Improve AI logic
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Urtzi Alfaro
288
services/production/app/api/ml_insights.py
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288
services/production/app/api/ml_insights.py
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"""
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ML Insights API Endpoints for Production Service
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Provides endpoints to trigger ML insight generation for:
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- Production yield predictions
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- Quality optimization
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- Process efficiency analysis
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"""
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from fastapi import APIRouter, Depends, HTTPException
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from pydantic import BaseModel, Field
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from typing import Optional, List
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from uuid import UUID
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from datetime import datetime, timedelta
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import structlog
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import pandas as pd
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from app.core.database import get_db
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from sqlalchemy.ext.asyncio import AsyncSession
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logger = structlog.get_logger()
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router = APIRouter(
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prefix="/api/v1/tenants/{tenant_id}/production/ml/insights",
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tags=["ML Insights"]
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)
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# ================================================================
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# REQUEST/RESPONSE SCHEMAS
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# ================================================================
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class YieldPredictionRequest(BaseModel):
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"""Request schema for yield prediction"""
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recipe_ids: Optional[List[str]] = Field(
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None,
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description="Specific recipe IDs to analyze. If None, analyzes all recipes"
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)
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lookback_days: int = Field(
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90,
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description="Days of historical production to analyze",
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ge=30,
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le=365
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)
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min_history_runs: int = Field(
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30,
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description="Minimum production runs required",
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ge=10,
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le=100
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)
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class YieldPredictionResponse(BaseModel):
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"""Response schema for yield prediction"""
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success: bool
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message: str
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tenant_id: str
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recipes_analyzed: int
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total_insights_generated: int
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total_insights_posted: int
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recipes_with_issues: int
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insights_by_recipe: dict
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errors: List[str] = []
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# ================================================================
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# API ENDPOINTS
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# ================================================================
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@router.post("/predict-yields", response_model=YieldPredictionResponse)
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async def trigger_yield_prediction(
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tenant_id: str,
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request_data: YieldPredictionRequest,
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db: AsyncSession = Depends(get_db)
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):
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"""
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Trigger yield prediction for production recipes.
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This endpoint:
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1. Fetches historical production data for specified recipes
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2. Runs the YieldInsightsOrchestrator to predict yields
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3. Generates insights about yield optimization opportunities
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4. Posts insights to AI Insights Service
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Args:
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tenant_id: Tenant UUID
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request_data: Prediction parameters
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db: Database session
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Returns:
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YieldPredictionResponse with prediction results
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"""
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logger.info(
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"ML insights yield prediction requested",
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tenant_id=tenant_id,
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recipe_ids=request_data.recipe_ids,
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lookback_days=request_data.lookback_days
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)
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try:
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# Import ML orchestrator and clients
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from app.ml.yield_insights_orchestrator import YieldInsightsOrchestrator
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from shared.clients.recipes_client import RecipesServiceClient
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from app.core.config import settings
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# Initialize orchestrator and recipes client
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orchestrator = YieldInsightsOrchestrator()
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recipes_client = RecipesServiceClient(settings)
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# Get recipes to analyze from recipes service via API
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if request_data.recipe_ids:
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# Fetch specific recipes
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recipes = []
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for recipe_id in request_data.recipe_ids:
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recipe = await recipes_client.get_recipe_by_id(
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recipe_id=recipe_id,
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tenant_id=tenant_id
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)
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if recipe:
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recipes.append(recipe)
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else:
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# Fetch all recipes for tenant (limit to 10)
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all_recipes = await recipes_client.get_all_recipes(tenant_id=tenant_id)
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recipes = all_recipes[:10] if all_recipes else [] # Limit to prevent timeout
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if not recipes:
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return YieldPredictionResponse(
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success=False,
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message="No recipes found for analysis",
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tenant_id=tenant_id,
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recipes_analyzed=0,
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total_insights_generated=0,
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total_insights_posted=0,
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recipes_with_issues=0,
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insights_by_recipe={},
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errors=["No recipes found"]
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)
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# Calculate date range for production history
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end_date = datetime.utcnow()
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start_date = end_date - timedelta(days=request_data.lookback_days)
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# Process each recipe
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total_insights_generated = 0
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total_insights_posted = 0
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recipes_with_issues = 0
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insights_by_recipe = {}
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errors = []
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for recipe in recipes:
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try:
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recipe_id = str(recipe['id'])
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recipe_name = recipe.get('name', 'Unknown Recipe')
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logger.info(f"Analyzing yield for {recipe_name} ({recipe_id})")
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# Fetch real production batch history from database
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from app.models.production import ProductionBatch, ProductionStatus
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from sqlalchemy import select
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batch_query = select(ProductionBatch).where(
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ProductionBatch.tenant_id == UUID(tenant_id),
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ProductionBatch.recipe_id == UUID(recipe_id), # Use the extracted UUID
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ProductionBatch.actual_start_time >= start_date,
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ProductionBatch.actual_start_time <= end_date,
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ProductionBatch.status == ProductionStatus.COMPLETED,
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ProductionBatch.actual_quantity.isnot(None)
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).order_by(ProductionBatch.actual_start_time)
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batch_result = await db.execute(batch_query)
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batches = batch_result.scalars().all()
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if len(batches) < request_data.min_history_runs:
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logger.warning(
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f"Insufficient production history for recipe {recipe_id}: "
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f"{len(batches)} batches < {request_data.min_history_runs} required"
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)
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continue
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# Create production history DataFrame from real batches
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production_data = []
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for batch in batches:
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# Calculate yield percentage
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if batch.planned_quantity and batch.actual_quantity:
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yield_pct = (batch.actual_quantity / batch.planned_quantity) * 100
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else:
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continue # Skip batches without complete data
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production_data.append({
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'production_date': batch.actual_start_time,
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'planned_quantity': float(batch.planned_quantity),
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'actual_quantity': float(batch.actual_quantity),
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'yield_percentage': yield_pct,
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'worker_id': batch.notes or 'unknown', # Use notes field or default
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'batch_number': batch.batch_number
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})
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if not production_data:
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logger.warning(
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f"No valid production data for recipe {recipe_id}"
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)
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continue
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production_history = pd.DataFrame(production_data)
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# Run yield analysis
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results = await orchestrator.analyze_and_post_insights(
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tenant_id=tenant_id,
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recipe_id=recipe_id,
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production_history=production_history,
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min_history_runs=request_data.min_history_runs
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)
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# Track results
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total_insights_generated += results['insights_generated']
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total_insights_posted += results['insights_posted']
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baseline_stats = results.get('baseline_stats', {})
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mean_yield = baseline_stats.get('mean_yield', 100)
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if mean_yield < 90:
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recipes_with_issues += 1
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insights_by_recipe[recipe_id] = {
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'recipe_name': recipe_name,
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'insights_posted': results['insights_posted'],
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'mean_yield': mean_yield,
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'patterns': len(results.get('patterns', []))
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}
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logger.info(
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f"Recipe {recipe_id} analysis complete",
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insights_posted=results['insights_posted'],
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mean_yield=mean_yield
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)
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except Exception as e:
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error_msg = f"Error analyzing recipe {recipe_id}: {str(e)}"
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logger.error(error_msg, exc_info=True)
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errors.append(error_msg)
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# Close orchestrator and clients
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await orchestrator.close()
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await recipes_client.close()
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# Build response
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response = YieldPredictionResponse(
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success=total_insights_posted > 0,
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message=f"Successfully analyzed {len([r for r in recipes if isinstance(r, dict)])} recipes, generated {total_insights_posted} insights",
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tenant_id=tenant_id,
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recipes_analyzed=len([r for r in recipes if isinstance(r, dict)]),
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total_insights_generated=total_insights_generated,
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total_insights_posted=total_insights_posted,
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recipes_with_issues=recipes_with_issues,
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insights_by_recipe=insights_by_recipe,
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errors=errors
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)
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logger.info(
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"ML insights yield prediction complete",
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tenant_id=tenant_id,
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total_insights=total_insights_posted,
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recipes_with_issues=recipes_with_issues
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)
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return response
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except Exception as e:
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logger.error(
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"ML insights yield prediction failed",
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tenant_id=tenant_id,
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error=str(e),
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exc_info=True
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)
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raise HTTPException(
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status_code=500,
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detail=f"Yield prediction failed: {str(e)}"
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)
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@router.get("/health")
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async def ml_insights_health():
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"""Health check for ML insights endpoints"""
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return {
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"status": "healthy",
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"service": "production-ml-insights",
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"endpoints": [
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"POST /ml/insights/predict-yields"
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]
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}
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