import os
from pathlib import Path

import typer
from datasets import load_dataset
from dotenv import load_dotenv
from rich import print

from utils import http_get, http_post

if Path(".env").is_file():
    load_dotenv(".env")

HF_TOKEN = os.getenv("HF_TOKEN")
AUTOTRAIN_TOKEN = os.getenv("AUTOTRAIN_TOKEN")
AUTOTRAIN_USERNAME = os.getenv("AUTOTRAIN_USERNAME")
AUTOTRAIN_BACKEND_API = os.getenv("AUTOTRAIN_BACKEND_API")

if "staging" in AUTOTRAIN_BACKEND_API:
    AUTOTRAIN_ENV = "staging"
else:
    AUTOTRAIN_ENV = "prod"


def main():
    logs_df = load_dataset("autoevaluate/evaluation-job-logs", use_auth_token=True, split="train").to_pandas()
    # Filter out
    #   - legacy AutoTrain submissions prior to project approvals was implemented
    #   - submissions for appropriate AutoTrain environment (staging vs prod)
    projects_df = logs_df.copy()[
        (~logs_df["project_id"].isnull()) & (logs_df.query(f"autotrain_env == '{AUTOTRAIN_ENV}'"))
    ]
    projects_to_approve = projects_df["project_id"].astype(int).tolist()

    for project_id in projects_to_approve:
        try:
            project_info = http_get(
                path=f"/projects/{project_id}",
                token=HF_TOKEN,
                domain=AUTOTRAIN_BACKEND_API,
            ).json()
            if project_info["status"] == 3 and project_info["training_status"] == "not_started":
                train_job_resp = http_post(
                    path=f"/projects/{project_id}/start_training",
                    token=HF_TOKEN,
                    domain=AUTOTRAIN_BACKEND_API,
                ).json()
                print(f"🏃‍♂️ Project {project_id} approval response: {train_job_resp}")
        except:
            print(f"There was a problem obtaining the project info for project ID {project_id}")
            pass


if __name__ == "__main__":
    typer.run(main)