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app.py

@@ -195,40 +195,25 @@ demo = gr.Interface(
         label="Survey Question"
     ),
     outputs=gr.Markdown(label="Classification Result"),
-    title="ESS Variable Classification",
+    title="ESS Variable Classifier Prototype",
     description="""
-    <div class="sikt-header">
-        <div style="display: flex; align-items: center; gap: 1.5rem; flex-wrap: wrap;">
-            <img src="https://modansa.blob.core.windows.net/testcontainer/Sikt-Prim%C3%A6rlogo-M%C3%B8rk_0.png" alt="Sikt Logo" class="sikt-logo">
-            <div style="flex: 1; min-width: 300px;">
-                <h3 style="margin: 0 0 0.5rem 0; color: var(--sds-color-text-secondary); font-size: 1.25rem; font-weight: 600;">
-                    ESS Variable Classifier
-                </h3>
-                <p style="margin: 0; color: var(--sds-color-text-primary); font-size: 0.95rem; line-height: 1.5;">
-                    Developed by <strong>Sikt</strong> – Norwegian Agency for Shared Services in Education and Research
-                </p>
-            </div>
+<div class="sikt-header">
+    <div style="display: flex; align-items: center; gap: 1.5rem; flex-wrap: wrap;">
+        <img src="https://modansa.blob.core.windows.net/testcontainer/Sikt-Prim%C3%A6rlogo-M%C3%B8rk_0.png" alt="Sikt Logo" class="sikt-logo">
+        <div style="flex: 1; min-width: 300px;">
+            <h3 style="margin: 0 0 0.5rem 0; color: #331c6c; font-size: 1.25rem; font-weight: 600;">
+                ESS Variable Classifier Prototype
+            </h3>
+            <p style="margin: 0; color: #1a1a1a; font-size: 0.95rem; line-height: 1.5;">
+                Developed by <strong>Sikt</strong> – Norwegian Agency for Shared Services in Education and Research
+            </p>
         </div>
     </div>
+</div>
 
-    <div style="margin: 1.5rem 0;">
-        <p style="font-size: 1.05rem; color: var(--sds-color-text-primary); line-height: 1.6;">
-            Automatically classify European Social Survey (ESS) questions into <strong>19 subject categories</strong>.
-            This AI model is fine-tuned from XLM-RoBERTa-Base and achieves <strong>83.8% accuracy</strong>.
-        </p>
+Automatically classify European Social Survey (ESS) questions into **19 subject categories**. This AI model is fine-tuned from XLM-RoBERTa-Base and achieves **83.8% accuracy**.
 
-        <div style="background: linear-gradient(135deg, #fff4e6 0%, #ffe8cc 100%); padding: 1rem; border-radius: 6px; margin-top: 1rem; border-left: 4px solid #ff9500;">
-            <p style="margin: 0; color: #8b5a00; font-weight: 600; font-size: 0.95rem;">
-                ⚠️ <strong>Prototype Notice</strong>
-            </p>
-            <p style="margin: 0.5rem 0 0 0; color: #8b5a00; font-size: 0.9rem; line-height: 1.5;">
-                This is a <strong>prototype model</strong> trained on <strong>582 samples</strong>.
-                Only <strong>8 categories</strong> have sufficient training data (≥20 samples) and can be considered reliable:
-                <strong>Education, Politics, Society and Culture, Demography, Labour and Employment, Health, Psychology, and Other</strong>.
-                Results for remaining categories should be interpreted with caution.
-            </p>
-        </div>
-    </div>
+**⚠️ Prototype Notice:** This model is trained on 582 samples. Only **8 categories** have reliable training data (≥20 samples): **Education, Politics, Society and Culture, Demography, Labour and Employment, Health, Psychology, and Other**. Results for other categories should be interpreted with caution.
     """,
     examples=examples,
     article="""