import pytest
import deepeval
from deepeval import assert_test
from deepeval.dataset import EvaluationDataset
from deepeval.test_case import LLMTestCase, SingleTurnParams
from deepeval.metrics import AnswerRelevancyMetric, GEval

# To run this file: deepeval test run <file_name>.py

dataset = EvaluationDataset(alias="My dataset", test_cases=[])


@pytest.mark.parametrize(
    "test_case",
    dataset.test_cases,
)
def test_everything(test_case: LLMTestCase):
    test_case = LLMTestCase(
        input="What if these shoes don't fit?",
        # Replace this with the actual output of your LLM application
        actual_output="We offer a 30-day full refund at no extra cost.",
        expected_output="You're eligible for a free full refund within 30 days of purchase.",
    )
    answer_relevancy_metric = AnswerRelevancyMetric(threshold=0.7)
    correctness_metric = GEval(
        name="Correctness",
        criteria="Correctness - determine if the actual output is correct according to the expected output.",
        evaluation_params=[
            SingleTurnParams.ACTUAL_OUTPUT,
            SingleTurnParams.EXPECTED_OUTPUT,
        ],
        strict_mode=True,
    )
    assert_test(test_case, [answer_relevancy_metric, correctness_metric])


@deepeval.log_hyperparameters(model="gpt-4", prompt_template="...")
def hyperparameters():
    return {"temperature": 1, "chunk size": 500}