utils.openai_client import get_openai_client
 
 SUMMARY_MODEL = "gpt-4o-mini"
@@ -60,19 +61,12 @@ def summarize_results(
         ],
     }
 
-    system_prompt = (
-        "You are a surveillance analyst. Review structured detections aligned to a mission and summarize actionable "
-        "insights, highlighting objects of interest, temporal trends, and any security concerns. "
-        "Base conclusions solely on the provided data; if nothing is detected, explicitly state that."
-    )
+    system_prompt = mission_summarizer_system_prompt()
     messages = [
         {"role": "system", "content": system_prompt},
         {
             "role": "user",
-            "content": (
-                "Use this JSON to summarize the mission outcome:\n"
-                f"{json.dumps(payload, ensure_ascii=False)}"
-            ),
+            "content": mission_summarizer_user_prompt(json.dumps(payload, ensure_ascii=False)),
         },
     ]
 

models/detectors/yolov8_defence.py

@@ -0,0 +1,12 @@
+from models.detectors.yolov8 import HuggingFaceYoloV8Detector
+
+
+class HuggingFaceYoloV8DefenceDetector(HuggingFaceYoloV8Detector):
+    """YOLOv8m detector fine-tuned on defence data hosted on Hugging Face."""
+
+    REPO_ID = "spencercdz/YOLOv8m_defence"
+    WEIGHT_FILE = "yolov8m_defence.pt"
+
+    def __init__(self, score_threshold: float = 0.3) -> None:
+        super().__init__(score_threshold=score_threshold)
+        self.name = "hf_yolov8_defence"

models/model_loader.py

@@ -5,12 +5,14 @@ from typing import Callable, Dict, Optional
 from models.detectors.base import ObjectDetector
 from models.detectors.owlv2 import Owlv2Detector
 from models.detectors.yolov8 import HuggingFaceYoloV8Detector
+from models.detectors.yolov8_defence import HuggingFaceYoloV8DefenceDetector
 
 DEFAULT_DETECTOR = "owlv2"
 
 _REGISTRY: Dict[str, Callable[[], ObjectDetector]] = {
     "owlv2": Owlv2Detector,
     "hf_yolov8": HuggingFaceYoloV8Detector,
+    "hf_yolov8_defence": HuggingFaceYoloV8DefenceDetector,
 }
 
 

prompt.py

@@ -0,0 +1,37 @@
+"""Centralized prompt builders for all LLM calls."""
+
+from __future__ import annotations
+
+from typing import Iterable
+
+
+def mission_planner_system_prompt() -> str:
+    return (
+        "You are a mission-planning assistant helping a vision system select which YOLO object "
+        "classes to detect. You must only reference the provided list of YOLO classes."
+    )
+
+
+def mission_planner_user_prompt(mission: str, available_classes: Iterable[str], top_k: int) -> str:
+    classes_blob = ", ".join(available_classes)
+    return (
+        f"Mission: {mission}\n"
+        f"Available YOLO classes: {classes_blob}\n"
+        "Return JSON with: mission (string) and classes (array). "
+        "Each entry needs name, score (0-1 float), rationale. "
+        f"Limit to at most {top_k} classes. Only choose names from the list."
+    )
+
+
+def mission_summarizer_system_prompt() -> str:
+    return (
+        "You are a surveillance analyst. Review structured detections aligned to a mission and "
+        "summarize actionable insights, highlighting objects of interest, temporal trends, and any "
+        "security concerns. Base conclusions solely on the provided data; if nothing is detected, "
+        "explicitly state that."
+    )
+
+
+def mission_summarizer_user_prompt(payload_json: str) -> str:
+    return "Use this JSON to summarize the mission outcome:\n" f"{payload_json}"
+