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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")
def main():
logs_df = load_dataset("autoevaluate/evaluation-job-logs", use_auth_token=True, split="train").to_pandas()
evaluated_projects_ds = load_dataset("autoevaluate/evaluated-project-ids", use_auth_token=True, split="train")
projects_df = logs_df.copy()[(~logs_df["project_id"].isnull()) & (logs_df["is_evaluated"] == False)]
projects_to_approve = projects_df["project_id"].astype(int).tolist()
for project_id in projects_to_approve:
project_status = http_get(
path=f"/projects/{project_id}",
token=HF_TOKEN,
domain=AUTOTRAIN_BACKEND_API,
).json()
if project_status["status"] == 3:
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}")
# if train_job_resp["approved"] == True:
# # Update evaluation status
if __name__ == "__main__":
typer.run(main)
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