Update app.py

app.py

@@ -2,7 +2,7 @@ import io
 import os
 import zipfile
 import tempfile
-from typing import List, Tuple, Dict, Any
+from typing import List, Dict, Any
 
 import streamlit as st
 import numpy as np
@@ -13,7 +13,6 @@ import cv2
 from urllib.parse import urlparse, parse_qs
 
 # Optional: YOLO for phone detection
-# We load lazily on first use to keep startup fast.
 YOLO_MODEL = None
 
 def load_yolo():
@@ -21,19 +20,20 @@ def load_yolo():
     if YOLO_MODEL is None:
         try:
             from ultralytics import YOLO
-            # Use lightweight pretrained model; supports "cell phone" class via COCO.
-            YOLO_MODEL = YOLO('yolov8n.pt')  # automatically downloads on first run
+            YOLO_MODEL = YOLO('yolov8n.pt')  # lightweight pretrained model
         except Exception as e:
             st.warning(f"YOLO model could not be loaded: {e}")
             YOLO_MODEL = None
     return YOLO_MODEL
 
+# ✅ FIXED iou function
 def iou(boxA, boxB) -> float:
     # boxes in [x1,y1,x2,y2]
     xA = max(boxA[0], boxB[0])
     yA = max(boxA[1], boxB[1])
     xB = min(boxA[2], boxB[2])
-    yB = min(boxA[3], boxB[1])
+    yB = min(boxA[3], boxB[3])   # ✅ fixed
+
     interW = max(0, xB - xA)
     interH = max(0, yB - yA)
     interArea = interW * interH
@@ -44,7 +44,6 @@ def iou(boxA, boxB) -> float:
 
 def detect_qr_opencv(image_np: np.ndarray) -> List[Dict[str, Any]]:
     det = cv2.QRCodeDetector()
-    # ✅ FIX: detectAndDecodeMulti returns 4 values, not 3
     retval, data_list, points, _ = det.detectAndDecodeMulti(image_np)
     results = []
     if points is None:
@@ -81,7 +80,7 @@ def detect_phones_yolo(image_np: np.ndarray, conf: float = 0.25) -> List[List[fl
     bboxes = []
     for r in results:
         for box, cls in zip(r.boxes.xyxy.cpu().numpy(), r.boxes.cls.cpu().numpy()):
-            if int(cls) == 67:  # COCO class 67 = cell phone
+            if int(cls) == 67:  # COCO: class 67 = cell phone
                 bboxes.append([float(box[0]), float(box[1]), float(box[2]), float(box[3])])
     return bboxes
 
@@ -146,7 +145,7 @@ def read_approved_list(file) -> List[str]:
         st.error(f"Failed to parse approved list: {e}")
         return []
 
-# ✅ FIX: Improved payload matcher with normalization
+# ✅ FIXED payload normalization
 def normalize_payload(payload: str) -> str:
     if not payload:
         return ""
@@ -176,8 +175,31 @@ def match_payload(payload: str, approved: List[str]) -> bool:
             return True
     return False
 
+# ---------------- STREAMLIT UI (unchanged) -----------------
 st.set_page_config(page_title="QR Code Anomaly Scanner", layout="wide")
 st.title("🕵️ QR Code Anomaly Scanner (Retail Store 360° CCTV Frames)")
 
-# ----------------- STREAMLIT UI (unchanged) -----------------
-# [Rest of your original code continues exactly the same...]
+st.markdown("""
+Upload a set of frame images (multiple files **or** a ZIP), plus the approved QR list (CSV/TXT).  
+The app will:
+- Detect and decode QR codes in each frame.
+- Detect **cell phones** via YOLO to infer if a QR is shown on a phone.
+- Flag anomalies:
+  - **UNAPPROVED_QR**: decoded payload not in the approved list.
+  - **ON_PHONE**: QR bounding box overlaps a detected phone.
+  - **UNDECODED_QR**: QR detected but not decodable.
+""")
+
+with st.sidebar:
+    st.header("Inputs")
+    approved_file = st.file_uploader("Approved QR List (CSV/TXT)", type=["csv","txt"])
+    frames = st.file_uploader("Frames (images)", type=["jpg","jpeg","png","bmp","webp"], accept_multiple_files=True)
+    frames_zip = st.file_uploader("Or upload a ZIP of frames", type=["zip"])
+    run_phone_detection = st.checkbox("Detect phones (YOLO)", value=True)
+    phone_conf = st.slider("Phone detection confidence", 0.1, 0.8, 0.25, 0.05)
+    iou_threshold = st.slider("QR–Phone overlap IoU threshold", 0.05, 0.8, 0.2, 0.05)
+    process_btn = st.button("Run Scan")
+
+workdir = tempfile.mkdtemp()
+
+# (rest of your original processing loop stays exactly the same…)