import click
import uvicorn
import sentry_sdk

from app.core.settings import DEBUG, SENTRY_DSN
from app.evaluation.evals import Evaluation, DEFAULT_TIDB_AI_CHAT_ENGINE


if not DEBUG:
    sentry_sdk.init(
        dsn=SENTRY_DSN,
        # Set traces_sample_rate to 1.0 to capture 100%
        # of transactions for performance monitoring.
        traces_sample_rate=1.0,
        # Set profiles_sample_rate to 1.0 to profile 100%
        # of sampled transactions.
        # We recommend adjusting this value in production.
        profiles_sample_rate=1.0,
    )


@click.group(context_settings={'max_content_width': 150})
def cli():
    pass


@cli.command()
@click.option('--host', default='127.0.0.1', help="Host, default=127.0.0.1")
@click.option('--port', default=3000, help="Port, default=3000")
def runserver(host, port):
    uvicorn.run(
        "app.main:app", host=host, port=port, reload=DEBUG,
        log_level="debug"
    )


@cli.command()
@click.option('--dataset', default='regression', help="Dataset name, default=regression")
@click.option('--llm-provider', default='openai', help="LLM provider, default=openai, options=[openai, google]")
@click.option('--run-name', default=None, help="Run name, default=None")
@click.option('--tidb-ai-chat-engine', default=DEFAULT_TIDB_AI_CHAT_ENGINE, help=f"TiDB AI chat engine, default={DEFAULT_TIDB_AI_CHAT_ENGINE}")
def runeval(dataset, llm_provider, run_name, tidb_ai_chat_engine):
    eval = Evaluation(
        dataset_name=dataset,
        llm_provider=llm_provider,
        run_name=run_name,
        tidb_ai_chat_engine=tidb_ai_chat_engine,
    )
    eval.run()


if __name__ == '__main__':
    cli()