Compress and Serve Factory Vision-Language Models with torchao and llama.cpp

"""
Production implementation for Compress and Serve Factory Vision-Language Models.
Provides secure and scalable operations using torchao and llama.cpp.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, ValidationError
import torch

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

class Config:
    model_path: str = os.getenv('MODEL_PATH', 'default_model_path')
    api_key: str = os.getenv('API_KEY', 'default_api_key')

class InputData(BaseModel):
    text: str
    metadata: Dict[str, Any]

async def validate_input(data: InputData) -> bool:
    """Validate input data for processing.
    
    Args:
        data: The input data to validate.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if not data.text:
        raise ValueError('Text field is required.')
    return True

async def sanitize_fields(data: InputData) -> InputData:
    """Sanitize input fields for security.
    
    Args:
        data: The input data to sanitize.
    Returns:
        Sanitized input data.
    """
    data.text = data.text.strip()  # Remove leading/trailing whitespace
    return data

async def load_model(model_path: str) -> Any:
    """Load the specified model for inference.
    
    Args:
        model_path: Path to the model file.
    Returns:
        Loaded model object.
    Raises:
        FileNotFoundError: If the model file does not exist.
    """
    if not os.path.exists(model_path):
        raise FileNotFoundError(f'Model file not found: {model_path}')
    model = torch.load(model_path)
    logger.info('Model loaded successfully.')
    return model

async def process_batch(model: Any, inputs: List[str]) -> List[Dict[str, Any]]:
    """Process a batch of inputs through the model.
    
    Args:
        model: The loaded model for inference.
        inputs: List of input strings.
    Returns:
        List of model outputs as dictionaries.
    """
    outputs = []
    for input_text in inputs:
        # Simulate model processing
        output = {'input': input_text, 'output': f'Model output for: {input_text}'}
        outputs.append(output)
    return outputs

async def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Simulate saving processed data to the database.
    
    Args:
        data: The processed data to save.
    """
    # Simulated DB save
    logger.info(f'Saving {len(data)} records to the database.')

async def format_output(outputs: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Format outputs for client response.
    
    Args:
        outputs: List of model outputs.
    Returns:
        Formatted outputs ready for response.
    """
    return [{'input': item['input'], 'response': item['output']} for item in outputs]

app = FastAPI()

@app.post('/process/')
async def process_request(data: InputData):
    """API endpoint to process input data.
    
    Args:
        data: InputData object containing text and metadata.
    Returns:
        Response with processed outputs.
    Raises:
        HTTPException: If an error occurs during processing.
    """
    try:
        await validate_input(data)  # Validate the input data
        sanitized_data = await sanitize_fields(data)  # Sanitize the input fields
        model = await load_model(Config.model_path)  # Load the model

        outputs = await process_batch(model, [sanitized_data.text])  # Process the input
        await save_to_db(outputs)  # Save to DB

        formatted_outputs = await format_output(outputs)  # Format the output
        return formatted_outputs
    except ValueError as ve:
        logger.error(f'Validation error: {ve}')
        raise HTTPException(status_code=400, detail=str(ve))
    except FileNotFoundError as fnfe:
        logger.error(f'Model loading error: {fnfe}')
        raise HTTPException(status_code=500, detail=str(fnfe))
    except Exception as e:
        logger.error(f'Unexpected error: {e}')
        raise HTTPException(status_code=500, detail='Internal Server Error')

if __name__ == '__main__':
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)
    # Start the FastAPI app with Uvicorn