Update app.py

app.py

@@ -158,6 +158,20 @@ def create_interface():
     with gr.Blocks() as interface:
         with gr.Tab("Demo"):
             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.
+
+                To provide a comprehensive overview of the system, the demo is divided into three key sections:
+                
+                Detection Demo – Input an email and see real-time classification results.
+                
+                Metrics Analysis – Gain insights into the performance of the model, including accuracy, precision, recall, and F1-score.
+                
+                Credits – Acknowledging the datasets, tools, and frameworks that made this project possible.
+                This project aims to enhance email security by identifying malicious messages with high accuracy, reducing the risk of scams and fraud. Feel free to explore the demo and see how AI is improving cybersecurity!
+                """)    
+            
     
             # Email Text Input
             email_input = gr.Textbox(
@@ -183,7 +197,17 @@ def create_interface():
                 outputs=[result_output, confidence_output]
             )
         
-        with gr.Tab("Analysis"):          
+        with gr.Tab("Analysis"):
+            with gr.Blocks() as interface:
+                gr.Markdown("## Dataset ")
+                # Top 10 words for spam
+                top_spam_words = get_top_words(df[df['label'] == "spam"]['text'], n=10)
+                gradio.Dataset(label="Top spam words:", top_spam_words)
+                
+                # Top 10 words for ham
+                top_ham_words = get_top_words(df[df['label'] == "ham"]['text'], n=10)
+                gradio.Dataset(label="Top ham words:", top_ham_words)
+            
             gr.Markdown("## 📊 Model Performance Analytics")
             with gr.Row():
                 gr.Textbox(value=performance_metrics["accuracy"], label="Accuracy", interactive=False)
@@ -191,7 +215,7 @@ def create_interface():
                 gr.Textbox(value=performance_metrics["recall"], label="Recall", interactive=False)
                 gr.Textbox(value=performance_metrics["f1_score"], label="F1 Score", interactive=False)
                 
-        with gr.Tab("Background"):
+        with gr.Tab("Glossary"):
             gr.Markdown(" ## Credits and Reference ")
         
     return interface