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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_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 requirement
projects_df = logs_df.copy()[(~logs_df["project_id"].isnull())]
# Filter IDs for appropriate AutoTrain env (staging vs prod)
projects_df = projects_df.copy().query(f"autotrain_env == '{AUTOTRAIN_ENV}'")
projects_to_approve = projects_df["project_id"].astype(int).tolist()
failed_approvals = []
print(f"🚀 Found {len(projects_to_approve)} evaluation projects to approve!")
for project_id in projects_to_approve:
print(f"Attempting to evaluate project ID {project_id} ...")
try:
project_info = http_get(
path=f"/projects/{project_id}",
token=HF_TOKEN,
domain=AUTOTRAIN_BACKEND_API,
).json()q
print(project_info)
# Only start evaluation for projects with completed data processing (status=3)
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}")
else:
print(f"💪 Project {project_id} has already been evaluated. Skipping ...")
except Exception as e:
print(f"There was a problem obtaining the project info for project ID {project_id}")
print(f"Error message: {e}")
failed_approvals.append(project_id)
pass
if len(failed_approvals) > 0:
print(f"🚨 Failed to approve {len(failed_approvals)} projects: {failed_approvals}")
if __name__ == "__main__":
typer.run(main)
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