Update app.py

app.py

@@ -82,9 +82,14 @@ th {
 td {
     background-color: #0d0d1a !important;
     color: #e0e0f0 !important;
-    padding: 8px 14px !important;
+    padding: 6px 14px !important;
     border-bottom: 1px solid #1e1e3a !important;
     font-size: 0.9em !important;
+    max-height: 42px !important;
+    overflow: hidden !important;
+    text-overflow: ellipsis !important;
+    white-space: nowrap !important;
+    vertical-align: middle !important;
 }
 tr:hover td { background-color: #1a1a2e !important; }
 .result-count p {
@@ -145,10 +150,10 @@ def build_stats():
             lines.append(f"`{bar}` **{val}** — {count} ({pct}%)")
         return "\n\n".join(lines)
 
-    platforms   = section("By Platform",              df["platform"].value_counts())
-    categories  = section("By Behavior Category",     df["behavior_category"].value_counts())
-    confidence  = section("By Interpretive Confidence", df["interpretive_confidence"].value_counts())
-    repro       = section("By Reproducibility Status", df["reproducibility_status"].value_counts())
+    platforms  = section("By Platform",               df["platform"].value_counts())
+    categories = section("By Behavior Category",      df["behavior_category"].value_counts())
+    confidence = section("By Interpretive Confidence",df["interpretive_confidence"].value_counts())
+    repro      = section("By Reproducibility Status", df["reproducibility_status"].value_counts())
 
     evidence_pct = round(df["raw_prompt_available"].astype(str).str.lower().eq("yes").mean() * 100)
     response_pct = round(df["raw_response_available"].astype(str).str.lower().eq("yes").mean() * 100)
@@ -177,8 +182,6 @@ def build_stats():
 """
     return summary
 
-initial_df = load_data()
-
 def submit_observation(
     f_date, platform, model_version, memory_enabled,
     window_type, prompt_class, behavior_category,
@@ -213,6 +216,8 @@ def submit_observation(
     else:
         return "⚠️ Saved locally but HF_TOKEN not set — won't persist after restart."
 
+initial_df = load_data()
+
 with gr.Blocks(title="Coherence Gap Explorer", css=custom_css) as demo:
     gr.Markdown("# Coherence Gap Explorer")
     gr.Markdown(
@@ -243,8 +248,11 @@ with gr.Blocks(title="Coherence Gap Explorer", css=custom_css) as demo:
             elem_classes=["result-count"]
         )
         table = gr.Dataframe(
-            value=initial_df, label="Observations",
-            interactive=False, wrap=True
+            value=initial_df,
+            label="Observations",
+            interactive=False,
+            wrap=False,
+            height=500
         )
         inputs = [platform_dd, category_dd, confidence_dd, search_box]
         for component in inputs: