#### To launch the script
# "python wcag_validator_RESTserver.py"


import sys
import argparse
import logging
import uvicorn
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
import warnings

warnings.filterwarnings("ignore")
from dotenv import load_dotenv, find_dotenv


from restserver.routers import (
    routes_health,
    routes_local_db,
    routes_wcag_alttext,
    routes_extract_images,
)

from dependences.utils import (
    db_persistence_startup,
    return_from_env_valid,
    disclaim_bool_string,
)


def server(connection_db, mllm_settings):
    # instantiate the app
    app = FastAPI(title="HIISlab WCAG Validator REST Server")

    app.add_middleware(
        CORSMiddleware,
        allow_origins=["*"],
        allow_credentials=True,
        allow_methods=["*"],
        allow_headers=["*"],
    )

    health_routes = routes_health.HealthRoutes()
    local_db_routes = routes_local_db.LocalDBRoutes(connection_db)
    wcag_alttext_routes = routes_wcag_alttext.WCAGAltTextValuationRoutes(
        connection_db, mllm_settings
    )
    extract_images_routes = routes_extract_images.ExtractImagesRoutes()

    app.include_router(health_routes.router, prefix="")
    app.include_router(local_db_routes.router, prefix="")
    app.include_router(wcag_alttext_routes.router, prefix="")
    app.include_router(extract_images_routes.router, prefix="")
    return app


def app_startup():
    connection_db = db_persistence_startup(table="wcag_validator_results")
    if disclaim_bool_string(return_from_env_valid("USE_OPENAI_MODEL", "False")) == True:
        openai_model = True
    else:
        openai_model = False

    if openai_model:
        mllm_end_point = return_from_env_valid("MLLM_END_POINT_OPENAI", "")
        mllm_api_key = return_from_env_valid("MLLM_API_KEY_OPENAI", "")
        mllm_model_id = return_from_env_valid("MLLM_MODEL_ID_OPENAI", "")
    else:
        mllm_end_point = return_from_env_valid("MLLM_END_POINT_LOCAL", "")
        mllm_api_key = return_from_env_valid("MLLM_API_KEY_LOCAL", "")
        mllm_model_id = return_from_env_valid("MLLM_MODEL_ID_LOCAL", "")

    print("mllm_end_point:", mllm_end_point)
    print("mllm_model_id:", mllm_model_id)
    mllm_model_settings = {
        "openai_model": openai_model,
        "mllm_end_point": mllm_end_point,
        "mllm_api_key": mllm_api_key,
        "mllm_model_id": mllm_model_id,
    }

    return connection_db, mllm_model_settings


def run_server(
    connection_db,
    mllm_settings,
    host,
    port,
):
    app = server(connection_db, mllm_settings)
    uvicorn.run(app, host=host, port=port)


def cli(sys_argv):
    # Import environment variables
    env_path = find_dotenv(filename=".env")

    if env_path == "":
        print(
            "rest server env path not found: service starting with the default params values"
        )

    _ = load_dotenv(env_path)  # read .env file

    connection_db, mllm_settings = app_startup()

    default_rest_port = return_from_env_valid("rest_port", 8000)
    default_rest_host = return_from_env_valid("rest_host", "0.0.0.0")

    parser = argparse.ArgumentParser()

    # ----------------
    # Server parameters
    # ----------------
    parser.add_argument(
        "-p",
        "--port",
        help="port for server (default: os.environ[REST_PORT] or 8000)",
        default=default_rest_port,
        type=int,
    )

    parser.add_argument(
        "-H",
        "--host",
        help="host for server (default: os.environ[REST_HOST] or 0.0.0.0)",
        default=default_rest_host,
    )

    args = parser.parse_args(sys_argv)
    logging.info("service started with cli args:%s", args)

    run_server(
        connection_db,
        mllm_settings,
        args.host,
        args.port,
    )


if __name__ == "__main__":
    cli(sys.argv[1:])