Add Priority Export feature for lowest-quality records

Feature: Export F-grade records for volunteer prioritization
- Added 'Priority Export' section to Data Export page
- Allows volunteers to download lowest-scoring organizations and sites
- Configurable export count with sliders (10-1000 orgs, 10-500 sites)
- Includes quality scores and recommendations in exported CSV
- Preview of top 10 priority records before download
- Helps volunteers focus on records needing most improvement

Perfect for identifying which records to work on first!

Author: jonero1

data_explorer.py

@@ -1457,6 +1457,117 @@ def display_data_export(sites: List[Dict], organizations: List[Dict]):
             mime="application/json",
             help="Download sites and organizations together in one JSON file"
         )
+    
+    # Priority Export - lowest quality records for volunteer work
+    st.markdown("---")
+    st.markdown("### 🎯 Priority Export (Lowest Quality Records)")
+    st.info("Export records with the lowest quality scores - these need the most volunteer attention!")
+    
+    from tackle_hunger.data_quality import calculate_organization_quality_score, calculate_site_quality_score
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        if organizations:
+            # Calculate quality scores for all organizations
+            org_scores = []
+            for org in organizations:
+                quality = calculate_organization_quality_score(org)
+                org_with_score = org.copy()
+                org_with_score['quality_score'] = quality['overall_score']
+                org_with_score['quality_grade'] = quality['grade']
+                org_with_score['recommendations'] = '; '.join(quality['recommendations'])
+                org_scores.append(org_with_score)
+            
+            # Sort by quality score (lowest first)
+            org_scores.sort(key=lambda x: x['quality_score'])
+            
+            # Filter to F-grade only
+            f_grade_orgs = [o for o in org_scores if o['quality_grade'] == 'F']
+            
+            st.metric("F-Grade Organizations", f"{len(f_grade_orgs):,}")
+            
+            # Number selector for export
+            num_to_export = st.slider(
+                "Number of lowest-scoring orgs to export:",
+                min_value=10,
+                max_value=min(1000, len(f_grade_orgs)) if f_grade_orgs else 10,
+                value=100,
+                step=10,
+                key="priority_org_count"
+            )
+            
+            if f_grade_orgs:
+                priority_orgs = f_grade_orgs[:num_to_export]
+                
+                # CSV export
+                csv_data = export_to_csv(priority_orgs, "priority_organizations")
+                st.download_button(
+                    label=f"⬇️ Download Top {num_to_export} Priority Orgs (CSV)",
+                    data=csv_data,
+                    file_name=f"priority_orgs_F_grade_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
+                    mime="text/csv",
+                    help=f"Download {num_to_export} organizations with lowest quality scores (F grade)"
+                )
+                
+                # Show preview
+                with st.expander("Preview Priority Organizations"):
+                    preview_df = pd.DataFrame(priority_orgs[:10])
+                    display_cols = ['name', 'quality_score', 'quality_grade', 'recommendations']
+                    available_cols = [col for col in display_cols if col in preview_df.columns]
+                    st.dataframe(preview_df[available_cols], use_container_width=True)
+        else:
+            st.info("No organizations to prioritize")
+    
+    with col2:
+        if sites:
+            # Calculate quality scores for all sites
+            site_scores = []
+            for site in sites:
+                quality = calculate_site_quality_score(site)
+                site_with_score = site.copy()
+                site_with_score['quality_score'] = quality['overall_score']
+                site_with_score['quality_grade'] = quality['grade']
+                site_with_score['recommendations'] = '; '.join(quality['recommendations'])
+                site_scores.append(site_with_score)
+            
+            # Sort by quality score (lowest first)
+            site_scores.sort(key=lambda x: x['quality_score'])
+            
+            # Filter to F-grade only
+            f_grade_sites = [s for s in site_scores if s['quality_grade'] == 'F']
+            
+            st.metric("F-Grade Sites", f"{len(f_grade_sites):,}")
+            
+            # Number selector for export
+            num_to_export = st.slider(
+                "Number of lowest-scoring sites to export:",
+                min_value=10,
+                max_value=min(500, len(f_grade_sites)) if f_grade_sites else 10,
+                value=50,
+                step=10,
+                key="priority_site_count"
+            )
+            
+            if f_grade_sites:
+                priority_sites = f_grade_sites[:num_to_export]
+                
+                # CSV export
+                csv_data = export_to_csv(priority_sites, "priority_sites")
+                st.download_button(
+                    label=f"⬇️ Download Top {num_to_export} Priority Sites (CSV)",
+                    data=csv_data,
+                    file_name=f"priority_sites_F_grade_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
+                    mime="text/csv",
+                    help=f"Download {num_to_export} sites with lowest quality scores (F grade)"
+                )
+                
+                # Show preview
+                with st.expander("Preview Priority Sites"):
+                    preview_df = pd.DataFrame(priority_sites[:10])
+                    display_cols = ['name', 'city', 'state', 'quality_score', 'quality_grade', 'recommendations']
+                    available_cols = [col for col in display_cols if col in preview_df.columns]
+                    st.dataframe(preview_df[available_cols], use_container_width=True)
 
 
 def main():