"""
WBS Level 3: Create a Work Breakdown Structure (WBS) from a project plan.

https://en.wikipedia.org/wiki/Work_breakdown_structure

The "progressive elaboration" technique is used to decompose big tasks into smaller, more manageable subtasks.
"""
import os
import json
import time
from dataclasses import dataclass
from math import ceil
from typing import List, Optional
from uuid import uuid4
from dataclasses import dataclass
from pydantic import BaseModel, Field
from llama_index.core.llms.llm import LLM
from src.format_json_for_use_in_query import format_json_for_use_in_query

class WBSSubtask(BaseModel):
    """
    A subtask.
    """
    name: str = Field(
        description="Short name of the subtask, such as: Prepare necessary documentation for permits, Conduct interviews with potential contractors, Secure final approval of funds."
    )
    description: str = Field(
        description="Longer text that describes the subtask in more detail, such as: Objective, Scope, Steps, Deliverables."
    )
    resources_needed: list[str] = Field(
        description="List of resources needed to complete the subtask. Example: ['Project manager', 'Architect', 'Engineer', 'Construction crew']."
    )

class WBSTaskDetails(BaseModel):
    """
    A big task in the project decomposed into a few smaller tasks.
    """
    subtasks: list[WBSSubtask] = Field(
        description="List of subtasks."
    )

QUERY_PREAMBLE = f"""
Decompose a big task into smaller, more manageable subtasks.

Split the task into 3 to 5 subtasks.

Pick a subtask name that is short, max 7 words or max 60 characters.

Don't enumerate the subtasks with integers or letters.

Don't assign a uuid to the subtask.

"""

@dataclass
class CreateWBSLevel3:
    """
    WBS Level 3: Creating a Work Breakdown Structure (WBS) from a project plan.
    """
    query: str
    response: dict
    metadata: dict
    tasks: list[dict]
    task_uuids: list[str]

    @classmethod
    def format_query(cls, plan_json: dict, wbs_level1_json: dict, wbs_level2_json: list, wbs_level2_task_durations_json: dict, decompose_task_id: str) -> str:
        if not isinstance(plan_json, dict):
            raise ValueError("Invalid plan_json.")
        if not isinstance(wbs_level1_json, dict):
            raise ValueError("Invalid wbs_level1_json.")
        if not isinstance(wbs_level2_json, list):
            raise ValueError("Invalid wbs_level1_json.")
        if not isinstance(wbs_level2_task_durations_json, list):
            raise ValueError("Invalid wbs_level2_task_durations_json.")
        if not isinstance(decompose_task_id, str):
            raise ValueError("Invalid decompose_task_id.")

        query = f"""
The project plan:
{format_json_for_use_in_query(plan_json)}

WBS Level 1:
{format_json_for_use_in_query(wbs_level1_json)}

WBS Level 2:
{format_json_for_use_in_query(wbs_level2_json)}

WBS Level 2 time estimates:
{format_json_for_use_in_query(wbs_level2_task_durations_json)}

Only decompose this task:
"{decompose_task_id}"
"""
        return query

    @classmethod
    def execute(cls, llm: LLM, query: str, decompose_task_id: str) -> 'CreateWBSLevel3':
        """
        Invoke LLM to decompose a big WBS level 2 task into smaller tasks.
        """
        if not isinstance(llm, LLM):
            raise ValueError("Invalid LLM instance.")
        if not isinstance(query, str):
            raise ValueError("Invalid query.")
        if not isinstance(decompose_task_id, str):
            raise ValueError("Invalid decompose_task_id.")

        start_time = time.perf_counter()

        sllm = llm.as_structured_llm(WBSTaskDetails)
        response = sllm.complete(QUERY_PREAMBLE + query)
        json_response = json.loads(response.text)

        end_time = time.perf_counter()
        duration = int(ceil(end_time - start_time))

        metadata = dict(llm.metadata)
        metadata["llm_classname"] = llm.class_name()
        metadata["duration"] = duration

        # Cleanup the json response from the LLM model, assign unique ids to each subtask.
        parent_task_id = decompose_task_id
        result_tasks = []
        result_task_uuids = []
        for subtask in json_response['subtasks']:
            name = subtask['name']
            description = subtask['description']
            resources_needed = subtask['resources_needed']
            uuid = str(uuid4())
            subtask_item = {
                "id": uuid,
                "name": name,
                "description": description,
                "resources_needed": resources_needed,
                "parent_id": parent_task_id
            }
            result_tasks.append(subtask_item)
            result_task_uuids.append(uuid)

        result = CreateWBSLevel3(
            query=query,
            response=json_response,
            metadata=metadata,
            tasks=result_tasks,
            task_uuids=result_task_uuids
        )
        return result    

    def raw_response_dict(self, include_metadata=True, include_query=True) -> dict:
        d = self.response.copy()
        if include_metadata:
            d['metadata'] = self.metadata
        if include_query:
            d['query'] = self.query
        return d

if __name__ == "__main__":
    from llama_index.llms.ollama import Ollama

    # TODO: Eliminate hardcoded paths
    basepath = '/Users/neoneye/Desktop/planexe_data'

    def load_json(relative_path: str) -> dict:
        path = os.path.join(basepath, relative_path)
        print(f"loading file: {path}")
        with open(path, 'r', encoding='utf-8') as f:
            the_json = json.load(f)
        return the_json

    plan_json = load_json('002-project_plan.json')
    wbs_level1_json = load_json('006-wbs_level1.json')
    wbs_level2_json = load_json('008-wbs_level2.json')
    wbs_level2_task_durations_json = load_json('012-task_durations.json')
    decompose_task_id = "1c690f4a-ae8e-493d-9e47-6da58ef5b24c"

    query = CreateWBSLevel3.format_query(plan_json, wbs_level1_json, wbs_level2_json, wbs_level2_task_durations_json, decompose_task_id)

    model_name = "llama3.1:latest"
    # model_name = "qwen2.5-coder:latest"
    # model_name = "phi4:latest"
    llm = Ollama(model=model_name, request_timeout=120.0, temperature=0.5, is_function_calling_model=False)

    print(f"Query: {query}")
    result = CreateWBSLevel3.execute(llm, query, decompose_task_id)

    print("\n\nResponse:")
    print(json.dumps(result.raw_response_dict(include_query=False), indent=2))

    print("\n\nExtracted tasks:")
    print(json.dumps(result.tasks, indent=2))