Start fixing forecast service API 13
This commit is contained in:
Urtzi Alfaro
@@ -63,6 +63,7 @@ class ForecastingService:
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# Generate prediction using ML service
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prediction_result = await self.prediction_service.predict(
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model_id=model_info["model_id"],
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model_path=model_info["model_path"],
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features=features,
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confidence_level=request.confidence_level
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)
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@@ -268,7 +269,7 @@ class ForecastingService:
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}
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# Add Spanish holidays
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features["is_holiday"] = await self._is_spanish_holiday(request.forecast_date)
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features["is_holiday"] = self._is_spanish_holiday(request.forecast_date)
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weather_data = await self._get_weather_forecast(tenant_id, 1)
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@@ -276,26 +277,26 @@ class ForecastingService:
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return features
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async def _is_spanish_holiday(self, forecast_date: date) -> bool:
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"""Check if date is a Spanish holiday"""
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def _is_spanish_holiday(self, date: datetime) -> bool:
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"""Check if a date is a major Spanish holiday"""
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month_day = (date.month, date.day)
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try:
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# Call data service for holiday information
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async with httpx.AsyncClient() as client:
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response = await client.get(
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f"{settings.DATA_SERVICE_URL}/api/v1/holidays/check",
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params={"date": forecast_date.isoformat()},
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headers={"X-Service-Auth": settings.SERVICE_AUTH_TOKEN}
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)
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if response.status_code == 200:
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return response.json().get("is_holiday", False)
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else:
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return False
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except Exception as e:
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logger.warning("Error checking holiday status", error=str(e))
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return False
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# Major Spanish holidays that affect bakery sales
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spanish_holidays = [
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(1, 1), # New Year
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(1, 6), # Epiphany (Reyes)
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(5, 1), # Labour Day
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(8, 15), # Assumption
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(10, 12), # National Day
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(11, 1), # All Saints
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(12, 6), # Constitution
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(12, 8), # Immaculate Conception
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(12, 25), # Christmas
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(5, 15), # San Isidro (Madrid patron saint)
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(5, 2), # Madrid Community Day
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]
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return month_day in spanish_holidays
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async def _get_weather_forecast(self, tenant_id: str, days: str) -> Dict[str, Any]:
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"""Get weather forecast for the date"""
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@@ -16,6 +16,7 @@ import numpy as np
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import pandas as pd
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import httpx
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from pathlib import Path
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import os
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from app.core.config import settings
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from shared.monitoring.metrics import MetricsCollector
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@@ -33,7 +34,7 @@ class PredictionService:
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self.model_cache = {}
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self.cache_ttl = 3600 # 1 hour cache
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async def predict(self, model_id: str, features: Dict[str, Any],
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async def predict(self, model_id: str, model_path: str, features: Dict[str, Any],
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confidence_level: float = 0.8) -> Dict[str, float]:
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"""Generate prediction using trained model"""
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@@ -45,7 +46,7 @@ class PredictionService:
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features_count=len(features))
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# Load model
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model = await self._load_model(model_id)
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model = await self._load_model(model_id, model_path)
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if not model:
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raise ValueError(f"Model {model_id} not found or failed to load")
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@@ -87,42 +88,31 @@ class PredictionService:
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error=str(e))
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raise
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async def _load_model(self, model_id: str):
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"""Load model from cache or training service"""
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async def _load_model(self, model_id: str, model_path: str):
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"""Load model from shared volume using API metadata"""
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# Check cache first
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if model_id in self.model_cache:
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cached_model, cached_time = self.model_cache[model_id]
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if (datetime.now() - cached_time).seconds < self.cache_ttl:
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logger.debug("Using cached model", model_id=model_id)
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return cached_model
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try:
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# Download model from training service
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async with httpx.AsyncClient(timeout=30.0) as client:
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response = await client.get(
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f"{settings.TRAINING_SERVICE_URL}/api/v1/models/{model_id}/download",
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headers={"X-Service-Auth": settings.SERVICE_AUTH_TOKEN}
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)
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# Load model directly from shared volume (fast!)
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if os.path.exists(model_path):
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with open(model_path, 'rb') as f:
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model = pickle.load(f)
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# Cache the model
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self.model_cache[model_id] = (model, datetime.now())
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logger.info(f"Model loaded from shared volume: {model_path}")
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return model
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else:
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logger.error(f"Model file not found: {model_path}")
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return None
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if response.status_code == 200:
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# Load model from bytes
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model_data = response.content
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model = pickle.loads(model_data)
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# Cache the model
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self.model_cache[model_id] = (model, datetime.now())
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logger.info("Model loaded successfully", model_id=model_id)
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return model
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else:
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logger.error("Failed to download model",
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model_id=model_id,
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status_code=response.status_code)
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return None
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except Exception as e:
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logger.error("Error loading model", model_id=model_id, error=str(e))
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logger.error(f"Error loading model: {e}")
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return None
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def _prepare_prophet_features(self, features: Dict[str, Any]) -> pd.DataFrame:
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