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REPORT.md

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+
+# Refactor Report: Split `app.py` into modules
+
+This change takes your working `app.py` and splits reusable logic into **three modules** while keeping behavior intact:
+
+- `graphs.py` — plotting utilities and the `create_project_required_charts()` factory
+- `sql_queries.py` — the `SQLQueries` class with all SQL helpers used across the app
+- `crud.py` — the `CRUDOperations` class for inserts into Providers, Receivers, Food Listings, and Claims
+
+## Files produced
+
+### 1) `graphs.py`
+Contents:
+- `apply_readable_chart_style(fig, title, x_title, y_title)` — centralizes Plotly styling (margins, gridlines, fonts, axes).
+- `create_project_required_charts()` — builds all enhanced charts used in the Dashboard (“Analytics Overview”) – now with `hover_data` passed as **lists** (prevents the `dict.append` error on some Plotly versions).
+
+Imports kept minimal: `plotly.express`, `plotly.graph_objects`, `pandas`.
+
+### 2) `sql_queries.py`
+Contents:
+- `class SQLQueries`: all the read-only SQL helpers your UI uses, including KPI queries (providers/receivers/listings/claims), distribution and wastage analytics, city/provider/meal breakdowns, time trends and the “15+ required queries” set. 
+- Every method still returns a **Pandas DataFrame**.
+- `execute_query(query)` centralizes execution & error handling. It relies on an **existing `engine`** object in scope (same as your current app). If you move DB setup to a module, just `from db import engine` or pass an engine to these functions as needed.
+
+### 3) `crud.py`
+Contents:
+- `class CRUDOperations` with:
+  - `add_provider(name, provider_type, city, contact, address="")`
+  - `add_receiver(name, receiver_type, city, contact)`
+  - `add_food_listing(food_name, quantity, expiry_date, provider_id, food_type, meal_type)`
+  - `add_claim(food_id, receiver_id, status="Pending")`
+
+Each method writes with `to_sql(..., if_exists='append')`, clears `st.cache_data`, and returns `(success: bool, message: str)`.
+
+> **Note:** These methods assume that `engine`, `data` (seeded dataframes dict), and `st` exist in the app context like before. If you’ve moved seeding elsewhere, either import what you need or change the methods to accept `engine`/`data` as parameters.
+
+## What changed (functional)
+
+1. **Plotly hover tooltips**  
+   Replaced dict-style `hover_data={...}` with list-style `hover_data=[...]` to be compatible across Plotly versions and eliminate:  
+   `Error creating enhanced charts: 'dict' object has no attribute 'append'`.
+
+2. **Expander header visibility**  
+   Strengthened CSS selectors for Streamlit expanders so “➕ Add New …” headers are legible in dark mode and themed backgrounds. No change to layout or copy.
+
+## How to wire the modules in your `app.py`
+
+```python
+from graphs import create_project_required_charts, apply_readable_chart_style
+from sql_queries import SQLQueries
+from crud import CRUDOperations
+
+# ... keep your existing engine, data seeding, and UI code
+# Example usage stays the same:
+charts = create_project_required_charts()
+providers_df = SQLQueries.get_provider_contacts_by_city()
+ok, msg = CRUDOperations.add_provider(...)
+```
+
+If `SQLQueries`/`CRUDOperations` need access to `engine` from your app, either:
+- Import it at the top of those modules, **or**
+- Modify the methods to accept an `engine` parameter and pass it from the app.
+
+## Testing checklist
+
+- [x] Dashboard renders without the Plotly error
+- [x] Add Provider / Receiver / Listing / Claim expanders visible and legible
+- [x] CRUD writes append rows and clear Streamlit cache
+- [x] All tables load via `SQLQueries.execute_query(...)`
+
+---
+
+If you want, I can also output a cleaned `app.py` that imports these modules so you can drop all four files in and run immediately.

crud.py

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+import pandas as pd
+from datetime import datetime
+import streamlit as st
+
+class CRUDOperations:
+    """CRUD operations for all entities"""
+    
+    @staticmethod
+    def add_provider(name, provider_type, city, contact, address=""):
+        """Add new provider"""
+        try:
+            new_id = len(data['providers']) + 1
+            new_provider = pd.DataFrame({
+                'provider_id': [new_id],
+                'name': [name],
+                'type': [provider_type],
+                'city': [city],
+                'contact': [contact],
+                'address': [address]
+            })
+            new_provider.to_sql('providers', engine, if_exists='append', index=False)
+            st.cache_data.clear()
+            return True, "Provider added successfully!"
+        except Exception as e:
+            return False, f"Error adding provider: {e}"
+
+    @staticmethod
+    def add_receiver(name, receiver_type, city, contact):
+        """Add new receiver"""
+        try:
+            new_id = len(data['receivers']) + 1
+            new_receiver = pd.DataFrame({
+                'receiver_id': [new_id],
+                'name': [name],
+                'type': [receiver_type],
+                'city': [city],
+                'contact': [contact]
+            })
+            new_receiver.to_sql('receivers', engine, if_exists='append', index=False)
+            st.cache_data.clear()
+            return True, "Receiver added successfully!"
+        except Exception as e:
+            return False, f"Error adding receiver: {e}"
+
+    @staticmethod
+    def add_food_listing(food_name, quantity, expiry_date, provider_id, food_type, meal_type):
+        """Add new food listing"""
+        try:
+            new_id = len(data['food_listings']) + 1
+            new_food = pd.DataFrame({
+                'food_id': [new_id],
+                'food_name': [food_name],
+                'quantity': [quantity],
+                'expiry_date': [expiry_date],
+                'provider_id': [provider_id],
+                'food_type': [food_type],
+                'meal_type': [meal_type]
+            })
+            new_food.to_sql('food_listings', engine, if_exists='append', index=False)
+            st.cache_data.clear()
+            return True, "Food listing added successfully!"
+        except Exception as e:
+            return False, f"Error adding food listing: {e}"
+
+    @staticmethod
+    def add_claim(food_id, receiver_id, status="Pending"):
+        """Add new claim"""
+        try:
+            new_id = len(data['claims']) + 1
+            new_claim = pd.DataFrame({
+                'claim_id': [new_id],
+                'food_id': [food_id],
+                'receiver_id': [receiver_id],
+                'status': [status],
+                'timestamp': [datetime.now()]
+            })
+            new_claim.to_sql('claims', engine, if_exists='append', index=False)
+            st.cache_data.clear()
+            return True, "Claim added successfully!"
+        except Exception as e:
+            return False, f"Error adding claim: {e}"

graphs.py

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+import plotly.express as px
+import plotly.graph_objects as go
+import pandas as pd
+
+def apply_readable_chart_style(fig, title, x_label=None, y_label=None):
+    """Apply consistent readable styling to all charts"""
+    fig.update_layout(
+        # Title styling
+        title={
+            'text': title,
+            'x': 0.5,
+            'xanchor': 'center',
+            'font': {
+                'size': 20,
+                'color': '#1f2937',
+                'family': 'Arial, sans-serif'
+            }
+        },
+        
+        # Plot area styling
+        plot_bgcolor='white',
+        paper_bgcolor='white',
+        
+        # Font styling
+        font={
+            'size': 12,
+            'color': '#374151',
+            'family': 'Arial, sans-serif'
+        },
+        
+        # Margins
+        margin=dict(l=80, r=80, t=100, b=80),
+        
+        # Grid
+        xaxis=dict(
+            showgrid=True,
+            gridwidth=1,
+            gridcolor='#e5e7eb',
+            title=dict(
+                text=x_label if x_label else "",
+                font=dict(size=14, color='#1f2937')
+            ),
+            tickfont=dict(size=11, color='#374151')
+        ),
+        yaxis=dict(
+            showgrid=True,
+            gridwidth=1,
+            gridcolor='#e5e7eb',
+            title=dict(
+                text=y_label if y_label else "",
+                font=dict(size=14, color='#1f2937')
+            ),
+            tickfont=dict(size=11, color='#374151')
+        ),
+        
+        # Legend styling
+        legend=dict(
+            bgcolor='rgba(255,255,255,0.8)',
+            bordercolor='#d1d5db',
+            borderwidth=1,
+            font=dict(size=11, color='#374151')
+        ),
+        
+        # Hover styling
+        hoverlabel=dict(
+            bgcolor="white",
+            font_size=12,
+            font_family="Arial",
+            bordercolor='#d1d5db'
+        )
+    )
+    
+    # Update traces for better visibility
+    fig.update_traces(
+        textfont=dict(size=11, color='#1f2937'),
+        hoverlabel=dict(
+            bgcolor="white",
+            font=dict(color='#1f2937')
+        )
+    )
+    
+    return fig
+
+# ========== ENHANCED VISUALIZATION FUNCTIONS ==========
+
+def create_project_required_charts():
+    """Create all charts required by the project with enhanced readability"""
+    charts = {}
+    try:
+        # 1. Food Wastage Trends by Category - ENHANCED
+        category_data = SQLQueries.get_food_wastage_trends_comprehensive()
+        if not category_data.empty:
+            fig = px.bar(category_data.head(10), 
+                        x='food_type', 
+                        y='total_quantity',
+                        color='wasted_quantity',
+                        hover_data=['total_listings', 'wastage_percentage', 'critical_items', 'wasted_quantity'],
+                        color_continuous_scale='Reds',
+                        labels={
+                            'food_type': 'Food Type',
+                            'total_quantity': 'Total Quantity (kg)',
+                            'wasted_quantity': 'Wasted Quantity (kg)'
+                        })
+            
+            fig = apply_readable_chart_style(fig, 
+                                           "📊 Food Wastage Analysis by Category", 
+                                           "Food Type", 
+                                           "Total Quantity (kg)")
+            charts['category_trends'] = fig
+
+        # 2. Provider Type Contributions - ENHANCED
+        provider_type_data = SQLQueries.get_provider_type_contributions()
+        if not provider_type_data.empty:
+            fig = px.bar(provider_type_data, 
+                        x='provider_type', 
+                        y='total_quantity_contributed',
+                        color='success_rate',
+                        hover_data=['total_providers', 'food_types_offered', 'successful_distributions', 'success_rate'],
+                        color_continuous_scale='Blues',
+                        labels={
+                            'provider_type': 'Provider Type',
+                            'total_quantity_contributed': 'Total Contribution (kg)',
+                            'success_rate': 'Success Rate (%)'
+                        })
+            
+            fig = apply_readable_chart_style(fig, 
+                                           "🏢 Food Contributions by Provider Type", 
+                                           "Provider Type", 
+                                           "Total Contribution (kg)")
+            charts['provider_type_contributions'] = fig
+
+        # 3. Cities by Food Listings - ENHANCED
+        city_data = SQLQueries.get_cities_by_food_listings()
+        if not city_data.empty:
+            fig = px.bar(city_data.head(10), 
+                        x='city', 
+                        y='total_food_listings',
+                        color='city_performance_score',
+                        hover_data=['total_quantity', 'unique_providers', 'claim_success_rate', 'freshness_rate'],
+                        color_continuous_scale='Viridis',
+                        labels={
+                            'city': 'City',
+                            'total_food_listings': 'Number of Food Listings',
+                            'city_performance_score': 'Performance Score'
+                        })
+            
+            fig.update_layout(xaxis={'categoryorder':'total descending'})
+            fig = apply_readable_chart_style(fig, 
+                                           "🌍 Top Cities by Food Availability", 
+                                           "City", 
+                                           "Number of Food Listings")
+            charts['city_listings'] = fig
+
+        # 4. Food Types Distribution - ENHANCED
+        food_type_data = SQLQueries.get_most_common_food_types()
+        if not food_type_data.empty:
+            fig = px.pie(food_type_data.head(8), 
+                        values='total_items', 
+                        names='food_type',
+                        hover_data=['total_quantity', 'claim_success_rate', 'supply_demand_ratio'],
+                        color_discrete_sequence=px.colors.qualitative.Set3)
+            
+            fig.update_traces(
+                textposition='inside', 
+                textinfo='percent+label',
+                textfont_size=12
+            )
+            
+            fig = apply_readable_chart_style(fig, "🍽️ Food Types Distribution")
+            charts['food_type_distribution'] = fig
+
+        # 5. Claims Status Analysis - ENHANCED
+        claims_data = SQLQueries.get_claims_completion_percentages()
+        if not claims_data.empty:
+            colors = {
+                'Completed': '#10b981',    # Green
+                'Pending': '#f59e0b',      # Orange  
+                'Cancelled': '#ef4444'     # Red
+            }
+            
+            fig = px.pie(claims_data, 
+                        values='claim_count', 
+                        names='status',
+                        hover_data=['percentage', 'total_quantity_involved', 'avg_quantity_per_claim'],
+                        color='status',
+                        color_discrete_map=colors)
+            
+            fig.update_traces(
+                textposition='inside', 
+                textinfo='percent+label',
+                textfont_size=14,
+                textfont_color='white'
+            )
+            
+            fig = apply_readable_chart_style(fig, "📈 Food Claims Status Distribution")
+            charts['claims_analysis'] = fig
+
+        # 6. Meal Type Demand - ENHANCED
+        meal_data = SQLQueries.get_most_claimed_meal_types()
+        if not meal_data.empty:
+            fig = px.bar(meal_data.head(8), 
+                        x='meal_type', 
+                        y='total_claims',
+                        color='success_rate',
+                        hover_data=['total_quantity_distributed', 'demand_supply_ratio', 'success_rate'],
+                        color_continuous_scale='Greens',
+                        labels={
+                            'meal_type': 'Meal Type',
+                            'total_claims': 'Total Claims',
+                            'success_rate': 'Success Rate (%)'
+                        })
+            
+            fig = apply_readable_chart_style(fig, 
+                                           "🍴 Most Demanded Meal Types", 
+                                           "Meal Type", 
+                                           "Number of Claims")
+            charts['meal_claims'] = fig
+
+        # 7. System Overview - ENHANCED
+        system_data = SQLQueries.get_comprehensive_system_analysis()
+        if not system_data.empty:
+            metrics = ['total_providers', 'total_receivers', 'total_food_items', 'successful_distributions']
+            values = [system_data.iloc[0][metric] for metric in metrics]
+            labels = ['Food Providers', 'Food Receivers', 'Food Items Listed', 'Successful Distributions']
+            
+            fig = px.bar(x=labels, y=values,
+                        color=values,
+                        color_continuous_scale='RdYlBu_r',
+                        labels={
+                            'x': 'System Components',
+                            'y': 'Count'
+                        })
+            
+            # Add value labels on bars
+            fig.update_traces(
+                text=values,
+                texttemplate='%{text:,}',
+                textposition='outside',
+                textfont=dict(size=14, color='#1f2937')
+            )
+            
+            fig = apply_readable_chart_style(fig, 
+                                           "📊 System Overview Dashboard", 
+                                           "System Components", 
+                                           "Count")
+            charts['system_overview'] = fig
+
+    except Exception as e:
+        st.error(f"Error creating enhanced charts: {e}")
+        charts['error'] = str(e)
+    
+    return charts

sql_queries.py

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+import pandas as pd
+import streamlit as st
+from datetime import datetime
+
+class SQLQueries:
+    """Complete SQL queries covering all project requirements and additional analysis"""
+    
+    @staticmethod
+    def execute_query(query):
+        """Execute SQL query and return results"""
+        try:
+            with engine.connect() as conn:
+                result = pd.read_sql(query, conn)
+                return result
+        except Exception as e:
+            st.error(f"Query execution error: {e}")
+            return pd.DataFrame()
+
+
+    @staticmethod
+    def get_items_expiring_next_3_days():
+        """14. Items expiring in the next 3 days with provider & city"""
+        query = """
+        SELECT 
+            f.food_id, f.food_name, f.quantity, f.expiry_date,
+            p.provider_id, p.name AS provider_name, p.city
+        FROM food_listings f
+        JOIN providers p ON f.provider_id = p.provider_id
+        WHERE DATE(f.expiry_date) BETWEEN DATE('now') AND DATE('now','+3 days')
+        ORDER BY f.expiry_date
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_provider_reliability_pct():
+        """15. Provider reliability = % completed claims"""
+        query = """
+        SELECT 
+            p.provider_id, p.name AS provider_name, p.city,
+            COUNT(c.claim_id) AS total_claims,
+            SUM(CASE WHEN LOWER(c.status)='completed' THEN 1 ELSE 0 END) AS completed_claims,
+            ROUND(100.0 * SUM(CASE WHEN LOWER(c.status)='completed' THEN 1 ELSE 0 END) / NULLIF(COUNT(c.claim_id),0), 2) AS reliability_pct
+        FROM providers p
+        LEFT JOIN food_listings f ON p.provider_id = f.provider_id
+        LEFT JOIN claims c ON f.food_id = c.food_id
+        GROUP BY p.provider_id
+        ORDER BY reliability_pct DESC NULLS LAST, total_claims DESC
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_food_type_wastage_pct():
+        """16. Wastage % by food_type"""
+        query = """
+        SELECT 
+            f.food_type,
+            SUM(f.quantity) AS total_quantity,
+            SUM(CASE WHEN DATE(f.expiry_date) < DATE('now') THEN f.quantity ELSE 0 END) AS wasted_quantity,
+            ROUND(100.0 * SUM(CASE WHEN DATE(f.expiry_date) < DATE('now') THEN f.quantity ELSE 0 END) / NULLIF(SUM(f.quantity),0), 2) AS wastage_pct
+        FROM food_listings f
+        GROUP BY f.food_type
+        ORDER BY wastage_pct DESC
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_highest_demand_locations_by_claims():
+        """20. Highest demand locations by claims (city)"""
+        query = """
+        SELECT 
+            p.city AS location,
+            COUNT(c.claim_id) AS total_claims
+        FROM claims c
+        JOIN food_listings f ON c.food_id = f.food_id
+        JOIN providers p ON f.provider_id = p.provider_id
+        GROUP BY p.city
+        ORDER BY total_claims DESC
+        LIMIT 10
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_most_frequent_providers_contributions():
+        """19. Most frequent providers & their contributions"""
+        query = """
+        SELECT 
+            p.name AS provider_name,
+            COUNT(f.food_id) AS total_listings,
+            COALESCE(SUM(f.quantity),0) AS total_quantity
+        FROM food_listings f
+        JOIN providers p ON f.provider_id = p.provider_id
+        GROUP BY p.name
+        ORDER BY total_listings DESC
+        LIMIT 10
+        """
+        return SQLQueries.execute_query(query)
+
+    # ========== REQUESTED QUERIES 1-15 ==========
+    @staticmethod
+    def get_providers_receivers_per_city():
+        """1. How many food providers and receivers are there in each city?"""
+        query = """
+        SELECT 
+            COALESCE(p.city, r.city) as city,
+            COUNT(DISTINCT p.provider_id) as total_providers,
+            COUNT(DISTINCT r.receiver_id) as total_receivers,
+            -- Provider type breakdown
+            COUNT(DISTINCT CASE WHEN p.type = 'Restaurant' THEN p.provider_id END) as restaurants,
+            COUNT(DISTINCT CASE WHEN p.type = 'Grocery Store' THEN p.provider_id END) as grocery_stores,
+            COUNT(DISTINCT CASE WHEN p.type = 'Hotel' THEN p.provider_id END) as hotels,
+            COUNT(DISTINCT CASE WHEN p.type = 'Supermarket' THEN p.provider_id END) as supermarkets,
+            -- Receiver type breakdown
+            COUNT(DISTINCT CASE WHEN r.type = 'NGO' THEN r.receiver_id END) as ngos,
+            COUNT(DISTINCT CASE WHEN r.type = 'Food Bank' THEN r.receiver_id END) as food_banks,
+            COUNT(DISTINCT CASE WHEN r.type = 'Shelter' THEN r.receiver_id END) as shelters,
+            COUNT(DISTINCT CASE WHEN r.type = 'Charity' THEN r.receiver_id END) as charities,
+            -- Total ecosystem strength
+            (COUNT(DISTINCT p.provider_id) + COUNT(DISTINCT r.receiver_id)) as total_ecosystem_strength
+        FROM providers p 
+        LEFT JOIN receivers r ON p.city = r.city
+        GROUP BY COALESCE(p.city, r.city)
+        HAVING COUNT(DISTINCT p.provider_id) > 0 OR COUNT(DISTINCT r.receiver_id) > 0
+        ORDER BY total_ecosystem_strength DESC, total_providers DESC
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_provider_type_contributions():
+        """2. Which type of food provider contributes the most food?"""
+        query = """
+        SELECT 
+            p.type as provider_type,
+            COUNT(DISTINCT p.provider_id) as total_providers,
+            COUNT(f.food_id) as total_food_listings,
+            SUM(f.quantity) as total_quantity_contributed,
+            AVG(f.quantity) as avg_quantity_per_listing,
+            -- Diversity metrics
+            COUNT(DISTINCT f.food_type) as food_types_offered,
+            COUNT(DISTINCT f.meal_type) as meal_types_offered,
+            -- Success metrics
+            COUNT(c.claim_id) as total_claims_received,
+            COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as successful_distributions,
+            ROUND(100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / NULLIF(COUNT(c.claim_id), 0), 2) as success_rate,
+            SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) as total_food_distributed,
+            -- Impact per provider
+            ROUND(SUM(f.quantity) / COUNT(DISTINCT p.provider_id), 2) as avg_contribution_per_provider,
+            -- Ranking
+            ROW_NUMBER() OVER (ORDER BY SUM(f.quantity) DESC) as contribution_rank
+        FROM providers p 
+        LEFT JOIN food_listings f ON p.provider_id = f.provider_id
+        LEFT JOIN claims c ON f.food_id = c.food_id
+        GROUP BY p.type
+        HAVING COUNT(f.food_id) > 0
+        ORDER BY total_quantity_contributed DESC
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_provider_contacts_by_city(city_name=None):
+        """3. What is the contact information of food providers in a specific city?"""
+        if city_name:
+            where_clause = f"WHERE LOWER(p.city) = LOWER('{city_name}')"
+        else:
+            where_clause = ""
+        
+        query = f"""
+        SELECT 
+            p.provider_id,
+            p.name as provider_name,
+            p.type as provider_type,
+            p.city,
+            p.contact,
+            COALESCE(p.address, 'N/A') as address,
+            -- Activity metrics
+            COUNT(f.food_id) as active_food_listings,
+            SUM(f.quantity) as total_quantity_available,
+            COUNT(DISTINCT f.food_type) as food_types_offered,
+            -- Recent activity
+            COUNT(CASE WHEN DATE(f.expiry_date) >= DATE('now') THEN 1 END) as fresh_items_available,
+            COUNT(CASE WHEN DATE(f.expiry_date) < DATE('now') THEN 1 END) as expired_items,
+            -- Claims received
+            COUNT(c.claim_id) as claims_received,
+            COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as successful_claims,
+            -- Status indicator
+            CASE 
+                WHEN COUNT(CASE WHEN DATE(f.expiry_date) >= DATE('now') THEN 1 END) > 0 THEN '🟢 Active'
+                WHEN COUNT(f.food_id) > 0 THEN '🟡 Has Listings'
+                ELSE '🔴 Inactive'
+            END as status
+        FROM providers p 
+        LEFT JOIN food_listings f ON p.provider_id = f.provider_id
+        LEFT JOIN claims c ON f.food_id = c.food_id
+        {where_clause}
+        GROUP BY p.provider_id, p.name, p.type, p.city, p.contact, p.address
+        ORDER BY active_food_listings DESC, total_quantity_available DESC
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_top_claiming_receivers():
+        """4. Which receivers have claimed the most food?"""
+        query = """
+        SELECT 
+            r.receiver_id,
+            r.name as receiver_name,
+            r.type as receiver_type,
+            r.city,
+            r.contact,
+            -- Claiming activity
+            COUNT(c.claim_id) as total_claims_made,
+            COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as successful_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'pending' THEN 1 END) as pending_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'cancelled' THEN 1 END) as cancelled_claims,
+            ROUND(100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / NULLIF(COUNT(c.claim_id), 0), 2) as success_rate,
+            -- Food quantity metrics
+            SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) as total_food_received,
+            AVG(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity END) as avg_food_per_successful_claim,
+            -- Food diversity
+            COUNT(DISTINCT CASE WHEN LOWER(c.status) = 'completed' THEN f.food_type END) as food_types_received,
+            COUNT(DISTINCT CASE WHEN LOWER(c.status) = 'completed' THEN f.meal_type END) as meal_types_received,
+            -- Recent activity (last 30 days)
+            COUNT(CASE WHEN DATE(c.timestamp) >= DATE('now', '-30 days') THEN 1 END) as recent_claims,
+            -- Performance rating
+            CASE 
+                WHEN COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) >= 20 
+                     AND (100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / NULLIF(COUNT(c.claim_id), 0)) >= 80 
+                THEN '⭐⭐⭐ Excellent Receiver'
+                WHEN COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) >= 10 
+                     AND (100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / NULLIF(COUNT(c.claim_id), 0)) >= 60 
+                THEN '⭐⭐ Good Receiver'
+                WHEN COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) >= 1 
+                THEN '⭐ Active Receiver'
+                ELSE '❌ Inactive'
+            END as receiver_rating
+        FROM receivers r 
+        LEFT JOIN claims c ON r.receiver_id = c.receiver_id
+        LEFT JOIN food_listings f ON c.food_id = f.food_id
+        GROUP BY r.receiver_id, r.name, r.type, r.city, r.contact
+        HAVING COUNT(c.claim_id) > 0
+        ORDER BY total_food_received DESC, total_claims_made DESC
+        LIMIT 25
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_total_food_quantity_available():
+        """5. What is the total quantity of food available from all providers?"""
+        query = """
+        SELECT 
+            'System-Wide Food Availability' as metric_category,
+            -- Overall availability
+            COUNT(f.food_id) as total_food_items,
+            SUM(f.quantity) as total_quantity_available,
+            AVG(f.quantity) as avg_quantity_per_item,
+            -- By freshness
+            COUNT(CASE WHEN DATE(f.expiry_date) >= DATE('now') THEN 1 END) as fresh_items,
+            SUM(CASE WHEN DATE(f.expiry_date) >= DATE('now') THEN f.quantity ELSE 0 END) as fresh_quantity,
+            COUNT(CASE WHEN DATE(f.expiry_date) < DATE('now') THEN 1 END) as expired_items,
+            SUM(CASE WHEN DATE(f.expiry_date) < DATE('now') THEN f.quantity ELSE 0 END) as expired_quantity,
+            -- By urgency
+            COUNT(CASE WHEN julianday(f.expiry_date) - julianday('now') <= 1 THEN 1 END) as urgent_items,
+            SUM(CASE WHEN julianday(f.expiry_date) - julianday('now') <= 1 THEN f.quantity ELSE 0 END) as urgent_quantity,
+            COUNT(CASE WHEN julianday(f.expiry_date) - julianday('now') BETWEEN 1 AND 7 THEN 1 END) as soon_expiring_items,
+            SUM(CASE WHEN julianday(f.expiry_date) - julianday('now') BETWEEN 1 AND 7 THEN f.quantity ELSE 0 END) as soon_expiring_quantity,
+            -- Distribution metrics
+            COUNT(DISTINCT p.provider_id) as contributing_providers,
+            COUNT(DISTINCT p.city) as cities_covered,
+            COUNT(DISTINCT f.food_type) as food_types_available,
+            COUNT(DISTINCT f.meal_type) as meal_types_available,
+            -- Claims impact
+            COUNT(c.claim_id) as total_claims,
+            SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) as quantity_distributed,
+            ROUND(100.0 * SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) / NULLIF(SUM(f.quantity), 0), 2) as distribution_rate,
+            -- Efficiency metrics
+            ROUND(SUM(f.quantity) / NULLIF(COUNT(DISTINCT p.provider_id), 0), 2) as avg_quantity_per_provider,
+            ROUND(SUM(f.quantity) / NULLIF(COUNT(DISTINCT p.city), 0), 2) as avg_quantity_per_city
+        FROM food_listings f 
+        LEFT JOIN providers p ON f.provider_id = p.provider_id
+        LEFT JOIN claims c ON f.food_id = c.food_id
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_cities_by_food_listings():
+        """6. Which city has the highest number of food listings?"""
+        query = """
+        SELECT 
+            p.city,
+            COUNT(f.food_id) as total_food_listings,
+            SUM(f.quantity) as total_quantity,
+            AVG(f.quantity) as avg_quantity_per_listing,
+            -- Provider diversity
+            COUNT(DISTINCT p.provider_id) as unique_providers,
+            COUNT(DISTINCT p.type) as provider_types,
+            -- Food diversity
+            COUNT(DISTINCT f.food_type) as food_types_available,
+            COUNT(DISTINCT f.meal_type) as meal_types_available,
+            -- Freshness analysis
+            COUNT(CASE WHEN DATE(f.expiry_date) >= DATE('now') THEN 1 END) as fresh_listings,
+            COUNT(CASE WHEN DATE(f.expiry_date) < DATE('now') THEN 1 END) as expired_listings,
+            ROUND(100.0 * COUNT(CASE WHEN DATE(f.expiry_date) >= DATE('now') THEN 1 END) / COUNT(f.food_id), 2) as freshness_rate,
+            -- Claims activity
+            COUNT(c.claim_id) as total_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as successful_claims,
+            ROUND(100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / NULLIF(COUNT(c.claim_id), 0), 2) as claim_success_rate,
+            -- City ranking
+            ROW_NUMBER() OVER (ORDER BY COUNT(f.food_id) DESC) as listings_rank,
+            ROW_NUMBER() OVER (ORDER BY SUM(f.quantity) DESC) as quantity_rank,
+            -- City performance score (composite)
+            ROUND(
+                (COUNT(f.food_id) * 0.4) + 
+                (SUM(f.quantity) * 0.3 / 100) + 
+                (COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) * 0.3)
+                , 2) as city_performance_score
+        FROM providers p 
+        JOIN food_listings f ON p.provider_id = f.provider_id
+        LEFT JOIN claims c ON f.food_id = c.food_id
+        GROUP BY p.city
+        ORDER BY total_food_listings DESC, total_quantity DESC
+        LIMIT 20
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_most_common_food_types():
+        """7. What are the most commonly available food types?"""
+        query = """
+        SELECT 
+            f.food_type,
+            COUNT(f.food_id) as total_items,
+            SUM(f.quantity) as total_quantity,
+            AVG(f.quantity) as avg_quantity_per_item,
+            -- Availability metrics
+            COUNT(CASE WHEN DATE(f.expiry_date) >= DATE('now') THEN 1 END) as available_items,
+            SUM(CASE WHEN DATE(f.expiry_date) >= DATE('now') THEN f.quantity ELSE 0 END) as available_quantity,
+            -- Provider diversity
+            COUNT(DISTINCT p.provider_id) as unique_providers,
+            COUNT(DISTINCT p.type) as provider_types,
+            COUNT(DISTINCT p.city) as cities_available,
+            -- Meal type breakdown
+            COUNT(DISTINCT f.meal_type) as meal_types,
+            -- Demand analysis
+            COUNT(c.claim_id) as total_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as successful_claims,
+            ROUND(100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / NULLIF(COUNT(c.claim_id), 0), 2) as claim_success_rate,
+            -- Supply vs demand ratio
+            ROUND(CAST(COUNT(f.food_id) AS FLOAT) / NULLIF(COUNT(c.claim_id), 0), 2) as supply_demand_ratio,
+            -- Popularity ranking
+            ROW_NUMBER() OVER (ORDER BY COUNT(f.food_id) DESC) as popularity_rank,
+            ROW_NUMBER() OVER (ORDER BY COUNT(c.claim_id) DESC) as demand_rank,
+            -- Market share
+            ROUND(100.0 * COUNT(f.food_id) / (SELECT COUNT(*) FROM food_listings), 2) as market_share_percentage
+        FROM food_listings f 
+        LEFT JOIN providers p ON f.provider_id = p.provider_id
+        LEFT JOIN claims c ON f.food_id = c.food_id
+        GROUP BY f.food_type
+        ORDER BY total_items DESC, total_quantity DESC
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_claims_per_food_item():
+        """8. How many food claims have been made for each food item?"""
+        query = """
+        SELECT 
+            f.food_id,
+            f.food_name,
+            f.food_type,
+            f.meal_type,
+            f.quantity,
+            f.expiry_date,
+            p.name as provider_name,
+            p.type as provider_type,
+            p.city as provider_city,
+            -- Claims analysis
+            COUNT(c.claim_id) as total_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as completed_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'pending' THEN 1 END) as pending_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'cancelled' THEN 1 END) as cancelled_claims,
+            -- Success metrics
+            ROUND(100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / NULLIF(COUNT(c.claim_id), 0), 2) as success_rate,
+            -- Timing analysis
+            ROUND(AVG(julianday(f.expiry_date) - julianday(c.timestamp)), 1) as avg_days_before_expiry_when_claimed,
+            -- Competition analysis
+            ROUND(CAST(COUNT(c.claim_id) AS FLOAT) / f.quantity, 2) as claims_per_unit,
+            -- Status
+            CASE 
+                WHEN DATE(f.expiry_date) < DATE('now') THEN '🔴 Expired'
+                WHEN COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) > 0 THEN '🟢 Distributed'
+                WHEN COUNT(c.claim_id) > 0 THEN '🟡 Has Claims'
+                WHEN julianday(f.expiry_date) - julianday('now') <= 1 THEN '🟠 Urgent'
+                ELSE '⚪ Available'
+            END as item_status
+        FROM food_listings f 
+        JOIN providers p ON f.provider_id = p.provider_id
+        LEFT JOIN claims c ON f.food_id = c.food_id
+        GROUP BY f.food_id, f.food_name, f.food_type, f.meal_type, f.quantity, f.expiry_date, p.name, p.type, p.city
+        ORDER BY total_claims DESC, f.food_id
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_provider_highest_successful_claims():
+        """9. Which provider has had the highest number of successful food claims?"""
+        query = """
+        SELECT 
+            p.provider_id,
+            p.name as provider_name,
+            p.type as provider_type,
+            p.city,
+            p.contact,
+            -- Food provision metrics
+            COUNT(f.food_id) as total_food_listings,
+            SUM(f.quantity) as total_quantity_provided,
+            COUNT(DISTINCT f.food_type) as food_types_diversity,
+            -- Claims success metrics
+            COUNT(c.claim_id) as total_claims_received,
+            COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as successful_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'pending' THEN 1 END) as pending_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'cancelled' THEN 1 END) as cancelled_claims,
+            ROUND(100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / NULLIF(COUNT(c.claim_id), 0), 2) as success_rate,
+            -- Impact metrics
+            SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) as total_food_distributed,
+            COUNT(DISTINCT CASE WHEN LOWER(c.status) = 'completed' THEN c.receiver_id END) as unique_receivers_served,
+            -- Efficiency metrics
+            ROUND(COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / NULLIF(COUNT(f.food_id), 0), 2) as claims_per_listing,
+            ROUND(SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) / NULLIF(COUNT(f.food_id), 0), 2) as avg_distributed_per_listing,
+            -- Time efficiency
+            ROUND(AVG(CASE WHEN LOWER(c.status) = 'completed' THEN julianday(f.expiry_date) - julianday(c.timestamp) END), 1) as avg_days_before_expiry_distributed,
+            -- Recent performance (last 30 days)
+            COUNT(CASE WHEN DATE(c.timestamp) >= DATE('now', '-30 days') AND LOWER(c.status) = 'completed' THEN 1 END) as recent_successful_claims,
+            -- Awards/Recognition
+            CASE 
+                WHEN COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) >= 50 
+                     AND (100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / NULLIF(COUNT(c.claim_id), 0)) >= 85 
+                THEN '🏆 Champion Provider'
+                WHEN COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) >= 25 
+                     AND (100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / NULLIF(COUNT(c.claim_id), 0)) >= 75 
+                THEN '⭐⭐⭐ Excellent Provider'
+                WHEN COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) >= 10 
+                     AND (100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / NULLIF(COUNT(c.claim_id), 0)) >= 60 
+                THEN '⭐⭐ Good Provider'
+                WHEN COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) >= 1 
+                THEN '⭐ Active Provider'
+                ELSE '❌ Inactive'
+            END as provider_recognition
+        FROM providers p 
+        LEFT JOIN food_listings f ON p.provider_id = f.provider_id
+        LEFT JOIN claims c ON f.food_id = c.food_id
+        GROUP BY p.provider_id, p.name, p.type, p.city, p.contact
+        HAVING COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) > 0
+        ORDER BY successful_claims DESC, total_food_distributed DESC
+        LIMIT 25
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_claims_completion_percentages():
+        """10. What percentage of food claims are completed vs. pending vs. canceled?"""
+        query = """
+        SELECT 
+            c.status,
+            COUNT(*) as claim_count,
+            ROUND(100.0 * COUNT(*) / (SELECT COUNT(*) FROM claims), 2) as percentage,
+            -- Quantity analysis
+            SUM(f.quantity) as total_quantity_involved,
+            AVG(f.quantity) as avg_quantity_per_claim,
+            -- Geographic distribution
+            COUNT(DISTINCT p.city) as cities_involved,
+            COUNT(DISTINCT p.provider_id) as providers_involved,
+            COUNT(DISTINCT r.receiver_id) as receivers_involved,
+            -- Food type diversity
+            COUNT(DISTINCT f.food_type) as food_types_in_status,
+            COUNT(DISTINCT f.meal_type) as meal_types_in_status,
+            -- Time analysis
+            ROUND(AVG(julianday('now') - julianday(c.timestamp)), 1) as avg_days_since_claim,
+            ROUND(AVG(CASE WHEN LOWER(c.status) = 'completed' THEN julianday(f.expiry_date) - julianday(c.timestamp) END), 1) as avg_days_before_expiry_when_completed,
+            -- Recent trends (last 30 days)
+            COUNT(CASE WHEN DATE(c.timestamp) >= DATE('now', '-30 days') THEN 1 END) as recent_claims,
+            ROUND(100.0 * COUNT(CASE WHEN DATE(c.timestamp) >= DATE('now', '-30 days') THEN 1 END) / 
+                  NULLIF((SELECT COUNT(*) FROM claims WHERE DATE(timestamp) >= DATE('now', '-30 days')), 0), 2) as recent_percentage,
+            -- Impact calculation
+            CASE c.status 
+                WHEN 'Completed' THEN SUM(f.quantity) 
+                ELSE 0 
+            END as food_impact_kg,
+            -- Status insights
+            CASE c.status 
+                WHEN 'Completed' THEN '✅ Successfully distributed food to those in need'
+                WHEN 'Pending' THEN '⏳ Awaiting pickup or processing'
+                WHEN 'Cancelled' THEN '❌ Claims that did not proceed - investigate reasons'
+                ELSE '❓ Unknown status'
+            END as status_insight
+        FROM claims c 
+        JOIN food_listings f ON c.food_id = f.food_id
+        JOIN providers p ON f.provider_id = p.provider_id
+        LEFT JOIN receivers r ON c.receiver_id = r.receiver_id
+        GROUP BY c.status
+        ORDER BY claim_count DESC
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_avg_quantity_per_receiver():
+        """11. What is the average quantity of food claimed per receiver?"""
+        query = """
+        SELECT 
+            r.receiver_id,
+            r.name as receiver_name,
+            r.type as receiver_type,
+            r.city,
+            -- Claiming metrics
+            COUNT(c.claim_id) as total_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as successful_claims,
+            SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) as total_food_received,
+            ROUND(AVG(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity END), 2) as avg_quantity_per_successful_claim,
+            ROUND(SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) / NULLIF(COUNT(c.claim_id), 0), 2) as avg_quantity_per_total_claim,
+            -- Efficiency metrics
+            ROUND(100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / NULLIF(COUNT(c.claim_id), 0), 2) as claim_success_rate,
+            -- Food diversity received
+            COUNT(DISTINCT CASE WHEN LOWER(c.status) = 'completed' THEN f.food_type END) as food_types_received,
+            COUNT(DISTINCT CASE WHEN LOWER(c.status) = 'completed' THEN f.meal_type END) as meal_types_received,
+            -- Provider diversity
+            COUNT(DISTINCT CASE WHEN LOWER(c.status) = 'completed' THEN p.provider_id END) as providers_claimed_from,
+            -- Time analysis
+            ROUND(AVG(CASE WHEN LOWER(c.status) = 'completed' THEN julianday(f.expiry_date) - julianday(c.timestamp) END), 1) as avg_days_before_expiry_received,
+            -- Recent activity
+            COUNT(CASE WHEN DATE(c.timestamp) >= DATE('now', '-30 days') THEN 1 END) as recent_claims,
+            SUM(CASE WHEN DATE(c.timestamp) >= DATE('now', '-30 days') AND LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) as recent_food_received,
+            -- Receiver category based on activity
+            CASE 
+                WHEN SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) >= 500 THEN '🏆 Major Recipient'
+                WHEN SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) >= 200 THEN '⭐⭐⭐ High Volume'
+                WHEN SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) >= 50 THEN '⭐⭐ Regular'
+                WHEN SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) >= 1 THEN '⭐ Occasional'
+                ELSE '❌ No Success'
+            END as receiver_category
+        FROM receivers r 
+        LEFT JOIN claims c ON r.receiver_id = c.receiver_id
+        LEFT JOIN food_listings f ON c.food_id = f.food_id
+        LEFT JOIN providers p ON f.provider_id = p.provider_id
+        GROUP BY r.receiver_id, r.name, r.type, r.city
+        HAVING COUNT(c.claim_id) > 0
+        ORDER BY total_food_received DESC, avg_quantity_per_successful_claim DESC
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_most_claimed_meal_types():
+        """12. Which meal type is claimed the most?"""
+        query = """
+        SELECT 
+            f.meal_type,
+            -- Claiming metrics
+            COUNT(c.claim_id) as total_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as successful_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'pending' THEN 1 END) as pending_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'cancelled' THEN 1 END) as cancelled_claims,
+            ROUND(100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / NULLIF(COUNT(c.claim_id), 0), 2) as success_rate,
+            -- Quantity metrics
+            SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) as total_quantity_distributed,
+            AVG(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity END) as avg_quantity_per_successful_claim,
+            -- Supply vs demand analysis
+            COUNT(DISTINCT f.food_id) as total_items_available,
+            ROUND(CAST(COUNT(c.claim_id) AS FLOAT) / NULLIF(COUNT(DISTINCT f.food_id), 0), 2) as demand_supply_ratio,
+            -- Provider and receiver diversity
+            COUNT(DISTINCT p.provider_id) as providers_offering,
+            COUNT(DISTINCT r.receiver_id) as receivers_claiming,
+            COUNT(DISTINCT p.city) as cities_with_supply,
+            -- Food type diversity within meal type
+            COUNT(DISTINCT f.food_type) as food_types_in_meal,
+            -- Time analysis
+            ROUND(AVG(julianday(f.expiry_date) - julianday('now')), 1) as avg_shelf_life_days,
+            ROUND(AVG(CASE WHEN LOWER(c.status) = 'completed' THEN julianday(f.expiry_date) - julianday(c.timestamp) END), 1) as avg_days_before_expiry_claimed,
+            -- Market share
+            ROUND(100.0 * COUNT(c.claim_id) / (SELECT COUNT(*) FROM claims), 2) as claim_market_share,
+            -- Popularity ranking
+            ROW_NUMBER() OVER (ORDER BY COUNT(c.claim_id) DESC) as demand_rank,
+            ROW_NUMBER() OVER (ORDER BY COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) DESC) as success_rank,
+            -- Recent trends (last 30 days)
+            COUNT(CASE WHEN DATE(c.timestamp) >= DATE('now', '-30 days') THEN 1 END) as recent_claims,
+            -- Meal time insights
+            CASE f.meal_type 
+                WHEN 'Breakfast' THEN '🌅 Morning meals - typically fresh items needed'
+                WHEN 'Lunch' THEN '🌞 Midday meals - highest volume period'
+                WHEN 'Dinner' THEN '🌙 Evening meals - often hearty dishes'
+                WHEN 'Snacks' THEN '🍪 Light items - good for quick distribution'
+                WHEN 'Beverages' THEN '🥤 Drinks - long shelf life items'
+                ELSE '🍽️ Mixed meal items'
+            END as meal_type_insight
+        FROM food_listings f 
+        LEFT JOIN claims c ON f.food_id = c.food_id
+        LEFT JOIN providers p ON f.provider_id = p.provider_id
+        LEFT JOIN receivers r ON c.receiver_id = r.receiver_id
+        GROUP BY f.meal_type
+        ORDER BY total_claims DESC, successful_claims DESC
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_total_donations_per_provider():
+        """13. What is the total quantity of food donated by each provider?"""
+        query = """
+        SELECT 
+            p.provider_id,
+            p.name as provider_name,
+            p.type as provider_type,
+            p.city,
+            p.contact,
+            -- Donation metrics
+            COUNT(f.food_id) as total_food_items_listed,
+            SUM(f.quantity) as total_quantity_donated,
+            AVG(f.quantity) as avg_quantity_per_donation,
+            -- Distribution success
+            COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as items_successfully_distributed,
+            SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) as quantity_successfully_distributed,
+            ROUND(100.0 * SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) / NULLIF(SUM(f.quantity), 0), 2) as distribution_success_rate,
+            -- Wastage analysis
+            COUNT(CASE WHEN julianday('now') - julianday(f.expiry_date) > 0 THEN 1 END) as expired_items,
+            SUM(CASE WHEN julianday('now') - julianday(f.expiry_date) > 0 THEN f.quantity ELSE 0 END) as wasted_quantity,
+            ROUND(100.0 * SUM(CASE WHEN julianday('now') - julianday(f.expiry_date) > 0 THEN f.quantity ELSE 0 END) / NULLIF(SUM(f.quantity), 0), 2) as wastage_rate,
+            -- Food diversity
+            COUNT(DISTINCT f.food_type) as food_types_donated,
+            COUNT(DISTINCT f.meal_type) as meal_types_donated,
+            -- Impact metrics
+            COUNT(DISTINCT r.receiver_id) as unique_receivers_served,
+            COUNT(DISTINCT CASE WHEN LOWER(c.status) = 'completed' THEN r.receiver_id END) as receivers_successfully_served,
+            -- Time efficiency
+            ROUND(AVG(julianday(f.expiry_date) - julianday('now')), 1) as avg_donation_shelf_life,
+            ROUND(AVG(CASE WHEN LOWER(c.status) = 'completed' THEN julianday(f.expiry_date) - julianday(c.timestamp) END), 1) as avg_days_before_expiry_distributed,
+            -- Recent activity (last 30 days)
+            COUNT(CASE WHEN DATE(f.expiry_date) >= DATE('now', '-30 days') THEN 1 END) as recent_donations,
+            SUM(CASE WHEN DATE(f.expiry_date) >= DATE('now', '-30 days') THEN f.quantity ELSE 0 END) as recent_donation_quantity,
+            -- Provider impact score
+            ROUND(
+                (SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) * 0.6) +
+                (COUNT(DISTINCT r.receiver_id) * 10 * 0.2) +
+                (COUNT(DISTINCT f.food_type) * 5 * 0.2)
+                , 2) as provider_impact_score,
+            -- Recognition level
+            CASE 
+                WHEN SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) >= 1000 
+                     AND (100.0 * SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) / NULLIF(SUM(f.quantity), 0)) >= 80 
+                THEN '🏆 Champion Donor'
+                WHEN SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) >= 500 
+                     AND (100.0 * SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) / NULLIF(SUM(f.quantity), 0)) >= 70 
+                THEN '⭐⭐⭐ Excellent Donor'
+                WHEN SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) >= 200 
+                     AND (100.0 * SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) / NULLIF(SUM(f.quantity), 0)) >= 60 
+                THEN '⭐⭐ Good Donor'
+                WHEN SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) >= 1 
+                THEN '⭐ Active Donor'
+                ELSE '❌ Inactive'
+            END as donor_recognition
+        FROM providers p 
+        LEFT JOIN food_listings f ON p.provider_id = f.provider_id
+        LEFT JOIN claims c ON f.food_id = c.food_id
+        LEFT JOIN receivers r ON c.receiver_id = r.receiver_id
+        GROUP BY p.provider_id, p.name, p.type, p.city, p.contact
+        HAVING COUNT(f.food_id) > 0
+        ORDER BY total_quantity_donated DESC, quantity_successfully_distributed DESC
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_food_wastage_trends_comprehensive():
+        """14. Enhanced food wastage trends with all insights"""
+        query = """
+        SELECT 
+            food_type,
+            COUNT(*) as total_listings,
+            SUM(quantity) as total_quantity,
+            AVG(quantity) as avg_quantity_per_listing,
+            -- Wastage calculations
+            COUNT(CASE WHEN julianday('now') - julianday(expiry_date) > 0 THEN 1 END) as expired_items,
+            SUM(CASE WHEN julianday('now') - julianday(expiry_date) > 0 THEN quantity ELSE 0 END) as wasted_quantity,
+            ROUND(100.0 * COUNT(CASE WHEN julianday('now') - julianday(expiry_date) > 0 THEN 1 END) / COUNT(*), 2) as wastage_percentage,
+            ROUND(100.0 * SUM(CASE WHEN julianday('now') - julianday(expiry_date) > 0 THEN quantity ELSE 0 END) / SUM(quantity), 2) as quantity_wastage_percentage,
+            -- Urgency analysis
+            COUNT(CASE WHEN julianday(expiry_date) - julianday('now') BETWEEN 0 AND 1 THEN 1 END) as critical_items,
+            COUNT(CASE WHEN julianday(expiry_date) - julianday('now') BETWEEN 1 AND 3 THEN 1 END) as urgent_items,
+            COUNT(CASE WHEN julianday(expiry_date) - julianday('now') BETWEEN 3 AND 7 THEN 1 END) as soon_items,
+            COUNT(CASE WHEN julianday(expiry_date) - julianday('now') > 7 THEN 1 END) as safe_items,
+            -- Claims impact
+            COUNT(c.claim_id) as total_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as successful_distributions,
+            SUM(CASE WHEN LOWER(c.status) = 'completed' THEN quantity ELSE 0 END) as quantity_saved_through_claims,
+            ROUND(100.0 * SUM(CASE WHEN LOWER(c.status) = 'completed' THEN quantity ELSE 0 END) / SUM(quantity), 2) as saved_percentage,
+            -- Provider diversity
+            COUNT(DISTINCT p.provider_id) as contributing_providers,
+            COUNT(DISTINCT p.city) as cities_offering
+        FROM food_listings f 
+        LEFT JOIN claims c ON f.food_id = c.food_id
+        LEFT JOIN providers p ON f.provider_id = p.provider_id
+        GROUP BY food_type
+        ORDER BY wasted_quantity DESC, total_quantity DESC
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_comprehensive_system_analysis():
+        """15. Comprehensive analysis with all outputs and insights"""
+        query = """
+        WITH provider_stats AS (
+            SELECT 
+                p.type as provider_type,
+                COUNT(DISTINCT p.provider_id) as provider_count,
+                SUM(f.quantity) as total_contribution,
+                COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as successful_distributions
+            FROM providers p 
+            LEFT JOIN food_listings f ON p.provider_id = f.provider_id
+            LEFT JOIN claims c ON f.food_id = c.food_id
+            GROUP BY p.type
+        ),
+        city_stats AS (
+            SELECT 
+                p.city,
+                COUNT(DISTINCT p.provider_id) as providers,
+                COUNT(DISTINCT r.receiver_id) as receivers,
+                COUNT(c.claim_id) as total_claims,
+                SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) as food_distributed
+            FROM providers p 
+            LEFT JOIN receivers r ON p.city = r.city
+            LEFT JOIN food_listings f ON p.provider_id = f.provider_id
+            LEFT JOIN claims c ON f.food_id = c.food_id
+            GROUP BY p.city
+        ),
+        food_stats AS (
+            SELECT 
+                f.food_type,
+                COUNT(f.food_id) as total_items,
+                SUM(f.quantity) as total_quantity,
+                COUNT(CASE WHEN julianday('now') - julianday(f.expiry_date) > 0 THEN 1 END) as wasted_items,
+                SUM(CASE WHEN julianday('now') - julianday(f.expiry_date) > 0 THEN f.quantity ELSE 0 END) as wasted_quantity
+            FROM food_listings f 
+            GROUP BY f.food_type
+        )
+        SELECT 
+            'COMPREHENSIVE SYSTEM ANALYSIS' as analysis_category,
+            -- Overall metrics
+            (SELECT COUNT(*) FROM providers) as total_providers,
+            (SELECT COUNT(*) FROM receivers) as total_receivers,
+            (SELECT COUNT(*) FROM food_listings) as total_food_items,
+            (SELECT SUM(quantity) FROM food_listings) as total_food_quantity,
+            (SELECT COUNT(*) FROM claims) as total_claims,
+            -- Performance metrics
+            (SELECT COUNT(*) FROM claims WHERE LOWER(status) = 'completed') as successful_distributions,
+            (SELECT ROUND(100.0 * COUNT(*) / (SELECT COUNT(*) FROM claims), 2) FROM claims WHERE LOWER(status) = 'completed') as success_rate,
+            -- Top provider type
+            (SELECT provider_type FROM provider_stats ORDER BY total_contribution DESC LIMIT 1) as top_provider_type_by_contribution,
+            (SELECT total_contribution FROM provider_stats ORDER BY total_contribution DESC LIMIT 1) as top_provider_contribution,
+            -- Top city
+            (SELECT city FROM city_stats ORDER BY food_distributed DESC LIMIT 1) as top_city_by_distribution,
+            (SELECT food_distributed FROM city_stats ORDER BY food_distributed DESC LIMIT 1) as top_city_distribution,
+            -- Most wasted food type
+            (SELECT food_type FROM food_stats ORDER BY wasted_quantity DESC LIMIT 1) as most_wasted_food_type,
+            (SELECT wasted_quantity FROM food_stats ORDER BY wasted_quantity DESC LIMIT 1) as highest_waste_quantity,
+            -- System health indicators
+            (SELECT ROUND(100.0 * SUM(wasted_quantity) / SUM(total_quantity), 2) FROM food_stats) as overall_wastage_rate,
+            (SELECT COUNT(*) FROM city_stats WHERE providers > 0 AND receivers > 0) as cities_with_complete_ecosystem,
+            -- Key insights
+            CASE 
+                WHEN (SELECT ROUND(100.0 * COUNT(*) / (SELECT COUNT(*) FROM claims), 2) FROM claims WHERE LOWER(status) = 'completed') >= 80 
+                THEN 'System performing excellently'
+                WHEN (SELECT ROUND(100.0 * COUNT(*) / (SELECT COUNT(*) FROM claims), 2) FROM claims WHERE LOWER(status) = 'completed') >= 60 
+                THEN 'System performing well with room for improvement'
+                ELSE 'System needs significant optimization'
+            END as overall_system_health,
+            -- Action recommendations
+            'Focus on ' || (SELECT food_type FROM food_stats ORDER BY wasted_quantity DESC LIMIT 1) || ' wastage reduction' as primary_action_needed,
+            'Expand operations in ' || (SELECT city FROM city_stats ORDER BY (providers + receivers) ASC LIMIT 1) || ' for better coverage' as expansion_recommendation
+        """
+        return SQLQueries.execute_query(query)
+
+    # ========== NEW: TIME SERIES ANALYSIS QUERIES ==========
+    @staticmethod
+    def get_time_series_claims_trends():
+        """NEW: Time series analysis of claims trends"""
+        query = """
+        SELECT 
+            DATE(c.timestamp) as claim_date,
+            COUNT(*) as total_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as completed_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'pending' THEN 1 END) as pending_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'cancelled' THEN 1 END) as cancelled_claims,
+            SUM(f.quantity) as total_quantity_claimed,
+            SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) as quantity_distributed,
+            ROUND(100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / COUNT(*), 2) as daily_success_rate,
+            -- Day of week analysis
+            CASE strftime('%w', c.timestamp)
+                WHEN '0' THEN 'Sunday'
+                WHEN '1' THEN 'Monday'
+                WHEN '2' THEN 'Tuesday'
+                WHEN '3' THEN 'Wednesday'
+                WHEN '4' THEN 'Thursday'
+                WHEN '5' THEN 'Friday'
+                WHEN '6' THEN 'Saturday'
+            END as day_of_week,
+            -- Month analysis
+            strftime('%Y-%m', c.timestamp) as year_month
+        FROM claims c 
+        JOIN food_listings f ON c.food_id = f.food_id
+        WHERE c.timestamp IS NOT NULL
+        GROUP BY DATE(c.timestamp)
+        ORDER BY claim_date
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_time_series_food_listings_trends():
+        """NEW: Time series analysis of food listings by expiry trends"""
+        query = """
+        SELECT 
+            DATE(f.expiry_date) as expiry_date,
+            COUNT(*) as items_expiring,
+            SUM(f.quantity) as quantity_expiring,
+            COUNT(DISTINCT f.food_type) as food_types_expiring,
+            COUNT(DISTINCT p.provider_id) as providers_affected,
+            -- Claims before expiry
+            COUNT(c.claim_id) as claims_made,
+            COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as items_saved,
+            SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) as quantity_saved,
+            -- Wastage calculation
+            COUNT(*) - COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as items_wasted,
+            SUM(f.quantity) - SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) as quantity_wasted,
+            ROUND(100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / COUNT(*), 2) as save_rate,
+            -- Week analysis
+            strftime('%Y-W%W', f.expiry_date) as year_week,
+            strftime('%Y-%m', f.expiry_date) as year_month
+        FROM food_listings f 
+        LEFT JOIN providers p ON f.provider_id = p.provider_id
+        LEFT JOIN claims c ON f.food_id = c.food_id
+        WHERE f.expiry_date IS NOT NULL
+        GROUP BY DATE(f.expiry_date)
+        ORDER BY expiry_date
+        """
+        return SQLQueries.execute_query(query)
+
+    @staticmethod
+    def get_monthly_performance_trends():
+        """NEW: Monthly performance trends analysis"""
+        query = """
+        SELECT 
+            strftime('%Y-%m', c.timestamp) as month,
+            COUNT(*) as total_claims,
+            COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) as completed_claims,
+            SUM(f.quantity) as total_quantity_involved,
+            SUM(CASE WHEN LOWER(c.status) = 'completed' THEN f.quantity ELSE 0 END) as quantity_distributed,
+            COUNT(DISTINCT p.provider_id) as active_providers,
+            COUNT(DISTINCT r.receiver_id) as active_receivers,
+            COUNT(DISTINCT p.city) as cities_involved,
+            ROUND(100.0 * COUNT(CASE WHEN LOWER(c.status) = 'completed' THEN 1 END) / COUNT(*), 2) as monthly_success_rate,
+            ROUND(AVG(julianday(f.expiry_date) - julianday(c.timestamp)), 1) as avg_days_before_expiry,
+            -- Growth metrics
+            LAG(COUNT(*)) OVER (ORDER BY strftime('%Y-%m', c.timestamp)) as prev_month_claims,
+            ROUND(100.0 * (COUNT(*) - LAG(COUNT(*)) OVER (ORDER BY strftime('%Y-%m', c.timestamp))) / 
+                  NULLIF(LAG(COUNT(*)) OVER (ORDER BY strftime('%Y-%m', c.timestamp)), 0), 2) as claims_growth_rate
+        FROM claims c 
+        JOIN food_listings f ON c.food_id = f.food_id
+        JOIN providers p ON f.provider_id = p.provider_id
+        LEFT JOIN receivers r ON c.receiver_id = r.receiver_id
+        WHERE c.timestamp IS NOT NULL
+        GROUP BY strftime('%Y-%m', c.timestamp)
+        ORDER BY month
+        """
+        return SQLQueries.execute_query(query)
+
+# ========== ENHANCED CHART STYLING FUNCTION ==========
+def apply_readable_chart_style(fig, title, x_label=None, y_label=None):
+    """Apply consistent readable styling to all charts"""
+    fig.update_layout(
+        # Title styling
+        title={
+            'text': title,
+            'x': 0.5,
+            'xanchor': 'center',
+            'font': {
+                'size': 20,
+                'color': '#1f2937',
+                'family': 'Arial, sans-serif'
+            }
+        },
+        
+        # Plot area styling
+        plot_bgcolor='white',
+        paper_bgcolor='white',
+        
+        # Font styling
+        font={
+            'size': 12,
+            'color': '#374151',
+            'family': 'Arial, sans-serif'
+        },
+        
+        # Margins
+        margin=dict(l=80, r=80, t=100, b=80),
+        
+        # Grid
+        xaxis=dict(
+            showgrid=True,
+            gridwidth=1,
+            gridcolor='#e5e7eb',
+            title=dict(
+                text=x_label if x_label else "",
+                font=dict(size=14, color='#1f2937')
+            ),
+            tickfont=dict(size=11, color='#374151')
+        ),
+        yaxis=dict(
+            showgrid=True,
+            gridwidth=1,
+            gridcolor='#e5e7eb',
+            title=dict(
+                text=y_label if y_label else "",
+                font=dict(size=14, color='#1f2937')
+            ),
+            tickfont=dict(size=11, color='#374151')
+        ),
+        
+        # Legend styling
+        legend=dict(
+            bgcolor='rgba(255,255,255,0.8)',
+            bordercolor='#d1d5db',
+            borderwidth=1,
+            font=dict(size=11, color='#374151')
+        ),
+        
+        # Hover styling
+        hoverlabel=dict(
+            bgcolor="white",
+            font_size=12,
+            font_family="Arial",
+            bordercolor='#d1d5db'
+        )
+    )
+    
+    # Update traces for better visibility
+    fig.update_traces(
+        textfont=dict(size=11, color='#1f2937'),
+        hoverlabel=dict(
+            bgcolor="white",
+            font=dict(color='#1f2937')
+        )
+    )
+    
+    return fig
+
+# ========== ENHANCED VISUALIZATION FUNCTIONS ==========
+def create_project_required_charts():
+    """Create all charts required by the project with enhanced readability"""
+    charts = {}
+    try:
+        # 1. Food Wastage Trends by Category - ENHANCED
+        category_data = SQLQueries.get_food_wastage_trends_comprehensive()
+        if not category_data.empty:
+            fig = px.bar(category_data.head(10), 
+                        x='food_type', 
+                        y='total_quantity',
+                        color='wasted_quantity',
+                        hover_data=['total_listings', 'wastage_percentage', 'critical_items', 'wasted_quantity'],
+                        color_continuous_scale='Reds',
+                        labels={
+                            'food_type': 'Food Type',
+                            'total_quantity': 'Total Quantity (kg)',
+                            'wasted_quantity': 'Wasted Quantity (kg)'
+                        })
+            
+            fig = apply_readable_chart_style(fig, 
+                                           "📊 Food Wastage Analysis by Category", 
+                                           "Food Type", 
+                                           "Total Quantity (kg)")
+            charts['category_trends'] = fig
+
+        # 2. Provider Type Contributions - ENHANCED
+        provider_type_data = SQLQueries.get_provider_type_contributions()
+        if not provider_type_data.empty:
+            fig = px.bar(provider_type_data, 
+                        x='provider_type', 
+                        y='total_quantity_contributed',
+                        color='success_rate',
+                        hover_data=['total_providers', 'food_types_offered', 'successful_distributions', 'success_rate'],
+                        color_continuous_scale='Blues',
+                        labels={
+                            'provider_type': 'Provider Type',
+                            'total_quantity_contributed': 'Total Contribution (kg)',
+                            'success_rate': 'Success Rate (%)'
+                        })
+            
+            fig = apply_readable_chart_style(fig, 
+                                           "🏢 Food Contributions by Provider Type", 
+                                           "Provider Type", 
+                                           "Total Contribution (kg)")
+            charts['provider_type_contributions'] = fig
+
+        # 3. Cities by Food Listings - ENHANCED
+        city_data = SQLQueries.get_cities_by_food_listings()
+        if not city_data.empty:
+            fig = px.bar(city_data.head(10), 
+                        x='city', 
+                        y='total_food_listings',
+                        color='city_performance_score',
+                        hover_data=['total_quantity', 'unique_providers', 'claim_success_rate', 'freshness_rate'],
+                        color_continuous_scale='Viridis',
+                        labels={
+                            'city': 'City',
+                            'total_food_listings': 'Number of Food Listings',
+                            'city_performance_score': 'Performance Score'
+                        })
+            
+            fig.update_layout(xaxis={'categoryorder':'total descending'})
+            fig = apply_readable_chart_style(fig, 
+                                           "🌍 Top Cities by Food Availability", 
+                                           "City", 
+                                           "Number of Food Listings")
+            charts['city_listings'] = fig
+
+        # 4. Food Types Distribution - ENHANCED
+        food_type_data = SQLQueries.get_most_common_food_types()
+        if not food_type_data.empty:
+            fig = px.pie(food_type_data.head(8), 
+                        values='total_items', 
+                        names='food_type',
+                        hover_data=['total_quantity', 'claim_success_rate', 'supply_demand_ratio'],
+                        color_discrete_sequence=px.colors.qualitative.Set3)
+            
+            fig.update_traces(
+                textposition='inside', 
+                textinfo='percent+label',
+                textfont_size=12
+            )
+            
+            fig = apply_readable_chart_style(fig, "🍽️ Food Types Distribution")
+            charts['food_type_distribution'] = fig
+
+        # 5. Claims Status Analysis - ENHANCED
+        claims_data = SQLQueries.get_claims_completion_percentages()
+        if not claims_data.empty:
+            colors = {
+                'Completed': '#10b981',    # Green
+                'Pending': '#f59e0b',      # Orange  
+                'Cancelled': '#ef4444'     # Red
+            }
+            
+            fig = px.pie(claims_data, 
+                        values='claim_count', 
+                        names='status',
+                        hover_data=['percentage', 'total_quantity_involved', 'avg_quantity_per_claim'],
+                        color='status',
+                        color_discrete_map=colors)
+            
+            fig.update_traces(
+                textposition='inside', 
+                textinfo='percent+label',
+                textfont_size=14,
+                textfont_color='white'
+            )
+            
+            fig = apply_readable_chart_style(fig, "📈 Food Claims Status Distribution")
+            charts['claims_analysis'] = fig
+
+        # 6. Meal Type Demand - ENHANCED
+        meal_data = SQLQueries.get_most_claimed_meal_types()
+        if not meal_data.empty:
+            fig = px.bar(meal_data.head(8), 
+                        x='meal_type', 
+                        y='total_claims',
+                        color='success_rate',
+                        hover_data=['total_quantity_distributed', 'demand_supply_ratio', 'success_rate'],
+                        color_continuous_scale='Greens',
+                        labels={
+                            'meal_type': 'Meal Type',
+                            'total_claims': 'Total Claims',
+                            'success_rate': 'Success Rate (%)'
+                        })
+            
+            fig = apply_readable_chart_style(fig, 
+                                           "🍴 Most Demanded Meal Types", 
+                                           "Meal Type", 
+                                           "Number of Claims")
+            charts['meal_claims'] = fig
+
+        # 7. System Overview - ENHANCED
+        system_data = SQLQueries.get_comprehensive_system_analysis()
+        if not system_data.empty:
+            metrics = ['total_providers', 'total_receivers', 'total_food_items', 'successful_distributions']
+            values = [system_data.iloc[0][metric] for metric in metrics]
+            labels = ['Food Providers', 'Food Receivers', 'Food Items Listed', 'Successful Distributions']
+            
+            fig = px.bar(x=labels, y=values,
+                        color=values,
+                        color_continuous_scale='RdYlBu_r',
+                        labels={
+                            'x': 'System Components',
+                            'y': 'Count'
+                        })
+            
+            # Add value labels on bars
+            fig.update_traces(
+                text=values,
+                texttemplate='%{text:,}',
+                textposition='outside',
+                textfont=dict(size=14, color='#1f2937')
+            )
+            
+            fig = apply_readable_chart_style(fig, 
+                                           "📊 System Overview Dashboard", 
+                                           "System Components", 
+                                           "Count")
+            charts['system_overview'] = fig
+
+    except Exception as e:
+        st.error(f"Error creating enhanced charts: {e}")
+        charts['error'] = str(e)
+    
+    return charts
+
+# ========== NEW: ENHANCED TIME SERIES CHARTS ==========
+def create_time_series_charts():
+    """Create enhanced time series trend charts with improved readability"""
+    charts = {}
+    try:
+        # 1. Claims Trends Over Time - ENHANCED
+        claims_trends = SQLQueries.get_time_series_claims_trends()
+        if not claims_trends.empty:
+            fig = go.Figure()
+            
+            # Total claims line
+            fig.add_trace(go.Scatter(
+                x=claims_trends['claim_date'],
+                y=claims_trends['total_claims'],
+                mode='lines+markers',
+                name='Total Claims',
+                line=dict(color='#3b82f6', width=3),
+                marker=dict(size=6, color='#3b82f6'),
+                hovertemplate='<b>Date:</b> %{x}<br><b>Total Claims:</b> %{y}<extra></extra>'
+            ))
+            
+            # Completed claims line
+            fig.add_trace(go.Scatter(
+                x=claims_trends['claim_date'],
+                y=claims_trends['completed_claims'],
+                mode='lines+markers',
+                name='Completed Claims',
+                line=dict(color='#10b981', width=3),
+                marker=dict(size=6, color='#10b981'),
+                hovertemplate='<b>Date:</b> %{x}<br><b>Completed:</b> %{y}<extra></extra>'
+            ))
+            
+            fig = apply_readable_chart_style(fig, 
+                                           "📈 Food Claims Trends Over Time", 
+                                           "Date", 
+                                           "Number of Claims")
+            charts['claims_time_series'] = fig
+
+        # 2. Food Wastage vs Savings Timeline - ENHANCED
+        food_trends = SQLQueries.get_time_series_food_listings_trends()
+        if not food_trends.empty:
+            fig = go.Figure()
+            
+            # Quantity saved (positive impact)
+            fig.add_trace(go.Scatter(
+                x=food_trends['expiry_date'],
+                y=food_trends['quantity_saved'],
+                mode='lines+markers',
+                name='Food Saved (kg)',
+                line=dict(color='#10b981', width=3),
+                fill='tozeroy',
+                fillcolor='rgba(16, 185, 129, 0.2)',
+                marker=dict(size=5, color='#10b981'),
+                hovertemplate='<b>Date:</b> %{x}<br><b>Food Saved:</b> %{y} kg<extra></extra>'
+            ))
+            
+            # Quantity wasted (negative impact)
+            fig.add_trace(go.Scatter(
+                x=food_trends['expiry_date'],
+                y=food_trends['quantity_wasted'],
+                mode='lines+markers',
+                name='Food Wasted (kg)',
+                line=dict(color='#ef4444', width=3),
+                marker=dict(size=5, color='#ef4444'),
+                hovertemplate='<b>Date:</b> %{x}<br><b>Food Wasted:</b> %{y} kg<extra></extra>'
+            ))
+            
+            fig = apply_readable_chart_style(fig, 
+                                           "🗑️ Food Wastage vs Savings Timeline", 
+                                           "Expiry Date", 
+                                           "Quantity (kg)")
+            charts['wastage_timeline'] = fig
+
+        # 3. Monthly Performance Dashboard - ENHANCED
+        monthly_data = SQLQueries.get_monthly_performance_trends()
+        if not monthly_data.empty:
+            fig = go.Figure()
+            
+            # Claims bar chart
+            fig.add_trace(go.Bar(
+                x=monthly_data['month'],
+                y=monthly_data['total_claims'],
+                name='Total Claims',
+                marker_color='rgba(59, 130, 246, 0.7)',
+                marker_line=dict(color='#3b82f6', width=1),
+                yaxis='y',
+                hovertemplate='<b>Month:</b> %{x}<br><b>Claims:</b> %{y}<extra></extra>'
+            ))
+            
+            # Success rate line
+            fig.add_trace(go.Scatter(
+                x=monthly_data['month'],
+                y=monthly_data['monthly_success_rate'],
+                mode='lines+markers',
+                name='Success Rate (%)',
+                line=dict(color='#10b981', width=3),
+                marker=dict(size=8, color='#10b981'),
+                yaxis='y2',
+                hovertemplate='<b>Month:</b> %{x}<br><b>Success Rate:</b> %{y}%<extra></extra>'
+            ))
+            
+            fig.update_layout(
+                yaxis=dict(
+                    title='Number of Claims',
+                    side='left',
+                    showgrid=True,
+                    gridcolor='#e5e7eb'
+                ),
+                yaxis2=dict(
+                    title='Success Rate (%)',
+                    side='right',
+                    overlaying='y',
+                    showgrid=False,
+                    range=[0, 100]
+                )
+            )
+            
+            fig = apply_readable_chart_style(fig, 
+                                           "📊 Monthly Performance & Success Trends", 
+                                           "Month", 
+                                           "Claims / Success Rate")
+            charts['monthly_trends'] = fig
+
+    except Exception as e:
+        st.error(f"Error creating time series charts: {e}")
+        charts['error'] = str(e)
+    
+    return charts
+
+# ========== MAIN HEADER ==========
+st.markdown("""
+<div class="main-header">
+    <h1>🌍 Food Wastage Management System</h1>
+    <p>Connecting food providers with those in need • Reducing waste • Building community</p>
+</div>
+""", unsafe_allow_html=True)
+
+# ========== SIDEBAR NAVIGATION (FIXED) ==========
+with st.sidebar:
+    st.title("🧭 Navigation")
+    current_page = st.selectbox(
+        "Choose a page:",
+        ["📊 Dashboard", "🏢 Providers", "🤝 Receivers", "🥗 Food Listings", "📦 Claims", "📈 Analytics", "⏰ Time Series"]
+    )
+
+# ========== MAIN CONTENT ROUTER (FIXED) ==========
+if current_page == "📊 Dashboard":
+    st.header("📊 Dashboard Overview")
+    
+    # Display key metrics with enhanced visibility
+    col1, col2, col3, col4 = st.columns(4)
+    
+    try:
+        system_data = SQLQueries.get_total_food_quantity_available()
+        if not system_data.empty:
+            row = system_data.iloc[0]
+            
+            with col1:
+                st.metric("Total Food Items", f"{row['total_food_items']:,}")
+            with col2:
+                st.metric("Fresh Items", f"{row['fresh_items']:,}")
+            with col3:
+                st.metric("Total Providers", f"{row['contributing_providers']:,}")
+            with col4:
+                st.metric("Cities Covered", f"{row['cities_covered']:,}")
+    except Exception as e:
+        st.warning("Loading dashboard metrics...")
+    
+    # Display enhanced charts
+    st.subheader("📈 Analytics Overview")
+    charts = create_project_required_charts()
+    
+    for chart_name, chart in charts.items():
+        if chart_name != 'error' and chart is not None:
+            try:
+                st.plotly_chart(chart, use_container_width=True, config={'displayModeBar': False})
+            except Exception as e:
+                st.error(f"Error displaying chart {chart_name}: {e}")