from __future__ import annotations

import json
import logging
from typing import Any, Dict

from langchain_core.messages import HumanMessage

from ai_business_automation_agent.prompts.reporting_prompt import REPORTING_PROMPT
from ai_business_automation_agent.utils import append_agent_log, parse_llm_json

logger = logging.getLogger(__name__)


def run_reporting_agent(state: Dict[str, Any], llm) -> Dict[str, Any]:
    prompt = REPORTING_PROMPT.format(
        email_content=state.get("email_content", ""),
        extracted_json=json.dumps(state.get("extracted_data") or {}, ensure_ascii=False),
        vendor_verification_json=json.dumps(state.get("vendor_verification") or {}, ensure_ascii=False),
        validation_json=json.dumps(state.get("validation_status") or {}, ensure_ascii=False),
        decision_json=json.dumps(state.get("decision") or {}, ensure_ascii=False),
        erp_json=json.dumps(state.get("erp_update_status") or {}, ensure_ascii=False),
    )
    resp = llm.invoke([HumanMessage(content=prompt)])
    text = getattr(resp, "content", str(resp))
    parsed, err = parse_llm_json(text)

    updates: Dict[str, Any] = {}
    if err:
        logger.warning("Reporting JSON parse error: %s", err)
        updates["report"] = (
            "REPORT GENERATION FAILED\n\n"
            f"Error: {err}\n\n"
            "Raw model output:\n"
            f"{text}"
        )
        updates.update(append_agent_log(state, agent="reporting", event="error", payload={"error": err}))
    else:
        updates["report"] = parsed.get("report", "")
        updates.update(append_agent_log(state, agent="reporting", event="ok", payload=parsed))

    updates.update(append_agent_log(state, agent="reporting", event="prompt", payload={"prompt": prompt}))
    updates.update(append_agent_log(state, agent="reporting", event="raw_response", payload={"text": text}))
    return updates