Deploy Predictive Supply Chain Agents with AutoGen and FastAPI

                      
                     
"""
Production implementation for deploying predictive supply chain agents.
Provides secure, scalable operations using FastAPI, with AutoGen for data handling.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import asyncio
import httpx
from fastapi import FastAPI, HTTPException, Request
from pydantic import BaseModel, validator

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

class Config:
    database_url: str = os.getenv('DATABASE_URL')
    api_url: str = os.getenv('API_URL')

class InputData(BaseModel):
    id: int
    name: str
    quantity: int

    @validator('quantity')
    def validate_quantity(cls, v):
        if v < 0:
            raise ValueError('Quantity must be non-negative')
        return v

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'id' not in data or 'name' not in data:
        raise ValueError('Missing required fields')
    return True

async def fetch_data(api_endpoint: str) -> Optional[Dict[str, Any]]:
    """Fetch data from external API.
    
    Args:
        api_endpoint: The API endpoint to call
    Returns:
        The JSON response as a dictionary
    Raises:
        Exception: If fetching fails
    """
    try:
        async with httpx.AsyncClient() as client:
            response = await client.get(api_endpoint)
            response.raise_for_status()  # Raises HTTPError for bad responses
            return response.json()
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save data to the database.
    
    Args:
        data: The data to save
    Raises:
        Exception: If saving fails
    """
    # Simulating a save operation
    logger.info(f'Saving data: {data}')

async def process_batch(data_list: List[InputData]) -> None:
    """Process a batch of input data.
    
    Args:
        data_list: List of input data
    Raises:
        Exception: If processing fails
    """
    for data in data_list:
        try:
            await validate_input(data.dict())
            await save_to_db(data.dict())
        except Exception as e:
            logger.error(f'Error processing {data}: {e}')  # Log errors

app = FastAPI()

@app.post('/predict/')
async def predict(input_data: List[InputData]):
    """Endpoint to receive input data for prediction.
    
    Args:
        input_data: List of input data
    Returns:
        Confirmation message
    Raises:
        HTTPException: For validation errors
    """
    try:
        await process_batch(input_data)
        return {'message': 'Data processed successfully'}
    except HTTPException as e:
        logger.warning(f'HTTP exception: {e.detail}')
        raise
    except Exception as e:
        logger.error(f'General error: {e}')
        raise HTTPException(status_code=500, detail='Internal server error')

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