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ShelfMind AI β Smart Retail Shelf Intelligence
================================================
Complete retail shelf monitoring and inventory optimization system.
Run: streamlit run app/dashboard.py
"""
import streamlit as st
import numpy as np
import pandas as pd
import json
import os
import sys
import io
import time
import base64
import hashlib
import re
import requests
from pathlib import Path
from datetime import datetime, timedelta
from PIL import Image, ImageDraw, ImageFont
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Database module β add src/ to path for HF Spaces
SRC_DIR = Path(__file__).resolve().parent
sys.path.insert(0, str(SRC_DIR))
from db import (
setup_database, add_product, get_products, get_product_count,
get_next_product_id, delete_product, clear_all_products, get_catalog_as_dict,
save_planogram_db, get_planograms, delete_planogram,
log_compliance, log_alert, get_compliance_logs_as_list,
get_analytics_summary, get_alerts_history,
)
# ββ Path Setup ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
ROOT = Path(__file__).resolve().parent.parent
MODEL_DIR = ROOT / "models"
VIZ_DIR = ROOT / "models" / "training_visualizations"
CATALOG_DIR = ROOT / "data" / "store_catalog"
REF_IMG_DIR = CATALOG_DIR / "reference_images"
PLANOGRAM_DIR = ROOT / "data" / "store_planograms"
COMPLIANCE_DIR = ROOT / "data" / "compliance_logs"
# Ensure directories exist
for d in [MODEL_DIR, CATALOG_DIR, REF_IMG_DIR, PLANOGRAM_DIR, COMPLIANCE_DIR]:
d.mkdir(parents=True, exist_ok=True)
# ββ Auto-download models from Hugging Face Hub ββββββββββββββββββββββββββββ
HF_REPO_ID = "kush5699/ShelfMind-AI-Models" # Your HF model repo
YOLO_MODEL_NAME = "runs_detect_shelfmind_models_yolo26s_1280_v2_weights_best.pt"
YOLO_MODEL_PATH = MODEL_DIR / YOLO_MODEL_NAME
DINOV2_FINETUNED_NAME = "dinov2_shelf_finetuned.pth"
DINOV2_PROJECTOR_NAME = "dinov2_projector.pth"
DINOV2_FINETUNED_PATH = MODEL_DIR / DINOV2_FINETUNED_NAME
DINOV2_PROJECTOR_PATH = MODEL_DIR / DINOV2_PROJECTOR_NAME
RFDETR_MODEL_NAME = "shelfmind_output_rfdetr_shelf_best.pt"
RFDETR_MODEL_PATH = MODEL_DIR / RFDETR_MODEL_NAME
def download_model_from_hf(filename, destination):
"""Download a model file from HF Hub if not already cached."""
if destination.exists():
return str(destination)
try:
from huggingface_hub import hf_hub_download
path = hf_hub_download(
repo_id=HF_REPO_ID,
filename=filename,
local_dir=str(MODEL_DIR),
)
print(f"[OK] Downloaded {filename} from HF Hub")
return path
except Exception as e:
print(f"[WARNING] Could not download {filename}: {e}")
return None
# Initialize SQLite database
setup_database()
# ββ Page Config βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
st.set_page_config(
page_title="ShelfMind AI β Smart Retail Shelf Intelligence",
page_icon="π§ ",
layout="wide",
initial_sidebar_state="collapsed",
)
# ββ Premium CSS Theme βββββββββββββββββββββββββββββββββββββββββββββββββββββ
st.markdown("""
<style>
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&display=swap');
/* βββ Global βββ */
.stApp {
background: linear-gradient(135deg, #0a0a1a 0%, #0d1b2a 40%, #1b1b3a 100%);
font-family: 'Inter', sans-serif;
}
header[data-testid="stHeader"] { background: transparent; }
.block-container { padding: 1rem 2rem; max-width: 1400px; }
/* βββ Tab Styling (Pill Navigation) βββ */
.stTabs [data-baseweb="tab-list"] {
gap: 4px;
background: rgba(255,255,255,0.03);
border-radius: 16px;
padding: 6px 8px;
border: 1px solid rgba(255,255,255,0.06);
backdrop-filter: blur(20px);
}
.stTabs [data-baseweb="tab"] {
border-radius: 12px;
padding: 12px 24px;
color: #8892b0;
font-weight: 600;
font-size: 14px;
transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
border: 1px solid transparent;
}
.stTabs [data-baseweb="tab"]:hover {
color: #ccd6f6;
background: rgba(255,255,255,0.04);
}
.stTabs [aria-selected="true"] {
background: linear-gradient(135deg, #00d4aa 0%, #00b4d8 100%) !important;
color: #0a0a1a !important;
font-weight: 700;
box-shadow: 0 4px 20px rgba(0,212,170,0.3);
border: 1px solid rgba(0,212,170,0.3);
}
/* βββ Glassmorphism Metric Cards βββ */
.metric-card {
background: rgba(255,255,255,0.04);
border: 1px solid rgba(255,255,255,0.08);
border-radius: 20px;
padding: 24px 16px;
text-align: center;
backdrop-filter: blur(16px);
transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
position: relative;
overflow: hidden;
}
.metric-card::before {
content: '';
position: absolute;
top: 0; left: 0; right: 0;
height: 3px;
background: linear-gradient(90deg, #00d4aa, #00b4d8, #7b68ee);
opacity: 0;
transition: opacity 0.3s ease;
}
.metric-card:hover {
border-color: rgba(0,212,170,0.25);
transform: translateY(-4px);
box-shadow: 0 12px 40px rgba(0,212,170,0.12);
}
.metric-card:hover::before { opacity: 1; }
.metric-value {
font-size: 2.4rem;
font-weight: 800;
background: linear-gradient(135deg, #00d4aa, #00b4d8);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
line-height: 1.2;
letter-spacing: -0.5px;
}
.metric-label {
font-size: 0.75rem;
color: #6b7b9e;
margin-top: 8px;
font-weight: 600;
text-transform: uppercase;
letter-spacing: 1.5px;
}
/* βββ Product Cards βββ */
.product-card {
background: rgba(255,255,255,0.04);
border: 1px solid rgba(255,255,255,0.08);
border-radius: 16px;
padding: 16px;
text-align: center;
transition: all 0.3s ease;
}
.product-card:hover {
border-color: rgba(0,180,216,0.3);
box-shadow: 0 8px 24px rgba(0,180,216,0.08);
transform: translateY(-2px);
}
.product-card img { border-radius: 12px; }
/* βββ Alert Cards βββ */
.alert-critical {
background: linear-gradient(135deg, rgba(255,67,67,0.12), rgba(255,67,67,0.03));
border-left: 4px solid #ff4343;
border-radius: 12px;
padding: 16px 20px;
margin: 8px 0;
color: #ff8a8a;
font-size: 14px;
backdrop-filter: blur(10px);
animation: slideIn 0.3s ease-out;
}
.alert-warning {
background: linear-gradient(135deg, rgba(255,170,0,0.12), rgba(255,170,0,0.03));
border-left: 4px solid #ffaa00;
border-radius: 12px;
padding: 16px 20px;
margin: 8px 0;
color: #ffd066;
font-size: 14px;
backdrop-filter: blur(10px);
}
.alert-ok {
background: linear-gradient(135deg, rgba(0,212,170,0.12), rgba(0,212,170,0.03));
border-left: 4px solid #00d4aa;
border-radius: 12px;
padding: 16px 20px;
margin: 8px 0;
color: #66ffd9;
font-size: 14px;
backdrop-filter: blur(10px);
}
.alert-info {
background: linear-gradient(135deg, rgba(0,180,216,0.12), rgba(0,180,216,0.03));
border-left: 4px solid #00b4d8;
border-radius: 12px;
padding: 16px 20px;
margin: 8px 0;
color: #66d9f0;
font-size: 14px;
backdrop-filter: blur(10px);
}
/* βββ Section Headers βββ */
.section-header {
font-size: 1.5rem;
font-weight: 800;
background: linear-gradient(135deg, #e6f1ff, #ccd6f6);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
margin: 20px 0 12px 0;
display: flex;
align-items: center;
gap: 10px;
letter-spacing: -0.3px;
}
/* βββ Status Badges βββ */
.badge-ok { background: #00d4aa18; color: #00d4aa; padding: 4px 14px; border-radius: 20px; font-size: 12px; font-weight: 600; border: 1px solid #00d4aa30; }
.badge-warn { background: #ffaa0018; color: #ffaa00; padding: 4px 14px; border-radius: 20px; font-size: 12px; font-weight: 600; border: 1px solid #ffaa0030; }
.badge-critical { background: #ff434318; color: #ff4343; padding: 4px 14px; border-radius: 20px; font-size: 12px; font-weight: 600; border: 1px solid #ff434330; }
/* βββ Sidebar βββ */
[data-testid="stSidebar"] {
background: linear-gradient(180deg, #0d1b2a 0%, #1b1b3a 100%);
border-right: 1px solid rgba(255,255,255,0.06);
}
/* βββ Hero Section βββ */
.hero-title {
font-size: 2.2rem;
font-weight: 800;
background: linear-gradient(135deg, #00d4aa 0%, #00b4d8 50%, #7b68ee 100%);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
margin-bottom: 4px;
letter-spacing: -0.5px;
}
.hero-subtitle {
color: #6b7b9e;
font-size: 0.95rem;
margin-bottom: 24px;
line-height: 1.6;
}
/* βββ Buttons βββ */
.stButton > button[kind="primary"] {
background: linear-gradient(135deg, #00d4aa 0%, #00b4d8 100%) !important;
border: none !important;
border-radius: 12px !important;
font-weight: 700 !important;
letter-spacing: 0.3px;
transition: all 0.3s ease !important;
box-shadow: 0 4px 16px rgba(0,212,170,0.25);
}
.stButton > button[kind="primary"]:hover {
transform: translateY(-2px) !important;
box-shadow: 0 8px 28px rgba(0,212,170,0.35) !important;
}
.stButton > button[kind="secondary"] {
background: rgba(255,255,255,0.05) !important;
border: 1px solid rgba(255,255,255,0.12) !important;
border-radius: 12px !important;
color: #ccd6f6 !important;
transition: all 0.3s ease !important;
}
.stButton > button[kind="secondary"]:hover {
background: rgba(255,255,255,0.08) !important;
border-color: rgba(0,212,170,0.3) !important;
}
/* βββ Inputs βββ */
.stTextInput > div > div > input,
.stNumberInput > div > div > input,
.stSelectbox > div > div {
background: rgba(255,255,255,0.04) !important;
border: 1px solid rgba(255,255,255,0.1) !important;
border-radius: 10px !important;
color: #e6f1ff !important;
transition: border-color 0.3s ease;
}
.stTextInput > div > div > input:focus,
.stNumberInput > div > div > input:focus {
border-color: #00d4aa !important;
box-shadow: 0 0 0 2px rgba(0,212,170,0.15) !important;
}
/* βββ Radio Buttons βββ */
.stRadio > div {
background: rgba(255,255,255,0.02);
border-radius: 12px;
padding: 4px;
}
/* βββ Expander βββ */
.streamlit-expanderHeader {
background: rgba(255,255,255,0.03) !important;
border-radius: 12px !important;
border: 1px solid rgba(255,255,255,0.06) !important;
font-weight: 600;
}
/* βββ Images βββ */
[data-testid="stImage"] {
border-radius: 16px;
overflow: hidden;
border: 1px solid rgba(255,255,255,0.08);
}
/* βββ Dividers βββ */
hr {
border-color: rgba(255,255,255,0.06) !important;
margin: 20px 0 !important;
}
/* βββ Footer βββ */
.footer {
text-align: center;
padding: 32px;
color: #4a5568;
font-size: 12px;
margin-top: 48px;
border-top: 1px solid rgba(255,255,255,0.04);
}
/* βββ Scrollbar βββ */
::-webkit-scrollbar { width: 6px; }
::-webkit-scrollbar-track { background: transparent; }
::-webkit-scrollbar-thumb { background: rgba(255,255,255,0.1); border-radius: 3px; }
::-webkit-scrollbar-thumb:hover { background: rgba(255,255,255,0.2); }
/* βββ Animations βββ */
@keyframes slideIn {
from { opacity: 0; transform: translateX(-10px); }
to { opacity: 1; transform: translateX(0); }
}
@keyframes pulse {
0%, 100% { opacity: 1; }
50% { opacity: 0.7; }
}
/* βββ Better DataFrames βββ */
.stDataFrame { border-radius: 16px; overflow: hidden; }
/* βββ Form Container βββ */
[data-testid="stForm"] {
background: rgba(255,255,255,0.02);
border: 1px solid rgba(255,255,255,0.06);
border-radius: 16px;
padding: 20px;
}
</style>
""", unsafe_allow_html=True)
# ββ Header ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
st.markdown('<div class="hero-title">π§ ShelfMind AI</div>', unsafe_allow_html=True)
st.markdown('<div class="hero-subtitle">Smart Retail Shelf Intelligence β Computer Vision-Driven Inventory Monitoring & Demand Optimization</div>', unsafe_allow_html=True)
# ββ Model Loading βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
@st.cache_resource
def load_yolo():
"""Load YOLO model for product detection."""
try:
from ultralytics import YOLO
# Try local model first, then download from HF Hub
if not YOLO_MODEL_PATH.exists():
download_model_from_hf(YOLO_MODEL_NAME, YOLO_MODEL_PATH)
if YOLO_MODEL_PATH.exists():
model = YOLO(str(YOLO_MODEL_PATH))
model.to("cpu")
print(f"[OK] YOLO26s v2 loaded (1280px, mAP50=91.7%)")
return model
# Fallback to pretrained YOLO26s
model = YOLO("yolo26s.pt")
model.to("cpu")
return model
except Exception as e:
st.error(f"YOLO load failed: {e}")
return None
@st.cache_resource
def load_rfdetr():
"""Load fine-tuned RF-DETR model for product detection (PyTorch Lightning checkpoint)."""
rfdetr_path = RFDETR_MODEL_PATH
if not rfdetr_path.exists():
download_model_from_hf(RFDETR_MODEL_NAME, rfdetr_path)
if not rfdetr_path.exists():
print(f"[WARNING] RF-DETR model not found")
return None
try:
import torch
# RF-DETR needs CUDA; if not available, skip gracefully
if not torch.cuda.is_available():
print("[WARNING] RF-DETR requires GPU (CUDA). Running on CPU β RF-DETR disabled.")
return None
from rfdetr import RFDETRBase
model = RFDETRBase(pretrain_weights=str(rfdetr_path))
print(f"[OK] RF-DETR loaded ({rfdetr_path.stat().st_size/1e6:.1f} MB)")
return model
except TypeError:
try:
from rfdetr import RFDETRBase
model = RFDETRBase(checkpoint=str(rfdetr_path))
print(f"[OK] RF-DETR loaded ({rfdetr_path.stat().st_size/1e6:.1f} MB)")
return model
except Exception as e:
print(f"[ERROR] RF-DETR load failed: {e}")
return None
except ImportError:
print("[ERROR] rfdetr package not installed")
return None
except Exception as e:
print(f"[ERROR] RF-DETR load failed: {e}")
return None
@st.cache_resource
def load_dinov2():
"""Load pretrained DINOv2 for product embedding."""
try:
import torch
model = torch.hub.load("facebookresearch/dinov2", "dinov2_vits14", pretrained=True)
model = model.to("cpu")
model.eval()
return model
except Exception as e:
st.error(f"DINOv2 load failed: {e}")
return None
@st.cache_resource
def load_dinov2_finetuned():
"""Load fine-tuned DINOv2 with projector for product embedding.
Returns (backbone, projector) tuple.
"""
import torch
import torch.nn as nn
finetuned_path = DINOV2_FINETUNED_PATH
projector_path = DINOV2_PROJECTOR_PATH
# Auto-download from HF Hub if not present
if not finetuned_path.exists():
download_model_from_hf(DINOV2_FINETUNED_NAME, finetuned_path)
if not projector_path.exists():
download_model_from_hf(DINOV2_PROJECTOR_NAME, projector_path)
try:
# Load base model
model = torch.hub.load("facebookresearch/dinov2", "dinov2_vitb14", pretrained=True)
if finetuned_path.exists():
state = torch.load(str(finetuned_path), map_location="cpu", weights_only=True)
model.load_state_dict(state, strict=False)
print(f"[OK] DINOv2 fine-tuned backbone loaded ({finetuned_path.stat().st_size/1e6:.1f} MB)")
else:
print(f"[WARNING] Fine-tuned weights not found at {finetuned_path}")
return None
model = model.to("cpu")
model.eval()
# Build and load projector: 768 β 2048 β 2048 β 256
projector = None
if projector_path.exists():
projector = nn.Sequential(
nn.Linear(768, 2048), # net.0
nn.BatchNorm1d(2048), # net.1
nn.ReLU(inplace=True), # net.2
nn.Linear(2048, 2048), # net.3
nn.BatchNorm1d(2048), # net.4
nn.ReLU(inplace=True), # net.5
nn.Linear(2048, 256), # net.6
)
proj_state = torch.load(str(projector_path), map_location="cpu", weights_only=True)
# Strip 'net.' prefix: checkpoint has 'net.0.weight' but Sequential expects '0.weight'
cleaned_state = {k.replace("net.", "", 1): v for k, v in proj_state.items()}
projector.load_state_dict(cleaned_state, strict=True)
projector = projector.to("cpu")
projector.eval()
print(f"[OK] DINOv2 projector loaded (768β256, {projector_path.stat().st_size/1e6:.1f} MB)")
return (model, projector)
except Exception as e:
print(f"[ERROR] DINOv2 fine-tuned load failed: {e}")
return None
def get_embedding(model_or_tuple, image):
"""Get DINOv2 embedding for an image.
Accepts either a plain model or a (backbone, projector) tuple from load_dinov2_finetuned.
"""
import torch
from torchvision import transforms
# Unpack if tuple (fine-tuned model with projector)
if isinstance(model_or_tuple, tuple):
model, projector = model_or_tuple
else:
model, projector = model_or_tuple, None
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
img_tensor = transform(image.convert("RGB")).unsqueeze(0).to("cpu")
with torch.no_grad():
backbone_emb = model(img_tensor) # (1, 768) or (1, 384)
if projector is not None:
embedding = projector(backbone_emb).squeeze().numpy() # (256,)
else:
embedding = backbone_emb.squeeze().numpy()
return embedding / np.linalg.norm(embedding) # L2 normalize
def get_robust_embedding(model, image, return_views=False):
"""Generate robust embedding by averaging 15 augmented views of 1 photo.
Invariance types covered:
- Translational: center, left, right, top, bottom crops
- Scale: zoom in (80%) and zoom out (110%)
- Rotational: Β±5Β° slight rotation
- Photometric: brightness Β±20%, contrast +20%, saturation Β±20%
- Mirror: horizontal flip
- Blur: Gaussian blur (simulates out-of-focus / motion blur)
If return_views=True, returns (embedding, views_list, view_names)
"""
import torch
from torchvision import transforms
from PIL import ImageEnhance, ImageOps, ImageFilter
img = image.convert("RGB")
w, h = img.size
view_names = []
views = []
# 1. Original
views.append(img)
view_names.append("Original")
# 2. Horizontal flip (mirror invariance)
views.append(ImageOps.mirror(img))
view_names.append("H-Flip")
# 3. Center crop 80% (scale invariance - zoom in)
mw, mh = int(w * 0.1), int(h * 0.1)
views.append(img.crop((mw, mh, w - mw, h - mh)))
view_names.append("Center 80%")
# 4. Left-shifted crop (translational invariance)
views.append(img.crop((0, mh, int(w * 0.85), h - mh)))
view_names.append("Left Crop")
# 5. Right-shifted crop (translational invariance)
views.append(img.crop((int(w * 0.15), mh, w, h - mh)))
view_names.append("Right Crop")
# 6. Top crop (translational invariance)
views.append(img.crop((mw, 0, w - mw, int(h * 0.85))))
view_names.append("Top Crop")
# 7. Bottom crop (translational invariance β was missing)
views.append(img.crop((mw, int(h * 0.15), w - mw, h)))
view_names.append("Bottom Crop")
# 8. Slight rotation +5Β° (rotational invariance)
views.append(img.rotate(-5, expand=False, fillcolor=(128, 128, 128)))
view_names.append("Rotate +5Β°")
# 9. Slight rotation -5Β° (rotational invariance)
views.append(img.rotate(5, expand=False, fillcolor=(128, 128, 128)))
view_names.append("Rotate -5Β°")
# 10. Brightness +20% (photometric invariance)
views.append(ImageEnhance.Brightness(img).enhance(1.2))
view_names.append("Bright +20%")
# 11. Brightness -20% (shadow/dark shelf conditions)
views.append(ImageEnhance.Brightness(img).enhance(0.8))
view_names.append("Bright -20%")
# 12. Contrast +20% (photometric invariance)
views.append(ImageEnhance.Contrast(img).enhance(1.2))
view_names.append("Contrast +20%")
# 13. Saturation +20% (color temperature variation)
views.append(ImageEnhance.Color(img).enhance(1.2))
view_names.append("Saturation +20%")
# 14. Saturation -20% (faded/washed-out lighting)
views.append(ImageEnhance.Color(img).enhance(0.8))
view_names.append("Saturation -20%")
# 15. Gaussian blur (out-of-focus / motion blur during live scan)
views.append(img.filter(ImageFilter.GaussianBlur(radius=1.5)))
view_names.append("Blur Ο=1.5")
# Compute embeddings for all views and average
embeddings = []
for view in views:
emb = get_embedding(model, view)
embeddings.append(emb)
avg_emb = np.mean(embeddings, axis=0)
normalized = avg_emb / np.linalg.norm(avg_emb) # L2 normalize
if return_views:
return normalized, views, view_names
return normalized
# ββ Auto-Crop & OCR Helpers βββββββββββββββββββββββββββββββββββββββββββββββ
def auto_crop_product(image, yolo_model, conf=0.3, padding=5):
"""Auto-crop product from image using YOLO β GrabCut fallback.
Strategy:
1. Try YOLO detection (works for shelf/multi-product scenes)
2. If YOLO misses β use GrabCut foreground segmentation
(works for single product close-ups on table/plain background)
Returns: (cropped_image, bbox) or (original, None)
"""
import cv2
img_np = np.array(image)
# ββ Step 1: Try YOLO detection ββββββββββββββββββββββββββββββββββββ
results = yolo_model(img_np, conf=conf, imgsz=640, verbose=False)
best_crop = None
best_area = 0
best_bbox = None
for r in results:
for box in r.boxes:
x1, y1, x2, y2 = map(int, box.xyxy[0].tolist())
h, w = img_np.shape[:2]
x1 = max(0, x1 - padding)
y1 = max(0, y1 - padding)
x2 = min(w, x2 + padding)
y2 = min(h, y2 + padding)
area = (x2 - x1) * (y2 - y1)
if area > best_area:
best_area = area
best_crop = image.crop((x1, y1, x2, y2))
best_bbox = (x1, y1, x2, y2)
if best_crop:
# ββ Refine YOLO crop with GrabCut for tighter boundary ββ
try:
crop_np = cv2.cvtColor(np.array(best_crop), cv2.COLOR_RGB2BGR)
ch, cw = crop_np.shape[:2]
if ch > 50 and cw > 50: # Only refine if crop is large enough
margin_x = max(5, int(cw * 0.05))
margin_y = max(5, int(ch * 0.05))
rect = (margin_x, margin_y, cw - 2 * margin_x, ch - 2 * margin_y)
mask = np.zeros((ch, cw), np.uint8)
bgd_model = np.zeros((1, 65), np.float64)
fgd_model = np.zeros((1, 65), np.float64)
cv2.grabCut(crop_np, mask, rect, bgd_model, fgd_model, 3, cv2.GC_INIT_WITH_RECT)
fg_mask = np.where((mask == cv2.GC_FGD) | (mask == cv2.GC_PR_FGD), 255, 0).astype(np.uint8)
contours, _ = cv2.findContours(fg_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if contours:
largest = max(contours, key=cv2.contourArea)
x, y, rw, rh = cv2.boundingRect(largest)
refine_ratio = (rw * rh) / (cw * ch)
if 0.20 < refine_ratio < 0.95: # Reasonable refinement
rx1 = max(0, x - 3)
ry1 = max(0, y - 3)
rx2 = min(cw, x + rw + 3)
ry2 = min(ch, y + rh + 3)
refined = best_crop.crop((rx1, ry1, rx2, ry2))
return refined, best_bbox
except Exception:
pass # Fall back to YOLO crop if GrabCut fails
return best_crop, best_bbox
# ββ Step 2: GrabCut foreground segmentation (fast) βββββββββββββββββ
# Downsize for speed, then map bbox back to original
try:
h, w = img_np.shape[:2]
max_dim = 480
scale = 1.0
if max(h, w) > max_dim:
scale = max_dim / max(h, w)
small = cv2.resize(cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR),
(int(w * scale), int(h * scale)))
else:
small = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
sh, sw = small.shape[:2]
margin_x, margin_y = int(sw * 0.10), int(sh * 0.10)
rect = (margin_x, margin_y, sw - 2 * margin_x, sh - 2 * margin_y)
mask = np.zeros((sh, sw), np.uint8)
bgd_model = np.zeros((1, 65), np.float64)
fgd_model = np.zeros((1, 65), np.float64)
cv2.grabCut(small, mask, rect, bgd_model, fgd_model, 3, cv2.GC_INIT_WITH_RECT)
fg_mask = np.where((mask == cv2.GC_FGD) | (mask == cv2.GC_PR_FGD), 255, 0).astype(np.uint8)
contours, _ = cv2.findContours(fg_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if contours:
largest = max(contours, key=cv2.contourArea)
x, y, cw, ch = cv2.boundingRect(largest)
crop_ratio = (cw * ch) / (sw * sh)
# Skip crop if product already fills >60% of frame
# (cropping would cut off parts of the product)
if crop_ratio > 0.60:
return image, None
if crop_ratio > 0.05:
inv = 1.0 / scale
pad = padding
x1 = max(0, int(x * inv) - pad)
y1 = max(0, int(y * inv) - pad)
x2 = min(w, int((x + cw) * inv) + pad)
y2 = min(h, int((y + ch) * inv) + pad)
cropped = image.crop((x1, y1, x2, y2))
return cropped, (x1, y1, x2, y2)
except Exception:
pass
return image, None
def detect_all_products(image, det_model, conf=0.3, padding=3):
"""Detect ALL products in a shelf image. Supports both YOLO (ultralytics) and RF-DETR (rfdetr package).
Returns list of (crop, bbox, confidence).
"""
img_np = np.array(image)
h, w = img_np.shape[:2]
crops = []
# Check if this is an rfdetr model
is_rfdetr = 'RFDETR' in type(det_model).__name__
if is_rfdetr:
# RF-DETR predict returns a supervision.Detections object
try:
detections = det_model.predict(image, threshold=conf)
# supervision.Detections has .xyxy (ndarray) and .confidence (ndarray)
boxes = detections.xyxy # shape (N, 4) β [x1,y1,x2,y2]
scores = detections.confidence # shape (N,)
if boxes is not None and len(boxes) > 0:
for i in range(len(boxes)):
x1, y1, x2, y2 = map(int, boxes[i])
x1 = max(0, x1 - padding)
y1 = max(0, y1 - padding)
x2 = min(w, x2 + padding)
y2 = min(h, y2 + padding)
crop = image.crop((x1, y1, x2, y2))
bbox = (x1, y1, x2, y2)
score = float(scores[i]) if scores is not None else 1.0
crops.append((crop, bbox, score))
except Exception as e:
st.error(f"RF-DETR prediction error: {e}")
else:
# Ultralytics YOLO API
results = det_model(img_np, conf=conf, imgsz=1280, verbose=False)
for r in results:
for box in r.boxes:
x1, y1, x2, y2 = map(int, box.xyxy[0].tolist())
x1 = max(0, x1 - padding)
y1 = max(0, y1 - padding)
x2 = min(w, x2 + padding)
y2 = min(h, y2 + padding)
crop = image.crop((x1, y1, x2, y2))
bbox = (x1, y1, x2, y2)
crops.append((crop, bbox, float(box.conf[0])))
return crops
@st.cache_resource
def load_ocr():
"""Load EasyOCR reader for product text recognition."""
try:
import easyocr
reader = easyocr.Reader(["en"], gpu=False, verbose=False)
return reader
except Exception as e:
st.warning(f"OCR not available: {e}")
return None
def extract_text_from_crop(ocr_reader, crop_image):
"""Extract product name + quantity from crop using OCR.
Strategy:
1. Find the LARGEST text on the label (= brand name, in big font)
2. Extract quantity pattern (2.25L, 330ml, 500g, etc.)
3. Return: "BrandName Quantity"
"""
if ocr_reader is None:
return ""
try:
# Resize for speed β OCR is very slow on full-res phone images
img = crop_image.copy()
max_dim = 640
if max(img.size) > max_dim:
ratio = max_dim / max(img.size)
img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
img_np = np.array(img)
results = ocr_reader.readtext(img_np, detail=1, paragraph=False)
if not results:
return ""
# ββ Step 1: Find brand name (largest text by bbox area) ββββββββ
brand_name = ""
max_area = 0
for (bbox, text, conf) in results:
text = text.strip()
if conf < 0.2 or len(text) < 2:
continue
# Skip common non-brand words
skip_words = {"ingredients", "nutrition", "serving", "energy",
"protein", "sugar", "sodium", "carbohydrate", "fat",
"total", "added", "manufactured", "contains", "per",
"information", "flavours", "natural", "artificial",
"carbonated", "water", "values", "approximate"}
if text.lower() in skip_words:
continue
# Calculate bbox area (larger text = more prominent = brand name)
xs = [p[0] for p in bbox]
ys = [p[1] for p in bbox]
area = (max(xs) - min(xs)) * (max(ys) - min(ys))
if area > max_area:
max_area = area
brand_name = text
# ββ Step 2: Extract quantity pattern (2.25L, 330ml, 500g) βββββ
quantity = ""
all_text = " ".join([t for (_, t, c) in results if c > 0.2])
qty_match = re.search(
r'(\d+\.?\d*)\s*(ml|ML|mL|ltr|LTR|Ltr|[lL]|[gG]|[kK][gG])\b',
all_text
)
if qty_match:
quantity = qty_match.group(0).strip()
# ββ Combine βββββββββββββββββββββββββββββββββββββββββββββββββββ
parts = [p for p in [brand_name, quantity] if p]
return " ".join(parts) if parts else ""
except Exception:
return ""
def scan_barcode(image):
"""Detect and decode barcodes using OpenCV's built-in BarcodeDetector.
Tries multiple rotations and preprocessing for robustness.
Returns barcode string or None.
"""
try:
import cv2
detector = cv2.barcode.BarcodeDetector()
img_np = np.array(image)
# Try 4 rotations Γ 2 preprocessing methods
for rotation in [0, 90, 180, 270]:
if rotation == 0:
rotated = img_np
else:
rotated = np.array(image.rotate(-rotation, expand=True))
# Method 1: Original
gray = cv2.cvtColor(rotated, cv2.COLOR_RGB2GRAY)
ok, decoded_info, _, _ = detector.detectAndDecodeMulti(gray)
if ok and decoded_info is not None:
for info in decoded_info:
if info and len(info) >= 4:
return info
# Method 2: Enhanced contrast
gray_eq = cv2.equalizeHist(gray)
ok, decoded_info, _, _ = detector.detectAndDecodeMulti(gray_eq)
if ok and decoded_info is not None:
for info in decoded_info:
if info and len(info) >= 4:
return info
return None
except Exception:
return None
def lookup_barcode_info(barcode_number):
"""Lookup product info from barcode using Open Food Facts API (free).
Note: Many Indian products are NOT in the database β that's normal.
Returns dict with name, category, brand or empty dict.
"""
try:
url = f"https://world.openfoodfacts.org/api/v0/product/{barcode_number}.json"
headers = {"User-Agent": "ShelfMind/1.0 (retail-compliance)"}
resp = requests.get(url, headers=headers, timeout=5)
if resp.status_code == 200 and resp.text:
import json
try:
data = json.loads(resp.text)
except json.JSONDecodeError:
return {}
if data.get("status") == 1:
product = data.get("product", {})
name = product.get("product_name", "")
brand = product.get("brands", "")
category = product.get("categories", "").split(",")[0].strip() if product.get("categories") else ""
quantity = product.get("quantity", "")
# Build a useful product name
full_name = ""
if brand and name:
full_name = f"{brand} {name}"
elif name:
full_name = name
elif brand:
full_name = brand
if quantity and quantity not in full_name:
full_name = f"{full_name} {quantity}".strip()
return {
"name": full_name,
"category": category,
"brand": brand,
"quantity": quantity,
}
except Exception:
pass
return {}
def get_crop_geometry(bbox):
"""Get geometry features from bounding box for size-based discrimination."""
if bbox is None:
return {"width": 0, "height": 0, "aspect_ratio": 0, "area": 0}
x1, y1, x2, y2 = bbox
w = x2 - x1
h = y2 - y1
return {
"width": w,
"height": h,
"aspect_ratio": round(h / max(w, 1), 2),
"area": w * h,
}
def cluster_unique_products(crops_data, dinov2_model, similarity_threshold=0.85):
"""Cluster detected crops using HDBSCAN with size-aware distance fusion.
HDBSCAN: Automatically determines the optimal number of clusters without
requiring a similarity threshold. Finds dense regions in embedding space
and treats sparse points as noise (assigned to nearest cluster).
Size fusion: Injects bounding box area ratio (25% weight) so different-sized
packages of the same brand get separated.
Falls back to Agglomerative (complete linkage) if HDBSCAN is not installed.
Returns: (unique_products, embeddings, cluster_labels)
"""
if not crops_data:
return [], np.array([]), np.array([])
# Get embeddings for all crops
embeddings = []
areas = []
for crop, bbox, conf in crops_data:
emb = get_embedding(dinov2_model, crop)
embeddings.append(emb)
# Compute bounding box area for size-aware fusion
x1, y1, x2, y2 = bbox
areas.append((x2 - x1) * (y2 - y1))
embeddings = np.array(embeddings, dtype=np.float32)
areas = np.array(areas, dtype=np.float32)
n = len(embeddings)
if n == 1:
cluster_labels = np.array([0])
unique_products = [{
"crop": crops_data[0][0],
"bbox": crops_data[0][1],
"confidence": crops_data[0][2],
"count": 1,
"embedding": embeddings[0].tolist(),
"geometry": get_crop_geometry(crops_data[0][1]),
"member_bboxes": [crops_data[0][1]],
}]
return unique_products, embeddings, cluster_labels
# ββ Build combined distance matrix (visual + size) ββ
# Visual distance: 1 - cosine_similarity
visual_sim = np.dot(embeddings, embeddings.T) # cosine sim (already L2-normalized)
visual_dist = 1.0 - visual_sim
# Size distance: 1 - min(a,b)/max(a,b) for each pair
size_dist = np.zeros((n, n), dtype=np.float32)
for i in range(n):
for j in range(i + 1, n):
size_ratio = min(areas[i], areas[j]) / max(areas[i], areas[j]) if max(areas[i], areas[j]) > 0 else 1.0
sd = 1.0 - size_ratio
size_dist[i, j] = sd
size_dist[j, i] = sd
# Combined distance: 75% visual + 25% size
combined_dist = 0.75 * visual_dist + 0.25 * size_dist
# ββ Agglomerative Clustering with complete linkage ββ
from scipy.cluster.hierarchy import linkage, fcluster
from scipy.spatial.distance import squareform
# Convert to condensed distance matrix for scipy
condensed = squareform(combined_dist, checks=False)
# Complete linkage: max distance between all pairs in two clusters
Z = linkage(condensed, method='complete')
# Cut tree at distance = (1 - similarity_threshold)
distance_threshold = 1.0 - similarity_threshold
cluster_labels = fcluster(Z, t=distance_threshold, criterion='distance') - 1 # 0-indexed
# ββ Build cluster groups ββ
clusters = {}
for idx, label in enumerate(cluster_labels):
if label not in clusters:
clusters[label] = []
clusters[label].append(idx)
# Sort clusters by size (largest first)
sorted_label_groups = sorted(clusters.items(), key=lambda x: len(x[1]), reverse=True)
# Re-label so that largest cluster = 0
label_remap = {}
for new_label, (old_label, members) in enumerate(sorted_label_groups):
label_remap[old_label] = new_label
cluster_labels = np.array([label_remap[l] for l in cluster_labels], dtype=int)
# Build results with representative crop info
unique_products = []
for new_label, (old_label, members) in enumerate(sorted_label_groups):
# Pick the highest-confidence crop as representative
best_idx = max(members, key=lambda m: crops_data[m][2])
crop, bbox, conf = crops_data[best_idx]
member_bboxes = [crops_data[m][1] for m in members]
unique_products.append({
"crop": crop,
"bbox": bbox,
"confidence": conf,
"count": len(members),
"embedding": embeddings[best_idx].tolist(),
"geometry": get_crop_geometry(bbox),
"member_bboxes": member_bboxes,
})
return unique_products, embeddings, cluster_labels
# ββ Product Catalog Management ββββββββββββββββββββββββββββββββββββββββββββ
def load_catalog():
"""Load product catalog from SQLite database."""
return get_catalog_as_dict()
def save_catalog(catalog):
"""Save product catalog β handled by db.add_product() now."""
pass # Individual products are saved via db.add_product()
def build_faiss_index(catalog):
"""Build FAISS index from catalog embeddings."""
import faiss
embeddings = []
valid_products = []
for p in catalog["products"]:
if "embedding" in p and p["embedding"] is not None:
embeddings.append(p["embedding"])
valid_products.append(p)
if not embeddings:
return None, []
dim = len(embeddings[0])
index = faiss.IndexFlatIP(dim) # Inner product (cosine on normalized vectors)
index.add(np.array(embeddings, dtype=np.float32))
return index, valid_products
def search_product(index, products, query_embedding, threshold=0.5):
"""Search FAISS index for matching product."""
if index is None or len(products) == 0:
return None, 0.0
query = np.array([query_embedding], dtype=np.float32)
scores, indices = index.search(query, 1)
score = float(scores[0][0])
if score >= threshold:
return products[int(indices[0][0])], score
return None, score
def search_product_with_size(index, products, query_embedding, query_bbox,
expected_products_on_shelf=None, threshold=0.5,
size_weight=0.15):
"""Enhanced search: DINOv2 visual similarity + bounding box size-ratio.
Combines visual embedding score with height-ratio comparison
to distinguish same-brand, different-size products.
Args:
index: FAISS index
products: List of indexed products
query_embedding: DINOv2 embedding of detected crop
query_bbox: (x1, y1, x2, y2) of detected product
expected_products_on_shelf: List of expected planogram positions with bbox info
threshold: Minimum combined score to accept
size_weight: How much size-ratio influences final score (0-1)
Returns:
(matched_product, combined_score)
"""
if index is None or len(products) == 0:
return None, 0.0
# Step 1: Get top-3 visual matches from FAISS
k = min(3, len(products))
query = np.array([query_embedding], dtype=np.float32)
scores, indices = index.search(query, k)
if float(scores[0][0]) < threshold:
return None, float(scores[0][0])
# If no expected products or no bbox β fall back to pure visual
if expected_products_on_shelf is None or query_bbox is None:
return products[int(indices[0][0])], float(scores[0][0])
# Step 2: Get detected product height
qx1, qy1, qx2, qy2 = query_bbox
query_height = qy2 - qy1
# Step 3: Score each candidate with size-ratio fusion
best_match = None
best_combined = 0.0
for rank in range(k):
visual_score = float(scores[0][rank])
if visual_score < threshold * 0.8: # Skip very low visual matches
continue
candidate = products[int(indices[0][rank])]
candidate_sku = candidate["sku"]
# Find this SKU's expected bbox in planogram
size_score = 1.0 # Default: no penalty
for exp_prod in expected_products_on_shelf:
if exp_prod.get("sku") == candidate_sku:
exp_bbox = exp_prod.get("bbox")
if exp_bbox and len(exp_bbox) == 4:
exp_height = exp_bbox[3] - exp_bbox[1]
if exp_height > 0 and query_height > 0:
# Height ratio β 1.0 = perfect match, <1.0 = size mismatch
height_ratio = min(query_height, exp_height) / max(query_height, exp_height)
size_score = height_ratio
break
# Fused score: visual * (1-w) + size * w
combined = visual_score * (1 - size_weight) + size_score * size_weight
if combined > best_combined:
best_combined = combined
best_match = candidate
if best_match and best_combined >= threshold:
return best_match, best_combined
# Fallback to top visual match
return products[int(indices[0][0])], float(scores[0][0])
# ββ Planogram Management (now via SQLite) βββββββββββββββββββββββββββββββββ
def load_planograms():
"""Load all planograms from SQLite database."""
return get_planograms()
def save_planogram(name, data):
"""Save planogram to SQLite database."""
save_planogram_db(name, data)
# ββ Alert Engine ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def send_mobile_alert(title, message, priority="high"):
"""Send push notification via ntfy.sh."""
topic = st.session_state.get("ntfy_topic", "shelfmind-alerts")
try:
# Remove emojis from title (HTTP headers must be latin-1 safe)
clean_title = title.encode("ascii", "ignore").decode("ascii").strip()
if not clean_title:
clean_title = "ShelfMind Alert"
requests.post(
f"https://ntfy.sh/{topic}",
data=message.encode("utf-8"),
headers={
"Title": clean_title,
"Priority": priority,
"Tags": "warning" if priority == "high" else "loudspeaker",
},
timeout=5,
)
return True
except Exception as e:
return False
def format_compliance_alert(shelf_name, issues, compliance_pct):
"""Generate formatted compliance alert HTML."""
if compliance_pct >= 90:
css_class = "alert-ok"
icon = "β
"
elif compliance_pct >= 70:
css_class = "alert-warning"
icon = "β οΈ"
else:
css_class = "alert-critical"
icon = "π΄"
issues_html = "<br>".join(f"β’ {issue}" for issue in issues)
return f"""<div class="{css_class}">
<strong>{icon} {shelf_name} β {compliance_pct:.0f}% Compliant</strong><br>
{issues_html}
</div>"""
# ββ YOLO Detection Helper ββββββββββββββββββββββββββββββββββββββββββββββββ
def run_detection(model, image, conf=0.25, max_det=200):
"""Run YOLO detection and return detections list."""
results = model.predict(image, conf=conf, max_det=max_det, device="cpu", verbose=False)
detections = []
for r in results:
for box in r.boxes:
x1, y1, x2, y2 = box.xyxy[0].tolist()
detections.append({
"bbox": [x1, y1, x2, y2],
"confidence": float(box.conf[0]),
"class": int(box.cls[0]) if box.cls is not None else 0,
})
return detections, results
def detect_shelf_levels(detections, img_height):
"""Auto-detect shelf levels from product Y-positions."""
if len(detections) < 3:
return [0.0, float(img_height)], 1
y_centers = sorted([(d["bbox"][1] + d["bbox"][3]) / 2 for d in detections])
gaps = [(y_centers[i] - y_centers[i-1], i) for i in range(1, len(y_centers))]
# Minimum gap must be at least 15% of image height to count as a new shelf
# This prevents splitting products on the same flat surface
min_shelf_gap = img_height * 0.15
# Also use statistical threshold: gap must be 3x the average small gap
small_gaps = sorted([g for g, _ in gaps])[:max(len(gaps)//2, 1)]
avg_small = sum(small_gaps) / len(small_gaps) if small_gaps else 10
stat_threshold = avg_small * 3
# Use the LARGER of the two thresholds
sig_threshold = max(min_shelf_gap, stat_threshold)
gaps_sorted = sorted(gaps, key=lambda x: x[0], reverse=True)
top_gaps = [(g, idx) for g, idx in gaps_sorted if g > sig_threshold][:6]
top_gaps_sorted = sorted(top_gaps, key=lambda x: y_centers[x[1]])
boundaries = [0.0]
for _, idx in top_gaps_sorted:
boundaries.append((y_centers[idx-1] + y_centers[idx]) / 2)
boundaries.append(float(img_height))
return boundaries, len(boundaries) - 1
def assign_to_shelves(detections, boundaries):
"""Assign each detection to a shelf level."""
shelf_assignments = {}
for det in detections:
y_center = (det["bbox"][1] + det["bbox"][3]) / 2
for s in range(len(boundaries) - 1):
if boundaries[s] <= y_center < boundaries[s + 1]:
shelf_id = s + 1
if shelf_id not in shelf_assignments:
shelf_assignments[shelf_id] = []
# Sort by x position (left to right)
det["shelf"] = shelf_id
shelf_assignments[shelf_id].append(det)
break
# Sort products in each shelf by x-position
for shelf_id in shelf_assignments:
shelf_assignments[shelf_id].sort(key=lambda d: d["bbox"][0])
return shelf_assignments
def draw_annotated_image(image, detections, product_labels=None):
"""Draw bounding boxes with confidence-colored labels on image."""
draw_img = image.copy()
draw = ImageDraw.Draw(draw_img)
try:
font = ImageFont.truetype("arial.ttf", 14)
except:
font = ImageFont.load_default()
for i, det in enumerate(detections):
x1, y1, x2, y2 = det["bbox"]
match_score = det.get("match_score", det.get("confidence", 0))
# Confidence-based coloring
if det.get("status") == "unknown" or match_score < 0.3:
color = "#ff4343" # Red β unknown
elif match_score >= 0.7:
color = "#00d4aa" # Green β high confidence
elif match_score >= 0.5:
color = "#ffaa00" # Yellow β medium
else:
color = "#ff8c00" # Orange β low
# Draw box
draw.rectangle([x1, y1, x2, y2], outline=color, width=2)
# Draw label
label = ""
if product_labels and i < len(product_labels):
label = product_labels[i]
elif "product_name" in det:
label = det["product_name"]
else:
label = f"P{i+1}"
text = f"{label} ({match_score:.0%})" if label else f"{match_score:.0%}"
bbox = draw.textbbox((x1, y1 - 18), text, font=font)
draw.rectangle([bbox[0]-2, bbox[1]-2, bbox[2]+2, bbox[3]+2], fill=color)
draw.text((x1, y1 - 18), text, fill="#0a0a1a", font=font)
return draw_img
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# ββ PHONE CAMERA CONFIG ββββββββββββββββββββββββββββββββββββββββββββββββββ
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def capture_from_phone(url, rotation=0):
"""Grab a single frame from IP Webcam stream with orientation fix."""
import cv2
from PIL import ImageOps
try:
# Use /shot.jpg for a single frame (more reliable than /video)
shot_url = url.replace("/video", "/shot.jpg").replace("/videofeed", "/shot.jpg")
if "/shot.jpg" not in shot_url:
shot_url = url.rstrip("/") + "/shot.jpg"
import urllib.request
with urllib.request.urlopen(shot_url, timeout=5) as resp:
arr = np.frombuffer(resp.read(), np.uint8)
img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
if img is not None:
pil_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
# Auto-fix EXIF orientation
try:
pil_img = ImageOps.exif_transpose(pil_img)
except Exception:
pass
# Apply manual rotation if needed
if rotation == 90:
pil_img = pil_img.rotate(-90, expand=True)
elif rotation == 180:
pil_img = pil_img.rotate(180, expand=True)
elif rotation == 270:
pil_img = pil_img.rotate(90, expand=True)
return pil_img
except Exception as e:
st.error(f"β Cannot connect to phone camera: {e}")
return None
# Phone camera global settings (collapsible)
with st.expander("π± Phone Camera Setup", expanded=False):
phone_cols = st.columns([2, 1, 1])
with phone_cols[0]:
phone_cam_url = st.text_input(
"IP Webcam URL",
value="http://192.168.1.5:8080",
key="global_phone_url",
help="Install 'IP Webcam' app on Android β Start Server β paste URL here"
)
with phone_cols[1]:
phone_rotation = st.selectbox(
"Rotation Fix",
[0, 90, 180, 270],
index=1,
format_func=lambda x: f"π {x}Β°" if x > 0 else "None",
key="phone_rotation",
help="If image appears sideways, change this"
)
with phone_cols[2]:
st.markdown("<br>", unsafe_allow_html=True)
if st.button("π Test Connection"):
test_img = capture_from_phone(phone_cam_url, phone_rotation)
if test_img:
st.success("β
Connected!")
st.image(test_img, caption="Phone camera preview", width='stretch')
else:
st.error("β Cannot reach phone camera")
st.markdown("""
**Quick Setup:** Install **IP Webcam** (Android) β Start Server β Enter URL above
Both phone & laptop must be on the **same WiFi network**
""")
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# ββ TABS ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
tab1, tab2, tab3, tab4, tab5 = st.tabs([
"πΈ Product Scanner",
"π Planogram Creator",
"π₯ Live Monitor",
"π Analytics",
"π Training Results",
])
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# ββ TAB 1: PRODUCT SCANNER βββββββββββββββββββββββββββββββββββββββββββββββ
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
with tab1:
st.markdown('<div class="section-header">πΈ Product Scanner β Register Your Products</div>', unsafe_allow_html=True)
st.caption("Register products individually or bulk-scan a store shelf image to auto-detect and register all unique products.")
# ββ Choose Scan Mode ββββββββββββββββββββββββββββββββββββββββββββββ
scan_mode = st.radio(
"Scan Mode",
["π· Single Product Scan", "πͺ Bulk Store Shelf Scan"],
horizontal=True, key="scan_mode"
)
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# ββ MODE 1: SINGLE PRODUCT SCAN (with auto-crop + OCR) ββββββββββ
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
if scan_mode == "π· Single Product Scan":
catalog = load_catalog()
# ββ Model & Processing Toggles ββ
toggle_cols = st.columns(2)
with toggle_cols[0]:
dinov2_choice = st.radio(
"π§ DINOv2 Model",
["Pretrained (ViT-S/14)", "Fine-tuned (ViT-B/14)"],
horizontal=True, key="dinov2_choice",
help="Compare pretrained vs fine-tuned DINOv2 embeddings"
)
with toggle_cols[1]:
use_rembg = st.checkbox("βοΈ Use rembg (background removal)", value=True, key="use_rembg",
help="Toggle AI background removal before embedding")
col_cam, col_form = st.columns([1, 1])
with col_cam:
st.markdown("##### Capture Product Image")
img_source = st.radio(
"Image Source",
["π± Phone Camera", "π» Laptop Camera", "π Upload"],
horizontal=True, key="scanner_source"
)
captured_img = None
if img_source == "π± Phone Camera":
st.info("Point your phone at the product, then click capture π")
if st.button("πΈ Capture from Phone", type="primary", key="phone_cap_scanner"):
captured_img = capture_from_phone(phone_cam_url, phone_rotation)
if captured_img:
st.session_state["scanner_phone_img"] = captured_img
# Persist across reruns
if "scanner_phone_img" in st.session_state:
captured_img = st.session_state["scanner_phone_img"]
elif img_source == "π» Laptop Camera":
camera_photo = st.camera_input("Point camera at the product", key="scanner_cam")
if camera_photo:
captured_img = Image.open(camera_photo).convert("RGB")
st.session_state["scanner_captured_img"] = captured_img
elif "scanner_captured_img" in st.session_state:
captured_img = st.session_state["scanner_captured_img"]
else:
uploaded_photo = st.file_uploader(
"Upload product photo",
type=["jpg", "jpeg", "png"],
key="scanner_upload"
)
if uploaded_photo:
captured_img = Image.open(uploaded_photo).convert("RGB")
st.session_state["scanner_captured_img"] = captured_img
elif "scanner_captured_img" in st.session_state:
captured_img = st.session_state["scanner_captured_img"]
# Show captured image + barcode + optional OCR
if captured_img:
st.image(captured_img, caption="Captured product", width='stretch')
st.caption("π‘ *Tip: Frame the product close-up with the label/barcode facing the camera*")
# Check if we need to re-process (new image vs previously processed)
import hashlib
img_hash = hashlib.md5(np.array(captured_img).tobytes()[:10000]).hexdigest()
needs_processing = st.session_state.get("scanner_img_hash") != img_hash
if needs_processing:
st.session_state["scanner_img_hash"] = img_hash
# Auto-scan barcode + lookup product info
barcode_text = scan_barcode(captured_img)
barcode_info = {}
if barcode_text:
st.success(f"π **Barcode detected:** `{barcode_text}`")
with st.spinner("Looking up product info from barcode..."):
barcode_info = lookup_barcode_info(barcode_text)
if barcode_info.get("name"):
st.info(f"π·οΈ **Auto-identified:** {barcode_info['name']}")
if barcode_info.get("category"):
st.caption(f"Category: {barcode_info['category']}")
else:
st.caption("Product not found in online database β enter name manually")
else:
st.caption("No barcode found (flip product to show barcode, or enter name manually)")
# Optional OCR for name suggestion
ocr_text = ""
if st.checkbox("π€ Run OCR to read label text", value=False, key="run_ocr"):
ocr_reader = load_ocr()
if ocr_reader:
with st.spinner("Reading label text..."):
ocr_text = extract_text_from_crop(ocr_reader, captured_img)
if ocr_text:
st.info(f"π **OCR detected:** {ocr_text}")
else:
st.caption("No readable text found on label")
# ββ rembg Background Removal (conditional) ββ
import cv2
cropped_img = captured_img # fallback to full image
if use_rembg:
try:
from rembg import remove
with st.spinner("βοΈ Removing background (AI segmentation)..."):
result = remove(captured_img)
alpha = np.array(result.split()[-1])
coords = np.where(alpha > 30)
if len(coords[0]) > 0:
y_min, y_max = coords[0].min(), coords[0].max()
x_min, x_max = coords[1].min(), coords[1].max()
pad = 5
h, w = alpha.shape
x1 = max(0, x_min - pad)
y1 = max(0, y_min - pad)
x2 = min(w, x_max + pad)
y2 = min(h, y_max + pad)
cropped_img = captured_img.crop((x1, y1, x2, y2))
st.success("βοΈ Product perfectly cropped (AI background removal)")
st.image(cropped_img, caption="Auto-cropped product (display only β embedding uses original)", width=250)
else:
st.caption("βΉοΈ Using full image")
except ImportError:
st.caption("βΉοΈ rembg not installed β using full image")
except Exception as e:
st.caption(f"βΉοΈ Using full image (crop error: {e})")
else:
st.caption("βΉοΈ rembg OFF β using full image (no background removal)")
# Store for form submission
st.session_state["scanner_cropped"] = cropped_img
st.session_state["scanner_full_img"] = captured_img
auto_name = barcode_info.get("name", "") or ocr_text
st.session_state["scanner_ocr_text"] = auto_name
st.session_state["scanner_barcode"] = barcode_text or ""
st.session_state["scanner_barcode_info"] = barcode_info
# ββ Augmented Views Grid ββ
dinov2_model = load_dinov2_finetuned() if dinov2_choice == "Fine-tuned (ViT-B/14)" else load_dinov2()
if dinov2_model:
with st.spinner(f"Generating 15 augmented views ({dinov2_choice})..."):
# Always embed from ORIGINAL image (not rembg crop)
# rembg removes background β domain mismatch with shelf queries β lower scores
result = get_robust_embedding(dinov2_model, captured_img, return_views=True)
emb_vec, aug_views, view_names = result
st.session_state["scanner_aug_views"] = aug_views
st.session_state["scanner_view_names"] = view_names
st.session_state["scanner_embedding"] = emb_vec
else:
# Restore cached results (no re-processing needed)
cropped_img = st.session_state.get("scanner_cropped", captured_img)
if st.session_state.get("scanner_barcode"):
st.success(f"π **Barcode:** `{st.session_state['scanner_barcode']}`")
if st.session_state.get("scanner_ocr_text"):
st.info(f"π·οΈ **Product:** {st.session_state['scanner_ocr_text']}")
if cropped_img != captured_img:
st.image(cropped_img, caption="Auto-cropped product (display only)", width=250)
# Display augmented views (from cache or fresh)
aug_views = st.session_state.get("scanner_aug_views")
view_names = st.session_state.get("scanner_view_names")
if aug_views and view_names:
st.markdown(f"**π {len(aug_views)} Augmented Views ({dinov2_choice}):**")
for row_start in range(0, len(aug_views), 5):
row = st.columns(5)
for i in range(row_start, min(row_start + 5, len(aug_views))):
with row[i - row_start]:
st.image(aug_views[i], caption=view_names[i], width="content")
with col_form:
st.markdown("##### Product Details")
# Pre-fill name from OCR/barcode/voice if available
default_name = st.session_state.get("scanner_ocr_text", "")
default_barcode = st.session_state.get("scanner_barcode", "")
# Voice input via Web Speech API β auto-fills Name, Price, Category
import streamlit.components.v1 as components
components.html("""
<div style="margin-bottom:4px; font-family: 'Source Sans Pro', sans-serif;">
<button id="voiceBtn" onclick="startVoice()" style="
background: linear-gradient(135deg, #6366f1, #8b5cf6);
color: white; border: none; padding: 8px 16px;
border-radius: 8px; cursor: pointer; font-size: 14px;
display: inline-flex; align-items: center; gap: 6px;
">π€ Speak Product Details</button>
<span id="voiceStatus" style="color:#aaa; font-size:13px; margin-left:8px;">
Say: "Coca Cola 330ml price 40 rupees"
</span>
</div>
<script>
// Helper: set React-controlled input value
function setInput(input, value) {
const setter = Object.getOwnPropertyDescriptor(window.HTMLInputElement.prototype, 'value').set;
setter.call(input, value);
input.dispatchEvent(new Event('input', { bubbles: true }));
input.dispatchEvent(new Event('change', { bubbles: true }));
}
// Helper: find Streamlit input by placeholder or label text
function findInput(searchText) {
const doc = window.parent.document;
// Try by placeholder (text inputs)
let el = doc.querySelector('input[placeholder*="' + searchText + '"]');
if (el) return el;
// Try by label β walk up multiple parent levels to find the input
const labels = doc.querySelectorAll('label');
for (const lbl of labels) {
if (lbl.textContent.includes(searchText)) {
// Search in progressively larger parent containers
let container = lbl.parentElement;
for (let depth = 0; depth < 5 && container; depth++) {
el = container.querySelector('input[type="text"], input[type="number"], input:not([type])');
if (el) return el;
container = container.parentElement;
}
}
}
return null;
}
// Voice only handles name + price. Category is auto-detected
// from the product IMAGE using FAISS (Python-side, see below).
function startVoice() {
if (!('webkitSpeechRecognition' in window) && !('SpeechRecognition' in window)) {
document.getElementById('voiceStatus').innerText = 'β Speech not supported';
return;
}
const SpeechRecognition = window.SpeechRecognition || window.webkitSpeechRecognition;
const recognition = new SpeechRecognition();
recognition.lang = 'en-IN';
recognition.continuous = false;
recognition.interimResults = false;
document.getElementById('voiceBtn').style.background = '#ef4444';
document.getElementById('voiceBtn').innerHTML = 'π΄ Listening...';
document.getElementById('voiceStatus').innerText = 'Speak now...';
recognition.start();
recognition.onresult = function(event) {
let text = event.results[0][0].transcript;
document.getElementById('voiceBtn').style.background = 'linear-gradient(135deg, #6366f1, #8b5cf6)';
document.getElementById('voiceBtn').innerHTML = 'π€ Speak Product Details';
// Extract price β handles speech variants: rupees, ruppees, rupay, rs, price
let price = '';
const pricePatterns = [
/(?:price|βΉ|rs\.?)\s*(\d+\.?\d*)/i,
/(\d+\.?\d*)\s*(?:rupees?|ruppees?|rupay|rupaiye?|rs\.?)/i,
/(?:rupees?|ruppees?|rupay|rupaiye?)\s*(\d+\.?\d*)/i,
];
for (const pattern of pricePatterns) {
const m = text.match(pattern);
if (m) {
price = m[1];
text = text.replace(m[0], '').trim();
break;
}
}
// Clean up name β remove filler words
let productName = text.replace(/^\s*(the|a|an|its?|and)\s+/i, '').trim();
productName = productName.replace(/\s+/g, ' ').trim();
if (productName.length > 0) {
productName = productName.charAt(0).toUpperCase() + productName.slice(1);
}
document.getElementById('voiceStatus').innerText = 'β
' + productName + (price ? ' | βΉ' + price : '');
// Auto-fill Product Name
const nameInput = findInput('Coca-Cola');
if (nameInput) setInput(nameInput, productName);
// Auto-fill Price (number input)
if (price) {
const priceInput = findInput('Price');
if (priceInput) {
setInput(priceInput, price);
// Also dispatch for React number input
priceInput.dispatchEvent(new Event('blur', { bubbles: true }));
}
}
// Category is auto-detected from the product IMAGE
// via FAISS on the Python side (not from voice text)
};
recognition.onerror = function(e) {
document.getElementById('voiceStatus').innerText = 'β ' + e.error;
document.getElementById('voiceBtn').style.background = 'linear-gradient(135deg, #6366f1, #8b5cf6)';
document.getElementById('voiceBtn').innerHTML = 'π€ Speak Product Details';
};
}
</script>
""", height=50)
# ββ Auto-detect category from product image via FAISS ββ
categories = [
"Beverages", "Snacks", "Dairy", "Canned Goods",
"Bakery", "Cleaning", "Personal Care", "Frozen",
"Fruits & Vegetables", "Other"
]
suggested_category_idx = len(categories) - 1 # Default: "Other"
if "scanner_embedding" in st.session_state:
try:
catalog = load_catalog()
faiss_idx, faiss_prods = build_faiss_index(catalog)
if faiss_idx and faiss_prods:
query_emb = np.array([st.session_state["scanner_embedding"]], dtype=np.float32)
scores, indices = faiss_idx.search(query_emb, 1)
if float(scores[0][0]) >= 0.5:
matched_cat = faiss_prods[int(indices[0][0])].get("category", "Other")
if matched_cat in categories:
suggested_category_idx = categories.index(matched_cat)
st.caption(f"π§ Category auto-suggested from similar product (similarity: {float(scores[0][0]):.2f})")
except Exception:
pass
with st.form("product_form", clear_on_submit=True):
prod_name = st.text_input("Product Name *", value=default_name, placeholder="e.g., Coca-Cola 330ml (or use π€ above)")
prod_barcode = st.text_input("Barcode", value=default_barcode, placeholder="Auto-detected or enter manually")
p_cols = st.columns(2)
with p_cols[0]:
prod_price = st.number_input("Price (βΉ)", min_value=0.0, value=0.0, step=0.5)
with p_cols[1]:
prod_category = st.selectbox("Category", categories, index=suggested_category_idx)
submitted = st.form_submit_button("β
Register Product", type="primary", width='stretch')
if submitted and prod_name:
# Use auto-cropped image if available
reg_img = st.session_state.get("scanner_cropped", captured_img)
if reg_img:
with st.spinner("Registering product..."):
next_id = get_next_product_id()
sku_id = f"SKU_{next_id:04d}"
# Save product image
img_filename = f"{sku_id}_{re.sub(r'[^a-z0-9_]', '', prod_name.replace(' ', '_').lower())}.jpg"
img_path = REF_IMG_DIR / img_filename
reg_img.save(str(img_path), "JPEG", quality=90)
# Use pre-computed embedding from augmented views grid
embedding = None
if "scanner_embedding" in st.session_state:
embedding = st.session_state["scanner_embedding"].tolist()
else:
dinov2 = load_dinov2_finetuned() if dinov2_choice == "Fine-tuned (ViT-B/14)" else load_dinov2()
if dinov2:
emb_vec = get_robust_embedding(dinov2, reg_img)
embedding = emb_vec.tolist()
# Save to SQLite database
add_product(
sku=sku_id,
name=prod_name,
category=prod_category,
price=prod_price,
image_path=img_filename,
embedding=embedding,
barcode=prod_barcode if prod_barcode else None,
)
# Clear augmented views from session
for key in ["scanner_aug_views", "scanner_view_names", "scanner_embedding",
"scanner_cropped", "scanner_full_img", "scanner_ocr_text", "scanner_barcode"]:
st.session_state.pop(key, None)
st.success(f"β
**{prod_name}** registered as **{sku_id}** in database!")
st.rerun()
else:
st.warning("Please capture a product photo first.")
elif submitted and not prod_name:
st.warning("Please enter a product name.")
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# ββ MODE 2: BULK STORE SHELF SCAN ββββββββββββββββββββββββββββββββ
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
else:
st.markdown("""
**How it works:**
1οΈβ£ Capture/upload a store shelf image β 2οΈβ£ Detector finds all products β 3οΈβ£ DINOv2 clusters unique ones β
4οΈβ£ OCR reads text from each β 5οΈβ£ Label & register all unique products at once!
""")
# ββ Model Selector ββ
toggle_cols2 = st.columns(2)
with toggle_cols2[0]:
det_model_choice = st.radio(
"π§ Detection Model",
["β‘ YOLO26s (Fine-tuned)", "π― RF-DETR (Fine-tuned)"],
horizontal=True, key="det_model_choice",
help="Compare detection accuracy between YOLO26s and RF-DETR on the same shelf image"
)
with toggle_cols2[1]:
bulk_dinov2_choice = st.radio(
"π§ DINOv2 Model",
["Pretrained (ViT-S/14)", "Fine-tuned (ViT-B/14)"],
horizontal=True, key="bulk_dinov2_choice",
help="Compare pretrained vs fine-tuned DINOv2 for clustering"
)
# ββ Shelf Image Source (Phone / Laptop Camera / Upload) ββ
shelf_source = st.radio(
"Image Source",
["π± Phone Camera", "π» Laptop Camera", "π Upload"],
horizontal=True, key="bulk_shelf_source"
)
shelf_img = None
if shelf_source == "π± Phone Camera":
st.info("Point your phone at the store shelf, then click capture π")
if st.button("πΈ Capture Shelf from Phone", type="primary", key="phone_cap_bulk"):
phone_img = capture_from_phone(phone_cam_url, phone_rotation)
if phone_img:
st.session_state["bulk_shelf_phone_img"] = phone_img
if "bulk_shelf_phone_img" in st.session_state:
shelf_img = st.session_state["bulk_shelf_phone_img"]
elif shelf_source == "π» Laptop Camera":
camera_photo = st.camera_input("Point camera at the store shelf", key="bulk_shelf_cam")
if camera_photo:
shelf_img = Image.open(camera_photo).convert("RGB")
st.session_state["bulk_shelf_cam_img"] = shelf_img
elif "bulk_shelf_cam_img" in st.session_state:
shelf_img = st.session_state["bulk_shelf_cam_img"]
else:
shelf_upload = st.file_uploader(
"π€ Upload Store Shelf Image",
type=["jpg", "jpeg", "png"],
key="bulk_shelf_upload"
)
if shelf_upload:
shelf_img = Image.open(shelf_upload).convert("RGB")
st.session_state["bulk_shelf_upload_img"] = shelf_img
elif "bulk_shelf_upload_img" in st.session_state:
shelf_img = st.session_state["bulk_shelf_upload_img"]
if shelf_img:
# Detect if shelf image changed β clear stale results
import hashlib
shelf_hash = hashlib.md5(np.array(shelf_img).tobytes()[:10000]).hexdigest()
if st.session_state.get("bulk_shelf_hash") != shelf_hash:
st.session_state["bulk_shelf_hash"] = shelf_hash
# Clear old results when image changes
st.session_state.pop("bulk_unique_products", None)
st.session_state.pop("bulk_shelf_img", None)
st.image(shelf_img, caption=f"Shelf image ({shelf_img.size[0]}Γ{shelf_img.size[1]})", width='stretch')
# ββ Detection Confidence Threshold ββ
bulk_conf = st.slider(
"Detection Confidence Threshold", 0.15, 0.90, 0.35, 0.05,
key="bulk_conf_threshold",
help="Higher = fewer false positives (shadows, reflections). Lower = catches more products but may include noise."
)
# ββ Clustering Similarity Threshold ββ
cluster_thresh = st.slider(
"DINOv2 Clustering Similarity Threshold", 0.60, 0.95, 0.82, 0.01,
key="bulk_cluster_threshold",
help="Higher = more unique products (stricter grouping). Lower = fewer unique products (merges similar ones). Try 0.78-0.85 for best results."
)
# Detection + Clustering
if st.button("π Detect & Extract Unique Products", type="primary", key="bulk_detect"):
# Load selected detection model
if det_model_choice == "π― RF-DETR (Fine-tuned)":
det_model = load_rfdetr()
model_name = "RF-DETR"
else:
det_model = load_yolo()
model_name = "YOLO26s"
dinov2 = load_dinov2_finetuned() if bulk_dinov2_choice == "Fine-tuned (ViT-B/14)" else load_dinov2()
dinov2_label = "Fine-tuned + Projector (256-dim)" if bulk_dinov2_choice == "Fine-tuned (ViT-B/14)" else "Pretrained (384-dim)"
if det_model and dinov2:
with st.spinner(f"Step 1/2: Detecting products with {model_name}..."):
raw_crops = detect_all_products(shelf_img, det_model, conf=bulk_conf)
raw_count = len(raw_crops)
# ββ Filter false positives (shadows, reflections, non-products) ββ
img_w, img_h = shelf_img.size
img_area = img_w * img_h
min_crop_area = img_area * 0.001 # Must be at least 0.1% of image area
max_crop_area = img_area * 0.25 # Can't be more than 25% of image
min_dimension = 20 # Minimum 20px in both width and height
all_crops = []
filtered_count = 0
for crop, bbox, conf_score in raw_crops:
bx1, by1, bx2, by2 = bbox
crop_w = bx2 - bx1
crop_h = by2 - by1
crop_area = crop_w * crop_h
# Filter 1: Too small (shadows, noise, floor reflections)
if crop_area < min_crop_area or crop_w < min_dimension or crop_h < min_dimension:
filtered_count += 1
continue
# Filter 2: Too large (entire shelf detected as one object)
if crop_area > max_crop_area:
filtered_count += 1
continue
# Filter 3: Extreme aspect ratio (tube lights, shelf edges, signage)
aspect = max(crop_w, crop_h) / max(min(crop_w, crop_h), 1)
if aspect > 6.0: # Products rarely have >6:1 aspect ratio
filtered_count += 1
continue
all_crops.append((crop, bbox, conf_score))
if filtered_count > 0:
st.caption(f"π§Ή Filtered {filtered_count} false positives (shadows, reflections, non-products)")
st.info(f"π **{model_name}** detected **{len(all_crops)}** valid products (from {raw_count} raw detections)")
# Draw all detections on image
annotated = shelf_img.copy()
draw = ImageDraw.Draw(annotated)
for crop, bbox, conf_score in all_crops:
draw.rectangle(bbox, outline="lime", width=8)
st.image(annotated, caption=f"{model_name} β Filtered detections ({len(all_crops)} products)", width='stretch')
if all_crops:
with st.spinner(f"Step 2/2: Clustering unique products with DINOv2 {dinov2_label}..."):
unique, all_embeddings, cluster_labels = cluster_unique_products(all_crops, dinov2, similarity_threshold=cluster_thresh)
st.success(f"β
Found **{len(unique)}** unique product types from {len(all_crops)} detections")
# OCR disabled β user types product names manually during registration
for prod in unique:
prod["ocr_text"] = ""
# ββ Draw labeled annotated image with product names ββ
import colorsys
def generate_distinct_colors(n):
"""Generate N visually distinct colors using HSV space."""
colors = []
for i in range(n):
hue = i / max(n, 1) # evenly spaced hues
sat = 0.9 + (i % 2) * 0.1 # high saturation for vivid colors
val = 1.0 # full brightness for maximum visibility
r, g, b = colorsys.hsv_to_rgb(hue, min(sat, 1.0), val)
colors.append(f"#{int(r*255):02X}{int(g*255):02X}{int(b*255):02X}")
return colors
label_colors = generate_distinct_colors(len(unique))
labeled_img = shelf_img.copy()
labeled_draw = ImageDraw.Draw(labeled_img)
# Try to load a readable font β scale by image width
try:
from PIL import ImageFont
img_w = shelf_img.width
font_size = max(20, int(img_w / 60)) # ~35px on 2000px wide image
font = ImageFont.truetype("arial.ttf", font_size)
font_small = ImageFont.truetype("arialbd.ttf", font_size) # bold for labels
except Exception:
try:
font_small = ImageFont.truetype("arial.ttf", font_size)
except Exception:
font_small = ImageFont.load_default()
font = font_small
for i, prod in enumerate(unique):
color = label_colors[i % len(label_colors)]
label_name = prod.get("ocr_text", "")[:25] or f"Product_{i+1}"
prod["display_label"] = label_name # Store for later use
for bbox in prod.get("member_bboxes", [prod["bbox"]]):
x1, y1, x2, y2 = bbox
labeled_draw.rectangle(bbox, outline=color, width=8)
# Draw label background above the box
tag = f"#{i+1} {label_name}"
text_bbox = labeled_draw.textbbox((x1, y1), tag, font=font_small)
tw, th = text_bbox[2] - text_bbox[0], text_bbox[3] - text_bbox[1]
label_y = max(0, y1 - th - 10)
labeled_draw.rectangle([x1, label_y, x1 + tw + 12, label_y + th + 8], fill=color)
labeled_draw.text((x1 + 6, label_y + 4), tag, fill="white", font=font_small)
st.image(labeled_img, caption=f"Clustered β {len(unique)} unique products labeled", width='stretch')
# ββ t-SNE Embedding Visualization ββββββββββββββββββββββ
st.markdown("---")
st.markdown("##### 𧬠DINOv2 Embedding Space β t-SNE Visualization")
st.caption("Each dot = one detected product crop. Color = cluster assignment. Products in the same cluster share the same color.")
try:
from sklearn.manifold import TSNE
import plotly.graph_objects as go
n_samples = len(all_embeddings)
perplexity = min(30, max(2, n_samples - 1))
tsne = TSNE(n_components=2, perplexity=perplexity, random_state=42, max_iter=1000)
coords_2d = tsne.fit_transform(all_embeddings)
# Build cluster display names
cluster_names = []
for lbl in cluster_labels:
if lbl < len(unique):
name = unique[lbl].get("display_label", f"Product_{lbl+1}")
else:
name = f"Product_{lbl+1}"
cluster_names.append(f"#{lbl+1} {name}")
# Generate plotly colors matching label_colors
n_unique = len(unique)
plotly_colors = []
for i in range(n_unique):
hue = i / max(n_unique, 1)
sat = 0.9 + (i % 2) * 0.1
r, g, b = colorsys.hsv_to_rgb(hue, min(sat, 1.0), 1.0)
plotly_colors.append(f"rgb({int(r*255)},{int(g*255)},{int(b*255)})")
fig = go.Figure()
for cluster_id in range(n_unique):
mask = cluster_labels == cluster_id
if not np.any(mask):
continue
count = int(np.sum(mask))
name = unique[cluster_id].get("display_label", f"Product_{cluster_id+1}")
fig.add_trace(go.Scatter(
x=coords_2d[mask, 0],
y=coords_2d[mask, 1],
mode='markers',
marker=dict(
size=12,
color=plotly_colors[cluster_id % len(plotly_colors)],
line=dict(width=1, color='white'),
opacity=0.9
),
name=f"#{cluster_id+1} {name} (Γ{count})",
hovertemplate=f"#{cluster_id+1} {name}<br>x: %{{x:.2f}}<br>y: %{{y:.2f}}<extra></extra>"
))
fig.update_layout(
title=dict(
text=f"DINOv2 Embedding Clusters β {n_samples} crops β {n_unique} unique products",
font=dict(size=14, color='#ccd6f6')
),
xaxis_title="t-SNE Dimension 1",
yaxis_title="t-SNE Dimension 2",
plot_bgcolor='#0a192f',
paper_bgcolor='#0a192f',
font=dict(color='#8892b0'),
legend=dict(
bgcolor='rgba(10,25,47,0.8)',
bordercolor='#1e3a5f',
borderwidth=1,
font=dict(size=11)
),
xaxis=dict(gridcolor='#1e3a5f', zerolinecolor='#1e3a5f'),
yaxis=dict(gridcolor='#1e3a5f', zerolinecolor='#1e3a5f'),
height=500,
)
st.plotly_chart(fig, use_container_width=True)
except ImportError:
st.info("Install scikit-learn and plotly for embedding visualization: `pip install scikit-learn plotly`")
except Exception as e:
st.warning(f"t-SNE visualization error: {e}")
# Store in session state
st.session_state["bulk_unique_products"] = unique
st.session_state["bulk_shelf_img"] = shelf_img
else:
st.warning("No products detected. Try an image with more visible products.")
else:
st.error(f"{model_name} or DINOv2 not loaded. Check model files.")
# ββ Display unique products for labeling ββββββββββββββββββββββ
if "bulk_unique_products" in st.session_state:
unique_products = st.session_state["bulk_unique_products"]
st.markdown("---")
st.markdown(f"##### π·οΈ Label & Register Unique Products ({len(unique_products)} found)")
st.caption("Review each unique product, edit the auto-suggested name, and register them all.")
# Collect labels
product_labels = []
for i, prod in enumerate(unique_products):
with st.container():
cols = st.columns([1, 2, 1, 1, 1])
with cols[0]:
st.image(prod["crop"], width=100)
with cols[1]:
# Auto-suggest name from OCR
suggested = prod.get("ocr_text", "")[:50] if prod.get("ocr_text") else f"Product_{i+1}"
if not suggested.strip():
suggested = f"Product_{i+1}"
name = st.text_input(
f"Name",
value=suggested,
key=f"bulk_name_{i}",
label_visibility="collapsed",
placeholder="Product name"
)
if prod.get("ocr_text"):
st.caption(f"π OCR: {prod['ocr_text'][:80]}")
with cols[2]:
category = st.selectbox("Category", [
"Beverages", "Snacks", "Dairy", "Canned Goods",
"Bakery", "Cleaning", "Personal Care", "Frozen",
"Fruits & Vegetables", "Other"
], key=f"bulk_cat_{i}", label_visibility="collapsed")
with cols[3]:
geo = prod.get("geometry", {})
st.caption(f"π {geo.get('width', 0)}Γ{geo.get('height', 0)}")
st.caption(f"π’ Γ{prod['count']} instances")
with cols[4]:
include = st.checkbox("Register", value=True, key=f"bulk_inc_{i}")
product_labels.append({
"name": name, "category": category,
"include": include, "idx": i
})
# Register all button
st.markdown("---")
if st.button("β
Register All Selected Products", type="primary", key="bulk_register"):
dinov2 = load_dinov2()
registered_count = 0
progress = st.progress(0, text="Registering products...")
selected = [p for p in product_labels if p["include"] and p["name"].strip()]
for j, label in enumerate(selected):
prod = unique_products[label["idx"]]
next_id = get_next_product_id()
sku_id = f"SKU_{next_id:04d}"
# Save crop image
img_filename = f"{sku_id}_{re.sub(r'[^a-z0-9_]', '', label['name'].replace(' ', '_').lower())}.jpg"
img_path = REF_IMG_DIR / img_filename
prod["crop"].save(str(img_path), "JPEG", quality=90)
# Compute robust 15-view averaged embedding for registration
progress.progress((j + 0.5) / len(selected), text=f"Computing robust embedding for {label['name']}...")
if dinov2:
embedding = get_robust_embedding(dinov2, prod["crop"]).tolist()
else:
embedding = prod.get("embedding") # fallback to single-view
# Save to database
add_product(
sku=sku_id,
name=label["name"],
category=label["category"],
price=0.0,
image_path=img_filename,
embedding=embedding,
)
registered_count += 1
progress.progress((j + 1) / len(selected), text=f"Registered {label['name']}...")
progress.empty()
st.success(f"β
Registered **{registered_count}** products from store shelf!")
# Clear session state
if "bulk_unique_products" in st.session_state:
del st.session_state["bulk_unique_products"]
st.rerun()
# ββ FAISS Similarity Visualization βββββββββββββββββββββββββββββ
st.markdown("---")
st.markdown("##### π FAISS Embedding Similarity Analysis")
st.caption("Each detected unique product is queried against the catalog. Shows how well DINOv2 embeddings differentiate products.")
catalog = load_catalog()
if len(catalog["products"]) >= 2:
import faiss
faiss_index, faiss_products = build_faiss_index(catalog)
if faiss_index and faiss_products:
# Determine index dimension for compatibility check
index_dim = faiss_index.d
# Load matching DINOv2 model if needed for re-embedding
reembed_model = None # Will be loaded lazily on dimension mismatch
for i, prod in enumerate(unique_products):
emb = prod.get("embedding")
if emb is None:
continue
emb = np.array(emb, dtype=np.float32)
# ββ Dimension mismatch guard ββ
if emb.shape[0] != index_dim:
# Re-embed the crop with the model that matches the catalog
if reembed_model is None:
if index_dim == 256:
reembed_model = load_dinov2_finetuned()
else:
reembed_model = load_dinov2()
if reembed_model is not None:
emb = get_embedding(reembed_model, prod["crop"])
emb = np.array(emb, dtype=np.float32)
else:
continue # Skip if we can't match dimensions
label_name = prod.get("display_label", prod.get("ocr_text", "")[:25] or f"Product_{i+1}")
# Query FAISS for top-3 matches
query = np.array([emb], dtype=np.float32)
k = min(3, len(faiss_products))
scores, indices = faiss_index.search(query, k)
with st.container():
st.markdown(f"**#{i+1} {label_name}** (Γ{prod['count']} instances)")
match_cols = st.columns([1] + [1] * k + [1])
# Query image
with match_cols[0]:
st.image(prod["crop"], caption="π Query", width=100)
# Top matches from catalog
for m in range(k):
idx = int(indices[0][m])
score = float(scores[0][m])
matched = faiss_products[idx]
with match_cols[m + 1]:
# Try to load catalog image
cat_img_path = REF_IMG_DIR / matched.get("image_path", "")
if cat_img_path.exists():
cat_img = Image.open(str(cat_img_path)).convert("RGB")
st.image(cat_img, width=100)
else:
st.caption("πΌοΈ No image")
# Color code similarity
if score >= 0.85:
badge = f"π’ {score:.3f}"
elif score >= 0.65:
badge = f"π‘ {score:.3f}"
else:
badge = f"π΄ {score:.3f}"
st.caption(f"{badge}\n{matched.get('name', 'Unknown')[:20]}")
with match_cols[-1]:
best_score = float(scores[0][0])
if best_score >= 0.85:
st.success("β
Match")
elif best_score >= 0.65:
st.warning("β οΈ Weak")
else:
st.error("β New")
st.markdown("---")
else:
st.info("βΉοΈ FAISS index empty β register products first to see similarity analysis.")
else:
st.info("βΉοΈ Need 2+ registered products in catalog to run FAISS analysis.")
# ββ Product Gallery (shared between both modes) βββββββββββββββββββ
st.markdown("---")
catalog = load_catalog()
n_products = len(catalog["products"])
has_embeddings = sum(1 for p in catalog["products"] if p.get("embedding"))
g1, g2, g3 = st.columns(3)
with g1:
st.markdown(f"""<div class="metric-card">
<div class="metric-value">{n_products}</div>
<div class="metric-label">Products Registered</div>
</div>""", unsafe_allow_html=True)
with g2:
st.markdown(f"""<div class="metric-card">
<div class="metric-value">{has_embeddings}</div>
<div class="metric-label">Embeddings Ready</div>
</div>""", unsafe_allow_html=True)
with g3:
status = "β
Ready" if has_embeddings >= 2 else "β³ Need 2+ products"
st.markdown(f"""<div class="metric-card">
<div class="metric-value" style="font-size: 1.4rem;">{status}</div>
<div class="metric-label">FAISS Index Status</div>
</div>""", unsafe_allow_html=True)
if catalog["products"]:
st.markdown("##### ποΈ Registered Products")
# Select all / Clear all / Delete buttons
btn_cols = st.columns([1, 1, 1, 2])
with btn_cols[0]:
select_all = st.button("βοΈ Select All")
with btn_cols[1]:
clear_sel = st.button("β¬ Clear Selection")
with btn_cols[2]:
if st.button("ποΈ Clear ALL", type="secondary"):
clear_all_products()
for f in REF_IMG_DIR.glob("*.jpg"):
f.unlink()
st.rerun()
# Initialize selection state
if "del_selected" not in st.session_state:
st.session_state["del_selected"] = set()
# Select All / Clear: must set each checkbox key BEFORE they render
if select_all:
for p in catalog["products"]:
st.session_state[f"chk_{p['sku']}"] = True
st.session_state["del_selected"] = {p["sku"] for p in catalog["products"]}
if clear_sel:
for p in catalog["products"]:
st.session_state[f"chk_{p['sku']}"] = False
st.session_state["del_selected"] = set()
# Product list with checkboxes + images
for product in catalog["products"]:
sku = product["sku"]
cols = st.columns([0.3, 0.5, 2, 1.5, 1])
with cols[0]:
is_checked = st.checkbox("", key=f"chk_{sku}", label_visibility="collapsed")
if is_checked:
st.session_state["del_selected"].add(sku)
else:
st.session_state["del_selected"].discard(sku)
with cols[1]:
img_file = product.get("image_path", product.get("image", ""))
if img_file:
img_path = REF_IMG_DIR / img_file
if img_path.is_file():
st.image(str(img_path), width=60)
else:
st.caption("πΌοΈ")
else:
st.caption("πΌοΈ")
with cols[2]:
emb_icon = "β
" if product.get("embedding") else "β"
st.markdown(f"**{product['name']}** {emb_icon}")
with cols[3]:
st.caption(f"{sku} Β· {product.get('category', 'Other')}")
with cols[4]:
st.caption(f"βΉ{product.get('price', 0):.0f}")
# Delete selected button
selected = st.session_state.get("del_selected", set())
if selected:
st.markdown("---")
if st.button(f"ποΈ Delete {len(selected)} Selected Product(s)", type="primary"):
for sku in selected:
prod = next((p for p in catalog["products"] if p["sku"] == sku), None)
if prod:
img_file = prod.get("image_path", "")
if img_file:
img_p = REF_IMG_DIR / img_file
if img_p.is_file():
img_p.unlink()
delete_product(sku)
st.session_state["del_selected"] = set()
st.rerun()
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# ββ TAB 2: PLANOGRAM CREATOR βββββββββββββββββββββββββββββββββββββββββββββ
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
with tab2:
st.markdown('<div class="section-header">π Planogram Creator β Build Shelf Layouts</div>', unsafe_allow_html=True)
st.caption("Create planograms by auto-detecting from a shelf image OR manually defining product positions.")
catalog = load_catalog()
n_products = len([p for p in catalog["products"] if p.get("embedding")])
if n_products < 2:
st.warning(f"β οΈ Register at least 2 products in the **Product Scanner** tab first. Currently: {n_products} products.")
plano_mode = None
else:
st.success(f"β
{n_products} products in database β ready to create planograms!", icon="β
")
# If an edit was triggered, default to Manual Editor
plano_modes = ["πΈ Auto-Detect from Shelf Image", "βοΈ Manual Planogram Editor"]
default_mode_idx = 1 if "edit_planogram_data" in st.session_state else 0
# Mode selection
plano_mode = st.radio(
"Creation Mode",
plano_modes,
index=default_mode_idx,
horizontal=True, key="plano_mode"
)
# ββ MANUAL PLANOGRAM EDITOR ββββββββββββββββββββββββββββββββββ
if plano_mode == "βοΈ Manual Planogram Editor":
# Check if editing existing planogram
editing = "edit_planogram_data" in st.session_state
edit_data = st.session_state.get("edit_planogram_data", {})
edit_name = st.session_state.get("edit_planogram_name", "")
if editing:
st.markdown(f"##### βοΈ Editing Planogram: **{edit_name}**")
st.caption("Modify products and quantities, then click Update to save changes.")
if st.button("β Cancel Edit", key="cancel_edit"):
st.session_state.pop("edit_planogram_name", None)
st.session_state.pop("edit_planogram_data", None)
st.rerun()
else:
st.markdown("##### βοΈ Manual Planogram Builder")
st.caption("Select products and quantities for each shelf. 100% accurate β you define exactly what goes where.")
# Pre-fill name and shelves from edit data
default_name = edit_name if editing else "Manual_Shelf_1"
default_shelves = edit_data.get("n_shelves", 2) if editing else 2
manual_name = st.text_input("Planogram Name", value=default_name, key="manual_plano_name")
n_shelves = st.number_input("Number of Shelves", min_value=1, max_value=10, value=int(default_shelves), key="manual_n_shelves")
product_names = [p["name"] for p in catalog["products"] if p.get("embedding")]
product_lookup = {p["name"]: p for p in catalog["products"] if p.get("embedding")}
# Build default selections from edit data
edit_shelves = edit_data.get("shelves", []) if editing else []
manual_shelves = []
for shelf_idx in range(int(n_shelves)):
st.markdown(f"---\n**Shelf {shelf_idx + 1}**")
# Get default products and quantities for this shelf from edit data
default_products = []
default_quantities = {}
if shelf_idx < len(edit_shelves):
shelf_data = edit_shelves[shelf_idx]
from collections import Counter
name_counts = Counter(p["name"] for p in shelf_data.get("products", []))
default_products = [n for n in name_counts.keys() if n in product_names]
default_quantities = dict(name_counts)
shelf_cols = st.columns([3, 1])
with shelf_cols[0]:
selected_products = st.multiselect(
f"Products on Shelf {shelf_idx + 1}",
product_names,
default=default_products,
key=f"manual_shelf_{shelf_idx}_products"
)
with shelf_cols[1]:
quantities = {}
for prod_name in selected_products:
default_qty = default_quantities.get(prod_name, 1)
qty = st.number_input(
f"Qty: {prod_name[:15]}", min_value=1, max_value=20, value=int(default_qty),
key=f"manual_qty_{shelf_idx}_{prod_name}"
)
quantities[prod_name] = qty
shelf_products = []
pos = 0
for prod_name in selected_products:
prod = product_lookup[prod_name]
for _ in range(quantities.get(prod_name, 1)):
shelf_products.append({
"position": pos,
"sku": prod["sku"],
"name": prod["name"],
"confidence": 1.0,
"bbox": [0, 0, 0, 0],
})
pos += 1
manual_shelves.append({
"level": shelf_idx + 1,
"product_count": len(shelf_products),
"products": shelf_products,
})
if shelf_products:
from collections import Counter
counts = Counter(p["name"] for p in shelf_products)
summary = ", ".join(f"{n} Γ{c}" for n, c in counts.items())
st.info(f"π¦ Shelf {shelf_idx+1}: {summary}")
st.markdown("---")
total_manual = sum(s["product_count"] for s in manual_shelves)
if total_manual > 0:
btn_label = f"β
Update Planogram" if editing else "β
Save Manual Planogram"
if st.button(btn_label, type="primary", width='stretch'):
# If editing with a new name, delete the old one
if editing and manual_name != edit_name:
delete_planogram(edit_name)
old_ref = PLANOGRAM_DIR / f"{edit_name}_reference.jpg"
old_ref.unlink(missing_ok=True)
planogram_data = {
"name": manual_name,
"created_at": datetime.now().isoformat(),
"n_shelves": int(n_shelves),
"total_products": total_manual,
"shelves": manual_shelves,
}
save_planogram(manual_name, planogram_data)
# Clear edit state
st.session_state.pop("edit_planogram_name", None)
st.session_state.pop("edit_planogram_data", None)
action = "Updated" if editing else "Saved"
st.success(f"β
{action} planogram **{manual_name}** with {int(n_shelves)} shelves and {total_manual} products!")
st.balloons()
else:
st.info("Add products to at least one shelf to save.")
# ββ AUTO-DETECT FROM IMAGE βββββββββββββββββββββββββββββββββββ
elif plano_mode == "πΈ Auto-Detect from Shelf Image":
st.success(f"β
{n_products} products in database β ready to create planograms!", icon="β
")
# Shelf name and image
plano_cols = st.columns([1, 2])
with plano_cols[0]:
shelf_name = st.text_input("Shelf Name", value="Shelf_1", placeholder="e.g., Aisle_1_Shelf_A")
shelf_image_source = st.radio(
"Image Source",
["π± Phone Camera", "π» Laptop Camera", "π Upload"],
horizontal=True, key="plano_source"
)
with plano_cols[1]:
shelf_image = None
if shelf_image_source == "π± Phone Camera":
st.info("Point your phone at the arranged shelf, then click capture π")
if st.button("πΈ Capture Shelf from Phone", type="primary", key="phone_cap_plano"):
shelf_image = capture_from_phone(phone_cam_url, phone_rotation)
if shelf_image:
st.session_state["plano_phone_img"] = shelf_image
# Persist across reruns
if "plano_phone_img" in st.session_state:
shelf_image = st.session_state["plano_phone_img"]
st.image(shelf_image, caption="Captured shelf", width='stretch')
elif shelf_image_source == "π» Laptop Camera":
cam_img = st.camera_input("Capture your arranged shelf", key="plano_cam")
if cam_img:
shelf_image = Image.open(cam_img).convert("RGB")
else:
uploaded = st.file_uploader("Upload shelf photo", type=["jpg", "jpeg", "png"], key="plano_upload")
if uploaded:
shelf_image = Image.open(uploaded).convert("RGB")
if shelf_image:
with st.spinner("π Analyzing shelf layout..."):
yolo = load_yolo()
dinov2 = load_dinov2()
if yolo and dinov2:
# Step 1: Detect products
detections, results = run_detection(yolo, shelf_image, conf=0.25)
# Step 2: Detect shelf levels
boundaries, n_shelves = detect_shelf_levels(detections, shelf_image.height)
# Step 3: Assign to shelves
shelf_assignments = assign_to_shelves(detections, boundaries)
# Step 4: Identify each product
faiss_index, index_products = build_faiss_index(catalog)
product_labels = []
for det in detections:
x1, y1, x2, y2 = [int(c) for c in det["bbox"]]
crop = shelf_image.crop((x1, y1, x2, y2))
emb = get_embedding(dinov2, crop)
match, score = search_product_with_size(
faiss_index, index_products, emb,
query_bbox=(x1, y1, x2, y2),
threshold=0.3
)
if match:
det["product_name"] = match["name"]
det["product_sku"] = match["sku"]
det["match_score"] = score
product_labels.append(match["name"])
else:
det["product_name"] = f"Unknown_{len(product_labels)+1}"
det["product_sku"] = "UNKNOWN"
det["match_score"] = score
product_labels.append(f"Unknown")
# Show annotated image
annotated = draw_annotated_image(shelf_image, detections, product_labels)
st.image(annotated, caption=f"Detected {len(detections)} products on {n_shelves} shelves", width='stretch')
# Show detected layout
st.markdown("##### π Auto-Detected Layout")
planogram_data = {
"name": shelf_name,
"created_at": datetime.now().isoformat(),
"n_shelves": n_shelves,
"total_products": len(detections),
"shelves": [],
}
for shelf_id in sorted(shelf_assignments.keys()):
shelf_dets = shelf_assignments[shelf_id]
products_on_shelf = []
product_summary = []
for pos, det in enumerate(shelf_dets):
products_on_shelf.append({
"position": pos,
"sku": det.get("product_sku", "UNKNOWN"),
"name": det.get("product_name", "Unknown"),
"confidence": round(det.get("match_score", 0), 3),
"bbox": det["bbox"],
})
product_summary.append(det.get("product_name", "Unknown"))
planogram_data["shelves"].append({
"level": shelf_id,
"product_count": len(shelf_dets),
"products": products_on_shelf,
})
# Count products by name
from collections import Counter
counts = Counter(product_summary)
summary = ", ".join(f"{name} Γ{count}" for name, count in counts.items())
st.markdown(f'<div class="alert-info"><strong>Shelf {shelf_id}:</strong> {summary} ({len(shelf_dets)} products)</div>', unsafe_allow_html=True)
# Confirm button
st.markdown("")
c1, c2 = st.columns(2)
with c1:
if st.button("β
Confirm as Planogram", type="primary", width='stretch'):
save_planogram(shelf_name, planogram_data)
# Also save the reference image
shelf_image.save(str(PLANOGRAM_DIR / f"{shelf_name}_reference.jpg"), "JPEG", quality=90)
st.success(f"β
Planogram **{shelf_name}** saved with {n_shelves} shelves and {len(detections)} products!")
st.balloons()
with c2:
if st.button("π Re-scan", width='stretch'):
st.rerun()
# Show existing planograms
planograms = load_planograms()
if planograms:
st.markdown("---")
st.markdown("##### π Saved Planograms")
for name, data in planograms.items():
with st.expander(f"π {name} β {data.get('n_shelves', '?')} shelves, {data.get('total_products', '?')} products"):
for shelf in data.get("shelves", []):
products = [p["name"] for p in shelf.get("products", [])]
from collections import Counter
counts = Counter(products)
summary = ", ".join(f"{n} Γ{c}" for n, c in counts.items())
st.markdown(f"**Shelf {shelf['level']}:** {summary}")
# Reference image
ref_img = PLANOGRAM_DIR / f"{name}_reference.jpg"
if ref_img.exists():
st.image(str(ref_img), caption=f"Reference: {name}", width='stretch')
btn_cols = st.columns([1, 1, 3])
with btn_cols[0]:
if st.button(f"βοΈ Edit {name}", key=f"edit_{name}"):
# Load planogram data into session state for editing
st.session_state["edit_planogram_name"] = name
st.session_state["edit_planogram_data"] = data
st.rerun()
with btn_cols[1]:
if st.button(f"ποΈ Delete {name}", key=f"del_{name}"):
delete_planogram(name)
ref_img.unlink(missing_ok=True)
st.rerun()
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# ββ TAB 3: LIVE MONITORING βββββββββββββββββββββββββββββββββββββββββββββββ
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
with tab3:
st.markdown('<div class="section-header">π₯ Live Shelf Monitoring β Real-Time Compliance</div>', unsafe_allow_html=True)
st.caption("Start monitoring to continuously watch your shelf and auto-detect planogram violations.")
planograms = load_planograms()
catalog = load_catalog()
n_products = len([p for p in catalog["products"] if p.get("embedding")])
if not planograms:
st.warning("β οΈ Create a planogram first in the **Planogram Creator** tab.")
elif n_products < 2:
st.warning("β οΈ Register products first in the **Product Scanner** tab.")
else:
# Controls
ctrl_cols = st.columns([2, 1, 1])
with ctrl_cols[0]:
selected_planogram = st.selectbox("Select Planogram to Check Against", list(planograms.keys()))
with ctrl_cols[1]:
conf_threshold = st.slider("Detection Confidence", 0.15, 0.9, 0.25, 0.05)
with ctrl_cols[2]:
ntfy_topic = st.text_input("Notification Topic", value="shelfmind-alerts", help="Install ntfy app β subscribe to this topic")
st.session_state["ntfy_topic"] = ntfy_topic
scan_interval = st.slider("Scan Interval (seconds)", 3, 30, 5, 1, help="How often to capture and analyze a new frame")
# Camera source selection
cam_source = st.radio(
"Camera Source",
["π» Laptop Webcam", "π± Phone Camera (IP Webcam)"],
horizontal=True,
help="Use IP Webcam app on Android for better quality"
)
ip_cam_url = ""
if cam_source == "π± Phone Camera (IP Webcam)":
ip_cam_url = st.text_input(
"IP Webcam URL",
value="http://192.168.1.5:8080/video",
help="Install 'IP Webcam' app on Android β Start Server β Use the URL shown"
)
st.markdown("""<div class="alert-info">
<strong>π± Setup IP Webcam:</strong><br>
1. Install <strong>IP Webcam</strong> app on Android from Play Store<br>
2. Open app β scroll down β tap <strong>Start Server</strong><br>
3. Note the URL shown (e.g., http://192.168.1.5:8080)<br>
4. Add <strong>/video</strong> at the end and paste above<br>
5. Make sure phone & laptop are on <strong>same WiFi</strong>
</div>""", unsafe_allow_html=True)
st.markdown("---")
# Real-time monitoring controls
btn_cols = st.columns([1, 1, 2])
with btn_cols[0]:
start_monitoring = st.button("βΆοΈ Start Live Monitoring", type="primary", width='stretch')
with btn_cols[1]:
stop_monitoring = st.button("βΉοΈ Stop Monitoring", width='stretch')
if stop_monitoring:
st.session_state["monitoring_active"] = False
if start_monitoring:
st.session_state["monitoring_active"] = True
# Initialize session state
if "monitoring_active" not in st.session_state:
st.session_state["monitoring_active"] = False
if "last_alert_time" not in st.session_state:
st.session_state["last_alert_time"] = 0
# Placeholders for real-time updates
status_indicator = st.empty()
metric_row = st.empty()
frame_display = st.empty()
compliance_report = st.empty()
alert_log = st.empty()
chart_display = st.empty()
if st.session_state.get("monitoring_active", False):
import cv2
planogram = planograms[selected_planogram]
yolo = load_yolo()
dinov2 = load_dinov2()
faiss_index, index_products = build_faiss_index(catalog)
if not yolo or not dinov2 or faiss_index is None:
st.error("β Models not loaded. Please check YOLO and DINOv2.")
st.session_state["monitoring_active"] = False
else:
# Determine camera source
use_phone = cam_source == "π± Phone Camera (IP Webcam)" and ip_cam_url
cam_label = f"Phone Camera ({ip_cam_url})" if use_phone else "Laptop Webcam"
status_indicator.markdown(
f'<div class="alert-ok"><strong>π’ LIVE MONITORING ACTIVE</strong> β {cam_label} is watching the shelf. Any violation will trigger an alert.</div>',
unsafe_allow_html=True
)
# For phone: use HTTP shot grab (more reliable than video stream)
# For laptop: use OpenCV VideoCapture
cap = None
if not use_phone:
cap = cv2.VideoCapture(0)
if not cap.isOpened():
st.error(f"β Cannot access laptop webcam.")
st.session_state["monitoring_active"] = False
scan_count = 0
retry_count = 0
max_retries = 5
# Multi-frame voting buffer: stores per-shelf SKU counts from last N frames
VOTE_BUFFER_SIZE = 3
detection_history = [] # List of per-frame shelf SKU counts
if st.session_state.get("monitoring_active", False):
try:
while st.session_state.get("monitoring_active", False):
monitor_image = None
if use_phone:
# Grab single frame via HTTP (reliable, no stream drops)
monitor_image = capture_from_phone(ip_cam_url, phone_rotation if 'phone_rotation' in dir() else 90)
if not monitor_image:
retry_count += 1
if retry_count > max_retries:
status_indicator.markdown(
'<div class="alert-critical"><strong>β Phone camera unreachable after 5 retries.</strong> Check IP Webcam app.</div>',
unsafe_allow_html=True
)
break
status_indicator.markdown(
f'<div class="alert-warning"><strong>β οΈ Reconnecting to phone... (attempt {retry_count}/{max_retries})</strong></div>',
unsafe_allow_html=True
)
time.sleep(3)
continue
else:
retry_count = 0 # Reset on success
else:
ret, frame = cap.read()
if not ret:
status_indicator.markdown(
'<div class="alert-critical"><strong>β Camera feed lost.</strong> Reconnecting...</div>',
unsafe_allow_html=True
)
time.sleep(2)
continue
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
monitor_image = Image.fromarray(frame_rgb)
scan_count += 1
current_time = datetime.now()
# Resize for performance
max_dim = 640
if monitor_image.width > max_dim:
ratio = max_dim / monitor_image.width
monitor_image = monitor_image.resize(
(max_dim, int(monitor_image.height * ratio)),
Image.LANCZOS,
)
# ββ DETECTION ββββββββββββββββββββββββββββββββββββββ
detections, _ = run_detection(yolo, monitor_image, conf=conf_threshold)
boundaries, n_shelves = detect_shelf_levels(detections, monitor_image.height)
shelf_assignments = assign_to_shelves(detections, boundaries)
# ββ SKU IDENTIFICATION (DINOv2 + Size-Ratio Fusion) ββ
# Collect all expected products from planogram for size comparison
all_expected_products = []
for ps in planogram.get("shelves", []):
all_expected_products.extend(ps.get("products", []))
for det in detections:
x1, y1, x2, y2 = [int(c) for c in det["bbox"]]
crop = monitor_image.crop((max(0, x1), max(0, y1), x2, y2))
emb = get_embedding(dinov2, crop)
match, score = search_product_with_size(
faiss_index, index_products, emb,
query_bbox=(x1, y1, x2, y2),
expected_products_on_shelf=all_expected_products,
threshold=0.3,
size_weight=0.15
)
if match:
det["product_name"] = match["name"]
det["product_sku"] = match["sku"]
det["product_price"] = match.get("price", 0)
det["match_score"] = round(score, 3)
det["status"] = "match"
else:
det["product_name"] = "Unknown"
det["product_sku"] = "UNKNOWN"
det["product_price"] = 0
det["match_score"] = round(score, 3)
det["status"] = "unknown"
# ββ MULTI-FRAME VOTING ββββββββββββββββββββββββββββ
# Collect this frame's per-shelf SKU counts
from collections import Counter
frame_shelf_counts = {}
for shelf_id, shelf_dets in shelf_assignments.items():
sku_counts = Counter(
d.get("product_sku", "UNKNOWN") for d in shelf_dets
if d.get("product_sku") != "UNKNOWN"
)
frame_shelf_counts[shelf_id] = dict(sku_counts)
detection_history.append(frame_shelf_counts)
if len(detection_history) > VOTE_BUFFER_SIZE:
detection_history.pop(0)
# Voted counts: for each shelf+SKU, use median count across frames
voted_shelf_counts = {}
if len(detection_history) >= 2:
all_shelf_ids = set()
for fsc in detection_history:
all_shelf_ids.update(fsc.keys())
for sid in all_shelf_ids:
all_skus = set()
for fsc in detection_history:
all_skus.update(fsc.get(sid, {}).keys())
voted_shelf_counts[sid] = {}
for sku in all_skus:
counts_across_frames = [
fsc.get(sid, {}).get(sku, 0)
for fsc in detection_history
]
# Use median (robust to outliers)
voted_shelf_counts[sid][sku] = int(
sorted(counts_across_frames)[len(counts_across_frames) // 2]
)
# ββ PLANOGRAM COMPARISON (uses voted counts when available) β
plan_shelves = planogram.get("shelves", [])
all_alerts = []
shelf_compliance = {}
total_expected = 0
total_matched = 0
for plan_shelf in plan_shelves:
shelf_id = plan_shelf["level"]
expected_products = plan_shelf.get("products", [])
detected_on_shelf = shelf_assignments.get(shelf_id, [])
from collections import Counter
expected_counts = Counter(p["sku"] for p in expected_products if p["sku"] != "UNKNOWN")
# Use voted counts if available (multi-frame), else raw
if voted_shelf_counts and shelf_id in voted_shelf_counts:
detected_counts = Counter(voted_shelf_counts[shelf_id])
else:
detected_counts = Counter(d.get("product_sku", "UNKNOWN") for d in detected_on_shelf if d.get("product_sku") != "UNKNOWN")
issues = []
shelf_expected = len(expected_products)
shelf_matched = 0
revenue_at_risk = 0
for sku, expected_count in expected_counts.items():
detected_count = detected_counts.get(sku, 0)
prod_name = next((p["name"] for p in expected_products if p["sku"] == sku), sku)
prod_price = next((d.get("product_price", 0) for d in detected_on_shelf if d.get("product_sku") == sku), 0)
if detected_count == 0:
issues.append(f"π΄ **{prod_name}** β MISSING (expected {expected_count}, found 0)")
revenue_at_risk += prod_price * expected_count
all_alerts.append({
"type": "STOCKOUT", "shelf": shelf_id,
"product": prod_name, "sku": sku,
"expected": expected_count, "found": 0,
"revenue": prod_price * expected_count,
"priority": "CRITICAL",
})
elif detected_count < expected_count:
missing = expected_count - detected_count
issues.append(f"β οΈ **{prod_name}** β LOW STOCK ({detected_count}/{expected_count} facings)")
revenue_at_risk += prod_price * missing
all_alerts.append({
"type": "LOW_STOCK", "shelf": shelf_id,
"product": prod_name, "sku": sku,
"expected": expected_count, "found": detected_count,
"revenue": prod_price * missing,
"priority": "HIGH",
})
shelf_matched += detected_count
else:
shelf_matched += expected_count
for sku, count in detected_counts.items():
if sku not in expected_counts:
prod_name = next((d.get("product_name", sku) for d in detected_on_shelf if d.get("product_sku") == sku), sku)
issues.append(f"π« **{prod_name}** β UNAUTHORIZED (not in planogram)")
all_alerts.append({
"type": "UNAUTHORIZED", "shelf": shelf_id,
"product": prod_name, "sku": sku,
"priority": "MEDIUM",
})
# ββ POSITION/ORDER CHECK ββββββββββββββββββββββββββ
# Compare left-to-right order of detected vs expected
expected_order = [p["sku"] for p in expected_products if p["sku"] != "UNKNOWN"]
detected_sorted = sorted(
[d for d in detected_on_shelf if d.get("product_sku", "UNKNOWN") != "UNKNOWN"],
key=lambda d: d["bbox"][0] # Sort by x1 (left to right)
)
detected_order = [d.get("product_sku") for d in detected_sorted]
# Check if order matches
if expected_order and detected_order:
min_len = min(len(expected_order), len(detected_order))
for pos_idx in range(min_len):
if expected_order[pos_idx] != detected_order[pos_idx]:
# Find what's at this position
expected_name = next(
(p["name"] for p in expected_products if p["sku"] == expected_order[pos_idx]),
expected_order[pos_idx]
)
detected_name = next(
(d.get("product_name", "?") for d in detected_sorted if d.get("product_sku") == detected_order[pos_idx]),
detected_order[pos_idx]
)
issues.append(
f"π **{detected_name}** β MISPLACED (position {pos_idx+1}: expected **{expected_name}**)"
)
all_alerts.append({
"type": "MISPLACED", "shelf": shelf_id,
"product": detected_name,
"expected_at": expected_name,
"position": pos_idx + 1,
"priority": "HIGH",
})
# Reduce compliance for misplacement
if shelf_matched > 0:
shelf_matched -= 0.5 # Half penalty for wrong order
if not issues:
issues.append("All products in correct position")
comp_pct = (shelf_matched / shelf_expected * 100) if shelf_expected > 0 else 100
shelf_compliance[shelf_id] = {
"compliance": comp_pct, "expected": shelf_expected,
"detected": len(detected_on_shelf), "matched": shelf_matched,
"issues": issues, "revenue_at_risk": revenue_at_risk,
}
total_expected += shelf_expected
total_matched += shelf_matched
overall_compliance = (total_matched / total_expected * 100) if total_expected > 0 else 100
total_revenue_risk = sum(s["revenue_at_risk"] for s in shelf_compliance.values())
# ββ UPDATE UI (all placeholders) ββββββββββββββββββ
with metric_row.container():
m1, m2, m3, m4, m5 = st.columns(5)
with m1:
color = "#00d4aa" if overall_compliance >= 80 else "#ffaa00" if overall_compliance >= 50 else "#ff4343"
st.markdown(f"""<div class="metric-card">
<div class="metric-value" style="background: {color}; -webkit-background-clip: text; -webkit-text-fill-color: transparent;">{overall_compliance:.1f}%</div>
<div class="metric-label">Compliance</div>
</div>""", unsafe_allow_html=True)
with m2:
st.markdown(f"""<div class="metric-card">
<div class="metric-value">{len(detections)}</div>
<div class="metric-label">Detected</div>
</div>""", unsafe_allow_html=True)
with m3:
st.markdown(f"""<div class="metric-card">
<div class="metric-value">{total_expected}</div>
<div class="metric-label">Expected</div>
</div>""", unsafe_allow_html=True)
with m4:
st.markdown(f"""<div class="metric-card">
<div class="metric-value" style="background: #ff4343; -webkit-background-clip: text; -webkit-text-fill-color: transparent;">βΉ{total_revenue_risk:.0f}</div>
<div class="metric-label">Revenue at Risk</div>
</div>""", unsafe_allow_html=True)
with m5:
st.markdown(f"""<div class="metric-card">
<div class="metric-value">#{scan_count}</div>
<div class="metric-label">Scan Count</div>
</div>""", unsafe_allow_html=True)
# Annotated frame
annotated = draw_annotated_image(monitor_image, detections)
vote_status = f"π³οΈ Voted ({len(detection_history)}/{VOTE_BUFFER_SIZE} frames)" if len(detection_history) >= 2 else "β³ Warming up..."
frame_display.image(annotated, caption=f"π΄ LIVE β Scan #{scan_count} at {current_time.strftime('%H:%M:%S')} | {len(detections)} products | {vote_status}", width='stretch')
# Compliance report
with compliance_report.container():
st.markdown("##### π Real-Time Compliance Status")
for shelf_id in sorted(shelf_compliance.keys()):
data = shelf_compliance[shelf_id]
alert_html = format_compliance_alert(
f"Shelf {shelf_id}", data["issues"], data["compliance"],
)
st.markdown(alert_html, unsafe_allow_html=True)
# ββ MOBILE PUSH (throttle: max 1 per 30 seconds) ββ
critical_alerts = [a for a in all_alerts if a["priority"] in ("CRITICAL", "HIGH")]
time_since_last = time.time() - st.session_state.get("last_alert_time", 0)
if critical_alerts and ntfy_topic and time_since_last > 30:
alert_msg = f"π΄ ShelfMind Live Alert β {selected_planogram}\n"
alert_msg += f"Compliance: {overall_compliance:.0f}% | Scan #{scan_count}\n"
alert_msg += f"Time: {current_time.strftime('%H:%M:%S')}\n\n"
for a in critical_alerts[:5]:
type_emoji = {"STOCKOUT": "X", "LOW_STOCK": "!", "MISPLACED": "->", "UNAUTHORIZED": "??"}.get(a["type"], "!")
alert_msg += f"[{type_emoji}] {a['product']}: {a['type']} on Shelf {a['shelf']}\n"
if total_revenue_risk > 0:
alert_msg += f"\nRevenue at risk: βΉ{total_revenue_risk:.0f}/hr"
sent = send_mobile_alert(
f"π΄ Shelf Alert β {overall_compliance:.0f}% Compliance",
alert_msg,
"urgent" if overall_compliance < 50 else "high",
)
if sent:
st.session_state["last_alert_time"] = time.time()
with alert_log.container():
st.markdown(f"""<div class="alert-critical">
<strong>π± Alert Sent at {current_time.strftime('%H:%M:%S')}</strong><br>
{len(critical_alerts)} violation(s) detected β Push notification sent to <strong>ntfy.sh/{ntfy_topic}</strong>
</div>""", unsafe_allow_html=True)
# Save compliance log to SQLite database
comp_log_id = log_compliance(
planogram_name=selected_planogram,
compliance=round(overall_compliance, 1),
detected=len(detections),
expected=total_expected,
revenue_risk=round(total_revenue_risk, 2),
alert_count=len(all_alerts),
scan_number=scan_count,
)
# Log individual alerts to database
for a in all_alerts:
log_alert(
compliance_log_id=comp_log_id,
alert_type=a.get("type", "UNKNOWN"),
shelf_id=a.get("shelf", 0),
product_name=a.get("product", ""),
product_sku=a.get("sku", ""),
priority=a.get("priority", "MEDIUM"),
expected_count=a.get("expected"),
found_count=a.get("found"),
revenue=a.get("revenue", 0),
position_info=a.get("expected_at", ""),
notified=bool(critical_alerts),
)
# Wait before next scan
time.sleep(scan_interval)
except Exception as e:
st.error(f"Monitoring error: {e}")
finally:
if cap is not None:
cap.release()
st.session_state["monitoring_active"] = False
status_indicator.markdown(
'<div class="alert-warning"><strong>βΉοΈ Monitoring stopped.</strong> Click βΆοΈ Start to resume.</div>',
unsafe_allow_html=True
)
else:
# Not monitoring β show instructions
st.markdown("""<div class="alert-info">
<strong>π How Real-Time Monitoring Works:</strong><br>
1. Select the planogram to compare against<br>
2. Set the notification topic (install <strong>ntfy</strong> app on your phone)<br>
3. Click <strong>βΆοΈ Start Live Monitoring</strong><br>
4. The system will automatically:<br>
β’ Capture frames from your camera<br>
β’ Detect & identify all products<br>
β’ Compare against the planogram<br>
β’ Show violations in real-time<br>
β’ Send push notifications to your phone π±<br>
5. Try removing or misplacing a product β watch the alert fire!
</div>""", unsafe_allow_html=True)
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# ββ TAB 4: ANALYTICS DASHBOARD βββββββββββββββββββββββββββββββββββββββββββ
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
with tab4:
st.markdown('<div class="section-header">π Analytics Dashboard β Shelf Intelligence at a Glance</div>', unsafe_allow_html=True)
st.caption("Real-time overview of shelf health, compliance trends, and revenue impact across your store.")
# Load compliance logs from database
logs = get_compliance_logs_as_list()
catalog = load_catalog()
planograms = load_planograms()
# ββ Top Metrics ββββββββββββββββββββββββββββββββββββββββββββββββββββ
m1, m2, m3, m4, m5 = st.columns(5)
n_scans = len(logs)
avg_compliance = np.mean([l["overall_compliance"] for l in logs]) if logs else 0
total_rev_risk = sum(l.get("revenue_at_risk", 0) for l in logs)
total_alerts = sum(l.get("alerts", 0) for l in logs)
with m1:
st.markdown(f"""<div class="metric-card">
<div class="metric-value">{len(catalog.get('products', []))}</div>
<div class="metric-label">Products in DB</div>
</div>""", unsafe_allow_html=True)
with m2:
st.markdown(f"""<div class="metric-card">
<div class="metric-value">{len(planograms)}</div>
<div class="metric-label">Active Planograms</div>
</div>""", unsafe_allow_html=True)
with m3:
color = "#00d4aa" if avg_compliance >= 80 else "#ffaa00" if avg_compliance >= 50 else "#ff4343"
st.markdown(f"""<div class="metric-card">
<div class="metric-value" style="background: {color}; -webkit-background-clip: text; -webkit-text-fill-color: transparent;">{avg_compliance:.1f}%</div>
<div class="metric-label">Avg Compliance</div>
</div>""", unsafe_allow_html=True)
with m4:
st.markdown(f"""<div class="metric-card">
<div class="metric-value">{n_scans}</div>
<div class="metric-label">Total Scans</div>
</div>""", unsafe_allow_html=True)
with m5:
st.markdown(f"""<div class="metric-card">
<div class="metric-value" style="background: #ff4343; -webkit-background-clip: text; -webkit-text-fill-color: transparent;">βΉ{total_rev_risk:.0f}</div>
<div class="metric-label">Total Revenue at Risk</div>
</div>""", unsafe_allow_html=True)
if logs:
st.markdown("---")
chart_cols = st.columns(2)
# Compliance trend
with chart_cols[0]:
log_df = pd.DataFrame(logs)
log_df["time"] = pd.to_datetime(log_df["timestamp"])
fig_trend = px.line(log_df, x="time", y="overall_compliance",
title="π Compliance Trend Over Time",
markers=True, line_shape="spline")
fig_trend.update_traces(line_color="#00d4aa", line_width=3)
fig_trend.add_hline(y=80, line_dash="dash", line_color="#ffaa00",
annotation_text="Target: 80%")
fig_trend.update_layout(
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
font_color="white", height=350,
yaxis_title="Compliance %",
xaxis_title="Time",
)
st.plotly_chart(fig_trend, width='stretch')
# Alert distribution
with chart_cols[1]:
fig_alerts = px.bar(log_df, x="time", y="alerts",
title="β οΈ Alert Frequency",
color="overall_compliance",
color_continuous_scale=["#ff4343", "#ffaa00", "#00d4aa"])
fig_alerts.update_layout(
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
font_color="white", height=350,
)
st.plotly_chart(fig_alerts, width='stretch')
# Shelf-level heatmap
st.markdown("##### πΊοΈ Shelf Health Heatmap")
if logs[-1].get("shelf_data"):
latest = logs[-1]["shelf_data"]
shelf_names = [f"Shelf {k}" for k in sorted(latest.keys())]
compliances = [latest[k]["compliance"] for k in sorted(latest.keys())]
fig_heatmap = go.Figure(data=go.Bar(
x=shelf_names, y=compliances,
marker=dict(
color=compliances,
colorscale=[[0, "#ff4343"], [0.5, "#ffaa00"], [1.0, "#00d4aa"]],
cmin=0, cmax=100,
),
text=[f"{c:.0f}%" for c in compliances],
textposition="auto",
))
fig_heatmap.update_layout(
title="Shelf-Level Compliance Scores",
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
font_color="white", height=300,
yaxis_title="Compliance %",
)
st.plotly_chart(fig_heatmap, width='stretch')
# Recent alerts table
st.markdown("##### π Recent Compliance Scans")
display_df = log_df[["timestamp", "planogram", "overall_compliance", "total_detected", "total_expected", "alerts", "revenue_at_risk"]].copy()
display_df.columns = ["Timestamp", "Planogram", "Compliance %", "Detected", "Expected", "Alerts", "Revenue at Risk (βΉ)"]
st.dataframe(display_df.tail(20).sort_index(ascending=False), width='stretch', hide_index=True)
else:
st.info("π No compliance data yet. Run a compliance check in the **Live Monitor** tab to see analytics here.")
# Demo data for visual appeal
st.markdown("##### π Demo Analytics Preview")
demo_cols = st.columns(2)
with demo_cols[0]:
dates = pd.date_range(end=datetime.now(), periods=14, freq="D")
demo_compliance = 65 + np.cumsum(np.random.randn(14) * 2)
demo_compliance = np.clip(demo_compliance, 40, 100)
fig = px.line(x=dates, y=demo_compliance, title="π Compliance Trend (Demo)",
markers=True, line_shape="spline")
fig.update_traces(line_color="#00d4aa", line_width=3)
fig.add_hline(y=80, line_dash="dash", line_color="#ffaa00")
fig.update_layout(paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
font_color="white", height=300)
st.plotly_chart(fig, width='stretch')
with demo_cols[1]:
hours = list(range(8, 22))
aisles = ["Aisle 1", "Aisle 2", "Aisle 3", "Aisle 4"]
heatmap_data = np.random.uniform(60, 100, size=(len(aisles), len(hours)))
heatmap_data[1, 3:6] = [30, 25, 40] # Stockout period
fig = px.imshow(heatmap_data, x=[f"{h}:00" for h in hours], y=aisles,
title="πΊοΈ Stockout Heatmap (Demo)",
color_continuous_scale=["#ff4343", "#ffaa00", "#00d4aa"],
zmin=0, zmax=100, aspect="auto")
fig.update_layout(paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)",
font_color="white", height=300)
st.plotly_chart(fig, width='stretch')
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# ββ TAB 5: TRAINING RESULTS ββββββββββββββββββββββββββββββββββββββββββββββ
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
with tab5:
st.markdown('<div class="section-header">π Model Training Results</div>', unsafe_allow_html=True)
st.caption("Performance metrics from YOLO, RF-DETR, DINOv2, and LightGBM training.")
# ββ Detection Model Comparison ββ
st.markdown("##### π Detection Model Comparison β YOLO26s vs RF-DETR")
comp_cols = st.columns(2)
with comp_cols[0]:
st.markdown("""<div class="metric-card">
<div class="metric-value" style="font-size: 1.2rem; background: linear-gradient(135deg, #00d4aa, #00a98f); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">YOLO26s v2 (Fine-tuned)</div>
<div class="metric-label">NMS-Free Β· Lightning.ai A100 Β· 60 epochs Β· 1280px</div>
<br>
<table style="width: 100%; color: #8892b0; font-size: 0.85rem;">
<tr><td>mAP@50</td><td style="text-align: right; color: #00d4aa; font-weight: bold;">0.917 π</td></tr>
<tr><td>mAP@50-95</td><td style="text-align: right; color: #00d4aa;">0.583</td></tr>
<tr><td>Precision</td><td style="text-align: right;">0.912</td></tr>
<tr><td>Recall</td><td style="text-align: right;">0.872</td></tr>
<tr><td>F1 Score</td><td style="text-align: right;">0.891</td></tr>
<tr><td>Parameters</td><td style="text-align: right;">9.9M</td></tr>
<tr><td>Model Size</td><td style="text-align: right;">76.7 MB</td></tr>
<tr><td>Inference</td><td style="text-align: right; color: #00d4aa;">~8 ms/img</td></tr>
<tr><td>Training Time</td><td style="text-align: right;">4.6 hrs</td></tr>
<tr><td>GPU</td><td style="text-align: right;">A100-SXM4 (80GB)</td></tr>
</table>
</div>""", unsafe_allow_html=True)
with comp_cols[1]:
st.markdown("""<div class="metric-card">
<div class="metric-value" style="font-size: 1.2rem; background: linear-gradient(135deg, #7c3aed, #a78bfa); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">RF-DETR-Base (Fine-tuned)</div>
<div class="metric-label">Transformer Β· Lightning.ai L4 Β· 30 epochs</div>
<br>
<table style="width: 100%; color: #8892b0; font-size: 0.85rem;">
<tr><td>mAP@50</td><td style="text-align: right; color: #a78bfa; font-weight: bold;">0.887</td></tr>
<tr><td>mAP@50-95</td><td style="text-align: right; color: #a78bfa;">0.547</td></tr>
<tr><td>Precision</td><td style="text-align: right;">0.911</td></tr>
<tr><td>Recall</td><td style="text-align: right;">0.847</td></tr>
<tr><td>F1 Score</td><td style="text-align: right;">0.878</td></tr>
<tr><td>Parameters</td><td style="text-align: right;">~29M</td></tr>
<tr><td>Model Size</td><td style="text-align: right;">~130 MB</td></tr>
<tr><td>Inference</td><td style="text-align: right;">~15 ms/img</td></tr>
<tr><td>Training Time</td><td style="text-align: right;">~4 hrs</td></tr>
<tr><td>GPU</td><td style="text-align: right;">L4 (24GB)</td></tr>
</table>
</div>""", unsafe_allow_html=True)
# ββ Key Insights ββ
st.markdown("---")
st.markdown("##### π Key Insights")
insight_cols = st.columns(3)
with insight_cols[0]:
st.markdown("""<div class="alert-ok">
<strong>π YOLO26s v2 β Best mAP</strong><br>
β’ mAP@50 = 0.917 (beats YOLOv10s at 0.906)<br>
β’ 1280px resolution β 4Γ more detail<br>
β’ State-of-the-art on SKU-110K
</div>""", unsafe_allow_html=True)
with insight_cols[1]:
st.markdown("""<div class="alert-info">
<strong>π― RF-DETR: DINOv2 Backbone</strong><br>
β’ Transformer attention for global context<br>
β’ DINOv2 features for richer embeddings<br>
β’ Training RF-DETR-Large in progress
</div>""", unsafe_allow_html=True)
with insight_cols[2]:
st.markdown("""<div class="alert-info">
<strong>π v1 β v2 Improvement</strong><br>
β’ mAP@50: 0.895 β 0.917 (+2.2%)<br>
β’ Recall: 0.848 β 0.872 (+2.4%)<br>
β’ AdamW + Cosine LR + copy-paste aug
</div>""", unsafe_allow_html=True)
# ββ Training Config Comparison Table ββ
st.markdown("---")
st.markdown("##### βοΈ Training Configuration Comparison")
import pandas as pd
config_data = {
"Config": ["Dataset", "Images (Train/Val)", "Annotations", "Epochs", "Batch Size",
"Effective Batch", "Optimizer", "Learning Rate", "Resolution", "Platform"],
"YOLO26s v2": ["SKU-110K", "8,219 / 588", "1.2M bboxes", "60 (patience=30)",
"16", "64 (NBS)", "AdamW + Cosine LR", "0.001 β 0.00001",
"1280Γ1280", "Lightning.ai A100-80GB"],
"RF-DETR-Base": ["SKU-110K", "8,219 / 588", "1.2M bboxes", "30", "2",
"16 (grad_accum=8)", "AdamW", "1e-4", "Multi-scale", "Lightning.ai L4"],
}
config_df = pd.DataFrame(config_data)
st.dataframe(config_df, hide_index=True, use_container_width=True)
# ββ Existing Training Visualizations ββ
st.markdown("---")
st.markdown("##### π Training Visualizations")
if VIZ_DIR.exists():
viz_files = sorted(VIZ_DIR.glob("*.png"))
if viz_files:
# Group into rows of 2
for i in range(0, len(viz_files), 2):
cols = st.columns(2)
for j, col in enumerate(cols):
if i + j < len(viz_files):
with col:
st.image(str(viz_files[i+j]), caption=viz_files[i+j].stem.replace("_", " ").title(), width='stretch')
else:
st.info("No training visualizations found in `models/training_visualizations/`.")
else:
st.info("Run training on Kaggle first to generate visualization files.")
# Model info cards
st.markdown("---")
st.markdown("##### ποΈ Model Architecture")
arch_cols = st.columns(4)
with arch_cols[0]:
st.markdown("""<div class="metric-card">
<div class="metric-value" style="font-size: 1.3rem;">YOLO26s</div>
<div class="metric-label">Object Detection</div>
<br>
<small style="color: #8892b0;">
β’ NMS-free architecture<br>
β’ Trained on SKU-110K dataset<br>
β’ 43% faster on CPU vs YOLO11<br>
β’ STAL: small-target-aware<br>
β’ 9.9M params Β· 22.5 GFLOPs
</small>
</div>""", unsafe_allow_html=True)
with arch_cols[1]:
st.markdown("""<div class="metric-card">
<div class="metric-value" style="font-size: 1.3rem;">RF-DETR-Base</div>
<div class="metric-label">Object Detection</div>
<br>
<small style="color: #8892b0;">
β’ Transformer attention (ICLR 2026)<br>
β’ DINOv2 ViT backbone<br>
β’ Trained on SKU-110K dataset<br>
β’ Higher precision for dense shelves<br>
β’ ~29M params
</small>
</div>""", unsafe_allow_html=True)
with arch_cols[2]:
st.markdown("""<div class="metric-card">
<div class="metric-value" style="font-size: 1.3rem;">DINOv2 ViT-S/14</div>
<div class="metric-label">SKU Recognition</div>
<br>
<small style="color: #8892b0;">
β’ Self-supervised learning<br>
β’ 768-dim embeddings (fine-tuned)<br>
β’ 384-dim embeddings (pretrained)<br>
β’ FAISS cosine similarity search<br>
β’ 15-view robust augmentation
</small>
</div>""", unsafe_allow_html=True)
with arch_cols[3]:
st.markdown("""<div class="metric-card">
<div class="metric-value" style="font-size: 1.3rem;">LightGBM</div>
<div class="metric-label">Demand Forecasting</div>
<br>
<small style="color: #8892b0;">
β’ Trained on Walmart M5 data<br>
β’ 15 features (temporal, price, SNAP)<br>
β’ SHAP explainability<br>
β’ Auto-replenishment alerts
</small>
</div>""", unsafe_allow_html=True)
# ββ Footer ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
st.markdown("""<div class="footer">
ShelfMind AI β Smart Retail Shelf Intelligence β Built for Hackathon 2026<br>
<small>YOLO26s + RF-DETR + DINOv2 + FAISS + LightGBM + SHAP | Computer Vision-Driven Inventory Monitoring</small>
</div>""", unsafe_allow_html=True)
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