143 lines
4.6 KiB
Python
143 lines
4.6 KiB
Python
# ================================================================
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# services/forecasting/app/api/predictions.py
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# ================================================================
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"""
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Prediction API endpoints - Real-time prediction capabilities
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"""
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import structlog
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from fastapi import APIRouter, Depends, HTTPException, status, Query
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from sqlalchemy.ext.asyncio import AsyncSession
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from typing import List, Dict, Any
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from datetime import date, datetime, timedelta
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from app.core.database import get_db
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from shared.auth.decorators import (
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get_current_user_dep,
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get_current_tenant_id_dep
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)
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from app.services.prediction_service import PredictionService
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from app.schemas.forecasts import ForecastRequest
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logger = structlog.get_logger()
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router = APIRouter()
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# Initialize service
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prediction_service = PredictionService()
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@router.post("/realtime")
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async def get_realtime_prediction(
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product_name: str,
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location: str,
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forecast_date: date,
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features: Dict[str, Any],
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tenant_id: str = Depends(get_current_tenant_id_dep)
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):
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"""Get real-time prediction without storing in database"""
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try:
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# Get latest model
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from app.services.forecasting_service import ForecastingService
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forecasting_service = ForecastingService()
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model_info = await forecasting_service._get_latest_model(
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tenant_id, product_name, location
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)
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if not model_info:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail=f"No trained model found for {product_name}"
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)
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# Generate prediction
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prediction = await prediction_service.predict(
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model_id=model_info["model_id"],
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features=features,
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confidence_level=0.8
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)
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return {
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"product_name": product_name,
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"location": location,
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"forecast_date": forecast_date,
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"predicted_demand": prediction["demand"],
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"confidence_lower": prediction["lower_bound"],
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"confidence_upper": prediction["upper_bound"],
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"model_id": model_info["model_id"],
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"model_version": model_info["version"],
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"generated_at": datetime.now(),
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"features_used": features
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}
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except HTTPException:
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raise
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except Exception as e:
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logger.error("Error getting realtime prediction", error=str(e))
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail="Internal server error"
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)
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@router.get("/quick/{product_name}")
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async def get_quick_prediction(
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product_name: str,
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location: str = Query(...),
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days_ahead: int = Query(1, ge=1, le=7),
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tenant_id: str = Depends(get_current_tenant_id_dep)
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):
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"""Get quick prediction for next few days"""
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try:
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# Generate predictions for the next N days
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predictions = []
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for day in range(1, days_ahead + 1):
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forecast_date = date.today() + timedelta(days=day)
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# Prepare basic features
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features = {
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"date": forecast_date.isoformat(),
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"day_of_week": forecast_date.weekday(),
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"is_weekend": forecast_date.weekday() >= 5,
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"business_type": "individual"
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}
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# Get model and predict
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from app.services.forecasting_service import ForecastingService
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forecasting_service = ForecastingService()
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model_info = await forecasting_service._get_latest_model(
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tenant_id, product_name, location
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)
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if model_info:
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prediction = await prediction_service.predict(
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model_id=model_info["model_id"],
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features=features
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)
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predictions.append({
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"date": forecast_date,
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"predicted_demand": prediction["demand"],
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"confidence_lower": prediction["lower_bound"],
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"confidence_upper": prediction["upper_bound"]
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})
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return {
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"product_name": product_name,
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"location": location,
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"predictions": predictions,
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"generated_at": datetime.now()
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}
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except Exception as e:
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logger.error("Error getting quick prediction", error=str(e))
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail="Internal server error"
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)
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