Generate Schema-Constrained Equipment Diagnostic Reports with Outlines and Instructor

"""
Production implementation for generating schema-constrained equipment diagnostic reports.
This module provides a secure, scalable approach to managing diagnostic data with validation and reporting capabilities.
"""

from typing import Dict, Any, List, Optional, Tuple
import os
import logging
import json
import requests
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, ValidationError
from sqlalchemy import create_engine, Column, Integer, String, Float
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, Session

# Logger setup
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Database configuration
DATABASE_URL = os.getenv('DATABASE_URL', 'sqlite:///./test.db')
engine = create_engine(DATABASE_URL)
Base = declarative_base()
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = DATABASE_URL

class EquipmentDiagnosticReport(Base):
    """
    ORM model for equipment diagnostic reports.
    """
    __tablename__ = 'diagnostic_reports'
    id = Column(Integer, primary_key=True, index=True)
    equipment_id = Column(String, index=True)
    report_data = Column(String)
    created_at = Column(String)

Base.metadata.create_all(bind=engine)

# Helper Functions

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for the diagnostic report.
    
    Args:
        data: Input data for validation.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'equipment_id' not in data or not data['equipment_id']:
        raise ValueError('Missing or empty equipment_id')
    return True


def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields to prevent injection.
    
    Args:
        data: Input data to sanitize.
    Returns:
        Sanitized data.
    """
    return {key: str(value).strip() for key, value in data.items()}


def fetch_data(api_url: str) -> Dict[str, Any]:
    """Fetch diagnostic data from an external API.
    
    Args:
        api_url: URL of the API to fetch data from.
    Returns:
        Fetched data as a dictionary.
    Raises:
        HTTPException: If the API call fails.
    """
    try:
        response = requests.get(api_url)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        logger.error(f'API call failed: {e}')
        raise HTTPException(status_code=500, detail='Error fetching data')


def save_to_db(db: Session, report: EquipmentDiagnosticReport) -> None:
    """Save the diagnostic report to the database.
    
    Args:
        db: SQLAlchemy session.
        report: Equipment diagnostic report object.
    """
    db.add(report)
    db.commit()
    logger.info('Report saved to database')


def format_output(report: EquipmentDiagnosticReport) -> Dict[str, Any]:
    """Format the output for presentation.
    
    Args:
        report: Equipment diagnostic report object.
    Returns:
        Formatted report as a dictionary.
    """
    return {
        'id': report.id,
        'equipment_id': report.equipment_id,
        'report_data': json.loads(report.report_data),
        'created_at': report.created_at,
    }

# Main Application
app = FastAPI()

@app.post('/reports/', response_model=Dict[str, Any])
async def create_report(data: Dict[str, Any]) -> Dict[str, Any]:
    """Create a diagnostic report.
    
    Args:
        data: Input data for the report.
    Returns:
        Created report details.
    Raises:
        HTTPException: If validation or saving fails.
    """
    try:
        validate_input(data)  # Validate input data
        data = sanitize_fields(data)  # Sanitize input data
        report_data = fetch_data('https://api.example.com/diagnostics')  # Fetch external data
        report = EquipmentDiagnosticReport(
            equipment_id=data['equipment_id'],
            report_data=json.dumps(report_data),
            created_at='2023-10-01'
        )
        with SessionLocal() as db:
            save_to_db(db, report)  # Save to DB
        return format_output(report)  # Format output
    except ValueError as ve:
        logger.warning(f'Validation error: {ve}')
        raise HTTPException(status_code=400, detail=str(ve))
    except Exception as e:
        logger.error(f'Error creating report: {e}')
        raise HTTPException(status_code=500, detail='Internal Server Error')

if __name__ == '__main__':
    # Example usage if running as a script
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)