dragonllm-finance-models / docs /STATUS_REPORT.md
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feat: Clean deployment to HuggingFace Space with model config test endpoint
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# πŸ“Š Status Report: LinguaCustodia API Refactoring
**Date**: September 30, 2025
**Current Status**: Configuration Layer Complete, Core Layer Pending
**Working Space**: https://huggingface.co/spaces/jeanbaptdzd/linguacustodia-financial-api
---
## βœ… WHAT WE'VE DONE
### **Phase 1: Problem Solving** βœ… COMPLETE
1. **Solved Truncation Issue**
- Problem: Responses were truncated at 76-80 tokens
- Solution: Applied respectful official configuration with anti-truncation measures
- Result: Now generating ~141 tokens with proper endings
- Status: βœ… **WORKING** in production
2. **Implemented Persistent Storage**
- Problem: Models reload every restart
- Solution: Added persistent storage detection and configuration
- Result: Storage-enabled app deployed
- Status: ⚠️ **PARTIAL** - Space variable not fully working yet
3. **Fixed Storage Configuration**
- Problem: App was calling `setup_storage()` on every request
- Solution: Call only once during startup, store globally
- Result: Cleaner, more efficient storage handling
- Status: βœ… **FIXED** in latest version
### **Phase 2: Code Quality** βœ… COMPLETE
4. **Created Refactored Version**
- Eliminated redundant code blocks
- Created `StorageManager` and `ModelManager` classes
- Reduced function length and complexity
- Status: βœ… **DONE** (`app_refactored.py`)
### **Phase 3: Architecture Design** βœ… COMPLETE
5. **Designed Configuration Pattern**
- Created modular configuration system
- Separated concerns (base, models, providers, logging)
- Implemented configuration classes
- Status: βœ… **DONE** in `config/` directory
6. **Created Configuration Files**
- `config/base_config.py` - Base application settings
- `config/model_configs.py` - Model registry and configs
- `config/provider_configs.py` - Provider configurations
- `config/logging_config.py` - Structured logging
- Status: βœ… **CREATED** and ready to use
7. **Documented Architecture**
- Created comprehensive architecture document
- Documented design principles
- Provided usage examples
- Listed files to keep/remove
- Status: βœ… **DOCUMENTED** in `docs/ARCHITECTURE.md`
---
## 🚧 WHAT WE NEED TO DO
### **Phase 4: Core Layer Implementation** πŸ”„ NEXT
**Priority**: HIGH
**Estimated Time**: 2-3 hours
Need to create:
1. **`core/storage_manager.py`**
- Handles storage detection and setup
- Uses configuration from `config/base_config.py`
- Manages HF_HOME and cache directories
- Implements fallback logic
2. **`core/model_loader.py`**
- Handles model authentication and loading
- Uses configuration from `config/model_configs.py`
- Manages memory cleanup
- Implements retry logic
3. **`core/inference_engine.py`**
- Handles inference requests
- Uses generation configuration
- Manages tokenization
- Implements error handling
### **Phase 5: Provider Layer Implementation** πŸ”„ PENDING
**Priority**: MEDIUM
**Estimated Time**: 3-4 hours
Need to create:
1. **`providers/base_provider.py`**
- Abstract base class for all providers
- Defines common interface
- Implements shared logic
2. **`providers/huggingface_provider.py`**
- Implements HuggingFace inference
- Uses transformers library
- Handles local model loading
3. **`providers/scaleway_provider.py`**
- Implements Scaleway API integration
- Handles API authentication
- Implements retry logic
- Status: STUB (API details needed)
4. **`providers/koyeb_provider.py`**
- Implements Koyeb API integration
- Handles deployment management
- Implements scaling logic
- Status: STUB (API details needed)
### **Phase 6: API Layer Refactoring** πŸ”„ PENDING
**Priority**: MEDIUM
**Estimated Time**: 2-3 hours
Need to refactor:
1. **`api/app.py`**
- Use new configuration system
- Use new core modules
- Remove old code
2. **`api/routes.py`**
- Extract routes from main app
- Use new inference engine
- Implement proper error handling
3. **`api/models.py`**
- Update Pydantic models
- Add validation
- Use configuration
### **Phase 7: File Cleanup** πŸ”„ PENDING
**Priority**: LOW
**Estimated Time**: 1 hour
Need to:
1. **Move test files to `tests/` directory**
2. **Remove redundant files** (see list in ARCHITECTURE.md)
3. **Update imports in remaining files**
4. **Update documentation**
### **Phase 8: Testing & Deployment** πŸ”„ PENDING
**Priority**: HIGH
**Estimated Time**: 2-3 hours
Need to:
1. **Test new architecture locally**
2. **Update Space deployment**
3. **Verify persistent storage works**
4. **Test inference endpoints**
5. **Monitor performance**
---
## πŸ“ CURRENT FILE STATUS
### **Production Files** (Currently Deployed)
```
app.py # v20.0.0 - Storage-enabled respectful config
requirements.txt # Production dependencies
Dockerfile # Docker configuration
```
### **New Architecture Files** (Created, Not Deployed)
```
config/
β”œβ”€β”€ __init__.py βœ… DONE
β”œβ”€β”€ base_config.py βœ… DONE
β”œβ”€β”€ model_configs.py βœ… DONE
β”œβ”€β”€ provider_configs.py βœ… DONE
└── logging_config.py βœ… DONE
core/ ⚠️ EMPTY - Needs implementation
providers/ ⚠️ EMPTY - Needs implementation
api/ ⚠️ EMPTY - Needs refactoring
```
### **Redundant Files** (To Remove)
```
space_app.py ❌ Remove
space_app_with_storage.py ❌ Remove
persistent_storage_app.py ❌ Remove
memory_efficient_app.py ❌ Remove
respectful_linguacustodia_config.py ❌ Remove
storage_enabled_respectful_app.py ❌ Remove
app_refactored.py ❌ Remove (after migration)
```
---
## 🎯 IMMEDIATE NEXT STEPS
### **Option A: Complete New Architecture** (Recommended for Production)
**Time**: 6-8 hours total
1. Implement core layer (2-3 hours)
2. Implement provider layer - HuggingFace only (2-3 hours)
3. Refactor API layer (2-3 hours)
4. Test and deploy (1-2 hours)
### **Option B: Deploy Current Working Version** (Quick Fix)
**Time**: 30 minutes
1. Fix persistent storage issue in current `app.py`
2. Test Space configuration
3. Deploy and verify
4. Continue architecture work later
### **Option C: Hybrid Approach** (Balanced)
**Time**: 3-4 hours
1. Fix persistent storage in current version (30 min)
2. Deploy working version (30 min)
3. Continue building new architecture in parallel (2-3 hours)
4. Migrate when ready
---
## πŸ“Š PRODUCTION STATUS
### **Current Space Status**
- **URL**: https://huggingface.co/spaces/jeanbaptdzd/linguacustodia-financial-api
- **Version**: 20.0.0 (Storage-Enabled Respectful Config)
- **Model**: LinguaCustodia/llama3.1-8b-fin-v0.3
- **Hardware**: T4 Medium GPU
- **Status**: βœ… RUNNING
### **What's Working**
βœ… API endpoints (`/`, `/health`, `/inference`, `/docs`)
βœ… Model loading and inference
βœ… Truncation fix (141 tokens vs 76-80)
βœ… Respectful official configuration
βœ… GPU memory management
### **What's Not Working**
❌ Persistent storage (still using ephemeral cache)
⚠️ Storage configuration shows 0GB free
⚠️ Models reload on every restart
---
## πŸ’‘ RECOMMENDATIONS
### **For Immediate Production Use:**
1. **Option B** - Fix the current version quickly
2. Get persistent storage working properly
3. Verify models cache correctly
### **For Long-term Scalability:**
1. Complete **Option A** - Build out the new architecture
2. This provides multi-provider support
3. Easier to maintain and extend
### **Best Approach:**
1. **Today**: Fix current version (Option B)
2. **This Week**: Complete new architecture (Option A)
3. **Migration**: Gradual cutover with testing
---
## ❓ QUESTIONS TO ANSWER
1. **What's the priority?**
- Fix current production issue immediately?
- Complete new architecture first?
- Hybrid approach?
2. **Do we need Scaleway/Koyeb now?**
- Or can we start with HuggingFace only?
- When do you need other providers?
3. **File cleanup now or later?**
- Clean up redundant files now?
- Or wait until migration complete?
---
## πŸ“ˆ SUCCESS METRICS
### **Completed** βœ…
- Truncation issue solved
- Code refactored with classes
- Configuration pattern designed
- Architecture documented
### **In Progress** πŸ”„
- Persistent storage working
- Core layer implementation
- Provider abstraction
### **Pending** ⏳
- Scaleway integration
- Koyeb integration
- Full file cleanup
- Complete migration
---
**SUMMARY**: We've made excellent progress on architecture design and problem-solving. The current version works (with truncation fix), but persistent storage needs fixing. We have a clear path forward with the new architecture.