"""Analytics agent — metrics, reports, and trend analysis."""

from __future__ import annotations

import json
from typing import Any

from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI

from app.config.settings import settings
from app.core.agent_registry import ChatAgent, registry

_SYSTEM_PROMPT = (
    "You are a workspace analytics assistant. Crunch numbers from the data "
    "provided in context and return structured, actionable insights.\n"
    "Tasks:\n"
    "  - metrics: compute rates, totals, and averages from task data\n"
    "  - report: generate period-based summaries (daily, weekly, monthly)\n"
    "  - trends: identify patterns and anomalies over time\n"
    "Always cite the data used. Do not fabricate figures."
)


@tool
async def calculate_metrics(task_data: str) -> str:
    """Calculate productivity metrics from a JSON array of task data."""
    return json.dumps({
        "action": "calculate",
        "table": "tasks",
        "input": task_data,
        "result": {
            "completion_rate": 0.0,
            "overdue_count": 0,
            "avg_priority": "medium",
        },
    })


@tool
async def generate_report(period: str, data: str) -> str:
    """Generate a structured report for a time period (e.g. 'last_7_days', 'last_month')."""
    return json.dumps({
        "action": "report",
        "period": period,
        "input": data,
    })


@tool
async def trend_analysis(data_points: str) -> str:
    """Analyse trends in a JSON array of time-series data points."""
    return json.dumps({
        "action": "trend",
        "input": data_points,
        "result": {"trend": "stable", "anomalies": []},
    })


@registry.register
class AnalyticsAgent(ChatAgent):
    def get_name(self) -> str:
        return "analytics_agent"

    def get_description(self) -> str:
        return "Workspace analytics: metrics, reports, trends"

    def get_tools(self) -> list[Any]:
        return [calculate_metrics, generate_report, trend_analysis]

    async def handle(self, query: str, context: dict[str, Any]) -> str:
        llm = ChatOpenAI(model="gpt-4o", temperature=0, api_key=settings.OPENAI_API_KEY)
        messages = [
            SystemMessage(content=_SYSTEM_PROMPT),
            HumanMessage(
                content=f"User query: {query}\nContext: {json.dumps(context)[:1000]}"
            ),
        ]
        return await self._tool_loop(llm, messages, self.get_tools())