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docs/plans/final-fixes.md
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# IGNY8 Launch Preparation - Task Organization
---
## 1. Critical Pre-Launch Fixes
### 1.1 Payment & Account System
| # | Task | Details | Priority |
|---|------|---------|----------|
| 1 | Payment method saving | Individual account payment method not saving | Critical |
| 2 | Country-specific methods | Remove all country-specific payment methods, use only global | Critical |
| 3 | Payment verification | Verification of online payment method success through bank account | Critical |
| 4 | Account edit form | Fix issue with payment method and account-specific edit form update | High |
### 1.2 Backend Issues
| # | Task | Details | Priority |
|---|------|---------|----------|
| 1 | Django admin error | Backend Django admin keywords page returning 500 error | Critical |
| 2 | Delete functions | Many pages delete function not working in frontend (images, image prompts, etc.) | High |
| 3 | Soft deletion verification | Verify soft deletion criteria and how it works across system | Medium |
### 1.3 Data Integrity
| # | Task | Details | Priority |
|---|------|---------|----------|
| 1 | Status updates | Verification of all status update operations | High |
| 2 | Related records deletion | Verify delete and related records deletion for planner and writer module submodules | High |
| 3 | CRUD verification | Clear definition and verification of CRUD operations on each page | High |
---
## 2. Automation Pipeline Fixes
### 2.1 Stage Card & Metrics Issues
| # | Task | Details | Priority |
|---|------|---------|----------|
| 1 | Stage card credits | Automation credit display in stage cards to be corrected | Critical |
| 2 | Stage 6 image generation | Image generation has different implementation than other AI functions - check how it works vs other stages to find the issue | Critical |
| 3 | Progress bar metrics | Stage card progress bar showing wrong counts (always 0/remaining, 0%) | Critical |
| 4 | Main pipeline progress | Main progress bar completes at 100% at stage 5 instead of stage 6 - needs fix | Critical |
| 5 | Credits accuracy | Update stage cards to read from correct backend fields/endpoints (reference: /account/usage/credits shows accurate data) | High |
### 2.2 Pipeline Verification
| # | Task | Details | Priority |
|---|------|---------|----------|
| 1 | End-to-end automation | Verify complete automation pipeline runs from clustering to publishing automatically | Critical |
| 2 | Credits consumption | Verify accurate credits and costs consumption throughout pipeline | Critical |
---
## 3. AI Provider Configuration
| # | Task | Details | Priority |
|---|------|---------|----------|
| 1 | Bria integration | Configuration of Bria image generation model | Medium |
| 2 | Anthropic integration | Configuration of Anthropic AI provider | Medium |
---
## 4. WordPress & Content Templates
### 4.1 Template Improvements
| # | Task | Details | Priority |
|---|------|---------|----------|
| 1 | Blog post template | Use same content template design as IGNY8 app in WordPress | High |
| 2 | Image handling | If only 3 images available, use them twice in template | High |
| 3 | Content length variants | Adjust templates and prompts for all 3 content lengths (500, 1000, 1500 words) | High |
| 4 | Hub page template | Create landing page style template for cluster hub pages | High |
| 5 | Archive templates | Apply hub template to category/tag archive term pages | Medium |
| 6 | Theme integration | Apply same layout/design with theme's colors for blog posts | Medium |
---
## 5. Frontend Marketing Site
| # | Task | Details | Priority |
|---|------|---------|----------|
| 1 | Site content update | Complete site content based on final docs, features, and help documentation | High |
| 2 | Pricing page | Simple pricing page with plans (Starter $49, Growth $149, Scale $349) | High |
---
## 6. Major Refactoring (High Effort)
### 6.1 Design System Consolidation
| Phase | Task | Details |
|-------|------|---------|
| 1 - Audit | Component inventory | Collect all page styling info: buttons, icons, colors, tables, standard components |
| 2 - Analysis | Source mapping | Document what format and from which sources styles are being used |
| 3 - Cleanup | Remove duplicates | Remove duplicate components and CSS styling systems |
| 4 - Refactor | Standardize | Refactor complete system to use single standard styling and layouts |
| 5 - Verify | Design system check | Ensure only initially planned standard styling remains |
---

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docs/plans/flexible-model-configuration-plan.md
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# Flexible Model Configuration System Plan
## Overview
This plan outlines how to implement a flexible model configuration system that allows:
- Adding/removing/activating models dynamically
- Configuring rates for each model
- Supporting multiple providers (OpenAI, Anthropic, Runware)
- Per-account model overrides
## Current State
### Model Rates (hardcoded in `ai/constants.py`)
```python
MODEL_RATES = {
'gpt-4.1': {'input': 2.00, 'output': 8.00}, # per 1M tokens
'gpt-4o-mini': {'input': 0.15, 'output': 0.60},
'gpt-4o': {'input': 2.50, 'output': 10.00},
'gpt-5.1': {'input': 1.25, 'output': 10.00},
'gpt-5.2': {'input': 1.75, 'output': 14.00},
}
IMAGE_MODEL_RATES = {
'dall-e-3': 0.040, # per image
'dall-e-2': 0.020,
'gpt-image-1': 0.042,
'gpt-image-1-mini': 0.011,
}
```
### Current Settings Architecture
- `GlobalIntegrationSettings` (singleton) - Platform-wide API keys and defaults
- `IntegrationSettings` (per-account) - Model/parameter overrides
- `GlobalAIPrompt` - Platform-wide prompt templates
- `AIPrompt` (per-account) - Custom prompt overrides
## Proposed Changes
### Phase 1: Database Model for AI Models
Create a new model `AIModel` to store model configurations:
```python
# backend/igny8_core/modules/system/global_settings_models.py
class AIModel(models.Model):
"""
Dynamic AI model configuration.
Replaces hardcoded MODEL_RATES and IMAGE_MODEL_RATES.
"""
PROVIDER_CHOICES = [
('openai', 'OpenAI'),
('anthropic', 'Anthropic'),
('runware', 'Runware'),
('google', 'Google AI'),
]
MODEL_TYPE_CHOICES = [
('text', 'Text Generation'),
('image', 'Image Generation'),
('embedding', 'Embedding'),
]
# Identification
model_id = models.CharField(
max_length=100,
unique=True,
help_text="Model identifier (e.g., 'gpt-4o-mini', 'claude-3-sonnet')"
)
display_name = models.CharField(
max_length=200,
help_text="User-friendly name (e.g., 'GPT-4o Mini')"
)
provider = models.CharField(max_length=50, choices=PROVIDER_CHOICES)
model_type = models.CharField(max_length=20, choices=MODEL_TYPE_CHOICES)
# Pricing (per 1M tokens for text, per image for image models)
input_rate = models.DecimalField(
max_digits=10,
decimal_places=4,
default=0,
help_text="Cost per 1M input tokens (text) or per request (image)"
)
output_rate = models.DecimalField(
max_digits=10,
decimal_places=4,
default=0,
help_text="Cost per 1M output tokens (text only)"
)
# Capabilities
max_tokens = models.IntegerField(
default=8192,
help_text="Maximum tokens for this model"
)
supports_json_mode = models.BooleanField(
default=True,
help_text="Whether model supports JSON response format"
)
supports_vision = models.BooleanField(
default=False,
help_text="Whether model supports image input"
)
# Status
is_active = models.BooleanField(default=True)
is_default = models.BooleanField(
default=False,
help_text="Use as default when no specific model is configured"
)
sort_order = models.IntegerField(default=0)
# Metadata
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
class Meta:
db_table = 'igny8_ai_models'
ordering = ['sort_order', 'display_name']
def __str__(self):
return f"{self.display_name} ({self.model_id})"
```
### Phase 2: Model Registry Service
Create a service layer to manage models:
```python
# backend/igny8_core/ai/model_registry.py
class ModelRegistry:
"""
Central registry for AI model configurations.
Provides caching and fallback logic.
"""
_cache = {}
_cache_ttl = 300 # 5 minutes
@classmethod
def get_model(cls, model_id: str) -> Optional[dict]:
"""Get model configuration by ID"""
# Check cache first
# Fallback to database
# Return dict with rates, capabilities, etc.
pass
@classmethod
def get_models_by_type(cls, model_type: str) -> List[dict]:
"""Get all active models of a type"""
pass
@classmethod
def get_default_model(cls, model_type: str = 'text') -> dict:
"""Get default model for a type"""
pass
@classmethod
def calculate_cost(
cls,
model_id: str,
input_tokens: int = 0,
output_tokens: int = 0,
image_count: int = 0
) -> float:
"""Calculate cost for an operation"""
pass
@classmethod
def is_model_supported(cls, model_id: str) -> bool:
"""Check if a model is configured and active"""
pass
```
### Phase 3: Update AICore to Use Registry
Modify `ai_core.py` to use the model registry:
```python
# In run_ai_request()
from igny8_core.ai.model_registry import ModelRegistry
# Replace hardcoded MODEL_RATES check
if not ModelRegistry.is_model_supported(model):
supported = ModelRegistry.get_models_by_type('text')
error_msg = f"Model '{model}' is not supported. Available models: {[m['model_id'] for m in supported]}"
# ...
# Replace hardcoded cost calculation
model_info = ModelRegistry.get_model(model)
if model_info:
cost = ModelRegistry.calculate_cost(
model_id=model,
input_tokens=input_tokens,
output_tokens=output_tokens
)
```
### Phase 4: Admin Interface
Add Django admin for managing models:
```python
# backend/igny8_core/modules/system/admin.py
@admin.register(AIModel)
class AIModelAdmin(admin.ModelAdmin):
list_display = ['model_id', 'display_name', 'provider', 'model_type', 'input_rate', 'output_rate', 'is_active', 'is_default']
list_filter = ['provider', 'model_type', 'is_active', 'is_default']
search_fields = ['model_id', 'display_name']
ordering = ['sort_order', 'display_name']
fieldsets = (
('Identification', {
'fields': ('model_id', 'display_name', 'provider', 'model_type')
}),
('Pricing', {
'fields': ('input_rate', 'output_rate')
}),
('Capabilities', {
'fields': ('max_tokens', 'supports_json_mode', 'supports_vision')
}),
('Status', {
'fields': ('is_active', 'is_default', 'sort_order')
}),
)
```
### Phase 5: Data Migration
Create a migration to seed initial models:
```python
# Migration file
def seed_initial_models(apps, schema_editor):
AIModel = apps.get_model('system', 'AIModel')
models = [
# OpenAI Text Models
{'model_id': 'gpt-4o-mini', 'display_name': 'GPT-4o Mini', 'provider': 'openai', 'model_type': 'text', 'input_rate': 0.15, 'output_rate': 0.60, 'is_default': True},
{'model_id': 'gpt-4o', 'display_name': 'GPT-4o', 'provider': 'openai', 'model_type': 'text', 'input_rate': 2.50, 'output_rate': 10.00},
{'model_id': 'gpt-4.1', 'display_name': 'GPT-4.1', 'provider': 'openai', 'model_type': 'text', 'input_rate': 2.00, 'output_rate': 8.00},
{'model_id': 'gpt-5.1', 'display_name': 'GPT-5.1', 'provider': 'openai', 'model_type': 'text', 'input_rate': 1.25, 'output_rate': 10.00, 'max_tokens': 16000},
{'model_id': 'gpt-5.2', 'display_name': 'GPT-5.2', 'provider': 'openai', 'model_type': 'text', 'input_rate': 1.75, 'output_rate': 14.00, 'max_tokens': 16000},
# Anthropic Text Models
{'model_id': 'claude-3-sonnet', 'display_name': 'Claude 3 Sonnet', 'provider': 'anthropic', 'model_type': 'text', 'input_rate': 3.00, 'output_rate': 15.00},
{'model_id': 'claude-3-opus', 'display_name': 'Claude 3 Opus', 'provider': 'anthropic', 'model_type': 'text', 'input_rate': 15.00, 'output_rate': 75.00},
{'model_id': 'claude-3-haiku', 'display_name': 'Claude 3 Haiku', 'provider': 'anthropic', 'model_type': 'text', 'input_rate': 0.25, 'output_rate': 1.25},
# OpenAI Image Models
{'model_id': 'dall-e-3', 'display_name': 'DALL-E 3', 'provider': 'openai', 'model_type': 'image', 'input_rate': 0.040, 'output_rate': 0},
{'model_id': 'dall-e-2', 'display_name': 'DALL-E 2', 'provider': 'openai', 'model_type': 'image', 'input_rate': 0.020, 'output_rate': 0},
{'model_id': 'gpt-image-1', 'display_name': 'GPT Image 1', 'provider': 'openai', 'model_type': 'image', 'input_rate': 0.042, 'output_rate': 0},
# Runware Image Models
{'model_id': 'runware:97@1', 'display_name': 'Runware 97@1', 'provider': 'runware', 'model_type': 'image', 'input_rate': 0.009, 'output_rate': 0},
]
for i, model in enumerate(models):
AIModel.objects.create(sort_order=i, **model)
```
### Phase 6: API Endpoints for Model Management
Add REST endpoints for managing models:
```python
# GET /api/v1/admin/ai-models/ - List all models
# POST /api/v1/admin/ai-models/ - Create new model
# PUT /api/v1/admin/ai-models/{id}/ - Update model
# DELETE /api/v1/admin/ai-models/{id}/ - Delete model
# POST /api/v1/admin/ai-models/{id}/toggle-active/ - Toggle active status
# POST /api/v1/admin/ai-models/{id}/set-default/ - Set as default
```
### Phase 7: Frontend Admin UI
Create admin UI for model management:
- List view with filtering/sorting
- Create/Edit form with validation
- Quick toggle for active/default status
- Price calculator preview
## Implementation Order
1. **Week 1**: Create `AIModel` model and migration
2. **Week 1**: Create `ModelRegistry` service
3. **Week 2**: Update `ai_core.py` to use registry
4. **Week 2**: Update `constants.py` to load from database
5. **Week 3**: Add Django admin interface
6. **Week 3**: Add API endpoints
7. **Week 4**: Create frontend admin UI
8. **Week 4**: Testing and documentation
## Backward Compatibility
- Keep `constants.py` as fallback if database is empty
- `ModelRegistry.get_model()` checks DB first, falls back to constants
- No changes to existing `GlobalIntegrationSettings` or `IntegrationSettings`
- Existing API calls continue to work unchanged
## Benefits
1. **No Code Changes for New Models**: Add models via admin UI
2. **Easy Price Updates**: Update rates without deployment
3. **Provider Flexibility**: Support any provider by adding models
4. **Per-Provider Settings**: Configure different capabilities per provider
5. **Audit Trail**: Track when models were added/modified
6. **A/B Testing**: Easily enable/disable models for testing

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