Update app.py

app.py

@@ -8,7 +8,6 @@ import streamlit as st
 import numpy as np
 import pandas as pd
 from PIL import Image, ImageDraw, ImageFont
-
 import cv2
 
 # Optional: YOLO for phone detection
@@ -40,7 +39,6 @@ def iou(boxA, boxB) -> float:
 
 def detect_qr_opencv(image_np: np.ndarray) -> List[Dict[str, Any]]:
     det = cv2.QRCodeDetector()
-    # ✅ Correct unpack
     retval, data_list, points, _ = det.detectAndDecodeMulti(image_np)
     results = []
     if points is None:
@@ -56,11 +54,7 @@ def detect_qr_opencv(image_np: np.ndarray) -> List[Dict[str, Any]]:
             })
         return results
 
-    if isinstance(data_list, (list, tuple)):
-        decoded_list = data_list
-    else:
-        decoded_list = [data_list] * len(points)
-
+    decoded_list = data_list if isinstance(data_list, (list, tuple)) else [data_list] * len(points)
     for i, quad in enumerate(points):
         pts = np.array(quad, dtype=np.float32).reshape(-1,2)
         x1, y1 = np.min(pts[:,0]), np.min(pts[:,1])
@@ -141,40 +135,36 @@ def read_approved_list(file) -> List[str]:
                 vals = df.iloc[:,0].dropna().astype(str).tolist()
         else:
             content = file.read().decode("utf-8", errors="ignore")
-            file.seek(0)  # ✅ reset pointer so file can be reused
+            file.seek(0)  # reset pointer
             vals = [line.strip() for line in content.splitlines() if line.strip()]
         return [v.strip() for v in vals if v.strip()]
     except Exception as e:
         st.error(f"Failed to parse approved list: {e}")
         return []
 
-# 🔹 FIX START: robust normalization + exact matching
+# --- FIXED match with normalization ---
+from urllib.parse import urlparse, parse_qs
 def normalize_payload(payload: str) -> str:
     if not payload:
         return ""
     p = payload.strip().lower()
-
     if p.startswith("upi://"):
         try:
-            from urllib.parse import urlparse, parse_qs
             parsed = urlparse(p)
             qs = parse_qs(parsed.query)
             if "pa" in qs:
                 return qs["pa"][0].strip().lower()
         except Exception:
             pass
-
     if "pa=" in p:
         try:
             part = p.split("pa=")[1].split("&")[0]
             return part.strip().lower()
         except Exception:
             pass
-
     for prefix in ["upi://", "http://", "https://"]:
         if p.startswith(prefix):
             p = p[len(prefix):]
-
     return p
 
 def match_payload(payload: str, approved: List[str]) -> bool:
@@ -186,32 +176,29 @@ def match_payload(payload: str, approved: List[str]) -> bool:
         if norm_payload == norm_a:
             return True
     return False
-# 🔹 FIX END
-
-st.set_page_config(page_title="QR Code Anomaly Scanner", layout="wide")
-st.title("🕵️ QR Code Anomaly Scanner (Retail Store 360° CCTV Frames)")
-
-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.
-Download the annotated images and a consolidated CSV report at the end.
-""")
+
+# --- UI ---
+st.set_page_config(page_title="QR Code Anomaly Scanner", page_icon="🕵️", layout="wide")
+
+st.markdown(
+    """
+    <div style="background-color:#4B8BBE;padding:15px;border-radius:10px;margin-bottom:20px;">
+        <h1 style="color:white;text-align:center;">🕵️ QR Code Anomaly Scanner</h1>
+        <p style="color:white;text-align:center;">AI-Powered Surveillance for Retail Store 360° CCTV Frames</p>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
 
 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")
+    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", use_container_width=True)
 
 workdir = tempfile.mkdtemp()
 
@@ -224,7 +211,7 @@ if process_btn:
     if not approved_list:
         st.warning("Approved list is empty or failed to parse. All decoded QR payloads will be treated as UNAPPROVED.")
     else:
-        st.success(f"Loaded {len(approved_list)} approved entries.")
+        st.success(f"✅ Loaded {len(approved_list)} approved entries.")
 
     img_paths = []
     for f in frames or []:
@@ -305,28 +292,49 @@ if process_btn:
 
         progress.progress((idx+1)/len(img_paths))
 
-    status.text("Completed.")
+    status.text("✅ Completed.")
     df = pd.DataFrame(rows)
 
-    st.subheader("Results")
-    st.dataframe(df, use_container_width=True)
-
-    st.markdown("### Summary")
+    # --- Summary metrics ---
+    st.markdown("### 📊 Summary Dashboard")
+    col1, col2, col3, col4, col5, col6 = st.columns(6)
     total_frames = len(img_paths)
     total_qr = int((df["qr_index"] >= 0).sum())
     unapproved = int((df["anomalies"].str.contains("UNAPPROVED_QR", na=False)).sum())
     on_phone = int((df["anomalies"].str.contains("ON_PHONE", na=False)).sum())
     undecoded = int((df["anomalies"].str.contains("UNDECODED_QR", na=False)).sum())
     no_qr = int((df["anomalies"] == "NO_QR_FOUND").sum())
-    st.write({
-        "frames_processed": total_frames,
-        "qr_detections": total_qr,
-        "unapproved_qr": unapproved,
-        "qr_on_phone": on_phone,
-        "undecoded_qr": undecoded,
-        "frames_with_no_qr": no_qr
-    })
 
+    col1.metric("Frames Processed", total_frames)
+    col2.metric("QR Detections", total_qr)
+    col3.metric("❌ Unapproved", unapproved)
+    col4.metric("📱 On Phone", on_phone)
+    col5.metric("⚠️ Undecoded", undecoded)
+    col6.metric("🚫 No QR Found", no_qr)
+
+    # --- Results Table ---
+    st.markdown("### 📝 Detailed Results")
+    def highlight_anomalies(val):
+        if "UNAPPROVED" in str(val):
+            return "background-color: #FFB6C1; color: black;"
+        elif "ON_PHONE" in str(val):
+            return "background-color: #FFD580; color: black;"
+        elif "UNDECODED" in str(val):
+            return "background-color: #B0C4DE; color: black;"
+        elif "NO_QR_FOUND" in str(val):
+            return "background-color: #D3D3D3; color: black;"
+        return ""
+
+    st.dataframe(df.style.applymap(highlight_anomalies, subset=["anomalies"]), use_container_width=True)
+
+    # --- Image Gallery ---
+    st.markdown("### 🖼️ Annotated Frames Preview")
+    cols = st.columns(3)
+    for idx, fname in enumerate(sorted(os.listdir(annotated_dir))):
+        with cols[idx % 3]:
+            st.image(os.path.join(annotated_dir, fname), caption=fname, use_container_width=True)
+
+    # --- Downloads ---
     csv_bytes = df.to_csv(index=False).encode("utf-8")
     st.download_button("⬇️ Download CSV Report", data=csv_bytes,
                        file_name="qr_anomaly_report.csv", mime="text/csv")
@@ -341,9 +349,3 @@ if process_btn:
 
 else:
     st.info("Upload inputs on the left and click **Run Scan** to begin.")
-    st.markdown("""
-**Tips**
-- Approved list can be **TXT** (one payload per line) or **CSV** (use a `payload` column or first column).  
-- For QR-on-phone detection, keep **Detect phones (YOLO)** enabled.  
-- Name frames with timestamps to correlate events later.
-""")