🏭 Backroom — Inventory Intelligence

Concept:
AI-driven inventory intelligence tool that bridges the backroom and sales floor.

Core Idea:
Use computer vision and data analytics to detect low stock, automate reorder insights, and visualize “shelf vs. backroom” discrepancies in real time. Backroom helps store operators and inventory managers save time, reduce waste, and maintain optimal stock levels.

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📁 Project Structure

backroom/
 ├─ app.py
 ├─ data/
 │   ├─ raw/            # original files (e.g., inventory.csv moves here)
 │   ├─ processed/      # cleaned CSVs written here
 │   └─ shelves/
 ├─ models/
 ├─ scripts/
 │   └─ clean_data.py   # CLI entrypoint that calls src.cleaning
 ├─ src/
 │   ├─ detect.py
 │   ├─ forecast.py
 │   ├─ cleaning.py     # ← your data cleaning functions live here
 │   └─ utils.py
 └─ README.md

🧾 Directory Overview

app.py

Main Streamlit or Gradio application that runs the Backroom UI.

data/

• raw/ — Stores unprocessed or incoming data (e.g., inventory.csv).
• processed/ — Holds cleaned and prepared datasets used for modeling.
• shelves/ — Sample shelf images or vision datasets.

models/

Contains trained or downloaded model weights (e.g., YOLO or ML models).

scripts/

• clean_data.py — Command-line script that calls src.cleaning to clean raw CSVs.

src/

• detect.py — Vision-based shelf gap detection logic.
• forecast.py — Reorder prediction and demand forecasting logic.
• cleaning.py — Data cleaning and preprocessing functions.
• utils.py — Generic helper utilities and shared functions.

README.md

Top-level documentation for installation, setup, and usage instructions.

🧠 Open Source Libraries / Tools Used

Library / Tool	Purpose
duckdb	Lightweight in-process analytical database for fast SQL queries on CSVs and Parquet files.
pandas	Data manipulation and cleaning.
numpy	Efficient numerical computation.
scikit-learn	Machine learning for forecasting, anomaly detection, and metrics.
ultralytics (YOLOv8)	Object detection for shelf gap and inventory visibility.
opencv-python	Image processing and visualization for shelf photo analysis.
streamlit or gradio	Rapid UI prototyping for model demos.
matplotlib / plotly	Visualization and dashboards for metrics and analytics.
python-dotenv	Manage environment variables for local and deployment setups.

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⚙️ Next Steps

1. Implement cleaning.py pipeline for robust data validation and deduplication.
2. Integrate detect.py with YOLOv8 lightweight weights for on-device inference.
3. Connect forecast.py to a simple reorder simulation using SKU-level historical data.
4. Wrap models into app.py Streamlit interface for demo submission.
5. Add notebooks/ directory for experimentation and model testing (optional).

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💡 Suggested Enhancements

• NLP Q&A module — “What do I need to restock today?” powered by duckdb + LLM query parser.
• Predictive alerts — Real-time notifications for projected stockouts in the next 48 hours.
• Inventory heatmaps — Visual overview of low-stock areas across multiple stores.
• API integration — Connect with ERP or POS systems for live data updates.

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Backroom — See. Sense. Restock.