From ce73eadd7490b7fe22fb0c71e40876c8d446ed90 Mon Sep 17 00:00:00 2001
From: everbarry <alessandro.preiti@protonmail.com>
Date: Wed, 18 Mar 2026 10:51:06 +0100
Subject: [PATCH] initial commit

---
 .gitignore     |   19 +
 README.md      |    0
 dashboard.py   | 1129 ++++++++++++++++++
 map.webp       |  Bin 0 -> 136646 bytes
 pyproject.toml |   24 +
 uv.lock        | 3075 ++++++++++++++++++++++++++++++++++++++++++++++++
 6 files changed, 4247 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 README.md
 create mode 100644 dashboard.py
 create mode 100644 map.webp
 create mode 100644 pyproject.toml
 create mode 100644 uv.lock

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..28a1183
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,19 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
+
+# Large model weights & dataset
+*.pt
+*.json
+wild-forest-animals-and-person-1/
+data/
+
+# Runtime cache
+_xai_cache/
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..e69de29
diff --git a/dashboard.py b/dashboard.py
new file mode 100644
index 0000000..2d4e310
--- /dev/null
+++ b/dashboard.py
@@ -0,0 +1,1129 @@
+"""
+Wildlife Monitoring Dashboard — Nationaal Park De Hoge Veluwe
+
+Flask app with two pages:
+  /        – fullscreen map with camera markers + togglable sidebar
+  /det/<id> – detection detail page with all XAI visualisations
+
+Run:  uv run python dashboard.py
+"""
+
+import csv
+import io
+import json
+import random
+import threading
+import uuid
+from datetime import datetime
+from pathlib import Path
+
+import numpy as np
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from PIL import Image
+
+import torch
+from torch import nn
+from torch.utils.data import Dataset, DataLoader
+from torchvision import transforms
+from torchvision.models import efficientnet_v2_s, EfficientNet_V2_S_Weights
+from pytorch_grad_cam import ScoreCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image
+from lime import lime_image
+from skimage.segmentation import mark_boundaries
+from flask import Flask, render_template_string, jsonify, send_from_directory, request
+
+# ── constants ─────────────────────────────────────────────────────────────────
+
+SEED = 42
+random.seed(SEED)
+np.random.seed(SEED)
+torch.manual_seed(SEED)
+
+CLASS_NAMES = ["bear", "deer", "fox", "hare", "moose", "person", "wolf"]
+WEIGHTS_PATH = Path("efficientnet_v2_wild_forest_animals.pt")
+DATASET_DIR = Path("wild-forest-animals-and-person-1")
+XAI_DIR = Path("_xai_cache")
+XAI_DIR.mkdir(exist_ok=True)
+
+# Camera positions as percentages of the map image (adjust to match map.webp)
+CAMERAS = {
+    "CAM-01": {"name": "Hubertus Trail",  "px": 50, "py": 15,
+               "desc": "Northern forest near Jachthuis Sint Hubertus"},
+    "CAM-02": {"name": "Otterlo Gate",    "px": 25, "py": 38,
+               "desc": "Western entrance, deciduous woodland"},
+    "CAM-03": {"name": "Kröller-Müller",  "px": 42, "py": 48,
+               "desc": "Central area near the museum"},
+    "CAM-04": {"name": "Hoenderloo Path", "px": 72, "py": 32,
+               "desc": "Eastern forest corridor"},
+    "CAM-05": {"name": "Deelense Veld",   "px": 55, "py": 68,
+               "desc": "Southern heathland near Schaarsbergen"},
+}
+
+SPECIES_ICON = {
+    "bear": "\U0001f43b", "deer": "\U0001f98c", "fox": "\U0001f98a",
+    "hare": "\U0001f407", "moose": "\U0001f98c", "person": "\U0001f9d1",
+    "wolf": "\U0001f43a",
+}
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# ── dataset ───────────────────────────────────────────────────────────────────
+
+class WildForestAnimalsDataset(Dataset):
+    def __init__(self, root, split, transform=None):
+        self.root, self.transform = Path(root), transform
+        split_dir = self.root / split
+        self.samples = []
+        with (split_dir / "_classes.csv").open(newline="") as f:
+            for row in csv.DictReader(f):
+                oh = [int(row[n]) for n in CLASS_NAMES]
+                try:
+                    label = oh.index(1)
+                except ValueError:
+                    continue
+                self.samples.append((split_dir / row["filename"], label))
+
+    def __len__(self):
+        return len(self.samples)
+
+    def __getitem__(self, idx):
+        p, l = self.samples[idx]
+        img = Image.open(p).convert("RGB")
+        return self.transform(img) if self.transform else img, l
+
+
+_eval_tf = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Resize((224, 224), antialias=True),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# ── model ─────────────────────────────────────────────────────────────────────
+
+print("Loading model and data …")
+model = efficientnet_v2_s(weights=EfficientNet_V2_S_Weights.IMAGENET1K_V1)
+model.classifier[1] = nn.Linear(model.classifier[1].in_features, len(CLASS_NAMES))
+model.load_state_dict(torch.load(WEIGHTS_PATH, map_location=device, weights_only=True))
+model.to(device).eval()
+
+test_ds = WildForestAnimalsDataset(DATASET_DIR, "test", transform=_eval_tf)
+train_ds = WildForestAnimalsDataset(DATASET_DIR, "train", transform=_eval_tf)
+
+_norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+_unnorm = transforms.Normalize(
+    mean=[-0.485 / 0.229, -0.456 / 0.224, -0.406 / 0.225],
+    std=[1 / 0.229, 1 / 0.224, 1 / 0.225],
+)
+score_cam = ScoreCAM(model=model, target_layers=[model.features[7][0]])
+lime_exp = lime_image.LimeImageExplainer(random_state=SEED)
+
+
+def _to_display(t):
+    return torch.clamp(_unnorm(t), 0, 1).numpy().transpose(1, 2, 0)
+
+
+def _lime_predict(images):
+    batch = torch.stack([
+        _norm(torch.tensor(im.transpose(2, 0, 1), dtype=torch.float32))
+        for im in images
+    ]).to(device)
+    with torch.no_grad():
+        return torch.nn.functional.softmax(model(batch), dim=1).cpu().numpy()
+
+
+print("Pre-computing training features …")
+
+
+def _extract_features(ds, bs=64):
+    loader = DataLoader(ds, batch_size=bs, shuffle=False)
+    feats, labs = [], []
+    with torch.no_grad():
+        for imgs, lbls in loader:
+            x = torch.flatten(model.avgpool(model.features(imgs.to(device))), 1)
+            feats.append(x.cpu())
+            labs.extend(lbls.tolist())
+    return torch.nn.functional.normalize(torch.cat(feats), dim=1), labs
+
+
+train_feats, train_labels = _extract_features(train_ds)
+print("Ready.")
+
+# ── XAI helpers ───────────────────────────────────────────────────────────────
+
+
+def _save_img(arr, path):
+    if arr.dtype != np.uint8:
+        arr = (np.clip(arr, 0, 1) * 255).astype(np.uint8)
+    Image.fromarray(arr).save(path)
+
+
+def _prob_chart(probs, pred_idx, path):
+    fig, ax = plt.subplots(figsize=(5, 2.6))
+    colors = ["#e8832a" if i == pred_idx else "#4a90d9" for i in range(len(CLASS_NAMES))]
+    bars = ax.barh(CLASS_NAMES, probs, color=colors)
+    ax.set_xlim(0, 1)
+    ax.set_xlabel("Probability")
+    for bar, p in zip(bars, probs):
+        if p > 0.05:
+            ax.text(bar.get_width() - 0.01,
+                    bar.get_y() + bar.get_height() / 2,
+                    f"{p:.2f}", va="center", ha="right",
+                    fontsize=8, color="white", fontweight="bold")
+    plt.tight_layout()
+    fig.savefig(path, dpi=120, bbox_inches="tight")
+    plt.close(fig)
+
+
+def compute_xai(det):
+    out = XAI_DIR / det["id"]
+    if (out / "meta.json").exists():
+        return
+    out.mkdir(exist_ok=True)
+
+    idx = det["idx"]
+    tensor, label = test_ds[idx]
+    img_path, _ = test_ds.samples[idx]
+    raw = np.array(
+        Image.open(img_path).convert("RGB").resize((224, 224)),
+        dtype=np.float64,
+    ) / 255.0
+    inp = tensor.unsqueeze(0).to(device)
+
+    _save_img(raw, out / "original.png")
+
+    with torch.no_grad():
+        probs = torch.nn.functional.softmax(model(inp), dim=1).cpu().squeeze()
+    pred_idx = probs.argmax().item()
+    entropy = -(probs * torch.log(probs + 1e-12)).sum().item()
+    _prob_chart(probs.numpy(), pred_idx, out / "chart.png")
+
+    hm = score_cam(input_tensor=inp, targets=None)
+    _save_img(
+        show_cam_on_image(raw.astype(np.float32), hm.squeeze(), use_rgb=True, image_weight=0.75),
+        out / "scorecam.png",
+    )
+
+    expl = lime_exp.explain_instance(raw, _lime_predict, top_labels=2, hide_color=0, num_samples=1000)
+    c1, c2 = expl.top_labels[0], expl.top_labels[1]
+    t1, m1 = expl.get_image_and_mask(c1, positive_only=False, num_features=10, hide_rest=False)
+    t2, m2 = expl.get_image_and_mask(c2, positive_only=False, num_features=10, hide_rest=False)
+    _save_img(mark_boundaries(t1, m1), out / "lime1.png")
+    _save_img(mark_boundaries(t2, m2), out / "lime2.png")
+
+    segs = expl.segments
+    w1, w2 = dict(expl.local_exp[c1]), dict(expl.local_exp[c2])
+    diff = np.zeros(segs.shape, dtype=np.float64)
+    for sid in np.unique(segs):
+        diff[segs == sid] = w1.get(sid, 0) - w2.get(sid, 0)
+    mx = max(np.abs(diff).max(), 1e-8)
+    diff /= mx
+    col = plt.cm.RdBu_r((diff + 1) / 2)[:, :, :3]
+    _save_img((0.6 * raw + 0.4 * col).clip(0, 1), out / "contrastive.png")
+
+    with torch.no_grad():
+        qf = torch.nn.functional.normalize(
+            torch.flatten(model.avgpool(model.features(inp)), 1).cpu(), dim=1,
+        ).squeeze()
+    sims, idxs = (train_feats @ qf).topk(3)
+    nbs = []
+    for k, (ni, ns) in enumerate(zip(idxs, sims)):
+        _save_img(_to_display(train_ds[ni.item()][0]), out / f"nb{k+1}.png")
+        nbs.append({"cls": CLASS_NAMES[train_labels[ni]], "sim": f"{ns:.3f}"})
+
+    meta = {
+        "pred": CLASS_NAMES[pred_idx],
+        "conf": round(probs[pred_idx].item() * 100, 1),
+        "ppl": round(float(np.exp(entropy)), 2),
+        "probs": {CLASS_NAMES[i]: round(float(p), 4) for i, p in enumerate(probs)},
+        "lime1_cls": CLASS_NAMES[c1],
+        "lime2_cls": CLASS_NAMES[c2],
+        "contrast_leg": f"Blue = {CLASS_NAMES[c1]}  |  Red = {CLASS_NAMES[c2]}",
+        "nbs": nbs,
+    }
+    (out / "meta.json").write_text(json.dumps(meta))
+
+
+# ── Flask app ─────────────────────────────────────────────────────────────────
+
+app = Flask(__name__)
+detections: list[dict] = []
+_xai_events: dict[str, threading.Event] = {}
+_xai_lock = threading.Lock()
+
+
+def _precompute_xai(det):
+    try:
+        with _xai_lock:
+            compute_xai(det)
+    finally:
+        ev = _xai_events.get(det["id"])
+        if ev:
+            ev.set()
+
+
+@app.route("/")
+@app.route("/home")
+def home():
+    return render_template_string(HOME_HTML, cameras=CAMERAS, class_names=CLASS_NAMES)
+
+
+@app.route("/det/<det_id>")
+def detail(det_id):
+    det = next((d for d in detections if d["id"] == det_id), None)
+    if det is None:
+        return "Detection not found", 404
+    return render_template_string(DETAIL_HTML, det=det, cameras=CAMERAS, class_names=CLASS_NAMES)
+
+
+@app.route("/api/simulate", methods=["POST"])
+def api_simulate():
+    idx = random.randint(0, len(test_ds) - 1)
+    cam_id = random.choice(list(CAMERAS.keys()))
+    tensor, label = test_ds[idx]
+    with torch.no_grad():
+        probs = torch.nn.functional.softmax(
+            model(tensor.unsqueeze(0).to(device)), dim=1,
+        ).cpu().squeeze()
+    pred_idx = probs.argmax().item()
+    det = {
+        "id": uuid.uuid4().hex[:8],
+        "idx": idx,
+        "cam": cam_id,
+        "cam_name": CAMERAS[cam_id]["name"],
+        "pred": CLASS_NAMES[pred_idx],
+        "conf": round(probs[pred_idx].item() * 100, 1),
+        "time": datetime.now().strftime("%H:%M:%S"),
+        "verified": False,
+        "manual": False,
+    }
+    detections.append(det)
+    out = XAI_DIR / det["id"]
+    out.mkdir(exist_ok=True)
+    img_path, _ = test_ds.samples[idx]
+    raw = np.array(
+        Image.open(img_path).convert("RGB").resize((224, 224)), dtype=np.float64,
+    ) / 255.0
+    _save_img(raw, out / "original.png")
+    ev = threading.Event()
+    _xai_events[det["id"]] = ev
+    threading.Thread(target=_precompute_xai, args=(det,), daemon=True).start()
+    return jsonify(det)
+
+
+@app.route("/api/xai/<det_id>")
+def api_xai(det_id):
+    det = next((d for d in detections if d["id"] == det_id), None)
+    if det is None:
+        return jsonify(error="not found"), 404
+    ev = _xai_events.get(det_id)
+    if ev:
+        ev.wait()
+    else:
+        compute_xai(det)
+    meta = json.loads((XAI_DIR / det_id / "meta.json").read_text())
+    base = f"/xai/{det_id}"
+    meta["urls"] = {
+        k: f"{base}/{k}.png"
+        for k in ["original", "chart", "scorecam", "lime1", "lime2", "contrastive",
+                   "nb1", "nb2", "nb3"]
+    }
+    return jsonify(meta)
+
+
+@app.route("/cam/<cam_id>")
+def camera(cam_id):
+    if cam_id not in CAMERAS:
+        return "Camera not found", 404
+    cam_dets = [d for d in reversed(detections) if d["cam"] == cam_id]
+    return render_template_string(CAM_HTML, cam_id=cam_id, cam=CAMERAS[cam_id], dets=cam_dets)
+
+
+@app.route("/api/verify/<det_id>", methods=["POST"])
+def api_verify(det_id):
+    det = next((d for d in detections if d["id"] == det_id), None)
+    if det is None:
+        return jsonify(error="not found"), 404
+    data = request.get_json()
+    if data.get("action") == "correct":
+        det["verified"] = True
+    elif data.get("action") == "wrong":
+        det["verified"] = True
+        det["manual"] = True
+        det["orig_pred"] = det["pred"]
+        det["orig_conf"] = det["conf"]
+        det["pred"] = data["true_class"]
+        det["conf"] = 100.0
+    return jsonify(det)
+
+
+@app.route("/api/detections")
+def api_detections():
+    return jsonify(detections)
+
+
+@app.route("/map.webp")
+def serve_map():
+    return send_from_directory(".", "map.webp")
+
+
+@app.route("/xai/<det_id>/<filename>")
+def serve_xai(det_id, filename):
+    return send_from_directory(str(XAI_DIR / det_id), filename)
+
+
+# ── HTML: home page ──────────────────────────────────────────────────────────
+
+HOME_HTML = r"""<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width,initial-scale=1">
+<title>De Hoge Veluwe — Wildlife Monitor</title>
+<style>
+*{margin:0;padding:0;box-sizing:border-box}
+body{font-family:system-ui,-apple-system,sans-serif;overflow:hidden;height:100vh;background:#0f172a}
+
+/* ── map ── */
+#map-wrap{position:relative;height:100vh;width:auto;margin:0 auto;display:inline-block}
+#map-outer{display:flex;justify-content:center;width:100vw;height:100vh;overflow:hidden}
+#map-img{height:100vh;width:auto;display:block;user-select:none;-webkit-user-drag:none}
+.cam-marker{
+  position:absolute;width:22px;height:22px;border-radius:50%;
+  background:#2563eb;border:3px solid white;
+  box-shadow:0 2px 10px rgba(0,0,0,.35);cursor:pointer;
+  transform:translate(-50%,-50%);transition:all .3s;z-index:10;
+  text-decoration:none;
+}
+.cam-marker.active{background:#ef4444;animation:pulse 1.4s infinite}
+.cam-label{
+  position:absolute;transform:translate(-50%,-100%);
+  background:rgba(0,0,0,.75);color:#fff;padding:3px 10px;border-radius:5px;
+  font-size:11px;white-space:nowrap;pointer-events:none;z-index:10;
+  backdrop-filter:blur(4px);
+}
+@keyframes pulse{
+  0%,100%{box-shadow:0 0 0 0 rgba(239,68,68,.45)}
+  50%{box-shadow:0 0 0 14px rgba(239,68,68,0)}
+}
+
+.wolf-warn{
+  position:absolute;width:120px;height:120px;border-radius:50%;
+  border:3px solid rgba(239,68,68,.7);
+  background:rgba(239,68,68,.1);
+  transform:translate(-50%,-50%);z-index:5;pointer-events:none;
+  animation:wolfPulse 2s ease-in-out infinite;
+  transition:opacity 1.5s ease;
+}
+.wolf-warn.fade{animation:none;opacity:0}
+.wolf-warn-label{
+  position:absolute;transform:translate(-50%,64px);z-index:6;
+  color:#ef4444;font-size:11px;font-weight:700;text-transform:uppercase;
+  letter-spacing:.5px;pointer-events:none;white-space:nowrap;
+  text-shadow:0 1px 4px rgba(0,0,0,.6);
+  transition:opacity 1.5s ease;
+}
+.wolf-warn-label.fade{opacity:0}
+@keyframes wolfPulse{
+  0%,100%{transform:translate(-50%,-50%) scale(1);opacity:.8}
+  50%{transform:translate(-50%,-50%) scale(1.15);opacity:.5}
+}
+
+/* ── title ── */
+#title{
+  position:fixed;top:16px;left:16px;z-index:50;
+  background:rgba(15,23,42,.88);backdrop-filter:blur(10px);
+  color:#fff;padding:14px 22px;border-radius:12px;
+}
+#title h1{font-size:17px;font-weight:700}
+#title p{font-size:11px;opacity:.65;margin-top:2px}
+
+/* ── toast ── */
+#toast{
+  position:fixed;top:20px;left:50%;z-index:200;
+  transform:translateX(-50%) translateY(-120px);
+  background:linear-gradient(135deg,#16a34a,#15803d);color:#fff;
+  padding:14px 26px;border-radius:12px;box-shadow:0 4px 24px rgba(0,0,0,.35);
+  font-size:14px;transition:transform .4s ease;white-space:nowrap;
+  backdrop-filter:blur(8px);
+}
+#toast.show{transform:translateX(-50%) translateY(0)}
+
+/* ── sidebar ── */
+#sidebar-toggle{
+  position:fixed;top:16px;right:16px;z-index:101;
+  background:rgba(15,23,42,.88);backdrop-filter:blur(10px);color:#fff;border:none;
+  padding:10px 18px;border-radius:8px;cursor:pointer;font-size:13px;font-weight:500;
+  transition:right .3s ease;
+}
+#sidebar-toggle.shifted{right:376px}
+
+#sidebar{
+  position:fixed;top:0;right:0;width:360px;height:100vh;
+  background:rgba(15,23,42,.96);backdrop-filter:blur(14px);color:#fff;
+  transform:translateX(100%);transition:transform .3s ease;z-index:100;
+  display:flex;flex-direction:column;
+}
+#sidebar.open{transform:translateX(0)}
+
+.sb-header{padding:24px 20px;border-bottom:1px solid rgba(255,255,255,.08);text-align:center}
+.sb-header h2{font-size:13px;opacity:.55;font-weight:400;margin-bottom:2px}
+.sb-count{font-size:34px;font-weight:700}
+
+.sb-chart{padding:16px 20px;border-bottom:1px solid rgba(255,255,255,.08)}
+.sb-chart h3{font-size:11px;opacity:.45;margin-bottom:10px;text-transform:uppercase;letter-spacing:.5px}
+.bar-row{display:flex;align-items:center;margin-bottom:5px;font-size:12px;cursor:pointer;
+  padding:3px 0;border-radius:5px;transition:background .15s}
+.bar-row:hover{background:rgba(255,255,255,.06)}
+.bar-row.selected{background:rgba(59,130,246,.15)}
+.bar-row.selected .bar-label{opacity:1;color:#60a5fa}
+.bar-row.selected .bar-fill{background:#f59e0b}
+.bar-label{width:58px;text-align:right;padding-right:8px;opacity:.7;transition:all .15s}
+.bar-fill{height:16px;background:#3b82f6;border-radius:3px;transition:width .5s ease,background .2s;min-width:2px}
+.bar-num{padding-left:6px;opacity:.45;font-size:11px}
+
+.heatmap-dot{
+  position:absolute;border-radius:50%;pointer-events:none;z-index:4;
+  transform:translate(-50%,-50%);
+  background:radial-gradient(circle,rgba(251,191,36,.55) 0%,rgba(251,191,36,.15) 60%,transparent 100%);
+  animation:heatFade .5s ease;
+}
+.heatmap-label{
+  position:absolute;transform:translate(-50%,0);z-index:6;pointer-events:none;
+  color:#fbbf24;font-size:11px;font-weight:700;white-space:nowrap;
+  text-shadow:0 1px 4px rgba(0,0,0,.7);
+}
+@keyframes heatFade{from{opacity:0;transform:translate(-50%,-50%) scale(.5)}to{opacity:1;transform:translate(-50%,-50%) scale(1)}}
+
+.sb-filters{padding:10px 20px;border-bottom:1px solid rgba(255,255,255,.08);display:flex;gap:6px}
+.sb-filter{
+  flex:1;padding:6px 0;border:none;border-radius:6px;cursor:pointer;
+  font-size:11px;font-weight:600;text-align:center;
+  background:rgba(255,255,255,.06);color:rgba(255,255,255,.5);transition:all .15s;
+}
+.sb-filter:hover{background:rgba(255,255,255,.1)}
+.sb-filter.active{background:#3b82f6;color:#fff}
+
+.sb-list{flex:1;overflow-y:auto;padding:6px 0}
+.det-link{
+  display:flex;align-items:center;padding:11px 20px;gap:12px;
+  border-bottom:1px solid rgba(255,255,255,.04);cursor:pointer;
+  text-decoration:none;color:inherit;transition:background .12s;
+}
+.det-link:hover{background:rgba(255,255,255,.07)}
+.det-icon{font-size:22px;flex-shrink:0}
+.det-info{flex:1}
+.det-species{font-weight:600;font-size:13px}
+.det-meta{font-size:11px;opacity:.45;margin-top:1px}
+.det-arrow{opacity:.3;font-size:18px}
+.det-badge{
+  font-size:10px;padding:2px 6px;border-radius:4px;font-weight:600;flex-shrink:0;
+}
+.det-badge.verified{background:rgba(34,197,94,.2);color:#22c55e}
+.det-badge.manual{background:rgba(234,179,8,.2);color:#eab308}
+.det-badge.unverified{background:rgba(148,163,184,.15);color:#64748b}
+.empty-state{padding:40px 20px;text-align:center;opacity:.4;font-size:13px;line-height:1.6}
+</style>
+</head>
+<body>
+
+<div id="title"><h1>Nationaal Park De Hoge Veluwe</h1><p>Wildlife Camera Monitoring</p></div>
+
+<!-- map -->
+<div id="map-outer">
+<div id="map-wrap">
+  <img id="map-img" src="/map.webp" alt="Park map" draggable="false">
+  {% for cid, c in cameras.items() %}
+  <a href="/cam/{{ cid }}" class="cam-marker" id="mk-{{ cid }}" style="left:{{ c.px }}%;top:{{ c.py }}%"></a>
+  <div class="cam-label" style="left:{{ c.px }}%;top:calc({{ c.py }}% - 18px)">{{ cid }} &middot; {{ c.name }}</div>
+  {% endfor %}
+</div>
+</div>
+
+<div id="toast"></div>
+<button id="sidebar-toggle" onclick="toggleSidebar()" class="shifted">&#9664; Close</button>
+
+<!-- sidebar -->
+<div id="sidebar" class="open">
+  <div class="sb-header">
+    <h2>Detections Today</h2>
+    <div class="sb-count" id="sb-count">0</div>
+  </div>
+  <div class="sb-chart">
+    <h3>Detections by Species</h3>
+    <div id="chart"></div>
+  </div>
+  <div class="sb-filters">
+    <button class="sb-filter active" data-filter="all" onclick="setFilter('all')">All</button>
+    <button class="sb-filter" data-filter="unverified" onclick="setFilter('unverified')">Unverified</button>
+    <button class="sb-filter" data-filter="verified" onclick="setFilter('verified')">Verified</button>
+    <button class="sb-filter" data-filter="corrected" onclick="setFilter('corrected')">Corrected</button>
+  </div>
+  <div class="sb-list" id="sb-list">
+    <div class="empty-state">No detections yet.<br>Detections appear automatically.</div>
+  </div>
+</div>
+
+<script>
+const CN = {{ class_names | tojson }};
+const CAMS = {{ cameras | tojson }};
+const ICONS = {bear:'\u{1F43B}',deer:'\u{1F98C}',fox:'\u{1F98A}',hare:'\u{1F407}',
+               moose:'\u{1F98C}',person:'\u{1F9D1}',wolf:'\u{1F43A}'};
+let dets=[], sbOpen=true, activeFilter='all', selectedSpecies=null;
+
+function toggleSidebar(){
+  sbOpen=!sbOpen;
+  document.getElementById('sidebar').classList.toggle('open',sbOpen);
+  const b=document.getElementById('sidebar-toggle');
+  b.classList.toggle('shifted',sbOpen);
+  b.innerHTML=sbOpen?'\u25C0 Close':'Detections \u25B6';
+}
+
+function toast(msg){
+  const t=document.getElementById('toast');
+  t.innerHTML=msg; t.classList.add('show');
+  setTimeout(()=>t.classList.remove('show'),3500);
+}
+
+function renderChart(){
+  const counts={};CN.forEach(c=>counts[c]=0);
+  dets.forEach(d=>counts[d.pred]=(counts[d.pred]||0)+1);
+  const mx=Math.max(...Object.values(counts),1);
+  let h='';
+  for(const[sp,n]of Object.entries(counts)){
+    const pct=(n/mx)*100;
+    const sel=sp===selectedSpecies?' selected':'';
+    h+=`<div class="bar-row${sel}" onclick="toggleHeatmap('${sp}')"><div class="bar-label">${sp}</div>
+        <div class="bar-fill" style="width:${pct}%;${n?'':'opacity:.25'}"></div>
+        <div class="bar-num">${n}</div></div>`;
+  }
+  document.getElementById('chart').innerHTML=h;
+}
+
+function toggleHeatmap(species){
+  selectedSpecies = selectedSpecies===species ? null : species;
+  renderChart();
+  renderHeatmap();
+}
+
+function renderHeatmap(){
+  document.querySelectorAll('.heatmap-dot,.heatmap-label').forEach(e=>e.remove());
+  if(!selectedSpecies) return;
+  const wrap=document.getElementById('map-wrap');
+  const camCounts={};
+  for(const cid in CAMS) camCounts[cid]=0;
+  dets.filter(d=>d.pred===selectedSpecies).forEach(d=>{camCounts[d.cam]=(camCounts[d.cam]||0)+1;});
+  const mx=Math.max(...Object.values(camCounts),1);
+  for(const[cid,n]of Object.entries(camCounts)){
+    if(!n) continue;
+    const c=CAMS[cid];
+    const size=60+Math.round((n/mx)*100);
+    const dot=document.createElement('div');
+    dot.className='heatmap-dot';
+    dot.style.left=c.px+'%';dot.style.top=c.py+'%';
+    dot.style.width=size+'px';dot.style.height=size+'px';
+    wrap.appendChild(dot);
+    const lbl=document.createElement('div');
+    lbl.className='heatmap-label';
+    lbl.style.left=c.px+'%';
+    lbl.style.top=`calc(${c.py}% + ${size/2+4}px)`;
+    lbl.textContent=`${n}\u00D7 ${selectedSpecies}`;
+    wrap.appendChild(lbl);
+  }
+}
+
+function setFilter(f){
+  activeFilter=f;
+  document.querySelectorAll('.sb-filter').forEach(b=>b.classList.toggle('active',b.dataset.filter===f));
+  renderList();
+}
+
+function renderList(){
+  document.getElementById('sb-count').textContent=dets.length;
+  let filtered=[...dets];
+  if(activeFilter==='unverified') filtered=filtered.filter(d=>!d.verified);
+  else if(activeFilter==='verified') filtered=filtered.filter(d=>d.verified&&!d.manual);
+  else if(activeFilter==='corrected') filtered=filtered.filter(d=>d.verified&&d.manual);
+  if(!filtered.length){
+    document.getElementById('sb-list').innerHTML=
+      `<div class="empty-state">${dets.length?'No detections match this filter.':'No detections yet.<br>Detections appear automatically.'}</div>`;
+    return;
+  }
+  let h='';
+  for(const d of[...filtered].reverse()){
+    const ic=ICONS[d.pred]||'';
+    const badge=d.verified
+      ?(d.manual?'<span class="det-badge manual">corrected</span>'
+                :'<span class="det-badge verified">verified</span>')
+      :'<span class="det-badge unverified">unverified</span>';
+    h+=`<a class="det-link" href="/det/${d.id}">
+      <div class="det-icon">${ic}</div>
+      <div class="det-info">
+        <div class="det-species">${d.pred[0].toUpperCase()+d.pred.slice(1)}
+          <span style="opacity:.45;font-weight:400"> \u2014 ${d.conf.toFixed(0)}%</span></div>
+        <div class="det-meta">${d.cam} \u00B7 ${d.cam_name} \u00B7 ${d.time}</div>
+      </div>
+      ${badge}
+      <div class="det-arrow">\u203A</div></a>`;
+  }
+  document.getElementById('sb-list').innerHTML=h;
+}
+
+function flashCam(cid){
+  document.querySelectorAll('.cam-marker').forEach(m=>m.classList.remove('active'));
+  const mk=document.getElementById('mk-'+cid);
+  if(mk){mk.classList.add('active');setTimeout(()=>mk.classList.remove('active'),6000);}
+}
+
+function showWolfWarning(cid){
+  const c=CAMS[cid]; if(!c)return;
+  const wrap=document.getElementById('map-wrap');
+  const old=document.getElementById('wolf-'+cid);
+  if(old)old.remove();
+  const oldLbl=document.getElementById('wolf-lbl-'+cid);
+  if(oldLbl)oldLbl.remove();
+  const circle=document.createElement('div');
+  circle.className='wolf-warn';circle.id='wolf-'+cid;
+  circle.style.left=c.px+'%';circle.style.top=c.py+'%';
+  const lbl=document.createElement('div');
+  lbl.className='wolf-warn-label';lbl.id='wolf-lbl-'+cid;
+  lbl.style.left=c.px+'%';lbl.style.top=c.py+'%';
+  lbl.textContent='\u26A0 Wolf detected';
+  wrap.appendChild(circle);wrap.appendChild(lbl);
+  setTimeout(()=>{circle.classList.add('fade');lbl.classList.add('fade');},10000);
+  setTimeout(()=>{circle.remove();lbl.remove();},11500);
+}
+
+let simulating=false;
+async function simulate(){
+  if(simulating)return;
+  simulating=true;
+  try{
+    const r=await fetch('/api/simulate',{method:'POST'});
+    const d=await r.json();
+    dets.push(d);
+    flashCam(d.cam);
+    if(d.pred==='wolf') showWolfWarning(d.cam);
+    toast(`<b>${ICONS[d.pred]||''} ${d.pred[0].toUpperCase()+d.pred.slice(1)}</b> detected at ${d.cam} (${d.cam_name}) \u2014 ${d.conf.toFixed(0)}%`);
+    renderList();renderChart();renderHeatmap();
+  }catch(e){}
+  simulating=false;
+}
+
+function autoSimLoop(){
+  const delay=5000+Math.random()*5000;
+  setTimeout(async()=>{await simulate();autoSimLoop();},delay);
+}
+
+(async function init(){
+  try{
+    const r=await fetch('/api/detections');
+    const existing=await r.json();
+    dets=existing;
+  }catch(e){}
+  renderChart();renderList();
+  autoSimLoop();
+})();
+</script>
+</body></html>"""
+
+# ── HTML: detail page ─────────────────────────────────────────────────────────
+
+DETAIL_HTML = r"""<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width,initial-scale=1">
+<title>Detection {{ det.id }} — De Hoge Veluwe</title>
+<style>
+*{margin:0;padding:0;box-sizing:border-box}
+body{font-family:system-ui,-apple-system,sans-serif;background:#0f172a;color:#e2e8f0;line-height:1.5}
+
+.topbar{
+  position:sticky;top:0;z-index:50;
+  background:rgba(15,23,42,.92);backdrop-filter:blur(12px);
+  padding:14px 24px;display:flex;align-items:center;gap:16px;
+  border-bottom:1px solid rgba(255,255,255,.06);
+}
+.back{
+  background:rgba(255,255,255,.08);color:#e2e8f0;border:none;
+  padding:8px 16px;border-radius:8px;cursor:pointer;font-size:13px;
+  transition:background .15s;text-decoration:none;
+}
+.back:hover{background:rgba(255,255,255,.14)}
+.topbar-info{flex:1}
+.topbar-title{font-size:15px;font-weight:700}
+.topbar-sub{font-size:12px;opacity:.55;margin-top:1px}
+
+.container{max-width:1200px;margin:0 auto;padding:28px 24px 60px}
+
+/* verification */
+.verify-bar{
+  margin-bottom:24px;padding:16px 22px;border-radius:12px;
+  background:rgba(255,255,255,.04);border:1px solid rgba(255,255,255,.06);
+  display:flex;align-items:center;gap:14px;flex-wrap:wrap;
+}
+.verify-bar.done{border-color:rgba(34,197,94,.25)}
+.vb-status{flex:1;font-size:14px}
+.vb-btn{
+  padding:8px 18px;border-radius:8px;border:none;cursor:pointer;
+  font-size:13px;font-weight:600;transition:opacity .15s;
+}
+.vb-btn:hover{opacity:.85}
+.vb-correct{background:#22c55e;color:#fff}
+.vb-wrong{background:#ef4444;color:#fff}
+.vb-submit{background:#3b82f6;color:#fff}
+.vb-cancel{background:rgba(255,255,255,.1);color:#e2e8f0}
+.vb-select{
+  padding:8px 12px;border-radius:8px;border:1px solid rgba(255,255,255,.15);
+  background:rgba(255,255,255,.08);color:#e2e8f0;font-size:13px;
+}
+.vb-badge{
+  display:inline-block;padding:3px 10px;border-radius:6px;font-size:12px;font-weight:600;
+}
+.vb-badge.verified{background:rgba(34,197,94,.2);color:#22c55e}
+.vb-badge.manual{background:rgba(234,179,8,.2);color:#eab308}
+
+/* two-column layout */
+.det-layout{display:grid;grid-template-columns:1fr 340px;gap:24px;align-items:start}
+@media(max-width:860px){.det-layout{grid-template-columns:1fr}}
+
+/* viewer */
+.viewer{
+  background:rgba(255,255,255,.04);border:1px solid rgba(255,255,255,.06);
+  border-radius:14px;overflow:hidden;
+}
+.viewer-head{
+  display:flex;align-items:center;padding:14px 18px;gap:12px;
+  border-bottom:1px solid rgba(255,255,255,.06);
+}
+.nav-btn{
+  width:38px;height:38px;border-radius:8px;border:none;cursor:pointer;
+  background:rgba(255,255,255,.08);color:#e2e8f0;font-size:18px;
+  display:flex;align-items:center;justify-content:center;transition:background .15s;flex-shrink:0;
+}
+.nav-btn:hover{background:rgba(255,255,255,.16)}
+.slide-title{flex:1;text-align:center;font-size:15px;font-weight:700}
+.slide-counter{font-size:12px;opacity:.4;font-weight:400}
+.viewer-img{position:relative;background:#000;min-height:300px;display:flex;align-items:center;justify-content:center}
+.viewer-img img{width:100%;display:block}
+.slide-cap{padding:12px 18px;font-size:13px;opacity:.6;min-height:42px}
+.slide-dots{display:flex;gap:7px;justify-content:center;padding:0 18px 16px}
+.dot{
+  width:9px;height:9px;border-radius:50%;border:none;cursor:pointer;
+  background:rgba(255,255,255,.15);transition:all .2s;padding:0;
+}
+.dot.active{background:#3b82f6;transform:scale(1.25)}
+.dot:hover{background:rgba(255,255,255,.35)}
+
+/* chart sidebar */
+.chart-panel{
+  background:rgba(255,255,255,.04);border:1px solid rgba(255,255,255,.06);
+  border-radius:14px;overflow:hidden;position:sticky;top:80px;
+}
+.chart-panel h3{
+  padding:16px 18px 0;font-size:12px;opacity:.45;text-transform:uppercase;letter-spacing:.4px;
+}
+.prob-chart{padding:14px 18px 6px}
+.prob-row{display:flex;align-items:center;margin-bottom:6px;font-size:12px}
+.prob-label{width:54px;text-align:right;padding-right:10px;opacity:.7;text-transform:capitalize}
+.prob-track{flex:1;height:20px;background:rgba(255,255,255,.06);border-radius:4px;overflow:hidden;position:relative}
+.prob-fill{height:100%;border-radius:4px;transition:width .6s ease;display:flex;align-items:center;justify-content:flex-end;padding-right:6px;min-width:0}
+.prob-fill span{font-size:10px;font-weight:700;color:#fff;opacity:.9}
+.prob-fill.top{background:linear-gradient(90deg,#e8832a,#f59e0b)}
+.prob-fill.other{background:linear-gradient(90deg,#3b82f6,#60a5fa)}
+.pred-info{padding:0 18px 16px;font-size:13px;line-height:1.7}
+.pred-info b{color:#fff}
+.pred-info .tag{
+  display:inline-block;padding:2px 8px;border-radius:5px;font-size:12px;font-weight:600;
+  margin-right:4px;
+}
+.tag-pred{background:rgba(59,130,246,.15);color:#60a5fa}
+
+.loading{
+  display:flex;flex-direction:column;align-items:center;justify-content:center;
+  padding:80px 0;gap:16px;
+}
+.spinner{
+  width:36px;height:36px;border:3px solid rgba(255,255,255,.12);
+  border-top-color:#3b82f6;border-radius:50%;animation:spin .7s linear infinite;
+}
+@keyframes spin{to{transform:rotate(360deg)}}
+#xai{display:none}
+</style>
+</head>
+<body>
+
+<div class="topbar">
+  <a class="back" href="/">&#8592; Dashboard</a>
+  <div class="topbar-info">
+    <div class="topbar-title">Detection {{ det.id }}</div>
+    <div class="topbar-sub">
+      {{ det.cam }} &middot; {{ cameras[det.cam].name }} &middot; {{ det.time }}
+    </div>
+  </div>
+</div>
+
+<div class="container">
+
+<!-- verification -->
+<div class="verify-bar {{ 'done' if det.verified else '' }}" id="verify-bar">
+{% if det.verified %}
+  {% if det.manual %}
+  <div class="vb-status"><span class="vb-badge manual">Manually corrected</span>
+    Model predicted <b>{{ det.orig_pred }}</b> &mdash; ranger corrected to <b>{{ det.pred }}</b></div>
+  {% else %}
+  <div class="vb-status"><span class="vb-badge verified">Verified correct</span>
+    A ranger confirmed this detection is accurate.</div>
+  {% endif %}
+{% else %}
+  <div class="vb-status" id="vb-prompt">Is this detection correct?</div>
+  <div id="vb-actions">
+    <button class="vb-btn vb-correct" onclick="verifyCorrect()">Correct</button>
+    <button class="vb-btn vb-wrong" onclick="showCorrectionForm()">Wrong</button>
+  </div>
+  <div id="vb-form" style="display:none">
+    <select class="vb-select" id="vb-class">
+    {% for c in class_names %}
+      <option value="{{ c }}">{{ c }}</option>
+    {% endfor %}
+    </select>
+    <button class="vb-btn vb-submit" onclick="submitCorrection()">Submit</button>
+    <button class="vb-btn vb-cancel" onclick="cancelCorrection()">Cancel</button>
+  </div>
+{% endif %}
+</div>
+
+<div id="loader" class="loading">
+  <div class="spinner"></div>
+  <div style="opacity:.5;font-size:14px">Computing explanations &hellip;</div>
+</div>
+
+<div id="xai">
+<div class="det-layout">
+  <!-- image viewer / carousel -->
+  <div class="viewer">
+    <div class="viewer-head">
+      <button class="nav-btn" onclick="prev()">&lsaquo;</button>
+      <div class="slide-title">
+        <span id="slide-title">Loading&hellip;</span>
+        <span class="slide-counter" id="slide-counter"></span>
+      </div>
+      <button class="nav-btn" onclick="next()">&rsaquo;</button>
+    </div>
+    <div class="viewer-img"><img id="slide-img" alt=""></div>
+    <div class="slide-cap" id="slide-cap"></div>
+    <div class="slide-dots" id="slide-dots"></div>
+  </div>
+
+  <!-- probability chart (always visible) -->
+  <div class="chart-panel">
+    <h3>Class Probabilities</h3>
+    <div class="prob-chart" id="prob-chart"></div>
+    <div class="pred-info" id="pred-info"></div>
+  </div>
+</div>
+</div><!-- /xai -->
+</div><!-- /container -->
+
+<script>
+let slides=[], cur=0;
+
+function render(){
+  const s=slides[cur];
+  document.getElementById('slide-img').src=s.src;
+  document.getElementById('slide-title').textContent=s.title;
+  document.getElementById('slide-cap').textContent=s.cap;
+  document.getElementById('slide-counter').textContent=`${cur+1} / ${slides.length}`;
+  document.querySelectorAll('.dot').forEach((d,i)=>d.classList.toggle('active',i===cur));
+}
+function prev(){cur=(cur-1+slides.length)%slides.length;render();}
+function next(){cur=(cur+1)%slides.length;render();}
+
+document.addEventListener('keydown',e=>{
+  if(e.key==='ArrowLeft')prev();
+  else if(e.key==='ArrowRight')next();
+});
+
+(async function(){
+  const r = await fetch('/api/xai/{{ det.id }}');
+  const d = await r.json();
+  const u = d.urls;
+
+  slides=[
+    {src:u.original, title:'Original Image',
+     cap:`Predicted: ${d.pred} (${d.conf}% confidence, perplexity ${d.ppl})`},
+    {src:u.scorecam, title:'ScoreCAM',
+     cap:'Highlights image regions the model attends to for its prediction.'},
+    {src:u.lime1, title:'LIME — '+d.lime1_cls,
+     cap:'Green superpixels support this class; red superpixels oppose it.'},
+    {src:u.lime2, title:'LIME — '+d.lime2_cls,
+     cap:'Green superpixels support this class; red superpixels oppose it.'},
+    {src:u.contrastive, title:'Contrastive Explanation',
+     cap:d.contrast_leg},
+  ];
+  d.nbs.forEach((nb,i)=>{
+    slides.push({src:u['nb'+(i+1)], title:'Nearest Neighbour '+(i+1),
+                 cap:nb.cls+' (cosine similarity '+nb.sim+')'});
+  });
+
+  let dots='';
+  slides.forEach((_,i)=>{dots+=`<button class="dot${i===0?' active':''}" onclick="cur=${i};render()"></button>`;});
+  document.getElementById('slide-dots').innerHTML=dots;
+
+  const probs=d.probs, mx=Math.max(...Object.values(probs),0.01);
+  let ch='';
+  for(const[cls,p] of Object.entries(probs)){
+    const pct=(p/mx)*100, isTop=cls===d.pred;
+    const lbl=p>=0.05?`<span>${(p*100).toFixed(1)}%</span>`:'';
+    ch+=`<div class="prob-row"><div class="prob-label">${cls}</div>
+      <div class="prob-track"><div class="prob-fill ${isTop?'top':'other'}" style="width:${pct}%">${lbl}</div></div></div>`;
+  }
+  document.getElementById('prob-chart').innerHTML=ch;
+  document.getElementById('pred-info').innerHTML=
+    `<span class="tag tag-pred">${d.pred}</span><br>`+
+    `Confidence <b>${d.conf}%</b> &middot; Perplexity <b>${d.ppl}</b>`;
+
+  document.getElementById('loader').style.display='none';
+  document.getElementById('xai').style.display='block';
+  render();
+})();
+
+async function verifyCorrect(){
+  await fetch('/api/verify/{{ det.id }}',{
+    method:'POST',headers:{'Content-Type':'application/json'},
+    body:JSON.stringify({action:'correct'})
+  });
+  const bar=document.getElementById('verify-bar');
+  bar.classList.add('done');
+  bar.innerHTML='<div class="vb-status"><span class="vb-badge verified">Verified correct</span> A ranger confirmed this detection is accurate.</div>';
+}
+
+function showCorrectionForm(){
+  document.getElementById('vb-actions').style.display='none';
+  document.getElementById('vb-form').style.display='flex';
+  document.getElementById('vb-prompt').textContent='Select the correct species:';
+}
+
+function cancelCorrection(){
+  document.getElementById('vb-actions').style.display='';
+  document.getElementById('vb-form').style.display='none';
+  document.getElementById('vb-prompt').textContent='Is this detection correct?';
+}
+
+async function submitCorrection(){
+  const cls=document.getElementById('vb-class').value;
+  await fetch('/api/verify/{{ det.id }}',{
+    method:'POST',headers:{'Content-Type':'application/json'},
+    body:JSON.stringify({action:'wrong',true_class:cls})
+  });
+  const bar=document.getElementById('verify-bar');
+  bar.classList.add('done');
+  bar.innerHTML=`<div class="vb-status"><span class="vb-badge manual">Manually corrected</span> Model predicted <b>{{ det.pred }}</b> \u2014 ranger corrected to <b>${cls}</b></div>`;
+}
+</script>
+</body></html>"""
+
+
+# ── HTML: camera feed page ────────────────────────────────────────────────────
+
+CAM_HTML = r"""<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width,initial-scale=1">
+<title>{{ cam_id }} — {{ cam.name }}</title>
+<style>
+*{margin:0;padding:0;box-sizing:border-box}
+body{font-family:system-ui,-apple-system,sans-serif;background:#0f172a;color:#e2e8f0;line-height:1.5}
+
+.topbar{
+  position:sticky;top:0;z-index:50;
+  background:rgba(15,23,42,.92);backdrop-filter:blur(12px);
+  padding:14px 24px;display:flex;align-items:center;gap:16px;
+  border-bottom:1px solid rgba(255,255,255,.06);
+}
+.back{
+  background:rgba(255,255,255,.08);color:#e2e8f0;border:none;
+  padding:8px 16px;border-radius:8px;cursor:pointer;font-size:13px;
+  transition:background .15s;text-decoration:none;
+}
+.back:hover{background:rgba(255,255,255,.14)}
+.topbar-info{flex:1}
+.topbar-title{font-size:17px;font-weight:700}
+.topbar-sub{font-size:12px;opacity:.55;margin-top:2px}
+
+.container{max-width:1200px;margin:0 auto;padding:28px 24px 60px}
+
+.grid{
+  display:grid;
+  grid-template-columns:repeat(auto-fill,minmax(200px,1fr));
+  gap:16px;
+}
+
+.card{
+  background:rgba(255,255,255,.04);border:1px solid rgba(255,255,255,.06);
+  border-radius:12px;overflow:hidden;text-decoration:none;color:inherit;
+  transition:transform .15s,box-shadow .15s;
+}
+.card:hover{transform:translateY(-3px);box-shadow:0 8px 24px rgba(0,0,0,.3)}
+.card img{width:100%;aspect-ratio:1;object-fit:cover;display:block}
+.card-body{padding:10px 14px}
+.card-species{font-weight:600;font-size:14px}
+.card-conf{font-size:12px;opacity:.5;margin-top:1px}
+.card-time{font-size:11px;opacity:.35;margin-top:3px}
+.card-badge{
+  display:inline-block;font-size:10px;padding:2px 6px;border-radius:4px;
+  font-weight:600;margin-top:4px;
+}
+.card-badge.verified{background:rgba(34,197,94,.2);color:#22c55e}
+.card-badge.manual{background:rgba(234,179,8,.2);color:#eab308}
+.card-badge.unverified{background:rgba(148,163,184,.15);color:#64748b}
+
+.empty{text-align:center;padding:60px 20px;opacity:.4;font-size:14px;line-height:1.6}
+</style>
+</head>
+<body>
+
+<div class="topbar">
+  <a class="back" href="/">&#8592; Dashboard</a>
+  <div class="topbar-info">
+    <div class="topbar-title">{{ cam_id }} &mdash; {{ cam.name }}</div>
+    <div class="topbar-sub">{{ cam.desc }}</div>
+  </div>
+</div>
+
+<div class="container">
+{% if dets %}
+<div class="grid">
+  {% for d in dets %}
+  <a class="card" href="/det/{{ d.id }}">
+    <img src="/xai/{{ d.id }}/original.png" alt="{{ d.pred }}">
+    <div class="card-body">
+      <div class="card-species">{{ d.pred | capitalize }}</div>
+      <div class="card-conf">{{ d.conf }}% confidence</div>
+      <div class="card-time">{{ d.time }}</div>
+      {% if d.verified %}
+        {% if d.manual %}
+        <div class="card-badge manual">corrected</div>
+        {% else %}
+        <div class="card-badge verified">verified</div>
+        {% endif %}
+      {% else %}
+      <div class="card-badge unverified">unverified</div>
+      {% endif %}
+    </div>
+  </a>
+  {% endfor %}
+</div>
+{% else %}
+<div class="empty">No detections from this camera yet.<br>Detections will appear here automatically.</div>
+{% endif %}
+</div>
+
+</body></html>"""
+
+
+if __name__ == "__main__":
+    app.run(debug=True, port=5000)
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index 0000000..158f332
--- /dev/null
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