Auto-GPT/agbenchmark/challenge.py

import glob
import os
import subprocess
from abc import ABC
from typing import Any, Dict, List

from agbenchmark.challenges.define_task_types import ChallengeData, Ground
from agbenchmark.start_benchmark import CURRENT_DIRECTORY


class Challenge(ABC):
    """The parent class to all specific challenges classes.
    Defines helper methods for running a challenge"""

    _data_cache: Dict[str, ChallengeData] = {}
    CHALLENGE_LOCATION: str = ""

    @property
    def data(self) -> ChallengeData:
        file_path = f"{CURRENT_DIRECTORY}/../{self.CHALLENGE_LOCATION}/data.json"
        if file_path not in Challenge._data_cache:
            Challenge._data_cache[file_path] = ChallengeData.deserialize(file_path)
        return Challenge._data_cache[file_path]

    @property
    def task(self) -> str:
        return self.data.task

    @property
    def dependencies(self) -> list:
        return self.data.dependencies

    def setup_challenge(self, config: Dict[str, Any], cutoff: int) -> None:
        from agbenchmark.agent_interface import copy_artifacts_into_workspace, run_agent

        copy_artifacts_into_workspace(
            config["workspace"], "artifacts_in", self.CHALLENGE_LOCATION
        )

        run_agent(self.task, config, self.CHALLENGE_LOCATION, cutoff)

        # hidden files are added after the agent runs. Hidden files can be python test files.
        # We copy them in the workspace to make it easy to import the code produced by the agent

        copy_artifacts_into_workspace(
            config["workspace"], "custom_python", self.CHALLENGE_LOCATION
        )

    def test_method(self, config: Dict[str, Any]) -> None:
        raise NotImplementedError

    @staticmethod
    def open_file(workspace: str, filename: str) -> str:
        script_dir = workspace
        workspace_dir = os.path.join(script_dir, filename)
        with open(workspace_dir, "r") as f:
            return f.read()

    def get_artifacts_out(self, workspace: str, file_patterns: list) -> List[str]:
        script_dir = workspace
        files_contents = []

        for file_pattern in file_patterns:
            # Check if it is a file extension
            if file_pattern.startswith("."):
                # Find all files with the given extension in the workspace
                matching_files = glob.glob(os.path.join(script_dir, "*" + file_pattern))
            else:
                # Otherwise, it is a specific file
                matching_files = [os.path.join(script_dir, file_pattern)]

            for file_path in matching_files:
                if self.data.ground.type == "execute_python_code":
                    result = subprocess.run(
                        ["python3", file_path],
                        cwd=os.path.abspath(workspace),
                        capture_output=True,
                        text=True,
                    )
                    files_contents.append(result.stdout)
                else:
                    with open(file_path, "r") as f:
                        files_contents.append(f.read())

        return files_contents

    @staticmethod
    def write_to_file(workspace: str, filename: str, content: str) -> None:
        script_dir = workspace
        print("Writing file at", script_dir)
        workspace_dir = os.path.join(script_dir, filename)

        # Open the file in write mode.
        with open(workspace_dir, "w") as f:
            # Write the content to the file.
            f.write(content)

    def get_filenames_in_workspace(self, workspace: str) -> List[str]:
        return [
            filename
            for filename in os.listdir(workspace)
            if os.path.isfile(os.path.join(workspace, filename))
        ]

    def scoring(self, content: str, ground: Ground) -> float:
        print("Scoring content: ", content)
        if ground.should_contain:
            for should_contain_word in ground.should_contain:
                if should_contain_word not in content:
                    print(f"Word that should exist - {should_contain_word}: False")
                    return 0.0
                else:
                    print(f"Word that should exist - {should_contain_word}: True")

        if ground.should_not_contain:
            for should_not_contain_word in ground.should_not_contain:
                if should_not_contain_word in content:
                    print(
                        f"Word that should not exist - {should_not_contain_word}: False"
                    )
                    return 0.0
                else:
                    print(
                        f"Word that should not exist - {should_not_contain_word}: True"
                    )

        return 1.0

    def get_scores(self, config: Dict[str, Any]) -> List[float]:
        files_contents = self.get_artifacts_out(
            config["workspace"], self.data.ground.files
        )

        scores = []
        for file_content in files_contents:
            score = self.scoring(file_content, self.data.ground)
            print("Your score is:", score)
            scores.append(score)

        return scores