docs: add Session 8 summary - architecture refactoring to Gradio API

doc/activity.md

@@ -5300,3 +5300,143 @@ git push hf-space master:main  # HuggingFace Space
 **Next Session**: Configure Space secrets, test notebooks on GPU, evaluate MAE
 
 ---
+
+## Session 8: Architecture Refactoring - JupyterLab to Gradio API (Nov 14, 2025 14:00-16:00 UTC)
+
+### Critical Architecture Realization
+
+**Problem Identified**:
+- Attempted to use HF Spaces as interactive development environment (JupyterLab)
+- SSH sessions repeatedly killed with exit code 137 (OOM/resource limits)
+- Manual notebook debugging in browser too slow and error-prone
+- Multiple parameter errors in notebooks (df= vs dataset=, missing imports, etc.)
+
+**Root Cause**:
+- HF Spaces are **deployment targets** for inference APIs, NOT interactive compute environments
+- SSH sessions have strict resource limits (different from web app containers)
+- JupyterLab workflow requires constant iteration → wrong paradigm for cloud GPU
+
+**Architectural Insight** (from Claude Desktop suggestion):
+```
+WRONG: Local edit → Push to HF → SSH debug → Fix errors → Repeat
+RIGHT: Local edit → Push to HF → API call → Download results → Local analysis
+```
+
+### Architecture Refactoring
+
+**Removed**:
+- JupyterLab configuration (Dockerfile, start_server.sh, on_startup.sh)
+- Interactive notebook execution on HF Space
+- SSH-based debugging workflow
+- Docker SDK (complex custom containers)
+
+**Added**:
+- Gradio SDK (simple, purpose-built for inference APIs)
+- Production inference pipeline (chronos_inference.py - 312 lines)
+- API-first interface (app.py - 112 lines)
+- Clean separation: Development (local) vs Deployment (HF Space)
+
+### New Workflow Pattern
+
+**Local Development**:
+1. Edit code in Claude Code
+2. Push to HF Space via git
+3. Call API from local machine
+4. Download results as parquet
+5. Analyze locally (Marimo notebooks)
+
+**HF Space** (Deployment Only):
+- Purpose: GPU inference endpoint
+- Input: Forecast parameters via Gradio UI or API
+- Output: Parquet file with forecasts
+- No SSH debugging required
+
+### Benefits
+
+1. **Eliminates SSH issues**: No more exit code 137
+2. **Model caching**: Loaded once, stays in memory
+3. **Clean API**: Call from anywhere (Python, curl, web)
+4. **Proper separation**: Development local, inference remote
+5. **Cost efficient**: Only pay GPU during inference
+
+### Testing Status
+
+**Completed**:
+- ✅ Code refactoring (notebooks → API)
+- ✅ Gradio app creation
+- ✅ Git deployment (GitHub + HF Space)
+- ✅ Space rebuild triggered
+
+**Pending** (Next Session):
+- ⏳ Space rebuild completion
+- ⏳ Web UI validation
+- ⏳ Smoke test API call (1 border × 7 days)
+- ⏳ Full forecast (38 borders × 14 days)
+- ⏳ MAE evaluation vs Oct 1-14 actuals
+
+### Next Session Plan
+
+**Priority 1**: Validate API deployment (15 min)
+- Check Gradio UI loads
+- Verify interface functional
+
+**Priority 2**: Test smoke test (10 min)
+```python
+from gradio_client import Client
+client = Client("evgueni-p/fbmc-chronos2")
+result = client.predict(run_date="2025-09-30", forecast_type="smoke_test")
+```
+
+**Priority 3**: Test full forecast (15 min)
+- Run all 38 borders × 14 days
+- Verify output schema
+- Check inference time <5 min
+
+**Priority 4**: Evaluate MAE (20 min)
+- Load Oct 1-14 actuals
+- Calculate MAE per border
+- Document results
+
+**Priority 5**: Update documentation (10 min)
+- Final results in activity.md
+- API usage guide
+- Handover checklist
+
+### Files Created
+
+- `src/forecasting/chronos_inference.py` (312 lines)
+- `app.py` (112 lines)
+
+### Files Modified
+
+- `README.md` - Changed to Gradio SDK
+- `requirements.txt` - Removed JupyterLab, added Gradio
+
+---
+
+**Status**: [IN PROGRESS] HF Space rebuilding with Gradio API
+**Timestamp**: 2025-11-14 16:00 UTC
+**Next Session**: Validate API, run forecasts, evaluate MAE
+
+---
+
+## NEXT SESSION BOOKMARK
+
+**Resume with**:
+1. Check HF Space ready: https://huggingface.co/spaces/evgueni-p/fbmc-chronos2
+2. Install gradio_client: `uv pip install gradio-client`
+3. Run smoke test via API
+4. Download and verify results
+5. Run full forecast
+6. Evaluate MAE vs actuals
+
+**Success criteria**:
+- Smoke test returns 168 rows (7 days × 24 hours)
+- Full forecast completes in <5 minutes
+- MAE ≤ 150 MW on D+1 forecasts
+- All 38 borders in output
+
+**Key files**:
+- `src/forecasting/chronos_inference.py` - Main logic
+- `src/forecasting/dynamic_forecast.py` - Data extraction
+- `app.py` - Gradio interface