src/envs.py

import os
import torch
from huggingface_hub import HfApi


# replace this with our token
TOKEN = os.environ.get("HF_TOKEN", None)

OWNER = "airlsyn"
REPO_ID = f"{OWNER}/leaderboard"
QUEUE_REPO = f"{OWNER}/requests"
RESULTS_REPO = f"{OWNER}/results"
LEADERBOARD_DATASET_REPO = f"{OWNER}/leaderboard_results"

CACHE_PATH=os.getenv("HF_HOME", ".")

# Local caches
EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue")
EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results")
EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk")
EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-results-bk")

DATA_LEADERBOARD_REPO = f"{OWNER}/leaderboard_dataset"
DATA_LEADERBOARD_PATH = os.path.join(CACHE_PATH, "leaderboard-bk")
DATA_LEADERBOARD_NAME = os.path.join(DATA_LEADERBOARD_PATH, "leaderboard_dataset_16k.csv")

DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') #"cpu"
API = HfApi(token=TOKEN)

LEADERBOARD_DATASET_PATH = "leaderboard_results/leaderboard_summaries.csv"
DATASET_PATH = "src/datasets/leaderboard_dataset.csv"
SAMPLE_DATASET_PATH = "src/datasets/sample_dataset.csv"
# HEM_PATH = 'vectara/HHEM-2.1'
HEM_PATH = 'vectara/hallucination_evaluation_model'

SYSTEM_PROMPT = "You are a chat bot answering questions using data. You must stick to the answers provided solely by the text in the passage provided."
USER_PROMPT = "You are asked the question 'Provide a concise summary of the following passage, covering the core pieces of information described': "