Update app.py

app.py

@@ -327,11 +327,7 @@ 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
 
@@ -579,19 +575,20 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), css=CUSTOM_CSS, title="
             gr.Dataframe(orders_df, wrap=True)
 
     with gr.Tab("⚠️ Predictive Maintenance"):
-        gr.HTML(data_badge(
-            "🧪 <b>Source:</b> randomly generated by the developer's code "
-            "(`src/data_generation.py`, fixed seed) — 1,000 synthetic sensor readings "
-            "(900 normal + 100 simulated-fault). Not logged from real equipment.<br>"
-            "<b>What it describes:</b> conveyor/crane motor sensor readings — temperature, "
-            "vibration, current draw, and belt speed — with the 'anomaly' readings modelled "
-            "on realistic failure signatures (elevated temp/vibration/current with reduced "
-            "belt speed, as seen with bearing wear or motor overload).<br>"
-            "<b>Function:</b> an Isolation Forest model trained unsupervised (it never sees "
-            "an anomaly label) learns what 'normal' sensor behaviour looks like, then flags "
-            "any new reading — like the ones you enter below — that deviates from it, as an "
-            "early-warning signal before an unplanned equipment stoppage."
-        ))
+        with gr.Accordion("ℹ️ About Data & Predictive System", open=False):
+            gr.HTML(data_badge(
+                "🧪 <b>Source:</b> randomly generated by the developer's code "
+                "(`src/data_generation.py`, fixed seed) — 1,000 synthetic sensor readings "
+                "(900 normal + 100 simulated-fault). Not logged from real equipment.<br>"
+                "<b>What it describes:</b> conveyor/crane motor sensor readings — temperature, "
+                "vibration, current draw, and belt speed — with the 'anomaly' readings modelled "
+                "on realistic failure signatures (elevated temp/vibration/current with reduced "
+                "belt speed, as seen with bearing wear or motor overload).<br>"
+                "<b>Function:</b> an Isolation Forest model trained unsupervised (it never sees "
+                "an anomaly label) learns what 'normal' sensor behaviour looks like, then flags "
+                "any new reading — like the ones you enter below — that deviates from it, as an "
+                "early-warning signal before an unplanned equipment stoppage."
+            ))
         preset_dropdown = gr.Dropdown(
             choices=list(ANOMALY_PRESETS.keys()), label="Load a preset reading", value="Normal reading"
         )