Update app.py

app.py

@@ -166,7 +166,7 @@ def create_interface():
     performance_metrics = generate_performance_metrics()
     with gr.Blocks() as interface:
         with gr.Tab(" 📨 Demo"):
-            gr.Markdown("📧🔍 Spam and Phishing Email Detection")
+            gr.Markdown(" # 📧🔍 Spam and Phishing Email Detection")
             gr.Markdown(
                 """
                 Welcome to the Spam and Phishing Email Detection Demo! This tool leverages DistilBERT, a lightweight yet powerful transformer model, to classify emails as ham (legitimate), spam, or phishing based on their content.
@@ -224,11 +224,11 @@ def create_interface():
             with gr.Column():
                 gr.Markdown(
                 """
-                ### Label Definitions
+                ## Label Definitions
                 - Spam: Unwanted or potentially harmful emails detected by the system.
                 - Ham: Legitimate and safe emails.
 
-                ### Evaluation Metrics
+                ## Evaluation Metrics
                 - Accuracy: Measures the percentage of correctly classified emails.
                 - Precision: Out of all emails classified as spam, how many were actually spam?
                 - Recall: Out of all actual spam emails, how many were identified correctly?