import argparse
import asyncio
import json
import statistics
from pathlib import Path

import deepeval.metrics
import wget
from deepeval.dataset import EvaluationDataset
from deepeval.test_case import LLMTestCase
from tqdm import tqdm

import cognee
import evals.deepeval_metrics
from cognee.api.v1.search import SearchType
from cognee.base_config import get_base_config
from cognee.infrastructure.llm.get_llm_client import get_llm_client
from cognee.infrastructure.llm.prompts import read_query_prompt, render_prompt


async def answer_without_cognee(instance):
    args = {
        "question": instance["question"],
        "context": instance["context"],
    }
    user_prompt = render_prompt("context_for_question.txt", args)
    system_prompt = read_query_prompt("answer_question.txt")

    llm_client = get_llm_client()
    answer_prediction = await llm_client.acreate_structured_output(
        text_input=user_prompt,
        system_prompt=system_prompt,
        response_model=str,
    )
    return answer_prediction

async def answer_with_cognee(instance):
    await cognee.prune.prune_data()
    await cognee.prune.prune_system(metadata=True)
    for (title, sentences) in instance["context"]:
        await cognee.add("\n".join(sentences), dataset_name = "HotPotQA")
    await cognee.cognify("HotPotQA")

    search_results = await cognee.search(
        SearchType.INSIGHTS, query_text=instance["question"]
    )
   
    args = {
        "question": instance["question"],
        "context": search_results,
    }
    user_prompt = render_prompt("context_for_question.txt", args)
    system_prompt = read_query_prompt("answer_question_kg.txt")

    llm_client = get_llm_client()
    answer_prediction = await llm_client.acreate_structured_output(
        text_input=user_prompt,
        system_prompt=system_prompt,
        response_model=str,
    )
    return answer_prediction


async def eval_answers(instances, answers, eval_metric):
    test_cases = []
    for i in range(len(answers)):
        instance = instances[i]
        answer = answers[i]
        test_case = LLMTestCase(
            input=instance["question"],
            actual_output=answer,
            expected_output=instance["answer"]
        )
        test_cases.append(test_case)
    evalset = EvaluationDataset(test_cases)
    evalresults = evalset.evaluate([eval_metric])
    return evalresults

async def eval_on_hotpotQA(answer_provider, num_samples, eval_metric):
    base_config = get_base_config()
    data_root_dir = base_config.data_root_directory
    filepath = data_root_dir / Path("hotpot_dev_fullwiki_v1.json")
    if not filepath.exists():
        url = 'http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_fullwiki_v1.json'
        wget.download(url, out=data_root_dir)
    with open(filepath, "r") as file:
        dataset = json.load(file)
    if not num_samples:
        num_samples = len(dataset)
    instances = dataset[:num_samples]
    answers = []
    for instance in tqdm(instances, desc="Getting answers"):
        answer = await answer_provider(instance)
        answers.append(answer)
    evalresults = await eval_answers(instances, answers, eval_metric)
    avg_score = statistics.mean([result.metrics_data[0].score for result in evalresults.test_results])
    return avg_score

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--with_cognee", action="store_true")
    parser.add_argument("--num_samples", type=int, default=500)
    parser.add_argument("--metric", type=str, default="correctness_metric")
    args = parser.parse_args()

    try:
        metric_cls = getattr(deepeval.metrics, args.metric)
        metric = metric_cls()
    except AttributeError:
        metric = getattr(evals.deepeval_metrics, args.metric)
    if args.with_cognee:
        answer_provider = answer_with_cognee
    else:
        answer_provider = answer_without_cognee
    avg_score = asyncio.run(eval_on_hotpotQA(answer_provider, args.num_samples, metric))
    print(f"Average {args.metric}: {avg_score}")