Update app.py

app.py

@@ -132,51 +132,51 @@ with gr.Blocks(title="DataForge - AI-Powered File Analysis") as demo:
         inputs=[user_question],
         outputs=[analysis_output]
     )
-
-    with gr.Tab("📊 Analysis Examples"):
-        gr.Markdown("## 💡 Example Questions by File Type")
+    
+    gr.Markdown("---")
+    gr.Markdown("## 💡 Example Questions by File Type")
+    
+    with gr.Accordion("🔐 Security Analysis Questions", open=False):
+        gr.Markdown("""
+        **For Log Files:**
+        - "Find any failed login attempts and suspicious IP addresses"
+        - "Identify potential security threats or anomalies"
+        - "Show me authentication errors and user access patterns"
+        - "Are there any brute force attacks or repeated failures?"
         
-        with gr.Accordion("🔐 Security Analysis Questions", open=False):
-            gr.Markdown("""
-            **For Log Files:**
-            - "Find any failed login attempts and suspicious IP addresses"
-            - "Identify potential security threats or anomalies"
-            - "Show me authentication errors and user access patterns"
-            - "Are there any brute force attacks or repeated failures?"
-            
-            **For Access Logs:**
-            - "Detect unusual access patterns or potential intrusions"
-            - "Find requests with suspicious user agents or payloads"
-            - "Identify high-frequency requests from single IPs"
-            """)
+        **For Access Logs:**
+        - "Detect unusual access patterns or potential intrusions"
+        - "Find requests with suspicious user agents or payloads"
+        - "Identify high-frequency requests from single IPs"
+        """)
+    
+    with gr.Accordion("⚡ Performance Analysis Questions", open=False):
+        gr.Markdown("""
+        **For Application Logs:**
+        - "Which API endpoints are slowest and why?"
+        - "Find performance bottlenecks and response time issues"
+        - "Show me timeout errors and failed requests"
+        - "What are the peak usage times and load patterns?"
         
-        with gr.Accordion("⚡ Performance Analysis Questions", open=False):
-            gr.Markdown("""
-            **For Application Logs:**
-            - "Which API endpoints are slowest and why?"
-            - "Find performance bottlenecks and response time issues"
-            - "Show me timeout errors and failed requests"
-            - "What are the peak usage times and load patterns?"
-            
-            **For System Logs:**
-            - "Identify resource usage spikes and memory issues"
-            - "Find database query performance problems"
-            - "Show me error rates and system health indicators"
-            """)
+        **For System Logs:**
+        - "Identify resource usage spikes and memory issues"
+        - "Find database query performance problems"
+        - "Show me error rates and system health indicators"
+        """)
+    
+    with gr.Accordion("📈 Data Analysis Questions", open=False):
+        gr.Markdown("""
+        **For CSV/Data Files:**
+        - "Analyze data distribution and find statistical insights"
+        - "Identify outliers and anomalies in the dataset"
+        - "What correlations exist between different columns?"
+        - "Generate a comprehensive data quality report"
         
-        with gr.Accordion("📈 Data Analysis Questions", open=False):
-            gr.Markdown("""
-            **For CSV/Data Files:**
-            - "Analyze data distribution and find statistical insights"
-            - "Identify outliers and anomalies in the dataset"
-            - "What correlations exist between different columns?"
-            - "Generate a comprehensive data quality report"
-            
-            **For JSON Files:**
-            - "Parse the structure and extract key information"
-            - "Find patterns in nested data and relationships"
-            - "Summarize the main data points and values"
-            """)
+        **For JSON Files:**
+        - "Parse the structure and extract key information"
+        - "Find patterns in nested data and relationships"
+        - "Summarize the main data points and values"
+        """)
 
 if __name__ == "__main__":
     demo.launch()