from detection import ml_detection, ml_utils
import base64
import json
import logging


logger = logging.getLogger()
logger.setLevel(logging.INFO)


# Find ML model type based on string request
def get_model_type(query_string):
    # Default ml model type
    if query_string == "":
        model_type = "facebook/detr-resnet-50"
    # Assess query string value
    elif "detr" in query_string:
        model_type = "facebook/" + query_string
    elif "yolos" in query_string:
        model_type = "hustvl/" + query_string
    else:
        raise Exception('Incorrect model type.')
    return model_type


# Run detection pipeline: load ML model, perform object detection and return json object
def detection_pipeline(model_type, image_bytes):
    # Load correct ML model
    processor, model = ml_detection.load_model(model_type)

    # Perform object detection
    results = ml_detection.object_detection(processor, model, image_bytes)

    # Convert dictionary of tensors to JSON object
    result_json_dict = ml_utils.convert_tensor_dict_to_json(results)

    return result_json_dict


def lambda_handler(event, context):
    try:
        # Get the model name from the query string parameters
        # Condition for local testing
        is_querystringparam = event.get('queryStringParameters')
        if is_querystringparam is not None:
            model_query = event['queryStringParameters'].get('model', '').lower()
        else:
            model_query = ""
        model_type = get_model_type(model_query)
        logger.info(f"Model query: {model_query}")
        logger.info(f"Model type: {model_type}")

        # Decode the base64-encoded image data from the event
        image_data = base64.b64decode(event['body'])
        result_dict = detection_pipeline(model_type, image_data)
        logger.info(f"API Results: {result_dict}")
        return {
            'statusCode': 200,
            'headers': {
                'Content-Type': 'application/json'
            },
            'body': json.dumps(result_dict),
        }
    except Exception as e:
        logger.info(f"API Error: {str(e)}")
        return {
            'statusCode': 500,
            'headers': {
                'Content-Type': 'application/json'
            },
            'body': json.dumps({'error': str(e)}),
        }