AI Researcher and Video Studio implementation complete

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# Image Studio Editing - Completion Summary
**Date**: Current Session
**Status**: ✅ **Backend Complete** - Ready for Frontend Integration
**Progress**: 5 Models Integrated, APIs Ready, Auto-Detection Implemented
---
## ✅ Completed Backend Implementation
### **1. Model Integration** ✅ (5/14 Models)
**Integrated Models**:
1.**Qwen Image Edit** ($0.02) - Basic, single-image
2.**Qwen Image Edit Plus** ($0.02) - Multi-image, ControlNet
3.**Google Nano Banana Pro Edit Ultra** ($0.15-0.18) - 4K/8K, premium
4.**Bytedance Seedream V4.5 Edit** ($0.04) - Reference-faithful, 4K
5.**FLUX Kontext Pro** ($0.04) - Typography, guidance scale
**Remaining**: 9 models (waiting for documentation)
---
### **2. Backend APIs** ✅ **COMPLETE**
#### **2.1 Get Available Models** ✅
**Endpoint**: `GET /api/image-studio/edit/models`
**Query Parameters**:
- `operation` (optional): Filter by operation type
- `tier` (optional): Filter by tier (budget, mid, premium)
**Response**:
```json
{
"models": [
{
"id": "qwen-edit-plus",
"name": "Qwen Image Edit Plus",
"description": "...",
"cost": 0.02,
"tier": "budget",
"max_resolution": [1536, 1536],
"capabilities": ["general_edit", "multi_image"],
"use_cases": ["Quick edits", "Batch editing"],
"features": ["ControlNet support", "Bilingual (CN/EN)"],
"supports_multi_image": true,
"supports_controlnet": true,
"languages": ["en", "zh"]
}
],
"total": 5
}
```
#### **2.2 Get Model Recommendations** ✅
**Endpoint**: `POST /api/image-studio/edit/recommend`
**Request Body**:
```json
{
"operation": "general_edit",
"image_resolution": { "width": 1024, "height": 1024 },
"user_tier": "free",
"preferences": {
"prioritize_cost": true,
"prioritize_quality": false
}
}
```
**Response**:
```json
{
"recommended_model": "qwen-edit",
"reason": "Lowest cost option, Supports 1024×1024 resolution, Budget-friendly for free tier",
"alternatives": [
{
"model_id": "qwen-edit-plus",
"name": "Qwen Image Edit Plus",
"cost": 0.02,
"reason": "Alternative: Budget tier, higher quality"
}
]
}
```
---
### **3. Auto-Detection & Routing** ✅ **COMPLETE**
**Implementation**: `EditStudioService._handle_general_edit()`
**Logic**:
1. **If model specified**: Use that model (WaveSpeed or HuggingFace)
2. **If no model specified** (general_edit operation):
- Auto-detect image resolution
- Call recommendation logic
- Auto-select recommended WaveSpeed model
- Fall back to HuggingFace if no WaveSpeed model matches
**Features**:
- ✅ Automatic model selection based on image resolution
- ✅ Cost-optimized by default (prioritize_cost: true)
- ✅ Logs auto-selection reason for transparency
- ✅ Graceful fallback to HuggingFace if needed
---
### **4. Recommendation Algorithm** ✅ **COMPLETE**
**Scoring Factors**:
1. **Cost** (weighted by `prioritize_cost` preference)
2. **Quality** (max resolution, weighted by `prioritize_quality`)
3. **User Tier** (free users → budget models, pro → premium)
4. **Image Resolution** (filters models that don't support input size)
**Scoring Formula**:
```python
score = (
(1.0 / cost) * cost_weight + # Lower cost = higher score
max_resolution / resolution_weight + # Higher res = higher score
tier_bonus # Based on user tier
)
```
**Result**: Returns best matching model with explanation and alternatives
---
### **5. Service Layer Methods** ✅ **COMPLETE**
**Added to `EditStudioService`**:
-`get_available_models()` - List models with metadata
-`recommend_model()` - Smart recommendation algorithm
-`_get_use_cases_for_model()` - Generate use cases from capabilities
-`_get_features_for_model()` - Generate feature list
**Added to `ImageStudioManager`**:
-`get_edit_models()` - Expose model listing
-`recommend_edit_model()` - Expose recommendations
---
## 📋 Frontend Integration (Pending)
### **Required Components**
1. **ModelSelector Component**
- Dropdown/select with search
- Group by tier
- Show cost and features
- Display recommendations
2. **ModelInfoCard Component**
- Model details
- Use cases
- Features
- Cost information
3. **ModelComparisonDialog Component**
- Side-by-side comparison
- Filterable table
- Quick select
4. **ModelRecommendationBadge Component**
- Show recommendation reason
- Dismissible
### **Integration Points**
1. **EditStudio.tsx**
- Add model selector to UI
- Call `/api/image-studio/edit/models` on load
- Call `/api/image-studio/edit/recommend` for auto-selection
- Display model info and cost
- Pass selected model to request
2. **useImageStudio Hook**
- Add `loadEditModels()` function
- Add `getModelRecommendation()` function
- Add model selection state
---
## 🎯 Current Status
| Component | Status | Notes |
|-----------|--------|-------|
| **Backend Models** | ✅ 5/14 | Qwen Edit, Qwen Edit Plus, Nano Banana, Seedream, FLUX Kontext Pro |
| **Backend APIs** | ✅ Complete | `/edit/models`, `/edit/recommend` |
| **Auto-Detection** | ✅ Complete | Smart routing when model not specified |
| **Recommendation** | ✅ Complete | Algorithm with scoring |
| **Service Layer** | ✅ Complete | All methods implemented |
| **Frontend UI** | ⏸️ Pending | Components need to be built |
---
## 📝 Next Steps
### **Immediate (Frontend)**
1. Create `ModelSelector` component
2. Create `ModelInfoCard` component
3. Create `ModelComparisonDialog` component
4. Integrate into `EditStudio.tsx`
5. Add API calls to `useImageStudio` hook
### **Future (More Models)**
1. Add remaining 9 editing models (once docs provided)
2. Enhance recommendation algorithm with usage history
3. Add model performance metrics
4. Add user feedback/rating system
---
## 🔧 API Usage Examples
### **Get Available Models**
```bash
curl -X GET "http://localhost:8000/api/image-studio/edit/models?operation=general_edit&tier=budget" \
-H "Authorization: Bearer ${TOKEN}"
```
### **Get Recommendation**
```bash
curl -X POST "http://localhost:8000/api/image-studio/edit/recommend" \
-H "Authorization: Bearer ${TOKEN}" \
-H "Content-Type: application/json" \
-d '{
"operation": "general_edit",
"image_resolution": { "width": 1024, "height": 1024 },
"user_tier": "free",
"preferences": { "prioritize_cost": true }
}'
```
### **Process Edit (with auto-detection)**
```bash
curl -X POST "http://localhost:8000/api/image-studio/edit/process" \
-H "Authorization: Bearer ${TOKEN}" \
-H "Content-Type: application/json" \
-d '{
"image_base64": "...",
"operation": "general_edit",
"prompt": "Change background to beach"
// model not specified - will auto-detect
}'
```
---
*Backend complete - Ready for frontend integration*