feat: enable blockchain, multi-modal RL, and edge GPU routers

- Enable blockchain router (uncommented in main.py)
- Create multi_modal_rl.py router with placeholder endpoints
- Create edge_gpu.py router with placeholder endpoints
- Remove warning messages for missing optional routers

apps/coordinator-api/src/app/main.py

@@ -350,11 +350,7 @@ def create_app() -> FastAPI:
     app.include_router(marketplace_offers, prefix="/v1")
 
     # Add blockchain router for CLI compatibility
-    # print(f"Adding blockchain router: {blockchain}")
-    # app.include_router(blockchain, prefix="/v1")
-    # BLOCKCHAIN ROUTER DISABLED - preventing monitoring calls
-    # Blockchain router disabled - preventing monitoring calls
-    print("Blockchain router disabled")
+    app.include_router(blockchain, prefix="/v1")
 
     # Add Prometheus metrics endpoint
     metrics_app = make_asgi_app()

apps/coordinator-api/src/app/routers/edge_gpu.py

@@ -0,0 +1,67 @@
+"""
+Edge GPU Router
+Handles edge GPU management endpoints
+"""
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from typing import Any
+
+router = APIRouter(prefix="/edge-gpu", tags=["edge-gpu"])
+
+
+class GPUProfile(BaseModel):
+    """GPU profile model"""
+    gpu_model: str
+    architecture: str
+    memory_gb: int
+    edge_optimized: bool
+
+
+class GPUMetrics(BaseModel):
+    """GPU metrics model"""
+    gpu_id: str
+    timestamp: str
+    utilization: float
+    memory_used: float
+    temperature: float
+
+
+@router.get("/profiles")
+async def list_profiles() -> dict:
+    """List available edge GPU profiles"""
+    return {"profiles": []}
+
+
+@router.get("/metrics/{gpu_id}")
+async def get_gpu_metrics(gpu_id: str) -> dict:
+    """Get metrics for a specific GPU"""
+    return {"gpu_id": gpu_id, "metrics": []}
+
+
+@router.post("/metrics")
+async def submit_metrics(metrics: GPUMetrics) -> dict:
+    """Submit GPU metrics"""
+    return {"status": "success", "gpu_id": metrics.gpu_id}
+
+
+@router.post("/discover")
+async def discover_edge_gpus(miner_id: str) -> dict[str, Any]:
+    """Discover and register edge GPUs for a miner"""
+    return {
+        "miner_id": miner_id,
+        "gpus": [],
+        "registered": 0,
+        "edge_optimized": 0,
+    }
+
+
+@router.post("/optimize")
+async def optimize_inference(gpu_id: str, model_name: str, request_data: dict) -> dict[str, Any]:
+    """Optimize ML inference request for edge GPU"""
+    return {
+        "gpu_id": gpu_id,
+        "model_name": model_name,
+        "optimized": True,
+        "latency_reduction": 0.0,
+    }

apps/coordinator-api/src/app/routers/multi_modal_rl.py

@@ -0,0 +1,45 @@
+"""
+Multi-modal RL Router
+Handles multi-modal reinforcement learning endpoints
+"""
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+
+router = APIRouter(prefix="/multi-modal-rl", tags=["multi-modal-rl"])
+
+
+class RLRequest(BaseModel):
+    """RL request model"""
+    model_id: str
+    input_data: dict
+
+
+class RLResponse(BaseModel):
+    """RL response model"""
+    status: str
+    result: dict
+
+
+@router.post("/train")
+async def train_model(request: RLRequest) -> RLResponse:
+    """Train multi-modal RL model"""
+    return RLResponse(
+        status="success",
+        result={"message": "Multi-modal RL training endpoint - placeholder"}
+    )
+
+
+@router.post("/inference")
+async def run_inference(request: RLRequest) -> RLResponse:
+    """Run inference on multi-modal RL model"""
+    return RLResponse(
+        status="success",
+        result={"message": "Multi-modal RL inference endpoint - placeholder"}
+    )
+
+
+@router.get("/models")
+async def list_models() -> dict:
+    """List available multi-modal RL models"""
+    return {"models": []}