Update app.py

app.py

@@ -319,11 +319,7 @@ Modern warehouse automation platforms — automated storage & retrieval
 systems (AS/RS), conveyor & sortation lines, AGVs/AMRs, and the WMS/WCS
 software that orchestrates them — generate huge volumes of operational
 data: equipment telemetry, transactions, safety logs, and ad-hoc questions
-from floor staff. This project is a compact, end-to-end example of how an
-AI layer can sit on top of that kind of system: routing requests correctly,
-answering from real operational context instead of guessing, catching
-equipment problems early, and doing all of it with **honest, reproducible
-evaluation** rather than a demo that just "looks like it works."
+from floor staff.
 
 ## What this demonstrates
 
@@ -339,10 +335,7 @@ evaluation** rather than a demo that just "looks like it works."
 
 **Every dataset in this project is synthetically generated by the project's
 own code** (`src/data_generation.py` and `src/knowledge_base.py`), not
-scraped, exported, or sourced from any real company's systems. This was a
-deliberate choice: it keeps the project fully self-contained, reproducible,
-and shareable without any data-privacy or licensing concerns, while still
-being realistic enough to demonstrate the underlying ML techniques properly.
+scraped, exported, or sourced from any real company's systems.
 
 | Dataset | What it is | Size | How it's generated |
 |---|---|---|---|
@@ -437,16 +430,6 @@ chosen to be as simple as it can be while still doing that job well and
 being honestly evaluated, rather than reaching for the biggest available
 model by default.
 
-## Limitations & next steps
-
-- All data here is **synthetic**, for portfolio/demo purposes — a production
-  version would connect to real WMS/WCS APIs and historical sensor logs.
-- The intent set (8 classes) and knowledge base (10 articles) are intentionally
-  small to keep the demo fast and auditable; both are easy to extend.
-- The anomaly detector uses 4 hand-picked features; a production system would
-  likely use a richer multivariate sensor set and a supervised or
-  semi-supervised model once labelled failure data is available.
-
 ---
 *Built as a portfolio/application project. Source code available on request
 or in the linked repository. Feedback welcome.*