Update hate_speech_demo.py

hate_speech_demo.py

@@ -810,7 +810,7 @@ def rate_user_input(user_input):
         # Create the popup div (initially hidden)
         knowledge_html = f"""
         <div id="{popup_id}" class="knowledge-popup" style="display: none;">
-            <div class="knowledge-popup-header">Supporting evidence for Contextual Oracle</div>
+            <div class="knowledge-popup-header">Supporting evidence for Contextual Policy Engine</div>
             <button class="knowledge-popup-close" 
                     onclick="this.parentElement.style.display='none'; 
                              document.getElementById('btn-{popup_id}').style.display='inline-block'; 
@@ -873,21 +873,21 @@ def create_gradio_app():
     # Use the custom CSS with PDF modal styling
     custom_css = CUSTOM_CSS
     
-    with gr.Blocks(title="Hate Speech Rating Oracle", theme=theme, css=custom_css) as app:
+    with gr.Blocks(title="Hate Speech Rating Contextual Policy Engine", theme=theme, css=custom_css) as app:
         # Add loading spinner
         loading_spinner = gr.HTML('<div id="loading-spinner"></div>')
         
         # Add the PDF modal HTML directly (defined at the top of the file)
         gr.HTML(PDF_MODAL_HTML)
 
-        gr.Markdown("# Safety Oracle for Rating Hate Speech [BETA]")
+        gr.Markdown("# Contextual Policy Engine for Rating Hate Speech [BETA]")
         gr.HTML("""
 <div style="margin-bottom: 20px;">
     <p>
     <strong>Assess whether user-generated social content contains hate speech using Contextual AI's State-of-the-Art Agentic RAG system.</strong>
 </p>
 <p>
-    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. Feedback is welcome as we work with design partners to bring this to production. Reach out to Aravind Mohan, Head of Data Science, at <a href="mailto:aravind.mohan@contextual.ai">aravind.mohan@contextual.ai</a>.
+    The Contextual Policy Engine's 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. Feedback is welcome as we work with design partners to bring this to production. Reach out to Aravind Mohan, Head of Data Science, at <a href="mailto:aravind.mohan@contextual.ai">aravind.mohan@contextual.ai</a>.
 </p>
     
     <h2>Instructions</h2>
@@ -935,7 +935,7 @@ def create_gradio_app():
             # 🌟 Contextual Safety Oracle
             gr.HTML("""
             <div class="result-header" style="display: flex; align-items: center; gap: 10px;">
-                <span>🌟 Contextual Safety Oracle</span>
+                <span>🌟 Contextual Policy Engine</span>
                 <a href="#" class="knowledge-button" onclick="openPolicyPopup(); return false;">View policy</a>
             </div>
             """)