kbt-devops
fe76bc7629
Implements FastAPI backend with ML model support for energy trading, including price prediction models and RL-based battery trading policy. Features dashboard, trading, backtest, and settings API routes with WebSocket support for real-time updates.
72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
from typing import List
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from fastapi import APIRouter, HTTPException
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from datetime import datetime
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from app.models.schemas import ModelInfo, TrainingRequest, TrainingStatus, PredictionResponse
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from app.services import MLService
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import uuid
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router = APIRouter()
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ml_service = MLService()
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_training_store: dict = {}
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@router.get("", response_model=List[ModelInfo])
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async def list_models():
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return ml_service.list_models()
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@router.post("/train")
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async def train_model(request: TrainingRequest):
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training_id = f"training_{uuid.uuid4().hex[:8]}"
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_training_store[training_id] = TrainingStatus(
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training_id=training_id,
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status="pending",
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progress=0.0,
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started_at=datetime.utcnow(),
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)
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return {"training_id": training_id, "status": _training_store[training_id]}
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@router.get("/{training_id}/status", response_model=TrainingStatus)
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async def get_training_status(training_id: str):
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if training_id not in _training_store:
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raise HTTPException(status_code=404, detail=f"Training job {training_id} not found")
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return _training_store[training_id]
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@router.get("/{model_id}/metrics")
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async def get_model_metrics(model_id: str):
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try:
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metrics = ml_service.get_model_metrics(model_id)
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return {"model_id": model_id, "metrics": metrics}
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except ValueError as e:
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raise HTTPException(status_code=404, detail=str(e))
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@router.post("/predict", response_model=PredictionResponse)
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async def predict(
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model_id: str,
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timestamp: datetime,
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features: dict = None,
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):
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try:
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result = ml_service.predict(model_id, timestamp, features)
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return PredictionResponse(**result)
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except ValueError as e:
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raise HTTPException(status_code=404, detail=str(e))
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@router.get("/{model_id}/feature-importance")
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async def get_feature_importance(model_id: str):
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try:
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importance = ml_service.get_feature_importance(model_id)
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return {"model_id": model_id, "feature_importance": importance}
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except ValueError as e:
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raise HTTPException(status_code=404, detail=str(e))
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