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import json
import shutil
import time
from pathlib import Path
from typing import Any
import requests
from datasets import load_dataset
from huggingface_hub import snapshot_download
from api.schemas import GaiaQuestion
from config import Settings
class GaiaApiClient:
"""Client for the Hugging Face AI Agents course evaluation API."""
def __init__(
self,
settings: Settings,
session: requests.Session | None = None,
) -> None:
self.settings = settings
self.session = session or requests.Session()
self.session.headers.update(
{
"User-Agent": "Vertex-Agent/1.0",
"Accept": "application/json",
}
)
def _request(
self,
method: str,
path: str,
**kwargs: Any,
) -> requests.Response:
"""Send a request to the course API."""
base_url = self.settings.course_api_url.rstrip("/")
url = f"{base_url}{path}"
response = self.session.request(
method=method,
url=url,
timeout=self.settings.request_timeout,
**kwargs,
)
response.raise_for_status()
return response
def get_questions(self) -> list[GaiaQuestion]:
"""Load local debug questions if available, otherwise use the course API."""
debug_file = Path("questions_debug.json")
if debug_file.exists():
print(f"Using local questions: {debug_file.resolve()}")
with debug_file.open(
"r",
encoding="utf-8",
) as f:
data = json.load(f)
if not isinstance(data, list):
raise RuntimeError(
"questions_debug.json must contain a JSON list."
)
return [
GaiaQuestion.from_api(item)
for item in data
]
print("Using course API questions...")
response = self._request(
method="GET",
path="/questions",
)
data = response.json()
if not isinstance(data, list):
raise RuntimeError(
"The /questions endpoint did not return a list."
)
return [
GaiaQuestion.from_api(item)
for item in data
]
def download_file(
self,
question: GaiaQuestion,
) -> Path | None:
"""
Download a question attachment.
The course API is attempted first. If that endpoint does not
provide the attachment, the official gated GAIA dataset is used
as a fallback.
"""
if not question.file_name:
return None
destination = (
Path(self.settings.download_dir)
/ question.task_id
/ question.file_name
)
destination.parent.mkdir(
parents=True,
exist_ok=True,
)
# Reuse an existing valid download.
if (
destination.exists()
and destination.is_file()
and destination.stat().st_size > 0
):
return destination
course_file = self._download_from_course_api(
task_id=question.task_id,
destination=destination,
)
if course_file is not None:
return course_file
return self._download_from_gaia_dataset(
question=question,
destination=destination,
)
def _download_from_course_api(
self,
task_id: str,
destination: Path,
) -> Path | None:
"""Try downloading an attachment from the course API."""
base_url = self.settings.course_api_url.rstrip("/")
url = f"{base_url}/files/{task_id}"
try:
response = self.session.get(
url=url,
timeout=self.settings.request_timeout,
)
except requests.RequestException as exc:
print(
"Course attachment request failed "
f"for {task_id}: {exc}"
)
return None
if response.status_code == 404:
print(
f"Course API attachment unavailable for {task_id}; "
"trying GAIA fallback."
)
return None
try:
response.raise_for_status()
except requests.RequestException as exc:
print(
"Course attachment download failed "
f"for {task_id}: {exc}"
)
return None
content = response.content
if not content:
print(
f"Course API returned an empty file for {task_id}; "
"trying GAIA fallback."
)
return None
destination.write_bytes(content)
if destination.stat().st_size == 0:
destination.unlink(missing_ok=True)
return None
return destination
def _download_from_gaia_dataset(
self,
question: GaiaQuestion,
destination: Path,
) -> Path:
"""Download an attachment from the official GAIA dataset."""
token = self.settings.hf_token
if not token:
raise RuntimeError(
"The course API did not provide the attachment and "
"HF_TOKEN is missing. Add a Hugging Face read token "
"to the .env file."
)
dataset_root = snapshot_download(
repo_id=self.settings.gaia_dataset_id,
repo_type="dataset",
token=token,
)
dataset = load_dataset(
dataset_root,
self.settings.gaia_dataset_config,
split=self.settings.gaia_dataset_split,
)
record = next(
(
item
for item in dataset
if str(item.get("task_id", "")).strip()
== question.task_id
),
None,
)
if record is None:
raise FileNotFoundError(
f"Task {question.task_id} was not found "
"in the GAIA dataset."
)
source_path = self._resolve_gaia_file_path(
dataset_root=Path(dataset_root),
record=record,
expected_file_name=question.file_name,
)
if source_path is None:
raise FileNotFoundError(
f"Could not locate attachment "
f"{question.file_name!r} for task "
f"{question.task_id} in the GAIA dataset."
)
destination.parent.mkdir(
parents=True,
exist_ok=True,
)
shutil.copy2(
source_path,
destination,
)
if (
not destination.exists()
or destination.stat().st_size == 0
):
raise RuntimeError(
f"The attachment for task {question.task_id} "
"was copied but the destination file is empty."
)
return destination
@staticmethod
def _resolve_gaia_file_path(
dataset_root: Path,
record: dict[str, Any],
expected_file_name: str,
) -> Path | None:
"""Resolve the attachment path inside a GAIA snapshot."""
candidates: list[Path] = []
raw_file_path = record.get("file_path")
if raw_file_path:
raw_path = Path(str(raw_file_path))
if raw_path.is_absolute():
candidates.append(raw_path)
else:
candidates.append(
dataset_root / raw_path
)
# Search by exact expected filename as a fallback.
candidates.extend(
dataset_root.rglob(expected_file_name)
)
for candidate in candidates:
if (
candidate.exists()
and candidate.is_file()
and candidate.stat().st_size > 0
):
return candidate.resolve()
return None
def submit(
self,
username: str,
agent_code: str,
answers: list[dict[str, str]],
) -> dict[str, Any]:
"""Submit 20 answers to the course scoring API."""
username = username.strip()
agent_code = agent_code.strip()
if not username:
raise ValueError(
"Hugging Face username is required."
)
if not agent_code:
raise ValueError(
"Public agent code URL is required."
)
if not agent_code.endswith("/tree/main"):
raise ValueError(
"agent_code must be a public Hugging Face Space "
"URL ending in /tree/main."
)
normalized_answers = self._validate_answers(
answers
)
payload = {
"username": username,
"agent_code": agent_code,
"answers": normalized_answers,
}
base_url = self.settings.course_api_url.rstrip("/")
url = f"{base_url}/submit"
headers = {
"Accept": "application/json",
"Content-Type": "application/json",
"User-Agent": "Vertex-Agent/1.0",
}
last_error: Exception | None = None
for attempt in range(1, 4):
try:
response = self.session.post(
url=url,
json=payload,
headers=headers,
timeout=(30, 300),
allow_redirects=False,
)
if response.status_code in {
301,
302,
303,
307,
308,
}:
location = response.headers.get(
"Location",
"unknown",
)
raise RuntimeError(
"The scoring API redirected the submission. "
f"HTTP {response.status_code}. "
f"Location: {location}"
)
if response.status_code in {401, 403}:
raise RuntimeError(
"The scoring API rejected access. "
f"HTTP {response.status_code}: "
f"{response.text[:1000]}"
)
if response.status_code == 422:
raise RuntimeError(
"The scoring API rejected the payload format. "
f"Response: {response.text[:2000]}"
)
response.raise_for_status()
try:
data = response.json()
except ValueError as exc:
raise RuntimeError(
"The scoring API returned a non-JSON response: "
f"{response.text[:2000]}"
) from exc
if not isinstance(data, dict):
raise RuntimeError(
"The scoring API returned an unexpected "
"response type."
)
return data
except requests.RequestException as exc:
last_error = exc
if attempt < 3:
delay_seconds = attempt * 3
print(
"Submission connection failed "
f"on attempt {attempt}/3: {exc}"
)
print(
f"Retrying in {delay_seconds} seconds..."
)
time.sleep(delay_seconds)
continue
except RuntimeError:
raise
raise RuntimeError(
"Submission failed after 3 attempts. "
f"Last connection error: {last_error}"
)
@staticmethod
def _validate_answers(
answers: list[dict[str, str]],
) -> list[dict[str, str]]:
"""Validate and normalize answers before submission."""
if not answers:
raise ValueError(
"Answers list cannot be empty."
)
if len(answers) != 20:
raise ValueError(
f"Expected 20 answers, but received "
f"{len(answers)}."
)
normalized: list[dict[str, str]] = []
seen_task_ids: set[str] = set()
empty_task_ids: list[str] = []
for index, item in enumerate(
answers,
start=1,
):
if not isinstance(item, dict):
raise ValueError(
f"Answer number {index} must be an object."
)
task_id = str(
item.get("task_id", "")
).strip()
submitted_answer = str(
item.get("submitted_answer", "")
).strip()
if not task_id:
raise ValueError(
f"Answer number {index} is missing task_id."
)
if task_id in seen_task_ids:
raise ValueError(
f"Duplicate task_id found: {task_id}"
)
seen_task_ids.add(task_id)
if not submitted_answer:
empty_task_ids.append(task_id)
normalized.append(
{
"task_id": task_id,
"submitted_answer": submitted_answer,
}
)
if empty_task_ids:
raise ValueError(
"Submission stopped because these tasks "
"have empty answers:\n"
+ "\n".join(empty_task_ids)
)
return normalized |