Update app.py

app.py

@@ -1901,48 +1901,45 @@ with gr.Blocks(
     </div>
     """)
     
-    # Enhanced tips and documentation
-    gr.Markdown("""
-    ---
-    ### 💡 Pro Tips:
-    
-    **🎯 Data Preparation:**
-    - ✅ Use descriptive column names (e.g., "Monthly_Revenue" instead of "Col1")
-    - ✅ Ensure consistent date formats (YYYY-MM-DD recommended)
-    - ✅ Remove completely empty rows/columns before upload
-    - ✅ For large files (>10MB), consider using sample size option
-    
-    **🔍 Analysis Optimization:**
-    - **Comprehensive**: Full statistical analysis with AI insights (recommended for business reports)
-    - **Quick**: Fast overview for initial data exploration
-    - **Statistical**: Focus on mathematical relationships and patterns
-    
-    **📊 Question Examples for Better AI Responses:**
-    - "What factors most strongly correlate with customer churn?"
-    - "Which time periods show the highest sales performance?"
-    - "Are there any data quality issues I should address?"
-    - "What are the key business opportunities in this dataset?"
-    
-    **📥 Export Recommendations:**
-    - **HTML**: Best for sharing interactive reports with stakeholders
-    - **Markdown**: Great for technical documentation and version control
-    
-    **⚡ Performance Notes:**
-    - Files under 5MB: Instant processing
-    - Files 5-20MB: ~5-10 seconds
-    - Files 20MB+: Consider sampling for faster results
-    
-    **🔧 Supported Formats & Limits:**
-    - **CSV/TSV**: Up to 100MB
-    - **Excel (XLSX/XLS)**: Up to 100MB  
-    - **JSON**: Flat or nested structures
-    - **Parquet**: High-performance columnar format
-    
-    **📞 Support & Contact:**
-    - 📱 WhatsApp: +8801719296601
-    - 📧 Email: https://tinyurl.com/email-for-contact
-    - 🕒 Response Time: Within 24 hours
-    """)
+    with gr.Accordion("💡 Pro Tips", open=False):
+        gr.Markdown("""
+        ### 🎯 Data Preparation:
+        - ✅ Use descriptive column names (e.g., "Monthly_Revenue" instead of "Col1")
+        - ✅ Ensure consistent date formats (YYYY-MM-DD recommended)
+        - ✅ Remove completely empty rows/columns before upload
+        - ✅ For large files (>10MB), consider using sample size option
+
+        ### 🔍 Analysis Optimization:
+        - **Comprehensive**: Full statistical analysis with AI insights (recommended for business reports)
+        - **Quick**: Fast overview for initial data exploration
+        - **Statistical**: Focus on mathematical relationships and patterns
+
+        ### 📊 Question Examples for Better AI Responses:
+        - "What factors most strongly correlate with customer churn?"
+        - "Which time periods show the highest sales performance?"
+        - "Are there any data quality issues I should address?"
+        - "What are the key business opportunities in this dataset?"
+
+        ### 📥 Export Recommendations:
+        - **HTML**: Best for sharing interactive reports with stakeholders
+        - **Markdown**: Great for technical documentation and version control
+
+        ### ⚡ Performance Notes:
+        - Files under 5MB: Instant processing
+        - Files 5-20MB: ~5-10 seconds
+        - Files 20MB+: Consider sampling for faster results
+
+        ### 🔧 Supported Formats & Limits:
+        - **CSV/TSV**: Up to 100MB
+        - **Excel (XLSX/XLS)**: Up to 100MB  
+        - **JSON**: Flat or nested structures
+        - **Parquet**: High-performance columnar format
+
+        ### 📞 Support & Contact:
+        - 📱 WhatsApp: +8801719296601
+        - 📧 Email: https://tinyurl.com/email-for-contact
+        - 🕒 Response Time: Within 24 hours
+        """)
 
 if __name__ == "__main__":
     # Enhanced launch configuration