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hate_speech_demo.py

@@ -103,6 +103,7 @@ body, .gradio-container {
     border-radius: 2px;
     box-shadow: 0 2px 5px rgba(0,0,0,0.1);
     padding: 5px;
+    margin-top: -10px;
     margin-bottom: 1px;
     transition: all 0.3s ease;
     background-color: #ffffff;
@@ -150,11 +151,8 @@ body, .gradio-container {
 }
 
 }
-.orange-button,
-.gray-button {
+.orange-button {
     font-family: 'All Round Gothic Demi', 'Poppins', sans-serif !important;
-    font-size: 14px !important;
-    font-weight: 600 !important;
     padding: 10px 15px !important;
     border-radius: 5px !important;
     box-shadow: 0 2px 5px rgba(0,0,0,0.1);
@@ -171,11 +169,8 @@ body, .gradio-container {
     /* Custom gray button style */
 .gray-button {
     font-family: 'All Round Gothic Demi', 'Poppins', sans-serif !important;
-    font-size: 14px !important;
-    font-weight: 600 !important;
     background: #4285F4 !important;
     color: #000000 !important;
-    font-weight: bold;
     border-radius: 5px;
     padding: 10px 15px;
     box-shadow: 0 2px 5px rgba(0,0,0,0.1);
@@ -790,18 +785,19 @@ def create_gradio_app():
     <strong>Assess whether user-generated social content contains hate speech using Contextual AI's State-of-the-Art Agentic RAG system.</strong>
 </p>
 <p>
-    Classifications are steerable and explainable as they are based on a policy document rather than parametric knowledge! This app also returns ratings from LlamaGuard 3.0 and the OpenAI Moderation API for you to compare. This is a demo from Contextual AI researchers. Feedback is welcome as we work with design partners to bring this to production.
+    Contextual's Safety Oracle classifications are steerable and explainable as they are based on a policy document rather than parametric knowledge. This app returns ratings from LlamaGuard 3.0, the OpenAI Moderation API and the Perspective API from Google Jigsaw for comparison. This is a demo from Contextual AI researchers. Feedback is welcome as we work with design partners to bring this to production. Reach out to Aravind Mohan, Head of Data Science, at aravind.mohan@contextual.ai.
 </p>
     
     <h2>Instructions</h2>
     <ul>
         <p>Enter user-generated content to receive an assessment from all four models, or use the 'Random Test Case' button to generate an example.</p>
+        <p><strong>Safety warning.</strong>Some of the randomly generated test cases contain hateful language that you might find offensive or upsetting.</p>
     </ul>
 
     <h2>How it works</h2>
     <p>
-    <strong>Document-grounded evaluations</strong> Every rating is directly tied to our 
-    <a href="#" onclick="openPolicyPopup(); return false;">hate speech policy document</a>, making our system far superior to other solutions that lack transparent decision criteria.<br>
+    <strong>Document-grounded evaluations</strong> ensure every rating is directly tied to our 
+    <a href="#" onclick="openPolicyPopup(); return false;">hate speech policy document</a>, making our system far superior to solutions that lack transparent decision criteria.<br>
     
     <strong>Adaptable policies</strong> mean the system can instantly evolve to match your requirements without retraining.<br>
     
@@ -815,20 +811,13 @@ def create_gradio_app():
     <a href='https://contextual.ai/blog/combining-rag-and-specialization/' target='_blank'>agent specialization</a> 
     to deliver superhuman performance in content evaluation tasks.
 </p>
-
-    <h2>Contact info</h2>
-    <p>Reach out to Aravind Mohan, Head of Data Science, at <a href="mailto:aravind.mohan@contextual.ai">aravind.mohan@contextual.ai</a> to find out more or sign up as a design partner.</p>
-<br><br>
-    <h2>‼️ SAFETY WARNING ‼️</h2>
-    <p>Some of the randomly generated test cases contain hateful language that you might find offensive or upsetting.</p>
-</div>
 """)
         
         with gr.Column():
             # Buttons (stacked or spaced however you like)
-            with gr.Row():
-                random_test_btn = gr.Button("Random Test Case", variant="secondary", elem_classes=["orange-button"])
-                rate_btn = gr.Button("Rate Content", variant="secondary", elem_classes=["gray-button"])
+            with gr.Row(equal_height=True) as button_row:
+                random_test_btn = gr.Button("Random Test Case", elem_classes=["orange-button"], scale=1)
+                rate_btn = gr.Button("Rate Content", elem_classes=["gray-button"], scale=1)
 
             # Input box
             user_input = gr.Textbox(