import asyncio
import pathlib
import os

import cognee
from cognee.api.v1.visualize.visualize import visualize_graph
from cognee.shared.logging_utils import setup_logging, ERROR
from cognee.api.v1.cognify.memify import memify
from cognee.modules.pipelines.tasks.task import Task
from cognee.tasks.memify.extract_subgraph import extract_subgraph
from cognee.modules.graph.utils import resolve_edges_to_text
from cognee.tasks.codingagents.coding_rule_associations import (
    add_rule_associations,
    get_existing_rules,
)

# Prerequisites:
# 1. Copy `.env.template` and rename it to `.env`.
# 2. Add your OpenAI API key to the `.env` file in the `LLM_API_KEY` field:
#    LLM_API_KEY = "your_key_here"


async def main():
    # Create a clean slate for cognee -- reset data and system state
    print("Resetting cognee data...")
    await cognee.prune.prune_data()
    await cognee.prune.prune_system(metadata=True)
    print("Data reset complete.\n")

    # cognee knowledge graph will be created based on this text
    text = """
    Natural language processing (NLP) is an interdisciplinary
    subfield of computer science and information retrieval.
    """

    coding_rules_text = """
    Code must be formatted by PEP8 standards.
    Typing and Docstrings must be added.
    """

    print("Adding text to cognee:")
    print(text.strip())
    # Add the text, and make it available for cognify
    await cognee.add(text)
    await cognee.add(coding_rules_text, node_set=["coding_rules"])
    print("Text added successfully.\n")

    # Use LLMs and cognee to create knowledge graph
    await cognee.cognify()
    print("Cognify process complete.\n")

    subgraph_extraction_tasks = [Task(extract_subgraph)]

    rule_association_tasks = [
        Task(resolve_edges_to_text, task_config={"batch_size": 10}),
        Task(
            add_rule_associations,
            rules_nodeset_name="coding_agent_rules",
            user_prompt_location="memify_coding_rule_association_agent_user.txt",
            system_prompt_location="memify_coding_rule_association_agent_system.txt",
        ),
    ]

    await memify(
        preprocessing_tasks=subgraph_extraction_tasks,
        processing_tasks=rule_association_tasks,
        node_name=["coding_rules"],
    )

    developer_rules = await get_existing_rules(rules_nodeset_name="coding_agent_rules")
    print(developer_rules)

    file_path = os.path.join(
        pathlib.Path(__file__).parent, ".artifacts", "graph_visualization.html"
    )
    await visualize_graph(file_path)


if __name__ == "__main__":
    logger = setup_logging(log_level=ERROR)
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    try:
        loop.run_until_complete(main())
    finally:
        loop.run_until_complete(loop.shutdown_asyncgens())