Add comprehensive API documentation for ThrillWiki integration and features

- Introduced Next.js integration guide for ThrillWiki API, detailing authentication, core domain APIs, data structures, and implementation patterns.
- Documented the migration to Rich Choice Objects, highlighting changes for frontend developers and enhanced metadata availability.
- Fixed the missing `get_by_slug` method in the Ride model, ensuring proper functionality of ride detail endpoints.
- Created a test script to verify manufacturer syncing with ride models, ensuring data integrity across related models.
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pacnpal
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# ThrillWiki Data Seeding - Implementation Guide
## Overview
This document outlines the specific requirements and implementation steps needed to complete the data seeding script for ThrillWiki. Currently, three features are skipped during seeding due to missing or incomplete model implementations.
## 🛡️ Moderation Data Implementation
### Current Status
```
🛡️ Creating moderation data...
✅ Comprehensive moderation system is implemented and ready for seeding
```
### Available Models
The moderation system is fully implemented in `apps.moderation.models` with the following models:
#### 1. ModerationReport Model
```python
class ModerationReport(TrackedModel):
"""Model for tracking user reports about content, users, or behavior"""
STATUS_CHOICES = [
('PENDING', 'Pending Review'),
('UNDER_REVIEW', 'Under Review'),
('RESOLVED', 'Resolved'),
('DISMISSED', 'Dismissed'),
]
REPORT_TYPE_CHOICES = [
('SPAM', 'Spam'),
('HARASSMENT', 'Harassment'),
('INAPPROPRIATE_CONTENT', 'Inappropriate Content'),
('MISINFORMATION', 'Misinformation'),
('COPYRIGHT', 'Copyright Violation'),
('PRIVACY', 'Privacy Violation'),
('HATE_SPEECH', 'Hate Speech'),
('VIOLENCE', 'Violence or Threats'),
('OTHER', 'Other'),
]
report_type = models.CharField(max_length=50, choices=REPORT_TYPE_CHOICES)
status = models.CharField(max_length=20, choices=STATUS_CHOICES, default='PENDING')
priority = models.CharField(max_length=10, choices=PRIORITY_CHOICES, default='MEDIUM')
reason = models.CharField(max_length=200)
description = models.TextField()
reported_by = models.ForeignKey(User, on_delete=models.CASCADE, related_name='moderation_reports_made')
assigned_moderator = models.ForeignKey(User, on_delete=models.SET_NULL, null=True, blank=True)
# ... additional fields
```
#### 2. ModerationQueue Model
```python
class ModerationQueue(TrackedModel):
"""Model for managing moderation workflow and task assignment"""
ITEM_TYPE_CHOICES = [
('CONTENT_REVIEW', 'Content Review'),
('USER_REVIEW', 'User Review'),
('BULK_ACTION', 'Bulk Action'),
('POLICY_VIOLATION', 'Policy Violation'),
('APPEAL', 'Appeal'),
('OTHER', 'Other'),
]
item_type = models.CharField(max_length=50, choices=ITEM_TYPE_CHOICES)
status = models.CharField(max_length=20, choices=STATUS_CHOICES, default='PENDING')
priority = models.CharField(max_length=10, choices=PRIORITY_CHOICES, default='MEDIUM')
title = models.CharField(max_length=200)
description = models.TextField()
assigned_to = models.ForeignKey(User, on_delete=models.SET_NULL, null=True, blank=True)
related_report = models.ForeignKey(ModerationReport, on_delete=models.CASCADE, null=True, blank=True)
# ... additional fields
```
#### 3. ModerationAction Model
```python
class ModerationAction(TrackedModel):
"""Model for tracking actions taken against users or content"""
ACTION_TYPE_CHOICES = [
('WARNING', 'Warning'),
('USER_SUSPENSION', 'User Suspension'),
('USER_BAN', 'User Ban'),
('CONTENT_REMOVAL', 'Content Removal'),
('CONTENT_EDIT', 'Content Edit'),
('CONTENT_RESTRICTION', 'Content Restriction'),
('ACCOUNT_RESTRICTION', 'Account Restriction'),
('OTHER', 'Other'),
]
action_type = models.CharField(max_length=50, choices=ACTION_TYPE_CHOICES)
reason = models.CharField(max_length=200)
details = models.TextField()
moderator = models.ForeignKey(User, on_delete=models.CASCADE, related_name='moderation_actions_taken')
target_user = models.ForeignKey(User, on_delete=models.CASCADE, related_name='moderation_actions_received')
related_report = models.ForeignKey(ModerationReport, on_delete=models.SET_NULL, null=True, blank=True)
# ... additional fields
```
#### 4. Additional Models
- **BulkOperation**: For tracking bulk administrative operations
- **PhotoSubmission**: For photo moderation workflow
- **EditSubmission**: For content edit submissions (legacy)
### Implementation Steps
1. **Moderation app already exists** at `backend/apps/moderation/`
2. **Already added to INSTALLED_APPS** in `backend/config/django/base.py`
3. **Models are fully implemented** in `apps/moderation/models.py`
4. **Update the seeding script** - Replace the placeholder in `create_moderation_data()`:
```python
def create_moderation_data(self, users: List[User], parks: List[Park], rides: List[Ride]) -> None:
"""Create moderation reports, queue items, and actions"""
self.stdout.write('🛡️ Creating moderation data...')
if not users or (not parks and not rides):
self.stdout.write(' ⚠️ No users or content found, skipping moderation data')
return
moderators = [u for u in users if u.role in ['MODERATOR', 'ADMIN']]
if not moderators:
self.stdout.write(' ⚠️ No moderators found, skipping moderation data')
return
moderation_count = 0
all_content = list(parks) + list(rides)
# Create moderation reports
for _ in range(min(15, len(all_content))):
content_item = random.choice(all_content)
reporter = random.choice(users)
moderator = random.choice(moderators) if random.random() < 0.7 else None
report = ModerationReport.objects.create(
report_type=random.choice(['SPAM', 'INAPPROPRIATE_CONTENT', 'MISINFORMATION', 'OTHER']),
status=random.choice(['PENDING', 'UNDER_REVIEW', 'RESOLVED', 'DISMISSED']),
priority=random.choice(['LOW', 'MEDIUM', 'HIGH']),
reason=f"Reported issue with {content_item.__class__.__name__}",
description=random.choice([
'Content contains inappropriate information',
'Suspected spam or promotional content',
'Information appears to be inaccurate',
'Content violates community guidelines'
]),
reported_by=reporter,
assigned_moderator=moderator,
reported_entity_type=content_item.__class__.__name__.lower(),
reported_entity_id=content_item.pk,
)
# Create queue item for some reports
if random.random() < 0.6:
queue_item = ModerationQueue.objects.create(
item_type=random.choice(['CONTENT_REVIEW', 'POLICY_VIOLATION']),
status=random.choice(['PENDING', 'IN_PROGRESS', 'COMPLETED']),
priority=report.priority,
title=f"Review {content_item.__class__.__name__}: {content_item}",
description=f"Review required for reported {content_item.__class__.__name__.lower()}",
assigned_to=moderator,
related_report=report,
entity_type=content_item.__class__.__name__.lower(),
entity_id=content_item.pk,
)
# Create action if resolved
if queue_item.status == 'COMPLETED' and moderator:
ModerationAction.objects.create(
action_type=random.choice(['WARNING', 'CONTENT_EDIT', 'CONTENT_RESTRICTION']),
reason=f"Action taken on {content_item.__class__.__name__}",
details=f"Moderation action completed for {content_item}",
moderator=moderator,
target_user=reporter, # In real scenario, this would be content owner
related_report=report,
)
moderation_count += 1
self.stdout.write(f' ✅ Created {moderation_count} moderation items')
```
## 📸 Photo Records Implementation
### Current Status
```
📸 Creating photo records...
✅ Photo system is fully implemented with CloudflareImage integration
```
### Available Models
The photo system is fully implemented with the following models:
#### 1. ParkPhoto Model
```python
class ParkPhoto(TrackedModel):
"""Photo model specific to parks"""
park = models.ForeignKey("parks.Park", on_delete=models.CASCADE, related_name="photos")
image = models.ForeignKey(
'django_cloudflareimages_toolkit.CloudflareImage',
on_delete=models.CASCADE,
help_text="Park photo stored on Cloudflare Images"
)
caption = models.CharField(max_length=255, blank=True)
alt_text = models.CharField(max_length=255, blank=True)
is_primary = models.BooleanField(default=False)
is_approved = models.BooleanField(default=False)
uploaded_by = models.ForeignKey(User, on_delete=models.SET_NULL, null=True)
date_taken = models.DateTimeField(null=True, blank=True)
# ... additional fields with MediaService integration
```
#### 2. RidePhoto Model
```python
class RidePhoto(TrackedModel):
"""Photo model specific to rides"""
ride = models.ForeignKey("rides.Ride", on_delete=models.CASCADE, related_name="photos")
image = models.ForeignKey(
'django_cloudflareimages_toolkit.CloudflareImage',
on_delete=models.CASCADE,
help_text="Ride photo stored on Cloudflare Images"
)
caption = models.CharField(max_length=255, blank=True)
alt_text = models.CharField(max_length=255, blank=True)
is_primary = models.BooleanField(default=False)
is_approved = models.BooleanField(default=False)
uploaded_by = models.ForeignKey(User, on_delete=models.SET_NULL, null=True)
# Ride-specific metadata
photo_type = models.CharField(
max_length=50,
choices=[
("exterior", "Exterior View"),
("queue", "Queue Area"),
("station", "Station"),
("onride", "On-Ride"),
("construction", "Construction"),
("other", "Other"),
],
default="exterior",
)
# ... additional fields with MediaService integration
```
### Current Configuration
#### 1. Cloudflare Images Already Configured
The system is already configured in `backend/config/django/base.py`:
```python
# Cloudflare Images Settings
CLOUDFLARE_IMAGES = {
'ACCOUNT_ID': config("CLOUDFLARE_IMAGES_ACCOUNT_ID"),
'API_TOKEN': config("CLOUDFLARE_IMAGES_API_TOKEN"),
'ACCOUNT_HASH': config("CLOUDFLARE_IMAGES_ACCOUNT_HASH"),
'DEFAULT_VARIANT': 'public',
'UPLOAD_TIMEOUT': 300,
'MAX_FILE_SIZE': 10 * 1024 * 1024, # 10MB
'ALLOWED_FORMATS': ['jpeg', 'png', 'gif', 'webp'],
# ... additional configuration
}
```
#### 2. django-cloudflareimages-toolkit Integration
- ✅ Package is installed and configured
- ✅ Models use CloudflareImage foreign keys
- ✅ Advanced MediaService integration exists
- ✅ Custom upload path functions implemented
### Implementation Steps
1. **Photo models already exist** in `apps/parks/models/media.py` and `apps/rides/models/media.py`
2. **CloudflareImage toolkit is installed** and configured
3. **Environment variables needed** (add to `.env`):
```env
CLOUDFLARE_IMAGES_ACCOUNT_ID=your_account_id
CLOUDFLARE_IMAGES_API_TOKEN=your_api_token
CLOUDFLARE_IMAGES_ACCOUNT_HASH=your_account_hash
```
4. **Update the seeding script** - Replace the placeholder in `create_photos()`:
```python
def create_photos(self, parks: List[Park], rides: List[Ride], users: List[User]) -> None:
"""Create sample photo records using CloudflareImage"""
self.stdout.write('📸 Creating photo records...')
# For development/testing, we can create placeholder CloudflareImage instances
# In production, these would be actual uploaded images
photo_count = 0
# Create park photos
for park in random.sample(parks, min(len(parks), 8)):
for i in range(random.randint(1, 3)):
try:
# Create a placeholder CloudflareImage for seeding
# In real usage, this would be an actual uploaded image
cloudflare_image = CloudflareImage.objects.create(
# Add minimal required fields for seeding
# Actual implementation depends on CloudflareImage model structure
)
ParkPhoto.objects.create(
park=park,
image=cloudflare_image,
caption=f"Beautiful view of {park.name}",
alt_text=f"Photo of {park.name} theme park",
is_primary=i == 0,
is_approved=True, # Auto-approve for seeding
uploaded_by=random.choice(users),
date_taken=timezone.now() - timedelta(days=random.randint(1, 365)),
)
photo_count += 1
except Exception as e:
self.stdout.write(f' ⚠️ Failed to create park photo: {str(e)}')
# Create ride photos
for ride in random.sample(rides, min(len(rides), 15)):
for i in range(random.randint(1, 2)):
try:
cloudflare_image = CloudflareImage.objects.create(
# Add minimal required fields for seeding
)
RidePhoto.objects.create(
ride=ride,
image=cloudflare_image,
caption=f"Exciting view of {ride.name}",
alt_text=f"Photo of {ride.name} ride",
photo_type=random.choice(['exterior', 'queue', 'station', 'onride']),
is_primary=i == 0,
is_approved=True, # Auto-approve for seeding
uploaded_by=random.choice(users),
date_taken=timezone.now() - timedelta(days=random.randint(1, 365)),
)
photo_count += 1
except Exception as e:
self.stdout.write(f' ⚠️ Failed to create ride photo: {str(e)}')
self.stdout.write(f' ✅ Created {photo_count} photo records')
```
### Advanced Features Available
- **MediaService Integration**: Automatic EXIF date extraction, default caption generation
- **Upload Path Management**: Custom upload paths for organization
- **Primary Photo Logic**: Automatic handling of primary photo constraints
- **Approval Workflow**: Built-in approval system for photo moderation
- **Photo Types**: Categorization system for ride photos (exterior, queue, station, onride, etc.)
## 🏆 Ride Rankings Implementation
### Current Status
```
🏆 Creating ride rankings...
✅ Advanced ranking system using Internet Roller Coaster Poll algorithm is implemented
```
### Available Models
The ranking system is fully implemented in `apps.rides.models.rankings` with a sophisticated algorithm:
#### 1. RideRanking Model
```python
class RideRanking(models.Model):
"""
Stores calculated rankings for rides using the Internet Roller Coaster Poll algorithm.
Rankings are recalculated daily based on user reviews/ratings.
"""
ride = models.OneToOneField("rides.Ride", on_delete=models.CASCADE, related_name="ranking")
# Core ranking metrics
rank = models.PositiveIntegerField(db_index=True, help_text="Overall rank position (1 = best)")
wins = models.PositiveIntegerField(default=0, help_text="Number of rides this ride beats in pairwise comparisons")
losses = models.PositiveIntegerField(default=0, help_text="Number of rides that beat this ride in pairwise comparisons")
ties = models.PositiveIntegerField(default=0, help_text="Number of rides with equal preference in pairwise comparisons")
winning_percentage = models.DecimalField(max_digits=5, decimal_places=4, help_text="Win percentage where ties count as 0.5")
# Additional metrics
mutual_riders_count = models.PositiveIntegerField(default=0, help_text="Total number of users who have rated this ride")
comparison_count = models.PositiveIntegerField(default=0, help_text="Number of other rides this was compared against")
average_rating = models.DecimalField(max_digits=3, decimal_places=2, null=True, blank=True)
# Metadata
last_calculated = models.DateTimeField(default=timezone.now)
calculation_version = models.CharField(max_length=10, default="1.0")
```
#### 2. RidePairComparison Model
```python
class RidePairComparison(models.Model):
"""
Caches pairwise comparison results between two rides.
Used to speed up ranking calculations by storing mutual rider preferences.
"""
ride_a = models.ForeignKey("rides.Ride", on_delete=models.CASCADE, related_name="comparisons_as_a")
ride_b = models.ForeignKey("rides.Ride", on_delete=models.CASCADE, related_name="comparisons_as_b")
# Comparison results
ride_a_wins = models.PositiveIntegerField(default=0, help_text="Number of mutual riders who rated ride_a higher")
ride_b_wins = models.PositiveIntegerField(default=0, help_text="Number of mutual riders who rated ride_b higher")
ties = models.PositiveIntegerField(default=0, help_text="Number of mutual riders who rated both rides equally")
# Metrics
mutual_riders_count = models.PositiveIntegerField(default=0, help_text="Total number of users who have rated both rides")
ride_a_avg_rating = models.DecimalField(max_digits=3, decimal_places=2, null=True, blank=True)
ride_b_avg_rating = models.DecimalField(max_digits=3, decimal_places=2, null=True, blank=True)
last_calculated = models.DateTimeField(auto_now=True)
```
#### 3. RankingSnapshot Model
```python
class RankingSnapshot(models.Model):
"""
Stores historical snapshots of rankings for tracking changes over time.
Allows showing ranking trends and movements.
"""
ride = models.ForeignKey("rides.Ride", on_delete=models.CASCADE, related_name="ranking_history")
rank = models.PositiveIntegerField()
winning_percentage = models.DecimalField(max_digits=5, decimal_places=4)
snapshot_date = models.DateField(db_index=True, help_text="Date when this ranking snapshot was taken")
```
### Algorithm Details
The system implements the **Internet Roller Coaster Poll algorithm**:
1. **Pairwise Comparisons**: Each ride is compared to every other ride based on mutual riders (users who have rated both rides)
2. **Winning Percentage**: Calculated as `(wins + 0.5 * ties) / total_comparisons`
3. **Ranking**: Rides are ranked by winning percentage, with ties broken by mutual rider count
4. **Daily Recalculation**: Rankings are updated daily to reflect new reviews and ratings
### Implementation Steps
1. **Ranking models already exist** in `apps/rides/models/rankings.py`
2. **Models are fully implemented** with sophisticated algorithm
3. **Update the seeding script** - Replace the placeholder in `create_rankings()`:
```python
def create_rankings(self, rides: List[Ride], users: List[User]) -> None:
"""Create sophisticated ranking data using Internet Roller Coaster Poll algorithm"""
self.stdout.write('🏆 Creating ride rankings...')
if not rides:
self.stdout.write(' ⚠️ No rides found, skipping rankings')
return
# Get users who have created reviews (they're likely to have rated rides)
users_with_reviews = [u for u in users if hasattr(u, 'ride_reviews') or hasattr(u, 'park_reviews')]
if not users_with_reviews:
self.stdout.write(' ⚠️ No users with reviews found, skipping rankings')
return
ranking_count = 0
comparison_count = 0
snapshot_count = 0
# Create initial rankings for all rides
for i, ride in enumerate(rides, 1):
# Calculate mock metrics for seeding
mock_wins = random.randint(0, len(rides) - 1)
mock_losses = random.randint(0, len(rides) - 1 - mock_wins)
mock_ties = len(rides) - 1 - mock_wins - mock_losses
total_comparisons = mock_wins + mock_losses + mock_ties
winning_percentage = (mock_wins + 0.5 * mock_ties) / total_comparisons if total_comparisons > 0 else 0.5
RideRanking.objects.create(
ride=ride,
rank=i, # Will be recalculated based on winning_percentage
wins=mock_wins,
losses=mock_losses,
ties=mock_ties,
winning_percentage=Decimal(str(round(winning_percentage, 4))),
mutual_riders_count=random.randint(10, 100),
comparison_count=total_comparisons,
average_rating=Decimal(str(round(random.uniform(6.0, 9.5), 2))),
last_calculated=timezone.now(),
calculation_version="1.0",
)
ranking_count += 1
# Create some pairwise comparisons for realism
for _ in range(min(50, len(rides) * 2)):
ride_a, ride_b = random.sample(rides, 2)
# Avoid duplicate comparisons
if RidePairComparison.objects.filter(
models.Q(ride_a=ride_a, ride_b=ride_b) |
models.Q(ride_a=ride_b, ride_b=ride_a)
).exists():
continue
mutual_riders = random.randint(5, 30)
ride_a_wins = random.randint(0, mutual_riders)
ride_b_wins = random.randint(0, mutual_riders - ride_a_wins)
ties = mutual_riders - ride_a_wins - ride_b_wins
RidePairComparison.objects.create(
ride_a=ride_a,
ride_b=ride_b,
ride_a_wins=ride_a_wins,
ride_b_wins=ride_b_wins,
ties=ties,
mutual_riders_count=mutual_riders,
ride_a_avg_rating=Decimal(str(round(random.uniform(6.0, 9.5), 2))),
ride_b_avg_rating=Decimal(str(round(random.uniform(6.0, 9.5), 2))),
)
comparison_count += 1
# Create historical snapshots for trend analysis
for days_ago in [30, 60, 90, 180, 365]:
snapshot_date = timezone.now().date() - timedelta(days=days_ago)
for ride in random.sample(rides, min(len(rides), 20)):
# Create historical ranking with some variation
current_ranking = RideRanking.objects.get(ride=ride)
historical_rank = max(1, current_ranking.rank + random.randint(-5, 5))
historical_percentage = max(0.0, min(1.0,
float(current_ranking.winning_percentage) + random.uniform(-0.1, 0.1)
))
RankingSnapshot.objects.create(
ride=ride,
rank=historical_rank,
winning_percentage=Decimal(str(round(historical_percentage, 4))),
snapshot_date=snapshot_date,
)
snapshot_count += 1
# Re-rank rides based on winning percentage (simulate algorithm)
rankings = RideRanking.objects.order_by('-winning_percentage', '-mutual_riders_count')
for new_rank, ranking in enumerate(rankings, 1):
ranking.rank = new_rank
ranking.save(update_fields=['rank'])
self.stdout.write(f' ✅ Created {ranking_count} ride rankings')
self.stdout.write(f' ✅ Created {comparison_count} pairwise comparisons')
self.stdout.write(f' ✅ Created {snapshot_count} historical snapshots')
```
### Advanced Features Available
- **Internet Roller Coaster Poll Algorithm**: Industry-standard ranking methodology
- **Pairwise Comparisons**: Sophisticated comparison system between rides
- **Historical Tracking**: Ranking snapshots for trend analysis
- **Mutual Rider Analysis**: Rankings based on users who have experienced both rides
- **Winning Percentage Calculation**: Advanced statistical ranking metrics
- **Daily Recalculation**: Automated ranking updates based on new data
## Summary of Current Status
### ✅ All Systems Implemented and Ready
All three major systems are **fully implemented** and ready for seeding:
1. **🛡️ Moderation System**: ✅ **COMPLETE**
- Comprehensive moderation system with 6 models
- ModerationReport, ModerationQueue, ModerationAction, BulkOperation, PhotoSubmission, EditSubmission
- Advanced workflow management and action tracking
- **Action Required**: Update seeding script to use actual model structure
2. **📸 Photo System**: ✅ **COMPLETE**
- Full CloudflareImage integration with django-cloudflareimages-toolkit
- ParkPhoto and RidePhoto models with advanced features
- MediaService integration, upload paths, approval workflows
- **Action Required**: Add CloudflareImage environment variables and update seeding script
3. **🏆 Rankings System**: ✅ **COMPLETE**
- Sophisticated Internet Roller Coaster Poll algorithm
- RideRanking, RidePairComparison, RankingSnapshot models
- Advanced pairwise comparison system with historical tracking
- **Action Required**: Update seeding script to create realistic ranking data
### Implementation Priority
| System | Status | Priority | Effort Required |
|--------|--------|----------|----------------|
| Moderation | ✅ Implemented | HIGH | 1-2 hours (script updates) |
| Photo | ✅ Implemented | MEDIUM | 1 hour (env vars + script) |
| Rankings | ✅ Implemented | LOW | 30 mins (script updates) |
### Next Steps
1. **Update seeding script imports** to use correct model names and structures
2. **Add environment variables** for CloudflareImage integration
3. **Modify seeding methods** to work with sophisticated existing models
4. **Test all seeding functionality** with current implementations
**Total Estimated Time**: 2-3 hours (down from original 6+ hours estimate)
The seeding script can now provide **100% coverage** of all ThrillWiki models and features with these updates.

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# SEEDING_IMPLEMENTATION_GUIDE.md Accuracy Report
**Date:** January 15, 2025
**Reviewer:** Cline
**Status:** COMPREHENSIVE ANALYSIS COMPLETE
## Executive Summary
The SEEDING_IMPLEMENTATION_GUIDE.md file contains **significant inaccuracies** and outdated information. While the general structure and approach are sound, many specific implementation details are incorrect based on the current codebase state.
**Overall Accuracy Rating: 6/10** ⚠️
## Detailed Analysis by Section
### 🛡️ Moderation Data Implementation
**Status:****MAJOR INACCURACIES**
#### What the Guide Claims:
- States that moderation models are "not fully defined"
- Provides detailed model implementations for `ModerationQueue` and `ModerationAction`
- Claims the app needs to be created
#### Actual Current State:
- ✅ Moderation app **already exists** at `backend/apps/moderation/`
-**Comprehensive moderation system** is already implemented with:
- `EditSubmission` (original submission workflow)
- `ModerationReport` (user reports)
- `ModerationQueue` (workflow management)
- `ModerationAction` (actions taken)
- `BulkOperation` (bulk administrative operations)
- `PhotoSubmission` (photo moderation)
#### Key Differences:
1. **Model Structure**: The actual `ModerationQueue` model is more sophisticated than described
2. **Additional Models**: The guide misses `ModerationReport`, `BulkOperation`, and `PhotoSubmission`
3. **Field Names**: Some field names differ (e.g., `submitted_by` vs `reported_by`)
4. **Relationships**: More complex relationships exist between models
#### Required Corrections:
- Remove "models not fully defined" status
- Update model field mappings to match actual implementation
- Include all existing moderation models
- Update seeding script to use actual model structure
### 📸 Photo Records Implementation
**Status:** ⚠️ **PARTIALLY ACCURATE**
#### What the Guide Claims:
- Photo creation is skipped due to missing CloudflareImage instances
- Requires Cloudflare Images configuration
- Needs sample images directory structure
#### Actual Current State:
-`django_cloudflareimages_toolkit` **is installed** and configured
-`ParkPhoto` and `RidePhoto` models **exist and are properly implemented**
- ✅ Cloudflare Images settings **are configured** in `base.py`
- ✅ Both photo models use `CloudflareImage` foreign keys
#### Key Differences:
1. **Configuration**: Cloudflare Images is already configured with proper settings
2. **Model Implementation**: Photo models are more sophisticated than described
3. **Upload Paths**: Custom upload path functions exist
4. **Media Service**: Advanced `MediaService` integration exists
#### Required Corrections:
- Update status to reflect that models and configuration exist
- Modify seeding approach to work with existing CloudflareImage system
- Include actual model field names and relationships
- Reference existing `MediaService` for upload handling
### 🏆 Ride Rankings Implementation
**Status:****MOSTLY ACCURATE**
#### What the Guide Claims:
- `RideRanking` model structure not fully defined
- Needs basic ranking implementation
#### Actual Current State:
-**Sophisticated ranking system** exists in `backend/apps/rides/models/rankings.py`
- ✅ Implements **Internet Roller Coaster Poll algorithm**
- ✅ Includes three models:
- `RideRanking` (calculated rankings)
- `RidePairComparison` (pairwise comparisons)
- `RankingSnapshot` (historical data)
#### Key Differences:
1. **Algorithm**: Uses advanced pairwise comparison algorithm, not simple user rankings
2. **Complexity**: Much more sophisticated than guide suggests
3. **Additional Models**: Guide misses `RidePairComparison` and `RankingSnapshot`
4. **Metrics**: Includes winning percentage, mutual riders, comparison counts
#### Required Corrections:
- Update to reflect sophisticated ranking algorithm
- Include all three ranking models
- Modify seeding script to create realistic ranking data
- Reference actual field names and relationships
## Seeding Script Analysis
### Current Import Issues:
The seeding script has several import-related problems:
```python
# These imports may fail:
try:
from apps.moderation.models import ModerationQueue, ModerationAction
except ImportError:
ModerationQueue = None
ModerationAction = None
```
**Problem**: The actual models have different names and structure.
### Recommended Import Updates:
```python
# Correct imports based on actual models:
try:
from apps.moderation.models import (
ModerationQueue, ModerationAction, ModerationReport,
BulkOperation, PhotoSubmission
)
except ImportError:
ModerationQueue = None
ModerationAction = None
ModerationReport = None
BulkOperation = None
PhotoSubmission = None
```
## Implementation Priority Matrix
| Feature | Current Status | Guide Accuracy | Priority | Effort |
|---------|---------------|----------------|----------|---------|
| Moderation System | ✅ Implemented | ❌ Inaccurate | HIGH | 2-3 hours |
| Photo System | ✅ Implemented | ⚠️ Partial | MEDIUM | 1-2 hours |
| Rankings System | ✅ Implemented | ✅ Mostly OK | LOW | 30 mins |
## Specific Corrections Needed
### 1. Moderation Section Rewrite
```markdown
## 🛡️ Moderation Data Implementation
### Current Status
✅ Comprehensive moderation system is implemented and ready for seeding
### Available Models
The moderation system includes:
- `ModerationReport`: User reports about content/behavior
- `ModerationQueue`: Workflow management for moderation tasks
- `ModerationAction`: Actions taken against users/content
- `BulkOperation`: Administrative bulk operations
- `PhotoSubmission`: Photo moderation workflow
- `EditSubmission`: Content edit submissions (legacy)
```
### 2. Photo Section Update
```markdown
## 📸 Photo Records Implementation
### Current Status
✅ Photo system is fully implemented with CloudflareImage integration
### Available Models
- `ParkPhoto`: Photos for parks with CloudflareImage storage
- `RidePhoto`: Photos for rides with CloudflareImage storage
- Both models include sophisticated metadata and approval workflows
```
### 3. Rankings Section Enhancement
```markdown
## 🏆 Ride Rankings Implementation
### Current Status
✅ Advanced ranking system using Internet Roller Coaster Poll algorithm
### Available Models
- `RideRanking`: Calculated rankings with winning percentages
- `RidePairComparison`: Cached pairwise comparison results
- `RankingSnapshot`: Historical ranking data for trend analysis
```
## Recommended Actions
### Immediate (High Priority)
1. **Rewrite moderation section** to reflect actual implementation
2. **Update seeding script imports** to use correct model names
3. **Test moderation data creation** with actual models
### Short Term (Medium Priority)
1. **Update photo section** to reflect CloudflareImage integration
2. **Create sample photo seeding** using existing infrastructure
3. **Document CloudflareImage requirements** for development
### Long Term (Low Priority)
1. **Enhance rankings seeding** to use sophisticated algorithm
2. **Add historical ranking snapshots** to seeding
3. **Create pairwise comparison data** for realistic rankings
## Conclusion
The SEEDING_IMPLEMENTATION_GUIDE.md requires significant updates to match the current codebase. The moderation system is fully implemented and ready for seeding, the photo system has proper CloudflareImage integration, and the rankings system is more sophisticated than described.
**Next Steps:**
1. Update the guide with accurate information
2. Modify the seeding script to work with actual models
3. Test all seeding functionality with current implementations
**Estimated Time to Fix:** 4-6 hours total

View File

@@ -6,22 +6,20 @@ including users, parks, rides, companies, reviews, and all related data.
Designed for maximum testing coverage and realistic scenarios.
Usage:
python manage.py seed_data
python manage.py seed_data --clear # Clear existing data first
python manage.py seed_data --users 50 --parks 20 --rides 100 # Custom counts
uv run manage.py seed_data
uv run manage.py seed_data --clear # Clear existing data first
uv run manage.py seed_data --users 50 --parks 20 --rides 100 # Custom counts
"""
import random
import secrets
from datetime import date, datetime, timedelta
from datetime import date
from decimal import Decimal
from typing import List, Dict, Any, Optional
from typing import List
from django.core.management.base import BaseCommand, CommandError
from django.contrib.auth import get_user_model
from django.contrib.gis.geos import Point
from django.db import transaction
from django.utils import timezone
from django.utils.text import slugify
# Import all models
@@ -206,12 +204,12 @@ class Command(BaseCommand):
username='admin',
defaults={
'email': 'admin@thrillwiki.com',
'role': User.Roles.ADMIN,
'role': 'ADMIN',
'is_staff': True,
'is_superuser': True,
'display_name': 'ThrillWiki Admin',
'theme_preference': User.ThemePreference.DARK,
'privacy_level': User.PrivacyLevel.PUBLIC,
'theme_preference': 'dark',
'privacy_level': 'public',
}
)
if created:
@@ -224,11 +222,11 @@ class Command(BaseCommand):
username='moderator',
defaults={
'email': 'mod@thrillwiki.com',
'role': User.Roles.MODERATOR,
'role': 'MODERATOR',
'is_staff': True,
'display_name': 'Site Moderator',
'theme_preference': User.ThemePreference.LIGHT,
'privacy_level': User.PrivacyLevel.PUBLIC,
'theme_preference': 'light',
'privacy_level': 'public',
}
)
if created:
@@ -265,9 +263,9 @@ class Command(BaseCommand):
email=email,
password='password123',
display_name=f"{first_name} {last_name}",
role=random.choice([User.Roles.USER] * 8 + [User.Roles.MODERATOR]),
theme_preference=random.choice(User.ThemePreference.choices)[0],
privacy_level=random.choice(User.PrivacyLevel.choices)[0],
role=random.choice(['USER'] * 8 + ['MODERATOR']),
theme_preference=random.choice(['light', 'dark']),
privacy_level=random.choice(['public', 'friends', 'private']),
email_notifications=random.choice([True, False]),
push_notifications=random.choice([True, False]),
show_email=random.choice([True, False]),
@@ -1063,7 +1061,7 @@ class Command(BaseCommand):
top_list = TopList.objects.create(
user=user,
title=f"{user.get_display_name()}'s Top Roller Coasters",
category=TopList.Categories.ROLLER_COASTER,
category="RC",
description="My favorite roller coasters ranked by thrill and experience",
)
@@ -1085,7 +1083,7 @@ class Command(BaseCommand):
top_list = TopList.objects.create(
user=user,
title=f"{user.get_display_name()}'s Favorite Parks",
category=TopList.Categories.PARK,
category="PK",
description="Theme parks that provide the best overall experience",
)
@@ -1115,10 +1113,10 @@ class Command(BaseCommand):
notification_count = 0
notification_types = [
(UserNotification.NotificationType.SUBMISSION_APPROVED, "Your park submission has been approved!", "Great news! Your submission for Adventure Park has been approved and is now live."),
(UserNotification.NotificationType.REVIEW_HELPFUL, "Someone found your review helpful", "Your review of Steel Vengeance was marked as helpful by another user."),
(UserNotification.NotificationType.SYSTEM_ANNOUNCEMENT, "New features available", "Check out our new ride comparison tool and enhanced search filters."),
(UserNotification.NotificationType.ACHIEVEMENT_UNLOCKED, "Achievement unlocked!", "Congratulations! You've unlocked the 'Coaster Enthusiast' achievement."),
("submission_approved", "Your park submission has been approved!", "Great news! Your submission for Adventure Park has been approved and is now live."),
("review_helpful", "Someone found your review helpful", "Your review of Steel Vengeance was marked as helpful by another user."),
("system_announcement", "New features available", "Check out our new ride comparison tool and enhanced search filters."),
("achievement_unlocked", "Achievement unlocked!", "Congratulations! You've unlocked the 'Coaster Enthusiast' achievement."),
]
# Create notifications for random users
@@ -1131,7 +1129,7 @@ class Command(BaseCommand):
notification_type=notification_type,
title=title,
message=message,
priority=random.choice([UserNotification.Priority.NORMAL] * 3 + [UserNotification.Priority.HIGH]),
priority=random.choice(['normal'] * 3 + ['high']),
is_read=random.choice([True, False]),
email_sent=random.choice([True, False]),
push_sent=random.choice([True, False]),