Update app.py

app.py

@@ -498,19 +498,22 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), css=CUSTOM_CSS, title="
         )
 
     with gr.Tab("💬 AI Assistant"):
-        gr.HTML(data_badge(
-            "🧪 <b>Source:</b> hand-written by the developer — 10 original knowledge-base "
-            "articles on warehouse operations (AS/RS, AGV/AMR, WMS, sortation, picking, "
-            "maintenance, safety) plus ~480 template-generated example queries. Not scraped "
-            "or sourced from any real company.<br>"
-            "<b>What it describes:</b> generic intralogistics/automation concepts and "
-            "operational scenarios (equipment faults, safety incidents, order/inventory "
-            "questions) — general domain knowledge, not any specific facility's live data.<br>"
-            "<b>Function:</b> answers free-text warehouse-ops questions by first classifying "
-            "the query's intent, retrieving the most relevant knowledge-base passage(s) "
-            "(RAG), then generating a grounded answer with a hosted LLM — falling back to "
-            "showing the retrieved passages directly if no LLM is available."
-        ))
+        with gr.Accordion("ℹ️ About this AI Assistant & Data Source", open=False):
+            gr.HTML(data_badge(
+                "🧪 <b>Source:</b> hand-written by the developer — 10 original knowledge-base "
+                "articles on warehouse operations (AS/RS, AGV/AMR, WMS, sortation, picking, "
+                "maintenance, safety) plus ~480 template-generated example queries. "
+                "Not scraped or sourced from any real company.<br>"
+                
+                "<b>What it describes:</b> generic intralogistics/automation concepts and "
+                "operational scenarios (equipment faults, safety incidents, order/inventory "
+                "questions) — general domain knowledge, not any specific facility's live data.<br>"
+                
+                "<b>Function:</b> answers free-text warehouse-ops questions by first classifying "
+                "the query's intent, retrieving the most relevant knowledge-base passage(s) "
+                "(RAG), then generating a grounded answer with a hosted LLM — falling back to "
+                "showing the retrieved passages directly if no LLM is available."
+            ))
         gr.ChatInterface(
             fn=chat_fn,
             type="messages",
@@ -529,20 +532,25 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), css=CUSTOM_CSS, title="
             test_llm_btn.click(test_llm_fn, inputs=None, outputs=test_llm_output)
 
     with gr.Tab("📦 Inventory & Order Query"):
-        gr.HTML(data_badge(
-            f"🧪 <b>Source:</b> randomly generated by the developer's code "
-            f"(`src/data_generation.py`, fixed seed) — {len(inventory_df)} synthetic SKU "
-            f"records and {len(orders_df)} synthetic orders. Not exported from any real "
-            "company's WMS.<br>"
-            "<b>What it describes:</b> a stand-in inventory table (SKU, category, zone, "
-            "on-hand units, reorder point, unit cost) and orders table (order ID, status, "
-            "line count, priority, zone) — realistic in shape and ranges, but fictional "
-            "records, not live warehouse data.<br>"
-            "<b>Function:</b> classifies whether a query is about inventory or order status, "
-            "extracts SKU/order-ID/zone identifiers with regex, and filters the "
-            "corresponding table — a lightweight stand-in for a natural-language WMS query "
-            "tool."
-        ))
+        with gr.Accordion("ℹ️ About Data & Query System", open=False):
+            gr.HTML(data_badge(
+                f"🧪 <b>Source:</b> randomly generated by the developer's code "
+                f"(<code>src/data_generation.py</code>, fixed seed) — "
+                f"{len(inventory_df)} synthetic SKU records and "
+                f"{len(orders_df)} synthetic orders. "
+                "Not exported from any real company's WMS.<br>"
+    
+                "<b>What it describes:</b> a stand-in inventory table "
+                "(SKU, category, zone, on-hand units, reorder point, unit cost) "
+                "and orders table (order ID, status, line count, priority, zone) — "
+                "realistic in shape and ranges, but fictional records, not live "
+                "warehouse data.<br>"
+    
+                "<b>Function:</b> classifies whether a query is about inventory or "
+                "order status, extracts SKU/order-ID/zone identifiers with regex, "
+                "and filters the corresponding table — a lightweight stand-in "
+                "for a natural-language WMS query tool."
+            ))
         with gr.Row():
             inv_input = gr.Textbox(label="Query", placeholder="How many units of SKU-1042 are in Zone B?", scale=4)
             inv_btn = gr.Button("Search", variant="primary", scale=1)
@@ -607,10 +615,11 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), css=CUSTOM_CSS, title="
         )
 
     with gr.Tab("📊 Model Evaluation"):
-        gr.HTML(data_badge(
-            "🧪 All charts below are computed on <b>held-out test data</b> by "
-            "<code>build_artifacts.py</code> — fully reproducible, not cherry-picked."
-        ))
+        with gr.Accordion("ℹ️ About Model Evaluation", open=False):
+            gr.HTML(data_badge(
+                "🧪 All charts below are computed on <b>held-out test data</b> "
+                "by <code>build_artifacts.py</code> — fully reproducible."
+            ))
 
         gr.Markdown("### 1. Intent Classifier — accuracy: **{:.1%}** · macro F1: **{:.1%}**".format(
             intent_eval.get("accuracy", 0), intent_eval.get("macro_f1", 0)
@@ -659,6 +668,6 @@ if __name__ == "__main__":
     # the Space if so.
     launch_kwargs = dict(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))
     try:
-        demo.launch(**launch_kwargs, ssr_mode=False)
+        demo.launch(**launch_kwargs, ssr=False)
     except TypeError:
         demo.launch(**launch_kwargs)