Sync from GitHub via hub-sync

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 pestdetectionai
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,10 +1,2 @@
----
-title: Web
-emoji: 🏢
-colorFrom: green
-colorTo: blue
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# web
+web

download_model.py

@@ -0,0 +1,23 @@
+from pathlib import Path
+from huggingface_hub import hf_hub_download
+
+MODEL_REPO = "underdogquality/yolo11s-pest-detection"
+MODEL_FILE = "best.pt"
+
+BASE_DIR = Path(__file__).resolve().parent
+MODEL_DIR = BASE_DIR / "models"
+MODEL_DIR.mkdir(parents=True, exist_ok=True)
+
+print("Downloading pest detection model...")
+print(f"Repo: {MODEL_REPO}")
+print(f"File: {MODEL_FILE}")
+
+downloaded_path = hf_hub_download(
+    repo_id=MODEL_REPO,
+    filename=MODEL_FILE,
+    local_dir=str(MODEL_DIR),
+    local_dir_use_symlinks=False
+)
+
+print("Model downloaded successfully:")
+print(downloaded_path)

filestructure.txt

@@ -0,0 +1,32 @@
+|-- .env
+|-- .github
+    |-- workflows
+        |-- sync-to-huggingface.yml
+|-- .gitignore
+|-- debug
+    |-- mask_20260506_044403_b6b1f10e7c884d7fabad5522bfa5d17f.jpg
+|-- download_model.py
+|-- LICENSE
+|-- main.py
+|-- models
+    |-- best.pt
+|-- README.md
+|-- requirements.txt
+|-- results
+    |-- result_20260506_044403_b6b1f10e7c884d7fabad5522bfa5d17f.jpg
+|-- uploads
+    |-- 20260506_044403_b6b1f10e7c884d7fabad5522bfa5d17f.jpeg
+|-- web
+    |-- access.html
+    |-- css
+        |-- app.css
+    |-- detail.html
+    |-- index.html
+    |-- js
+        |-- api.js
+        |-- dashboard.js
+        |-- detail.js
+        |-- logs.js
+    |-- logs.html
+|-- __pycache__
+    |-- main.cpython-310.pyc

main.py

@@ -0,0 +1,1802 @@
+from fastapi import FastAPI, UploadFile, File, HTTPException, Request, Query
+from fastapi.responses import JSONResponse, FileResponse, RedirectResponse
+from fastapi.staticfiles import StaticFiles
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+from pathlib import Path
+from datetime import datetime, timedelta
+from collections import Counter, defaultdict
+from dotenv import load_dotenv
+import uuid
+import cv2
+import numpy as np
+import os
+import base64
+import json
+import firebase_admin
+from firebase_admin import credentials, db
+import cloudinary
+import cloudinary.uploader
+
+
+# =========================================================
+# ENV
+# =========================================================
+
+load_dotenv()
+
+
+# =========================================================
+# CONFIG
+# =========================================================
+
+BASE_DIR = Path(__file__).resolve().parent
+
+MODEL_DIR = BASE_DIR / "models"
+
+MODEL_FILENAME = os.getenv("MODEL_FILENAME", "best.pt").strip()
+MODEL_PATH = Path(os.getenv("MODEL_PATH", str(MODEL_DIR / MODEL_FILENAME))).resolve()
+
+HF_MODEL_REPO = os.getenv(
+    "HF_MODEL_REPO",
+    "underdogquality/yolo11s-pest-detection"
+).strip()
+
+HF_MODEL_FILE = os.getenv(
+    "HF_MODEL_FILE",
+    MODEL_FILENAME
+).strip()
+
+HF_TOKEN = os.getenv("HF_TOKEN", "").strip() or None
+
+AUTO_DOWNLOAD_MODEL = os.getenv(
+    "AUTO_DOWNLOAD_MODEL",
+    "true"
+).strip().lower() in {"1", "true", "yes", "on"}
+
+UPLOAD_DIR = BASE_DIR / "uploads"
+RESULT_DIR = BASE_DIR / "results"
+DEBUG_DIR = BASE_DIR / "debug"
+WEB_DIR = BASE_DIR / "web"
+
+MODEL_PATH.parent.mkdir(parents=True, exist_ok=True)
+UPLOAD_DIR.mkdir(parents=True, exist_ok=True)
+RESULT_DIR.mkdir(parents=True, exist_ok=True)
+DEBUG_DIR.mkdir(parents=True, exist_ok=True)
+WEB_DIR.mkdir(parents=True, exist_ok=True)
+
+ALLOWED_EXTENSIONS = {".jpg", ".jpeg", ".png", ".webp", ".bmp"}
+
+APP_PUBLIC_BASE_URL = os.getenv("APP_PUBLIC_BASE_URL", "").strip()
+
+FIREBASE_DATABASE_URL = os.getenv("FIREBASE_DATABASE_URL", "").strip()
+FIREBASE_SERVICE_ACCOUNT_PATH = os.getenv("FIREBASE_SERVICE_ACCOUNT_PATH", "").strip()
+FIREBASE_SERVICE_ACCOUNT_JSON_B64 = os.getenv("FIREBASE_SERVICE_ACCOUNT_JSON_B64", "").strip()
+
+FIREBASE_LOGS_PATH = "/api/analyze/logs"
+
+CLOUDINARY_CLOUD_NAME = os.getenv("CLOUDINARY_CLOUD_NAME", "").strip()
+CLOUDINARY_API_KEY = os.getenv("CLOUDINARY_API_KEY", "").strip()
+CLOUDINARY_API_SECRET = os.getenv("CLOUDINARY_API_SECRET", "").strip()
+CLOUDINARY_FOLDER = os.getenv("CLOUDINARY_FOLDER", "smart-pest-detection").strip()
+
+# YOLO settings
+YOLO_CONFIDENCE = 0.08
+YOLO_IOU = 0.40
+YOLO_IMAGE_SIZE = 1280
+
+# YOLO filter
+MAX_YOLO_BOX_AREA_RATIO = 0.10
+LOW_CONF_LARGE_BOX_CONF = 0.20
+LOW_CONF_LARGE_BOX_AREA_RATIO = 0.040
+
+# CV proposal settings
+PROPOSAL_MIN_AREA = 10
+PROPOSAL_MAX_AREA_RATIO = 0.030
+PROPOSAL_MIN_WIDTH = 4
+PROPOSAL_MIN_HEIGHT = 4
+PROPOSAL_MAX_WIDTH_RATIO = 0.35
+PROPOSAL_MAX_HEIGHT_RATIO = 0.35
+
+GOOD_YOLO_CONFIDENCE = 0.24
+UNKNOWN_OVERLAP_WITH_GOOD_YOLO = 0.12
+
+FINAL_NMS_IOU = 0.14
+
+GREEN = (0, 255, 0)
+ORANGE = (0, 165, 255)
+BLACK = (0, 0, 0)
+
+
+# =========================================================
+# APP INIT
+# =========================================================
+
+app = FastAPI(
+    title="Smart Pest Trap Detection API",
+    description="YOLO pest identification + Cloudinary storage + Firebase logs + static website.",
+    version="9.1.0"
+)
+
+app.mount("/uploads", StaticFiles(directory=str(UPLOAD_DIR)), name="uploads")
+app.mount("/results", StaticFiles(directory=str(RESULT_DIR)), name="results")
+app.mount("/debug", StaticFiles(directory=str(DEBUG_DIR)), name="debug")
+app.mount("/web", StaticFiles(directory=str(WEB_DIR)), name="web")
+
+
+# =========================================================
+# GLOBALS
+# =========================================================
+
+model = None
+firebase_ready = False
+cloudinary_ready = False
+
+
+# =========================================================
+# STARTUP
+# =========================================================
+
+@app.on_event("startup")
+def startup():
+    load_model()
+    init_firebase()
+    init_cloudinary()
+
+
+def ensure_model_available():
+    """
+    Downloads the YOLO model on boot if models/best.pt is missing.
+    First boot needs internet. After download, it uses the local file.
+    """
+
+    MODEL_PATH.parent.mkdir(parents=True, exist_ok=True)
+
+    if MODEL_PATH.exists() and MODEL_PATH.stat().st_size > 0:
+        print("====================================")
+        print("[MODEL] Local model found")
+        print(f"[MODEL] Path: {MODEL_PATH}")
+        print("====================================")
+        return
+
+    if not AUTO_DOWNLOAD_MODEL:
+        raise RuntimeError(
+            f"Model not found: {MODEL_PATH}\n"
+            "AUTO_DOWNLOAD_MODEL=false, so boot download is disabled."
+        )
+
+    print("====================================")
+    print("[MODEL] Local model not found")
+    print("[MODEL] Downloading model from Hugging Face...")
+    print(f"[MODEL] Repo: {HF_MODEL_REPO}")
+    print(f"[MODEL] File: {HF_MODEL_FILE}")
+    print(f"[MODEL] Save to: {MODEL_PATH.parent}")
+    print("====================================")
+
+    try:
+        downloaded_path = hf_hub_download(
+            repo_id=HF_MODEL_REPO,
+            filename=HF_MODEL_FILE,
+            local_dir=str(MODEL_PATH.parent),
+            local_dir_use_symlinks=False,
+            token=HF_TOKEN
+        )
+
+        downloaded_path = Path(downloaded_path).resolve()
+
+        if downloaded_path != MODEL_PATH:
+            if downloaded_path.exists():
+                MODEL_PATH.write_bytes(downloaded_path.read_bytes())
+
+        if not MODEL_PATH.exists() or MODEL_PATH.stat().st_size <= 0:
+            raise RuntimeError(f"Downloaded model is missing or empty: {MODEL_PATH}")
+
+        print("====================================")
+        print("[MODEL] Download complete")
+        print(f"[MODEL] Path: {MODEL_PATH}")
+        print("====================================")
+
+    except Exception as e:
+        raise RuntimeError(
+            f"Model download failed.\n"
+            f"Repo: {HF_MODEL_REPO}\n"
+            f"File: {HF_MODEL_FILE}\n"
+            f"Target: {MODEL_PATH}\n"
+            f"Error: {e}"
+        )
+
+
+def load_model():
+    global model
+
+    ensure_model_available()
+
+    print("====================================")
+    print("[MODEL] Loading pest detection model")
+    print(f"[MODEL] Path: {MODEL_PATH}")
+    print("====================================")
+
+    model = YOLO(str(MODEL_PATH))
+
+    print("[MODEL] Loaded successfully")
+
+
+def init_firebase():
+    global firebase_ready
+
+    if not FIREBASE_DATABASE_URL:
+        print("[FIREBASE] Disabled: FIREBASE_DATABASE_URL is missing")
+        firebase_ready = False
+        return
+
+    try:
+        if firebase_admin._apps:
+            firebase_ready = True
+            print("[FIREBASE] Already initialized")
+            return
+
+        if FIREBASE_SERVICE_ACCOUNT_JSON_B64:
+            decoded = base64.b64decode(FIREBASE_SERVICE_ACCOUNT_JSON_B64).decode("utf-8")
+            service_account_info = json.loads(decoded)
+            cred = credentials.Certificate(service_account_info)
+            print("[FIREBASE] Using FIREBASE_SERVICE_ACCOUNT_JSON_B64")
+
+        elif FIREBASE_SERVICE_ACCOUNT_PATH:
+            service_account_path = Path(FIREBASE_SERVICE_ACCOUNT_PATH)
+
+            if not service_account_path.is_absolute():
+                service_account_path = BASE_DIR / service_account_path
+
+            if not service_account_path.exists():
+                print(f"[FIREBASE] Service account file not found: {service_account_path}")
+                firebase_ready = False
+                return
+
+            cred = credentials.Certificate(str(service_account_path))
+            print(f"[FIREBASE] Using service account file: {service_account_path}")
+
+        else:
+            print("[FIREBASE] Disabled: service account is missing")
+            firebase_ready = False
+            return
+
+        firebase_admin.initialize_app(
+            cred,
+            {
+                "databaseURL": FIREBASE_DATABASE_URL
+            }
+        )
+
+        firebase_ready = True
+        print("[FIREBASE] Initialized successfully")
+
+    except Exception as e:
+        firebase_ready = False
+        print(f"[FIREBASE] Init failed: {e}")
+
+
+def init_cloudinary():
+    global cloudinary_ready
+
+    if not CLOUDINARY_CLOUD_NAME or not CLOUDINARY_API_KEY or not CLOUDINARY_API_SECRET:
+        cloudinary_ready = False
+        print("[CLOUDINARY] Disabled: missing CLOUDINARY_CLOUD_NAME/API_KEY/API_SECRET")
+        return
+
+    try:
+        cloudinary.config(
+            cloud_name=CLOUDINARY_CLOUD_NAME,
+            api_key=CLOUDINARY_API_KEY,
+            api_secret=CLOUDINARY_API_SECRET,
+            secure=True
+        )
+
+        cloudinary_ready = True
+        print("[CLOUDINARY] Initialized successfully")
+
+    except Exception as e:
+        cloudinary_ready = False
+        print(f"[CLOUDINARY] Init failed: {e}")
+
+
+# =========================================================
+# BASIC HELPERS
+# =========================================================
+
+def now_dt():
+    return datetime.now()
+
+
+def now_string():
+    return now_dt().strftime("%Y-%m-%d %H:%M")
+
+
+def now_iso():
+    return now_dt().isoformat(timespec="seconds")
+
+
+def now_timestamp_ms():
+    return int(now_dt().timestamp() * 1000)
+
+
+def get_base_url(request: Request):
+    if APP_PUBLIC_BASE_URL:
+        return APP_PUBLIC_BASE_URL.rstrip("/")
+    return str(request.base_url).rstrip("/")
+
+
+def validate_image_file(file: UploadFile):
+    filename = file.filename or ""
+    ext = Path(filename).suffix.lower()
+
+    if ext not in ALLOWED_EXTENSIONS:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Invalid image type. Allowed: {', '.join(sorted(ALLOWED_EXTENSIONS))}"
+        )
+
+    return ext
+
+
+async def save_upload(file: UploadFile, ext: str) -> Path:
+    UPLOAD_DIR.mkdir(parents=True, exist_ok=True)
+
+    unique_name = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex}{ext}"
+    save_path = UPLOAD_DIR / unique_name
+
+    content = await file.read()
+
+    if not content:
+        raise HTTPException(status_code=400, detail="Uploaded image is empty.")
+
+    with open(save_path, "wb") as buffer:
+        buffer.write(content)
+
+    if not save_path.exists():
+        raise HTTPException(
+            status_code=500,
+            detail=f"Upload save failed. File was not created: {save_path}"
+        )
+
+    if save_path.stat().st_size <= 0:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Upload save failed. File is empty: {save_path}"
+        )
+
+    return save_path
+
+
+def safe_label(label: str):
+    return label.replace("_", " ").strip()
+
+
+def get_box(det):
+    b = det["box"]
+    return [float(b["x1"]), float(b["y1"]), float(b["x2"]), float(b["y2"])]
+
+
+def box_area(box):
+    x1, y1, x2, y2 = box
+    return max(0.0, x2 - x1) * max(0.0, y2 - y1)
+
+
+def clamp_box(box, width, height):
+    x1, y1, x2, y2 = box
+
+    x1 = max(0, min(float(x1), width - 1))
+    y1 = max(0, min(float(y1), height - 1))
+    x2 = max(0, min(float(x2), width - 1))
+    y2 = max(0, min(float(y2), height - 1))
+
+    if x2 < x1:
+        x1, x2 = x2, x1
+
+    if y2 < y1:
+        y1, y2 = y2, y1
+
+    return [x1, y1, x2, y2]
+
+
+def expand_box(box, pad, width, height):
+    x1, y1, x2, y2 = box
+    return clamp_box(
+        [x1 - pad, y1 - pad, x2 + pad, y2 + pad],
+        width,
+        height
+    )
+
+
+def iou(box_a, box_b):
+    ax1, ay1, ax2, ay2 = box_a
+    bx1, by1, bx2, by2 = box_b
+
+    ix1 = max(ax1, bx1)
+    iy1 = max(ay1, by1)
+    ix2 = min(ax2, bx2)
+    iy2 = min(ay2, by2)
+
+    iw = max(0.0, ix2 - ix1)
+    ih = max(0.0, iy2 - iy1)
+
+    inter = iw * ih
+    union = box_area(box_a) + box_area(box_b) - inter
+
+    if union <= 0:
+        return 0.0
+
+    return inter / union
+
+
+def intersection_ratio_small(box_a, box_b):
+    ax1, ay1, ax2, ay2 = box_a
+    bx1, by1, bx2, by2 = box_b
+
+    ix1 = max(ax1, bx1)
+    iy1 = max(ay1, by1)
+    ix2 = min(ax2, bx2)
+    iy2 = min(ay2, by2)
+
+    iw = max(0.0, ix2 - ix1)
+    ih = max(0.0, iy2 - iy1)
+
+    inter = iw * ih
+    smaller = min(box_area(box_a), box_area(box_b))
+
+    if smaller <= 0:
+        return 0.0
+
+    return inter / smaller
+
+
+def nms_detections(detections, iou_threshold=0.14, class_aware=False):
+    if not detections:
+        return []
+
+    detections = sorted(detections, key=lambda d: d["confidence"], reverse=True)
+    kept = []
+
+    while detections:
+        best = detections.pop(0)
+        kept.append(best)
+
+        remaining = []
+
+        for det in detections:
+            overlap = iou(get_box(best), get_box(det))
+
+            if class_aware:
+                if best["type"] == det["type"] and overlap > iou_threshold:
+                    continue
+            else:
+                if overlap > iou_threshold:
+                    continue
+
+            remaining.append(det)
+
+        detections = remaining
+
+    return kept
+
+
+# =========================================================
+# CLOUDINARY HELPERS
+# =========================================================
+
+def upload_image_to_cloudinary(file_path: Path, folder_name: str, public_id_prefix: str):
+    if not cloudinary_ready:
+        print("[CLOUDINARY] Skipped: cloudinary_ready=False")
+        return None
+
+    if not file_path:
+        print("[CLOUDINARY] Skipped: file_path is None")
+        return None
+
+    file_path = Path(file_path)
+
+    if not file_path.exists():
+        print(f"[CLOUDINARY] Skipped: file does not exist: {file_path}")
+        return None
+
+    if file_path.stat().st_size <= 0:
+        print(f"[CLOUDINARY] Skipped: file is empty: {file_path}")
+        return None
+
+    try:
+        public_id = f"{public_id_prefix}_{file_path.stem}"
+
+        result = cloudinary.uploader.upload(
+            str(file_path.resolve()),
+            folder=f"{CLOUDINARY_FOLDER}/{folder_name}",
+            public_id=public_id,
+            resource_type="image",
+            overwrite=True
+        )
+
+        return {
+            "secure_url": result.get("secure_url"),
+            "url": result.get("url"),
+            "public_id": result.get("public_id"),
+            "asset_id": result.get("asset_id"),
+            "format": result.get("format"),
+            "bytes": result.get("bytes"),
+            "width": result.get("width"),
+            "height": result.get("height")
+        }
+
+    except Exception as e:
+        print(f"[CLOUDINARY] Upload failed for {file_path}: {e}")
+        return None
+
+
+def upload_analysis_images_to_cloudinary(uploaded_path: Path, result_image_path: Path, debug_mask_path: Path | None):
+    timestamp_folder = datetime.now().strftime("%Y/%m/%d")
+
+    original_cloud = upload_image_to_cloudinary(
+        uploaded_path,
+        f"{timestamp_folder}/original",
+        "original"
+    )
+
+    annotated_cloud = upload_image_to_cloudinary(
+        result_image_path,
+        f"{timestamp_folder}/annotated",
+        "annotated"
+    )
+
+    debug_cloud = None
+
+    if debug_mask_path:
+        debug_cloud = upload_image_to_cloudinary(
+            debug_mask_path,
+            f"{timestamp_folder}/debug",
+            "debug"
+        )
+
+    return original_cloud, annotated_cloud, debug_cloud
+
+
+# =========================================================
+# IMAGE PREPROCESSING
+# =========================================================
+
+def remove_red_markup_if_present(image):
+    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+
+    lower_red1 = np.array([0, 80, 80])
+    upper_red1 = np.array([12, 255, 255])
+
+    lower_red2 = np.array([170, 80, 80])
+    upper_red2 = np.array([180, 255, 255])
+
+    mask1 = cv2.inRange(hsv, lower_red1, upper_red1)
+    mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
+
+    red_mask = cv2.bitwise_or(mask1, mask2)
+
+    if cv2.countNonZero(red_mask) < 50:
+        return image
+
+    return cv2.inpaint(image, red_mask, 5, cv2.INPAINT_TELEA)
+
+
+def enhance_image(image):
+    lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
+    l, a, b = cv2.split(lab)
+
+    clahe = cv2.createCLAHE(
+        clipLimit=3.5,
+        tileGridSize=(8, 8)
+    )
+
+    l2 = clahe.apply(l)
+    lab2 = cv2.merge((l2, a, b))
+    enhanced = cv2.cvtColor(lab2, cv2.COLOR_LAB2BGR)
+
+    blur = cv2.GaussianBlur(enhanced, (0, 0), 1.0)
+    sharp = cv2.addWeighted(enhanced, 1.8, blur, -0.8, 0)
+
+    return sharp
+
+
+def find_trap_floor_crop(image):
+    h, w = image.shape[:2]
+
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    blur = cv2.GaussianBlur(gray, (7, 7), 0)
+
+    _, thresh = cv2.threshold(
+        blur,
+        0,
+        255,
+        cv2.THRESH_BINARY + cv2.THRESH_OTSU
+    )
+
+    contours, _ = cv2.findContours(
+        thresh,
+        cv2.RETR_EXTERNAL,
+        cv2.CHAIN_APPROX_SIMPLE
+    )
+
+    if contours:
+        contours = sorted(contours, key=cv2.contourArea, reverse=True)
+
+        for cnt in contours[:10]:
+            x, y, cw, ch = cv2.boundingRect(cnt)
+            area = cw * ch
+            image_area = w * h
+
+            if area > image_area * 0.16 and cw > w * 0.25 and ch > h * 0.25:
+                pad = 2
+                x1 = max(0, x - pad)
+                y1 = max(0, y - pad)
+                x2 = min(w, x + cw + pad)
+                y2 = min(h, y + ch + pad)
+                return image[y1:y2, x1:x2].copy(), x1, y1
+
+    x1 = int(w * 0.16)
+    y1 = int(h * 0.24)
+    x2 = int(w * 0.82)
+    y2 = int(h * 0.82)
+
+    return image[y1:y2, x1:x2].copy(), x1, y1
+
+
+# =========================================================
+# YOLO DETECTION
+# =========================================================
+
+def yolo_predict(image, offset_x=0, offset_y=0, source_name="image"):
+    results = model.predict(
+        source=image,
+        imgsz=YOLO_IMAGE_SIZE,
+        conf=YOLO_CONFIDENCE,
+        iou=YOLO_IOU,
+        verbose=False
+    )
+
+    detections = []
+
+    if not results:
+        return detections
+
+    result = results[0]
+
+    if result.boxes is None or len(result.boxes) == 0:
+        return detections
+
+    names = result.names
+
+    for box in result.boxes:
+        cls_id = int(box.cls[0].item())
+        confidence = float(box.conf[0].item())
+        label = safe_label(names.get(cls_id, str(cls_id)))
+
+        xyxy = box.xyxy[0].cpu().numpy().astype(float)
+        x1, y1, x2, y2 = xyxy.tolist()
+
+        detections.append({
+            "type": label,
+            "confidence": round(confidence, 4),
+            "source": source_name,
+            "box": {
+                "x1": round(x1 + offset_x, 2),
+                "y1": round(y1 + offset_y, 2),
+                "x2": round(x2 + offset_x, 2),
+                "y2": round(y2 + offset_y, 2)
+            }
+        })
+
+    return detections
+
+
+def yolo_tiled(image, offset_x=0, offset_y=0):
+    detections = []
+
+    h, w = image.shape[:2]
+
+    tile_size = 448
+    overlap = 220
+    step = tile_size - overlap
+
+    y_positions = list(range(0, max(1, h - tile_size + 1), step))
+    x_positions = list(range(0, max(1, w - tile_size + 1), step))
+
+    if not y_positions:
+        y_positions = [0]
+
+    if not x_positions:
+        x_positions = [0]
+
+    last_y = max(0, h - tile_size)
+    last_x = max(0, w - tile_size)
+
+    if y_positions[-1] != last_y:
+        y_positions.append(last_y)
+
+    if x_positions[-1] != last_x:
+        x_positions.append(last_x)
+
+    for y in y_positions:
+        for x in x_positions:
+            tile = image[y:y + tile_size, x:x + tile_size].copy()
+
+            tile_detections = yolo_predict(
+                tile,
+                offset_x=offset_x + x,
+                offset_y=offset_y + y,
+                source_name="tile"
+            )
+
+            detections.extend(tile_detections)
+
+    return detections
+
+
+def filter_bad_yolo_boxes(detections, image_width, image_height):
+    filtered = []
+    image_area = image_width * image_height
+
+    for det in detections:
+        box = get_box(det)
+        area_ratio = box_area(box) / max(image_area, 1)
+        conf = det["confidence"]
+
+        bw = box[2] - box[0]
+        bh = box[3] - box[1]
+
+        if bw < 4 or bh < 4:
+            continue
+
+        if area_ratio > MAX_YOLO_BOX_AREA_RATIO:
+            continue
+
+        if conf < LOW_CONF_LARGE_BOX_CONF and area_ratio > LOW_CONF_LARGE_BOX_AREA_RATIO:
+            continue
+
+        filtered.append(det)
+
+    return filtered
+
+
+# =========================================================
+# COMPUTER VISION PROPOSAL DETECTOR
+# =========================================================
+
+def create_floor_mask(floor_crop):
+    enhanced = enhance_image(floor_crop)
+
+    gray = cv2.cvtColor(enhanced, cv2.COLOR_BGR2GRAY)
+    gray = cv2.GaussianBlur(gray, (3, 3), 0)
+
+    local_bg = cv2.GaussianBlur(gray, (0, 0), 19)
+    dark_diff = cv2.subtract(local_bg, gray)
+    _, dark_mask = cv2.threshold(dark_diff, 5, 255, cv2.THRESH_BINARY)
+
+    adaptive = cv2.adaptiveThreshold(
+        gray,
+        255,
+        cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+        cv2.THRESH_BINARY_INV,
+        31,
+        3
+    )
+
+    edges = cv2.Canny(gray, 18, 75)
+    edges = cv2.dilate(edges, np.ones((2, 2), np.uint8), iterations=1)
+
+    blackhat_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (19, 19))
+    blackhat = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, blackhat_kernel)
+    _, blackhat_mask = cv2.threshold(blackhat, 4, 255, cv2.THRESH_BINARY)
+
+    lab = cv2.cvtColor(enhanced, cv2.COLOR_BGR2LAB)
+    l, a, b = cv2.split(lab)
+
+    bg_l = cv2.GaussianBlur(l, (0, 0), 21)
+    bg_a = cv2.GaussianBlur(a, (0, 0), 21)
+    bg_b = cv2.GaussianBlur(b, (0, 0), 21)
+
+    diff_l = cv2.absdiff(l, bg_l)
+    diff_a = cv2.absdiff(a, bg_a)
+    diff_b = cv2.absdiff(b, bg_b)
+
+    color_diff = cv2.addWeighted(diff_l, 0.45, diff_a, 0.30, 0)
+    color_diff = cv2.addWeighted(color_diff, 1.0, diff_b, 0.25, 0)
+
+    _, color_mask = cv2.threshold(color_diff, 7, 255, cv2.THRESH_BINARY)
+
+    combined = cv2.bitwise_or(dark_mask, adaptive)
+    combined = cv2.bitwise_or(combined, edges)
+    combined = cv2.bitwise_or(combined, blackhat_mask)
+    combined = cv2.bitwise_or(combined, color_mask)
+
+    k2 = np.ones((2, 2), np.uint8)
+    k3 = np.ones((3, 3), np.uint8)
+
+    combined = cv2.morphologyEx(combined, cv2.MORPH_OPEN, k2, iterations=1)
+    combined = cv2.morphologyEx(combined, cv2.MORPH_CLOSE, k3, iterations=1)
+    combined = cv2.dilate(combined, k3, iterations=1)
+
+    return combined
+
+
+def score_candidate(floor_crop, x, y, w, h):
+    crop_h, crop_w = floor_crop.shape[:2]
+
+    x1 = max(0, x)
+    y1 = max(0, y)
+    x2 = min(crop_w, x + w)
+    y2 = min(crop_h, y + h)
+
+    roi = floor_crop[y1:y2, x1:x2]
+
+    if roi.size == 0:
+        return 0.0
+
+    gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
+
+    contrast = float(np.std(gray))
+    mean_darkness = 255.0 - float(np.mean(gray))
+
+    edges = cv2.Canny(gray, 20, 80)
+    edge_density = cv2.countNonZero(edges) / max(1, edges.shape[0] * edges.shape[1])
+
+    area = w * h
+    aspect = w / max(h, 1)
+
+    score = 0.0
+
+    if contrast >= 5:
+        score += 0.25
+
+    if contrast >= 10:
+        score += 0.25
+
+    if mean_darkness >= 35:
+        score += 0.20
+
+    if edge_density >= 0.015:
+        score += 0.20
+
+    if 0.12 <= aspect <= 8.5:
+        score += 0.10
+
+    if area >= 20:
+        score += 0.10
+
+    return min(score, 0.95)
+
+
+def proposal_detections_from_floor(original, floor_crop, floor_x, floor_y):
+    detections = []
+
+    mask = create_floor_mask(floor_crop)
+
+    crop_h, crop_w = floor_crop.shape[:2]
+    original_h, original_w = original.shape[:2]
+    crop_area = crop_w * crop_h
+
+    contours, _ = cv2.findContours(
+        mask,
+        cv2.RETR_EXTERNAL,
+        cv2.CHAIN_APPROX_SIMPLE
+    )
+
+    max_area = crop_area * PROPOSAL_MAX_AREA_RATIO
+    max_w = crop_w * PROPOSAL_MAX_WIDTH_RATIO
+    max_h = crop_h * PROPOSAL_MAX_HEIGHT_RATIO
+
+    for cnt in contours:
+        x, y, bw, bh = cv2.boundingRect(cnt)
+        area = cv2.contourArea(cnt)
+        box_area_local = bw * bh
+
+        if area < PROPOSAL_MIN_AREA:
+            continue
+
+        if box_area_local > max_area:
+            continue
+
+        if bw < PROPOSAL_MIN_WIDTH or bh < PROPOSAL_MIN_HEIGHT:
+            continue
+
+        if bw > max_w or bh > max_h:
+            continue
+
+        aspect = bw / max(bh, 1)
+
+        if aspect < 0.08 or aspect > 11.0:
+            continue
+
+        score = score_candidate(floor_crop, x, y, bw, bh)
+
+        if score < 0.22:
+            continue
+
+        x1 = floor_x + x
+        y1 = floor_y + y
+        x2 = floor_x + x + bw
+        y2 = floor_y + y + bh
+
+        x1, y1, x2, y2 = expand_box(
+            [x1, y1, x2, y2],
+            pad=4,
+            width=original_w,
+            height=original_h
+        )
+
+        detections.append({
+            "type": "unknown_pest",
+            "confidence": round(max(0.18, min(score, 0.60)), 4),
+            "source": "cv_proposal_counter",
+            "box": {
+                "x1": round(x1, 2),
+                "y1": round(y1, 2),
+                "x2": round(x2, 2),
+                "y2": round(y2, 2)
+            }
+        })
+
+    detections = nms_detections(
+        detections,
+        iou_threshold=0.10,
+        class_aware=False
+    )
+
+    return detections, mask
+
+
+def remove_unknown_duplicates(yolo_detections, unknown_detections):
+    good_yolo = [
+        d for d in yolo_detections
+        if d["confidence"] >= GOOD_YOLO_CONFIDENCE
+    ]
+
+    cleaned = []
+
+    for unknown in unknown_detections:
+        ub = get_box(unknown)
+
+        duplicate = False
+
+        for known in good_yolo:
+            kb = get_box(known)
+
+            overlap = intersection_ratio_small(ub, kb)
+
+            if overlap >= UNKNOWN_OVERLAP_WITH_GOOD_YOLO:
+                duplicate = True
+                break
+
+        if not duplicate:
+            cleaned.append(unknown)
+
+    return cleaned
+
+
+# =========================================================
+# FULL DETECTION PIPELINE
+# =========================================================
+
+def run_detection_pipeline(image_path: Path):
+    image_path = Path(image_path)
+
+    if not image_path.exists():
+        raise HTTPException(
+            status_code=500,
+            detail=f"Uploaded image file does not exist before processing: {image_path}"
+        )
+
+    original = cv2.imread(str(image_path))
+
+    if original is None:
+        raise HTTPException(status_code=400, detail="Unable to read uploaded image.")
+
+    original = remove_red_markup_if_present(original)
+
+    h, w = original.shape[:2]
+
+    floor_crop, floor_x, floor_y = find_trap_floor_crop(original)
+    enhanced_floor = enhance_image(floor_crop)
+
+    yolo_detections = []
+
+    yolo_detections.extend(
+        yolo_predict(
+            original,
+            offset_x=0,
+            offset_y=0,
+            source_name="original"
+        )
+    )
+
+    yolo_detections.extend(
+        yolo_predict(
+            floor_crop,
+            offset_x=floor_x,
+            offset_y=floor_y,
+            source_name="trap_floor"
+        )
+    )
+
+    yolo_detections.extend(
+        yolo_predict(
+            enhanced_floor,
+            offset_x=floor_x,
+            offset_y=floor_y,
+            source_name="enhanced_floor"
+        )
+    )
+
+    yolo_detections.extend(
+        yolo_tiled(
+            enhanced_floor,
+            offset_x=floor_x,
+            offset_y=floor_y
+        )
+    )
+
+    fixed_yolo = []
+
+    for det in yolo_detections:
+        b = det["box"]
+
+        x1, y1, x2, y2 = clamp_box(
+            [b["x1"], b["y1"], b["x2"], b["y2"]],
+            width=w,
+            height=h
+        )
+
+        det["box"] = {
+            "x1": round(x1, 2),
+            "y1": round(y1, 2),
+            "x2": round(x2, 2),
+            "y2": round(y2, 2)
+        }
+
+        fixed_yolo.append(det)
+
+    yolo_detections = filter_bad_yolo_boxes(
+        fixed_yolo,
+        image_width=w,
+        image_height=h
+    )
+
+    yolo_detections = nms_detections(
+        yolo_detections,
+        iou_threshold=0.20,
+        class_aware=True
+    )
+
+    unknown_detections, proposal_mask = proposal_detections_from_floor(
+        original,
+        floor_crop,
+        floor_x,
+        floor_y
+    )
+
+    unknown_detections = remove_unknown_duplicates(
+        yolo_detections,
+        unknown_detections
+    )
+
+    final_detections = []
+    final_detections.extend(yolo_detections)
+    final_detections.extend(unknown_detections)
+
+    final_detections = nms_detections(
+        final_detections,
+        iou_threshold=FINAL_NMS_IOU,
+        class_aware=False
+    )
+
+    return final_detections, original, proposal_mask
+
+
+# =========================================================
+# DRAWING AND RESPONSE HELPERS
+# =========================================================
+
+def draw_annotated_image(original_image, image_path: Path, detections):
+    RESULT_DIR.mkdir(parents=True, exist_ok=True)
+
+    image = original_image.copy()
+
+    for det in detections:
+        box = det["box"]
+        label = det["type"]
+        confidence = det["confidence"]
+
+        x1 = int(box["x1"])
+        y1 = int(box["y1"])
+        x2 = int(box["x2"])
+        y2 = int(box["y2"])
+
+        color = ORANGE if label == "unknown_pest" else GREEN
+
+        text = f"{label} {confidence:.2f}"
+
+        cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
+
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        font_scale = 0.55
+        thickness = 2
+
+        text_size, _ = cv2.getTextSize(text, font, font_scale, thickness)
+        text_w, text_h = text_size
+
+        label_y1 = max(y1 - text_h - 10, 0)
+        label_y2 = max(y1, text_h + 12)
+
+        cv2.rectangle(
+            image,
+            (x1, label_y1),
+            (min(x1 + text_w + 8, image.shape[1] - 1), label_y2),
+            color,
+            -1
+        )
+
+        cv2.putText(
+            image,
+            text,
+            (x1 + 4, max(y1 - 6, text_h + 4)),
+            font,
+            font_scale,
+            BLACK,
+            thickness,
+            cv2.LINE_AA
+        )
+
+    output_name = f"result_{image_path.stem}.jpg"
+    output_path = RESULT_DIR / output_name
+
+    success = cv2.imwrite(str(output_path), image)
+
+    if not success or not output_path.exists():
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to save annotated image: {output_path}"
+        )
+
+    return output_path
+
+
+def save_debug_mask(image_path: Path, mask):
+    if mask is None:
+        return None
+
+    DEBUG_DIR.mkdir(parents=True, exist_ok=True)
+
+    output_name = f"mask_{image_path.stem}.jpg"
+    output_path = DEBUG_DIR / output_name
+
+    success = cv2.imwrite(str(output_path), mask)
+
+    if not success or not output_path.exists():
+        print(f"[DEBUG] Failed to save debug mask: {output_path}")
+        return None
+
+    return output_path
+
+
+def build_summary(detections):
+    counts = Counter(det["type"] for det in detections)
+
+    return [
+        {
+            "type": pest_type,
+            "count": count
+        }
+        for pest_type, count in sorted(counts.items())
+    ]
+
+
+def get_local_image_urls(request: Request, uploaded_path: Path, result_image_path: Path, debug_mask_path: Path | None):
+    base_url = get_base_url(request)
+
+    original_image_url = f"{base_url}/uploads/{uploaded_path.name}"
+    annotated_image_url = f"{base_url}/results/{result_image_path.name}"
+
+    debug_mask_url = None
+    if debug_mask_path is not None:
+        debug_mask_url = f"{base_url}/debug/{debug_mask_path.name}"
+
+    return original_image_url, annotated_image_url, debug_mask_url
+
+
+# =========================================================
+# FIREBASE LOG FUNCTIONS
+# =========================================================
+
+def firebase_logs_ref():
+    return db.reference(FIREBASE_LOGS_PATH)
+
+
+def save_analysis_log_to_firebase(log_payload: dict):
+    if not firebase_ready:
+        return None
+
+    ref = firebase_logs_ref().push()
+    log_id = ref.key
+
+    log_payload["id"] = log_id
+    log_payload["firebase_saved"] = True
+    log_payload["firebase_path"] = f"{FIREBASE_LOGS_PATH}/{log_id}"
+
+    ref.set(log_payload)
+
+    return log_id
+
+
+def get_all_logs_from_firebase():
+    if not firebase_ready:
+        raise HTTPException(
+            status_code=503,
+            detail="Firebase is not initialized. Check FIREBASE_DATABASE_URL and service account."
+        )
+
+    raw = firebase_logs_ref().get()
+
+    if not raw:
+        return []
+
+    logs = []
+
+    for key, value in raw.items():
+        if not isinstance(value, dict):
+            continue
+
+        item = value
+        item["id"] = value.get("id", key)
+        logs.append(item)
+
+    return logs
+
+
+def get_log_from_firebase(log_id: str):
+    if not firebase_ready:
+        raise HTTPException(
+            status_code=503,
+            detail="Firebase is not initialized. Check FIREBASE_DATABASE_URL and service account."
+        )
+
+    item = firebase_logs_ref().child(log_id).get()
+
+    if not item:
+        raise HTTPException(status_code=404, detail="Log not found")
+
+    item["id"] = item.get("id", log_id)
+    return item
+
+
+def parse_date_filter(value: str | None, end_of_day=False):
+    if not value:
+        return None
+
+    try:
+        if len(value) == 10:
+            parsed = datetime.strptime(value, "%Y-%m-%d")
+            if end_of_day:
+                parsed = parsed.replace(hour=23, minute=59, second=59, microsecond=999000)
+            return parsed
+
+        return datetime.fromisoformat(value)
+
+    except Exception:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Invalid date format: {value}. Use YYYY-MM-DD or ISO datetime."
+        )
+
+
+def filter_logs(
+    logs,
+    pest_type=None,
+    date_from=None,
+    date_to=None,
+    min_total=None,
+    max_total=None,
+    search=None
+):
+    date_from_dt = parse_date_filter(date_from, end_of_day=False)
+    date_to_dt = parse_date_filter(date_to, end_of_day=True)
+
+    filtered = []
+
+    for item in logs:
+        total = int(item.get("total", 0) or 0)
+
+        if min_total is not None and total < min_total:
+            continue
+
+        if max_total is not None and total > max_total:
+            continue
+
+        timestamp_ms = item.get("timestamp_ms")
+
+        if timestamp_ms:
+            item_dt = datetime.fromtimestamp(int(timestamp_ms) / 1000)
+        else:
+            item_dt = None
+
+        if date_from_dt and item_dt and item_dt < date_from_dt:
+            continue
+
+        if date_to_dt and item_dt and item_dt > date_to_dt:
+            continue
+
+        data = item.get("data", [])
+        detections = item.get("detections", [])
+
+        if pest_type:
+            wanted = pest_type.lower().strip()
+            found_type = False
+
+            for row in data:
+                if str(row.get("type", "")).lower().strip() == wanted:
+                    found_type = True
+                    break
+
+            for det in detections:
+                if str(det.get("type", "")).lower().strip() == wanted:
+                    found_type = True
+                    break
+
+            if not found_type:
+                continue
+
+        if search:
+            s = search.lower().strip()
+            haystack = json.dumps(item, ensure_ascii=False).lower()
+            if s not in haystack:
+                continue
+
+        filtered.append(item)
+
+    return filtered
+
+
+def paginate_items(items, page, page_size):
+    if page <= 0:
+        page = 1
+
+    if page_size <= 0:
+        page_size = 10
+
+    if page_size > 100:
+        page_size = 100
+
+    total_items = len(items)
+    total_pages = max(1, (total_items + page_size - 1) // page_size)
+
+    if page > total_pages:
+        page_items = []
+    else:
+        start = (page - 1) * page_size
+        end = start + page_size
+        page_items = items[start:end]
+
+    return {
+        "page": page,
+        "page_size": page_size,
+        "total_items": total_items,
+        "total_pages": total_pages,
+        "has_next": page < total_pages,
+        "has_prev": page > 1,
+        "items": page_items
+    }
+
+
+def compact_log_item(item):
+    return {
+        "id": item.get("id"),
+        "datatime": item.get("datatime"),
+        "timestamp_ms": item.get("timestamp_ms"),
+        "total": item.get("total", 0),
+        "data": item.get("data", []),
+        "annotated_image": item.get("annotated_image"),
+        "original_image": item.get("original_image"),
+        "debug_mask": item.get("debug_mask"),
+        "cloudinary": item.get("cloudinary", {})
+    }
+
+
+# =========================================================
+# DASHBOARD HELPERS
+# =========================================================
+
+def build_dashboard_data(logs):
+    logs = sorted(logs, key=lambda x: int(x.get("timestamp_ms", 0) or 0), reverse=True)
+
+    today = datetime.now().date()
+    seven_days_ago = datetime.now() - timedelta(days=6)
+
+    total_logs = len(logs)
+    total_pests = sum(int(item.get("total", 0) or 0) for item in logs)
+
+    today_logs = []
+    last_7_days_logs = []
+
+    pest_counter = Counter()
+    daily_counter = defaultdict(int)
+    hourly_today_counter = defaultdict(int)
+
+    for item in logs:
+        timestamp_ms = item.get("timestamp_ms")
+
+        if timestamp_ms:
+            item_dt = datetime.fromtimestamp(int(timestamp_ms) / 1000)
+        else:
+            item_dt = None
+
+        item_total = int(item.get("total", 0) or 0)
+
+        for row in item.get("data", []):
+            pest_counter[row.get("type", "unknown")] += int(row.get("count", 0) or 0)
+
+        if item_dt:
+            day_key = item_dt.strftime("%Y-%m-%d")
+            daily_counter[day_key] += item_total
+
+            if item_dt.date() == today:
+                today_logs.append(item)
+                hour_key = item_dt.strftime("%H:00")
+                hourly_today_counter[hour_key] += item_total
+
+            if item_dt >= seven_days_ago:
+                last_7_days_logs.append(item)
+
+    today_pests = sum(int(item.get("total", 0) or 0) for item in today_logs)
+
+    top_pests = [
+        {
+            "type": pest_type,
+            "count": count
+        }
+        for pest_type, count in pest_counter.most_common(10)
+    ]
+
+    daily_chart = []
+
+    for i in range(6, -1, -1):
+        day = datetime.now() - timedelta(days=i)
+        key = day.strftime("%Y-%m-%d")
+        daily_chart.append(
+            {
+                "date": key,
+                "total": daily_counter.get(key, 0)
+            }
+        )
+
+    hourly_chart = []
+
+    for hour in range(24):
+        key = f"{hour:02d}:00"
+        hourly_chart.append(
+            {
+                "hour": key,
+                "total": hourly_today_counter.get(key, 0)
+            }
+        )
+
+    latest_log = logs[0] if logs else None
+
+    recent_logs = [compact_log_item(item) for item in logs[:10]]
+
+    return {
+        "summary": {
+            "total_logs": total_logs,
+            "total_pests": total_pests,
+            "today_logs": len(today_logs),
+            "today_pests": today_pests,
+            "last_7_days_logs": len(last_7_days_logs),
+            "last_7_days_pests": sum(int(item.get("total", 0) or 0) for item in last_7_days_logs),
+            "top_pests": top_pests
+        },
+        "chart": {
+            "daily_last_7_days": daily_chart,
+            "hourly_today": hourly_chart
+        },
+        "live_camera_stream": {
+            "latest": compact_log_item(latest_log) if latest_log else None,
+            "polling_route": "/api/live/latest",
+            "note": "Use latest.annotated_image as the latest processed camera frame. Frontend can poll every 1 to 3 seconds."
+        },
+        "logs": recent_logs
+    }
+
+
+# =========================================================
+# WEB UI ROUTES
+# =========================================================
+
+@app.get("/", include_in_schema=False)
+def web_root():
+    return RedirectResponse(url="/ui")
+
+
+@app.get("/ui", include_in_schema=False)
+def ui_dashboard():
+    index_path = WEB_DIR / "index.html"
+
+    if not index_path.exists():
+        raise HTTPException(
+            status_code=404,
+            detail=f"Missing web file: {index_path}"
+        )
+
+    return FileResponse(index_path)
+
+
+@app.get("/ui/logs", include_in_schema=False)
+def ui_logs():
+    logs_path = WEB_DIR / "logs.html"
+
+    if not logs_path.exists():
+        raise HTTPException(
+            status_code=404,
+            detail=f"Missing web file: {logs_path}"
+        )
+
+    return FileResponse(logs_path)
+
+
+@app.get("/ui/logs/{log_id}", include_in_schema=False)
+def ui_log_detail(log_id: str):
+    detail_path = WEB_DIR / "detail.html"
+
+    if not detail_path.exists():
+        raise HTTPException(
+            status_code=404,
+            detail=f"Missing web file: {detail_path}"
+        )
+
+    return FileResponse(detail_path)
+
+
+@app.get("/ui/access", include_in_schema=False)
+def ui_access():
+    access_path = WEB_DIR / "access.html"
+
+    if not access_path.exists():
+        raise HTTPException(
+            status_code=404,
+            detail=f"Missing web file: {access_path}"
+        )
+
+    return FileResponse(access_path)
+
+
+# =========================================================
+# API ROUTES
+# =========================================================
+
+@app.get("/api/status")
+def api_status():
+    return {
+        "message": "Smart Pest Trap Detection API is running",
+        "ui_route": "/ui",
+        "analyze_route": "/api/analyze",
+        "logs_route": "/api/logs",
+        "log_detail_route": "/api/logs/{id}",
+        "dashboard_route": "/api/dashboard",
+        "live_latest_route": "/api/live/latest",
+        "firebase_ready": firebase_ready,
+        "cloudinary_ready": cloudinary_ready,
+        "model_path": str(MODEL_PATH),
+        "model_exists": MODEL_PATH.exists(),
+        "auto_download_model": AUTO_DOWNLOAD_MODEL,
+        "hf_model_repo": HF_MODEL_REPO,
+        "hf_model_file": HF_MODEL_FILE,
+        "firebase_logs_path": FIREBASE_LOGS_PATH,
+        "cloudinary_folder": CLOUDINARY_FOLDER,
+        "field_name": "image"
+    }
+
+
+@app.post("/api/analyze")
+async def analyze_pest(request: Request, image: UploadFile = File(...)):
+    try:
+        ext = validate_image_file(image)
+        uploaded_path = await save_upload(image, ext)
+
+        detections, processed_original, proposal_mask = run_detection_pipeline(uploaded_path)
+
+        result_image_path = draw_annotated_image(
+            processed_original,
+            uploaded_path,
+            detections
+        )
+
+        debug_mask_path = save_debug_mask(
+            uploaded_path,
+            proposal_mask
+        )
+
+        data = build_summary(detections)
+        total = len(detections)
+
+        local_original_url, local_annotated_url, local_debug_url = get_local_image_urls(
+            request,
+            uploaded_path,
+            result_image_path,
+            debug_mask_path
+        )
+
+        original_cloud, annotated_cloud, debug_cloud = upload_analysis_images_to_cloudinary(
+            uploaded_path,
+            result_image_path,
+            debug_mask_path
+        )
+
+        original_image_url = original_cloud.get("secure_url") if original_cloud else local_original_url
+        annotated_image_url = annotated_cloud.get("secure_url") if annotated_cloud else local_annotated_url
+        debug_mask_url = debug_cloud.get("secure_url") if debug_cloud else local_debug_url
+
+        cloudinary_saved = bool(original_cloud and annotated_cloud)
+
+        log_payload = {
+            "id": None,
+            "datatime": now_string(),
+            "created_at": now_iso(),
+            "timestamp_ms": now_timestamp_ms(),
+            "data": data,
+            "total": total,
+            "detections": detections,
+
+            "original_image": original_image_url,
+            "annotated_image": annotated_image_url,
+            "debug_mask": debug_mask_url,
+
+            "local_images": {
+                "original_image": local_original_url,
+                "annotated_image": local_annotated_url,
+                "debug_mask": local_debug_url
+            },
+
+            "image_files": {
+                "original_filename": uploaded_path.name,
+                "annotated_filename": result_image_path.name,
+                "debug_mask_filename": debug_mask_path.name if debug_mask_path else None
+            },
+
+            "cloudinary_saved": cloudinary_saved,
+            "cloudinary": {
+                "original": original_cloud,
+                "annotated": annotated_cloud,
+                "debug_mask": debug_cloud
+            },
+
+            "firebase_saved": False,
+            "firebase_path": None,
+
+            "note": "Green boxes are YOLO identified pests. Orange boxes are counted pest-like objects that the model could not identify."
+        }
+
+        log_id = save_analysis_log_to_firebase(log_payload)
+
+        response = {
+            "datatime": log_payload["datatime"],
+            "id": log_id,
+            "data": data,
+            "total": total,
+            "detections": detections,
+            "original_image": original_image_url,
+            "annotated_image": annotated_image_url,
+            "debug_mask": debug_mask_url,
+            "cloudinary_saved": cloudinary_saved,
+            "firebase_saved": bool(log_id),
+            "firebase_path": f"{FIREBASE_LOGS_PATH}/{log_id}" if log_id else None,
+            "cloudinary": {
+                "original": original_cloud,
+                "annotated": annotated_cloud,
+                "debug_mask": debug_cloud
+            }
+        }
+
+        return JSONResponse(content=response)
+
+    except HTTPException:
+        raise
+
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Analysis failed: {str(e)}"
+        )
+
+
+@app.get("/api/logs")
+def list_logs(
+    page: int = Query(1, description="Page number. If page=0, it becomes page=1."),
+    page_size: int = Query(10, description="Items per page. Max 100."),
+    pest_type: str | None = Query(None, description="Filter by pest type, example: unknown_pest"),
+    date_from: str | None = Query(None, description="YYYY-MM-DD or ISO datetime"),
+    date_to: str | None = Query(None, description="YYYY-MM-DD or ISO datetime"),
+    min_total: int | None = Query(None),
+    max_total: int | None = Query(None),
+    search: str | None = Query(None),
+    sort: str = Query("desc", description="desc or asc")
+):
+    logs = get_all_logs_from_firebase()
+
+    logs = filter_logs(
+        logs,
+        pest_type=pest_type,
+        date_from=date_from,
+        date_to=date_to,
+        min_total=min_total,
+        max_total=max_total,
+        search=search
+    )
+
+    reverse = sort.lower() != "asc"
+
+    logs = sorted(
+        logs,
+        key=lambda x: int(x.get("timestamp_ms", 0) or 0),
+        reverse=reverse
+    )
+
+    logs = [compact_log_item(item) for item in logs]
+
+    result = paginate_items(logs, page, page_size)
+
+    return {
+        "success": True,
+        "filters": {
+            "pest_type": pest_type,
+            "date_from": date_from,
+            "date_to": date_to,
+            "min_total": min_total,
+            "max_total": max_total,
+            "search": search,
+            "sort": sort
+        },
+        **result
+    }
+
+
+@app.get("/api/logs/{log_id}")
+def get_log_detail(log_id: str):
+    item = get_log_from_firebase(log_id)
+
+    return {
+        "success": True,
+        "data": item
+    }
+
+
+@app.get("/api/dashboard")
+@app.get("/api/dashboard/")
+def dashboard():
+    logs = get_all_logs_from_firebase()
+    dashboard_data = build_dashboard_data(logs)
+
+    return {
+        "success": True,
+        "datatime": now_string(),
+        **dashboard_data
+    }
+
+
+@app.get("/api/live/latest")
+def live_latest():
+    logs = get_all_logs_from_firebase()
+
+    if not logs:
+        return {
+            "success": True,
+            "latest": None
+        }
+
+    logs = sorted(
+        logs,
+        key=lambda x: int(x.get("timestamp_ms", 0) or 0),
+        reverse=True
+    )
+
+    return {
+        "success": True,
+        "latest": compact_log_item(logs[0])
+    }

requirements.txt

@@ -0,0 +1,11 @@
+fastapi
+uvicorn[standard]
+python-multipart
+ultralytics
+opencv-python
+numpy
+pillow
+huggingface_hub
+firebase-admin
+python-dotenv
+cloudinary

web/access.html

@@ -0,0 +1,70 @@
+<!doctype html>
+<html lang="en">
+
+<head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width,initial-scale=1" />
+    <title>Access · Pest Detection AI</title>
+    <script src="https://cdn.tailwindcss.com"></script>
+    <link
+        href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&family=Space+Grotesk:wght@600;700&display=swap"
+        rel="stylesheet">
+    <link href="https://fonts.googleapis.com/css2?family=Material+Symbols+Outlined" rel="stylesheet">
+    <link rel="stylesheet" href="/web/css/app.css">
+</head>
+
+<body>
+    <header class="topbar">
+        <div style="display:flex;align-items:center;gap:28px"><a class="brand" href="/ui">Pest Detection AI</a>
+            <nav class="nav"><a href="/ui">Dashboard</a><a href="/ui/logs">Logs</a><a class="active"
+                    href="/ui/access">Access</a></nav>
+        </div>
+        <div class="avatar">AI</div>
+    </header>
+    <main class="shell">
+        <div class="label">Admin Portal</div>
+        <h1 style="font-size:36px;margin:4px 0 28px;color:var(--primary)">Access Management</h1>
+        <section class="card">
+            <div style="padding:0 24px;border-bottom:1px solid #dfe4e1;display:flex;gap:24px"><button class="btn"
+                    style="border:0;border-bottom:2px solid var(--primary);border-radius:0">Access
+                    Request</button><button class="btn" style="border:0;border-radius:0;color:#60716a">Users</button>
+            </div>
+            <div style="padding:24px">
+                <div class="toolbar" style="justify-content:space-between;margin-bottom:20px"><input class="input"
+                        style="min-width:360px" placeholder="Search by name or email..."><button class="btn"><span
+                            class="material-symbols-outlined">download</span> Export CSV</button></div>
+                <table class="table">
+                    <thead>
+                        <tr>
+                            <th>Name</th>
+                            <th>Email</th>
+                            <th>Requested Role</th>
+                            <th style="text-align:right">Action</th>
+                        </tr>
+                    </thead>
+                    <tbody id="requests"></tbody>
+                </table>
+            </div>
+        </section>
+        <div class="grid stats" style="margin-top:24px">
+            <div class="card pad">
+                <div class="label">Total Users</div>
+                <div class="stat">1,248</div>
+                <div class="good">↗ +12% this month</div>
+            </div>
+            <div class="card pad">
+                <div class="label">Pending Invites</div>
+                <div class="stat">42</div>
+                <div class="sub">Expires in 24 hours</div>
+            </div>
+            <div class="card pad">
+                <div class="label">System Health</div>
+                <div class="stat">99.9%</div>
+                <div class="sub">Authentication uptime</div>
+            </div>
+        </div>
+    </main>
+    <script>const data = [['JD', 'Julian D\'Arby', 'j.darby@sentinel-ops.io', 'Site Supervisor'], ['MA', 'Marcus Aurel', 'm.aurel@agri-tech.com', 'Data Analyst'], ['SL', 'Sarah Lane', 'sarah.lane@orchardview.net', 'Viewer']]; document.getElementById('requests').innerHTML = data.map(r => `<tr><td><div style="display:flex;align-items:center;gap:12px"><div class="avatar">${r[0]}</div><b>${r[1]}</b></div></td><td>${r[2]}</td><td>${r[3]}</td><td style="text-align:right"><button class="btn primary">Approve</button> <button class="btn danger">Reject</button></td></tr>`).join('')</script>
+</body>
+
+</html>

web/css/app.css

@@ -0,0 +1,4 @@
+:root{
+  --bg:#f8f9fa;--surface:#ffffff;--line:#dfe4e1;--muted:#4e616a;--muted2:#6b7680;--primary:#012d1d;--primary2:#06452e;--soft:#ecf8f1;--soft2:#f3f6f5;--danger:#ba1a1a;--orange:#f59e0b;--blue:#3b82f6;--purple:#a855f7;
+}
+*{box-sizing:border-box}body{margin:0;background:var(--bg);color:#17201c;font-family:Inter,system-ui,-apple-system,Segoe UI,Roboto,Arial,sans-serif}.mono,.label{font-family:"Space Grotesk",Inter,sans-serif}.material-symbols-outlined{font-variation-settings:'FILL' 0,'wght' 420,'GRAD' 0,'opsz' 24}.topbar{height:64px;background:#fff;border-bottom:1px solid #e7ece9;display:flex;align-items:center;justify-content:space-between;padding:0 28px;position:sticky;top:0;z-index:50}.brand{font-weight:800;color:var(--primary);font-size:20px;letter-spacing:-.02em}.nav{display:flex;gap:20px;align-items:center}.nav a{color:#64736c;text-decoration:none;font-weight:650;font-size:14px;padding:22px 0 18px;border-bottom:2px solid transparent}.nav a.active,.nav a:hover{color:var(--primary);border-color:var(--primary)}.avatar{width:34px;height:34px;border-radius:999px;border:2px solid #d9e2dd;background:linear-gradient(135deg,#d8efe1,#f0f5f2);display:grid;place-items:center;color:var(--primary);font-weight:800}.shell{max-width:1440px;margin:0 auto;padding:32px}.grid{display:grid;gap:24px}.stats{grid-template-columns:repeat(3,minmax(0,1fr))}.card{background:var(--surface);border:1px solid var(--line);border-radius:12px;box-shadow:0 2px 6px rgba(1,45,29,.035)}.card.pad{padding:22px}.label{text-transform:uppercase;font-size:12px;letter-spacing:.08em;font-weight:800;color:var(--muted)}.stat{font-family:"Space Grotesk";font-size:44px;line-height:1;color:var(--primary);font-weight:700}.sub{color:var(--muted2);font-size:14px}.good{color:#057347}.muted{color:var(--muted2)}.main-grid{grid-template-columns:7fr 3fr}.live-frame{position:relative;overflow:hidden;background:#07110d;border-radius:12px}.live-frame img{display:block;width:100%;height:100%;aspect-ratio:16/9;object-fit:cover}.badge{display:inline-flex;align-items:center;gap:8px;background:rgba(0,0,0,.55);backdrop-filter:blur(8px);color:#fff;border:1px solid rgba(255,255,255,.15);border-radius:999px;padding:8px 12px;font-size:12px;font-weight:800;text-transform:uppercase;letter-spacing:.04em}.live-badge{position:absolute;top:16px;left:16px}.dot{width:8px;height:8px;border-radius:50%;background:#ef4444;display:inline-block}.pulse{animation:pulse 1.5s infinite}@keyframes pulse{50%{opacity:.35}}.side-list{padding:22px}.det-row{display:flex;align-items:center;justify-content:space-between;padding:14px 0}.det-left{display:flex;align-items:center;gap:12px}.det-dot{width:8px;height:8px;border-radius:50%;background:#10b981}.det-count{font-family:"Space Grotesk";font-size:24px;font-weight:700}.chart{height:260px;display:flex;align-items:end;gap:8px;padding:24px 18px 8px;background-image:radial-gradient(#dfe4e1 1px,transparent 1px);background-size:24px 24px}.bar{flex:1;background:#b8efd2;border-radius:4px 4px 0 0;min-height:4px;transition:.2s}.bar.hot{background:#059669}.section-head{display:flex;align-items:end;justify-content:space-between;margin-bottom:16px}.toolbar{display:flex;gap:10px;align-items:center}.input{border:1px solid #d8e0db;background:#fff;border-radius:10px;padding:12px 14px;font:inherit;outline:none}.input:focus{border-color:#0a6b45;box-shadow:0 0 0 3px rgba(5,115,71,.08)}.btn{border:1px solid #cfd8d3;border-radius:10px;background:#fff;color:#24352e;padding:10px 14px;font-weight:750;cursor:pointer;text-decoration:none;display:inline-flex;gap:8px;align-items:center}.btn:hover{background:#f2f7f4}.btn.primary{background:var(--primary);color:#fff;border-color:var(--primary)}.btn.danger{border-color:#f0b7b7;color:#a40000}.table{width:100%;border-collapse:collapse}.table th{text-align:left;background:#f1f4f3;color:#51625b;font-family:"Space Grotesk";font-size:12px;letter-spacing:.08em;text-transform:uppercase;padding:14px 18px}.table td{padding:16px 18px;border-top:1px solid #ecf0ee;vertical-align:middle}.thumb{width:64px;height:64px;border-radius:8px;object-fit:cover;background:#e8efeb;border:1px solid #dce5e0}.thumb.sm{width:52px;height:52px}.log-row{cursor:pointer}.log-row:hover{background:#f6fbf8}.confbar{width:90px;height:7px;background:#dfe8e3;border-radius:99px;overflow:hidden}.confbar span{display:block;height:100%;background:#057347}.pager{display:flex;justify-content:space-between;align-items:center;padding:16px 20px;background:#f3f6f5;border-top:1px solid #dfe4e1}.detail-grid{grid-template-columns:2fr 1fr}.hero-img{width:100%;aspect-ratio:16/10;object-fit:contain;background:#07110d;border-radius:12px}.metric-grid{grid-template-columns:repeat(2,1fr)}.metric{display:flex;gap:14px;align-items:center;padding:18px}.iconbox{width:48px;height:48px;border-radius:999px;background:#eaf8f0;display:grid;place-items:center;color:#057347}.break-row{display:flex;align-items:center;justify-content:space-between;padding:14px 18px;border-top:1px solid #edf1ef}.dropzone{border:1px dashed #aab8b0;background:#fbfdfc;border-radius:14px;padding:18px;display:flex;flex-direction:column;gap:12px}.notice{background:#fff8e8;border:1px solid #f5d08a;color:#7a4d00;border-radius:10px;padding:12px 14px;font-size:13px}.loading{opacity:.6;pointer-events:none}.hide{display:none!important}.empty{padding:28px;text-align:center;color:#6d7a74}.mobile-only{display:none}@media(max-width:900px){.nav{display:none}.shell{padding:18px 14px 86px}.stats,.main-grid,.detail-grid,.metric-grid{grid-template-columns:1fr}.toolbar{flex-direction:column;align-items:stretch}.topbar{padding:0 16px}.table{min-width:760px}.mobile-only{display:block}.desktop-only{display:none}}

web/detail.html

@@ -0,0 +1,87 @@
+<!doctype html>
+<html lang="en">
+
+<head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width,initial-scale=1" />
+    <title>Detection Detail · Pest Detection AI</title>
+    <script src="https://cdn.tailwindcss.com"></script>
+    <link
+        href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&family=Space+Grotesk:wght@600;700&display=swap"
+        rel="stylesheet">
+    <link href="https://fonts.googleapis.com/css2?family=Material+Symbols+Outlined" rel="stylesheet">
+    <link rel="stylesheet" href="/web/css/app.css">
+</head>
+
+<body>
+    <header class="topbar">
+        <div style="display:flex;align-items:center;gap:14px"><a class="btn" href="/ui/logs"><span
+                    class="material-symbols-outlined">arrow_back</span></a><a class="brand" href="/ui">Pest Detection
+                AI</a></div>
+        <div class="avatar">AI</div>
+    </header>
+    <main class="shell">
+        <div class="section-head">
+            <div>
+                <div class="label">Logs / <span id="eventId">...</span></div>
+                <h1 style="font-size:36px;margin:4px 0;color:var(--primary)">Detection Detail</h1>
+            </div>
+            <div class="toolbar"><a id="downloadBtn" class="btn" target="_blank"><span
+                        class="material-symbols-outlined">download</span> Export Frame</a><button
+                    class="btn primary"><span class="material-symbols-outlined">verified</span> Validate
+                    Results</button></div>
+        </div>
+        <div class="grid detail-grid">
+            <section>
+                <div class="card pad"><img id="detailImage" class="hero-img" onerror="imgFallback(event)"></div>
+                <div class="grid metric-grid" style="margin-top:20px">
+                    <div class="card metric">
+                        <div class="iconbox"><span class="material-symbols-outlined">radar</span></div>
+                        <div>
+                            <div class="label">Detection Count</div>
+                            <div id="detCount" class="stat" style="font-size:32px">0</div>
+                        </div>
+                    </div>
+                    <div class="card metric">
+                        <div class="iconbox"><span class="material-symbols-outlined">query_stats</span></div>
+                        <div>
+                            <div class="label">Avg Confidence</div>
+                            <div id="avgConf" class="stat" style="font-size:32px">0%</div>
+                        </div>
+                    </div>
+                </div>
+            </section>
+            <aside class="grid">
+                <section class="card">
+                    <div style="padding:16px 18px;border-bottom:1px solid #e5ebe7;background:#f3f6f5">
+                        <div class="label">Event Metadata</div>
+                    </div>
+                    <div style="padding:20px">
+                        <div class="label">Timestamp</div>
+                        <p id="timestamp" style="font-weight:750"></p>
+                        <hr style="border:0;border-top:1px solid #edf1ef;margin:18px 0">
+                        <div class="label">Storage</div>
+                        <p id="storage" class="sub"></p>
+                        <p><a id="originalLink" target="_blank">Original image</a> · <a id="debugLink"
+                                target="_blank">Debug mask</a></p>
+                    </div>
+                </section>
+                <section class="card">
+                    <div
+                        style="padding:16px 18px;border-bottom:1px solid #e5ebe7;background:#f3f6f5;display:flex;justify-content:space-between">
+                        <div class="label">Pest Breakdown</div><b id="breakTotal">0 total</b>
+                    </div>
+                    <div id="breakdown"></div>
+                </section>
+                <section class="card pad"><label class="label">Analyst Notes</label><textarea class="input"
+                        style="width:100%;min-height:110px;margin-top:10px"
+                        placeholder="Add manual observation note..."></textarea><button class="btn"
+                        style="width:100%;justify-content:center;margin-top:12px">Save Analysis</button></section>
+            </aside>
+        </div>
+    </main>
+    <script src="/web/js/api.js"></script>
+    <script src="/web/js/detail.js"></script>
+</body>
+
+</html>

web/index.html

@@ -0,0 +1,109 @@
+<!doctype html>
+<html lang="en">
+
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width,initial-scale=1" />
+  <title>Pest Detection AI</title>
+  <script src="https://cdn.tailwindcss.com"></script>
+  <link
+    href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&family=Space+Grotesk:wght@600;700&display=swap"
+    rel="stylesheet">
+  <link href="https://fonts.googleapis.com/css2?family=Material+Symbols+Outlined" rel="stylesheet">
+  <link rel="stylesheet" href="/web/css/app.css">
+</head>
+
+<body>
+  <header class="topbar">
+    <div style="display:flex;align-items:center;gap:28px"><a class="brand" href="/ui">Pest Detection AI</a>
+      <nav class="nav"><a class="active" href="/ui">Dashboard</a><a href="/ui/logs">Logs</a><a
+          href="/ui/access">Access</a></nav>
+    </div>
+    <div style="display:flex;align-items:center;gap:12px"><span
+        class="material-symbols-outlined muted">notifications</span>
+      <div class="avatar">AI</div>
+    </div>
+  </header>
+  <main class="shell grid">
+    <section class="grid stats">
+      <div class="card pad">
+        <div class="label">Detections</div>
+        <div><span id="todayPests" class="stat">0</span> <span class="sub">today</span></div>
+        <div id="topToday" class="good">Loading...</div>
+      </div>
+      <div class="card pad">
+        <div class="label">Biodiversity</div>
+        <div><span id="biodiversity" class="stat">0</span></div>
+        <div class="sub">Type of pest today</div>
+      </div>
+      <div class="card pad">
+        <div class="label">Trend Analysis</div>
+        <div><span id="dailyAvg" class="stat">0</span> <span class="sub">daily average</span></div>
+        <div id="monthInfo" class="sub">Last 7 days analysis</div>
+      </div>
+    </section>
+    <section class="grid main-grid">
+      <div class="live-frame card">
+        <div class="badge live-badge"><span class="dot pulse"></span> Live feed: latest analysis</div><img
+          id="liveImage" onerror="imgFallback(event)" alt="Latest AI pest detection">
+        <div style="position:absolute;right:16px;bottom:16px;display:flex;gap:8px"><a id="openLatest" href="#"
+            class="btn" style="background:rgba(255,255,255,.12);color:#fff;border-color:rgba(255,255,255,.18)"><span
+              class="material-symbols-outlined">open_in_new</span></a></div>
+      </div>
+      <aside class="card side-list">
+        <div class="section-head">
+          <div class="label">Live Detections</div>
+          <div class="sub" id="updateAge">--</div>
+        </div>
+        <div id="liveList"></div>
+        <div style="border-top:1px solid #edf1ef;margin-top:22px;padding-top:18px">
+          <div class="sub" style="display:flex;justify-content:space-between;font-weight:800"><span>Total
+              Load</span><span id="loadPct">0%</span></div>
+          <div style="height:7px;background:#e0e8e3;border-radius:99px;margin-top:8px;overflow:hidden">
+            <div id="loadBar" style="height:100%;width:0;background:#059669"></div>
+          </div>
+        </div>
+      </aside>
+    </section>
+    <section class="card pad">
+      <div class="section-head">
+        <div>
+          <div class="label">24-hour trend</div>
+          <div class="sub">Detection frequency from Firebase logs</div>
+        </div>
+        <div class="toolbar"><button class="btn primary">24H</button><a class="btn" href="/ui/logs">View Logs</a></div>
+      </div>
+      <div id="hourlyChart" class="chart"></div>
+      <div
+        style="display:flex;justify-content:space-between;padding:0 18px 10px;color:#7c8b84;font-size:12px;font-weight:700">
+        <span>00:00</span><span>04:00</span><span>08:00</span><span>12:00</span><span>16:00</span><span>20:00</span><span>23:59</span>
+      </div>
+    </section>
+    <section class="card">
+      <div
+        style="padding:20px 24px;border-bottom:1px solid #edf1ef;display:flex;justify-content:space-between;gap:14px;align-items:center">
+        <h2 style="margin:0;color:var(--primary);font-size:22px">Detection Logs</h2><input id="searchBox" class="input"
+          placeholder="Search logs, species, timestamps...">
+      </div>
+      <div id="recentLogs"></div>
+      <div class="pager"><span id="logCount" class="sub">Loading...</span><a class="btn" href="/ui/logs">Load More
+          Entries</a></div>
+    </section>
+    <section class="card pad">
+      <div class="section-head">
+        <div>
+          <div class="label">Manual Upload Test</div>
+          <div class="sub">Upload an image to /api/analyze, then save image to Cloudinary and log to Firebase.</div>
+        </div>
+      </div>
+      <div class="dropzone"><input id="uploadInput" type="file" accept="image/*" class="input"><button id="uploadBtn"
+          class="btn primary"><span class="material-symbols-outlined">upload</span> Analyze Image</button>
+        <div id="uploadStatus" class="sub"></div>
+      </div>
+    </section>
+  </main>
+  <script src="/web/js/api.js"></script>
+  <script src="/web/js/dashboard.js"></script>
+</body>
+
+</html>

web/js/api.js

@@ -0,0 +1,39 @@
+const API = {
+  async get(path){
+    const res = await fetch(path, {headers:{'Accept':'application/json'}});
+    if(!res.ok) throw new Error(await res.text());
+    return res.json();
+  },
+  async postImage(path, file){
+    const fd = new FormData();
+    fd.append('image', file);
+    const res = await fetch(path, {method:'POST', body:fd});
+    if(!res.ok) throw new Error(await res.text());
+    return res.json();
+  }
+};
+function fmtNum(n){ return Number(n||0).toLocaleString(); }
+function pad2(n){ return String(n||0).padStart(2,'0'); }
+function fmtDate(ms, fallback=''){
+  if(!ms) return fallback || '-';
+  const d = new Date(Number(ms));
+  return d.toLocaleString([], {year:'numeric',month:'short',day:'2-digit',hour:'2-digit',minute:'2-digit'});
+}
+function fmtTime(ms){ if(!ms) return '-'; return new Date(Number(ms)).toLocaleTimeString([], {hour:'2-digit',minute:'2-digit'}); }
+function primaryType(item){
+  const data = item?.data || [];
+  if(!data.length) return 'No pest';
+  return [...data].sort((a,b)=>(b.count||0)-(a.count||0))[0].type || 'unknown_pest';
+}
+function avgConfidence(item){
+  const dets = item?.detections || [];
+  if(!dets.length) return 0;
+  return dets.reduce((s,d)=>s + Number(d.confidence||0),0) / dets.length;
+}
+function confidenceText(v){ return `${Math.round(Number(v||0)*100)}%`; }
+function imgFallback(e){
+  e.target.src='data:image/svg+xml;charset=UTF-8,'+encodeURIComponent(`<svg xmlns="http://www.w3.org/2000/svg" width="600" height="400"><rect width="100%" height="100%" fill="#e9efec"/><text x="50%" y="50%" dominant-baseline="middle" text-anchor="middle" fill="#52655d" font-family="Arial" font-size="22">No image</text></svg>`);
+}
+function setText(id, value){ const el=document.getElementById(id); if(el) el.textContent=value; }
+function setImg(id, src){ const el=document.getElementById(id); if(el){ el.src=src || ''; el.onerror=imgFallback; } }
+function escapeHtml(s){ return String(s??'').replace(/[&<>"]/g, c=>({'&':'&amp;','<':'&lt;','>':'&gt;','"':'&quot;'}[c])); }

web/js/dashboard.js

@@ -0,0 +1,41 @@
+let dashboardCache = null;
+async function loadDashboard(){
+  const dash = await API.get('/api/dashboard'); dashboardCache = dash;
+  const s = dash.summary || {}; const chart = dash.chart || {};
+  setText('todayPests', fmtNum(s.today_pests));
+  setText('biodiversity', fmtNum((s.top_pests||[]).length));
+  const avg = (s.last_7_days_pests || 0) / 7; setText('dailyAvg', Math.round(avg));
+  const top = (s.top_pests||[])[0]; setText('topToday', top ? `${fmtNum(top.count)} ${top.type} total` : 'No detections yet');
+  setText('monthInfo', `${fmtNum(s.last_7_days_pests)} pests in last 7 days`);
+  renderLive(dash.live_camera_stream?.latest); renderChart(chart.hourly_today||[]); renderRecent(dash.logs||[]);
+}
+async function loadLatest(){ try{ const r=await API.get('/api/live/latest'); renderLive(r.latest); }catch(e){ console.warn(e); } }
+function renderLive(item){
+  const list = document.getElementById('liveList');
+  if(!item){ setImg('liveImage',''); list.innerHTML='<div class="empty">No live detection yet</div>'; return; }
+  setImg('liveImage', item.annotated_image || item.original_image); document.getElementById('openLatest').href = `/ui/logs/${item.id}`;
+  const data = item.data || []; const total = item.total || 0; setText('updateAge', fmtTime(item.timestamp_ms));
+  list.innerHTML = data.length ? data.map((d,i)=>`<div class="det-row"><div class="det-left"><span class="det-dot" style="background:${['#10b981','#f59e0b','#3b82f6','#a855f7','#ef4444'][i%5]}"></span><span>${escapeHtml(d.type)}</span></div><span class="det-count">${pad2(d.count)}</span></div>`).join('') : '<div class="empty">No pest in latest frame</div>';
+  const pct = Math.min(100, Math.round((total/25)*100)); setText('loadPct', pct+'%'); document.getElementById('loadBar').style.width=pct+'%';
+}
+function renderChart(rows){
+  const el=document.getElementById('hourlyChart'); const max=Math.max(1,...rows.map(r=>r.total||0));
+  el.innerHTML = rows.map((r,i)=>`<div title="${r.hour}: ${r.total}" class="bar ${r.total===max?'hot':''}" style="height:${Math.max(4, (r.total/max)*92)}%"></div>`).join('');
+}
+function renderRecent(logs){
+  const root=document.getElementById('recentLogs'); setText('logCount', `Showing ${logs.length} latest entries`);
+  if(!logs.length){ root.innerHTML='<div class="empty">No logs found</div>'; return; }
+  root.innerHTML=logs.map(item=>{ const type=primaryType(item); const conf=avgConfidence(item); return `<div class="log-row" onclick="location.href='/ui/logs/${item.id}'" style="display:flex;align-items:center;padding:16px 20px;border-bottom:1px solid #f0f3f1"><img class="thumb" src="${item.annotated_image||item.original_image||''}" onerror="imgFallback(event)"><div style="margin-left:16px;flex:1"><b style="color:var(--primary)">${escapeHtml(type)}</b><div class="sub">Confidence ${confidenceText(conf)} · Total ${item.total||0}</div></div><div class="desktop-only sub" style="min-width:180px">${fmtDate(item.timestamp_ms,item.datatime)}</div><span class="material-symbols-outlined muted">chevron_right</span></div>`; }).join('');
+}
+document.getElementById('searchBox').addEventListener('input', async e=>{
+  const q=e.target.value.trim(); if(!q){ renderRecent(dashboardCache?.logs||[]); return; }
+  const r=await API.get(`/api/logs?page=1&page_size=10&search=${encodeURIComponent(q)}`); renderRecent(r.items||[]);
+});
+document.getElementById('uploadBtn').addEventListener('click', async ()=>{
+  const file=document.getElementById('uploadInput').files[0]; if(!file){ setText('uploadStatus','Choose image first.'); return; }
+  document.body.classList.add('loading'); setText('uploadStatus','Analyzing image...');
+  try{ const r=await API.postImage('/api/analyze', file); setText('uploadStatus',`Saved. Total detected: ${r.total}. Opening detail...`); setTimeout(()=>location.href=`/ui/logs/${r.id}`,700); }
+  catch(e){ setText('uploadStatus','Failed: '+e.message); }
+  finally{ document.body.classList.remove('loading'); }
+});
+loadDashboard().catch(e=>{ console.error(e); document.body.insertAdjacentHTML('afterbegin',`<div class="notice">Dashboard error: ${escapeHtml(e.message)}</div>`); }); setInterval(loadLatest, 5000);

web/js/detail.js

@@ -0,0 +1,8 @@
+function getId(){ return location.pathname.split('/').filter(Boolean).pop(); }
+async function loadDetail(){
+  const id=getId(); setText('eventId', id); const r=await API.get(`/api/logs/${id}`); const item=r.data;
+  setImg('detailImage', item.annotated_image||item.original_image); document.getElementById('downloadBtn').href=item.annotated_image||item.original_image||'#'; document.getElementById('originalLink').href=item.original_image||'#'; document.getElementById('debugLink').href=item.debug_mask||'#';
+  setText('detCount', pad2(item.total||0)); const conf=avgConfidence(item); setText('avgConf', confidenceText(conf)); setText('timestamp', fmtDate(item.timestamp_ms,item.datatime)); setText('storage', item.cloudinary_saved ? 'Cloudinary permanent storage' : 'Local temporary storage'); setText('breakTotal', `${item.total||0} total`);
+  const rows=item.data||[]; document.getElementById('breakdown').innerHTML = rows.length ? rows.map(row=>`<div class="break-row"><div style="display:flex;align-items:center;gap:12px"><div class="iconbox" style="width:34px;height:34px"><span class="material-symbols-outlined" style="font-size:18px">bug_report</span></div><b>${escapeHtml(row.type)}</b></div><span class="det-count">${row.count}</span></div>`).join('') : '<div class="empty">No pest breakdown</div>';
+}
+loadDetail().catch(e=>alert(e.message));

web/js/logs.js

@@ -0,0 +1,12 @@
+let page=1,pageSize=10,last=null;
+async function loadLogs(){
+  const q=document.getElementById('search').value.trim(); const p=document.getElementById('pestType').value.trim();
+  const url=`/api/logs?page=${page}&page_size=${pageSize}${q?`&search=${encodeURIComponent(q)}`:''}${p?`&pest_type=${encodeURIComponent(p)}`:''}`;
+  const r=await API.get(url); last=r; renderLogs(r.items||[]); setText('pageInfo',`Showing page ${r.page} of ${r.total_pages} · ${r.total_items} records`); document.getElementById('prevBtn').disabled=!r.has_prev; document.getElementById('nextBtn').disabled=!r.has_next;
+}
+function renderLogs(items){
+  const tb=document.getElementById('logTable');
+  if(!items.length){ tb.innerHTML='<tr><td colspan="6"><div class="empty">No logs found</div></td></tr>'; return; }
+  tb.innerHTML=items.map(item=>{ const type=primaryType(item); const conf=avgConfidence(item); return `<tr class="log-row" onclick="location.href='/ui/logs/${item.id}'"><td><img class="thumb sm" src="${item.annotated_image||item.original_image||''}" onerror="imgFallback(event)"></td><td><b style="color:var(--primary)">${escapeHtml(type)}</b><div class="sub">${escapeHtml(item.id||'')}</div></td><td><div style="display:flex;align-items:center;gap:10px"><div class="confbar"><span style="width:${Math.round(conf*100)}%"></span></div><b>${confidenceText(conf)}</b></div></td><td>${item.total||0}</td><td>${fmtDate(item.timestamp_ms,item.datatime)}</td><td style="text-align:right"><span class="material-symbols-outlined muted">open_in_new</span></td></tr>`; }).join('');
+}
+document.getElementById('filterBtn').onclick=()=>{page=1;loadLogs().catch(alert)}; document.getElementById('search').addEventListener('keydown',e=>{if(e.key==='Enter'){page=1;loadLogs().catch(alert)}}); document.getElementById('prevBtn').onclick=()=>{if(last?.has_prev){page--;loadLogs().catch(alert)}}; document.getElementById('nextBtn').onclick=()=>{if(last?.has_next){page++;loadLogs().catch(alert)}}; loadLogs().catch(e=>alert(e.message));

web/logs.html

@@ -0,0 +1,64 @@
+<!doctype html>
+<html lang="en">
+
+<head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width,initial-scale=1" />
+    <title>Detection Logs · Pest Detection AI</title>
+    <script src="https://cdn.tailwindcss.com"></script>
+    <link
+        href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&family=Space+Grotesk:wght@600;700&display=swap"
+        rel="stylesheet">
+    <link href="https://fonts.googleapis.com/css2?family=Material+Symbols+Outlined" rel="stylesheet">
+    <link rel="stylesheet" href="/web/css/app.css">
+</head>
+
+<body>
+    <header class="topbar">
+        <div style="display:flex;align-items:center;gap:28px"><a class="brand" href="/ui">Pest Detection AI</a>
+            <nav class="nav"><a href="/ui">Dashboard</a><a class="active" href="/ui/logs">Logs</a><a
+                    href="/ui/access">Access</a></nav>
+        </div>
+        <div class="avatar">AI</div>
+    </header>
+    <main class="shell">
+        <div class="section-head">
+            <div>
+                <div class="label">Platform archive</div>
+                <h1 style="font-size:36px;margin:4px 0;color:var(--primary)">Detection Logs</h1>
+                <div class="sub">Cloudinary thumbnails from Firebase Realtime Database logs.</div>
+            </div>
+        </div>
+        <div class="card pad" style="margin-bottom:20px">
+            <div class="toolbar"><input id="search" class="input" style="flex:1"
+                    placeholder="Filter by species, timestamp, or log id..."><input id="pestType" class="input"
+                    placeholder="Pest type"><button id="filterBtn" class="btn primary"><span
+                        class="material-symbols-outlined">filter_list</span> Filter</button></div>
+        </div>
+        <div class="card" style="overflow:hidden">
+            <div style="overflow-x:auto">
+                <table class="table">
+                    <thead>
+                        <tr>
+                            <th>Entity</th>
+                            <th>Identification</th>
+                            <th>Confidence</th>
+                            <th>Total</th>
+                            <th>Timestamp</th>
+                            <th style="text-align:right">Action</th>
+                        </tr>
+                    </thead>
+                    <tbody id="logTable"></tbody>
+                </table>
+            </div>
+            <div class="pager"><span id="pageInfo" class="sub">Loading...</span>
+                <div class="toolbar"><button id="prevBtn" class="btn">Previous</button><button id="nextBtn"
+                        class="btn">Next</button></div>
+            </div>
+        </div>
+    </main>
+    <script src="/web/js/api.js"></script>
+    <script src="/web/js/logs.js"></script>
+</body>
+
+</html>