"""LLM-backed agent and deterministic weather briefing helpers."""

from __future__ import annotations

import json
import os
import re
from dataclasses import dataclass
from typing import Any

import pandas as pd

from weatherpred.prompts import AGENT_RESPONSE_PROMPT, AGENT_SYSTEM_PROMPT
from weatherpred.tools import clamp_horizon, normalize_variable, run_tool


@dataclass
class AgentRun:
    response: str
    action: dict[str, Any]
    tool_result: dict[str, Any]
    comparison: pd.DataFrame | None = None


def has_gemini_key() -> bool:
    return bool(os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY"))


def make_rule_based_brief(comparison: pd.DataFrame, summary: dict[str, Any]) -> str:
    warmest = comparison.sort_values("model_high_c", ascending=False).iloc[0]
    rainiest = comparison.sort_values("api_rain_mm", ascending=False).iloc[0]
    confidence = round(summary["avg_model_confidence"] * 100)
    return (
        f"The ONNX model expects the warmest day around {warmest['date']} "
        f"at {warmest['model_high_c']:.1f}C, while Open-Meteo peaks at "
        f"{summary['max_api_high_c']:.1f}C. API rainfall is highest on "
        f"{rainiest['date']} at {rainiest['api_rain_mm']:.1f} mm. "
        f"The model confidence proxy averages {confidence}%, based on historical "
        "seasonal variability rather than a calibrated meteorological probability."
    )


def make_gemini_brief(comparison: pd.DataFrame, summary: dict[str, Any]) -> str:
    if not has_gemini_key():
        return make_rule_based_brief(comparison, summary)

    try:
        from google import genai
    except ImportError:
        return make_rule_based_brief(comparison, summary)

    prompt = (
        "Write a concise weather analyst briefing for Tokyo. Compare an ONNX "
        "model forecast against Open-Meteo API forecast. Mention uncertainty and "
        "avoid claiming this model is production-grade.\n\n"
        f"Summary: {json.dumps(summary)}\n"
        f"Forecast rows: {comparison.to_json(orient='records')}"
    )
    try:
        client = genai.Client()
        response = client.models.generate_content(
            model=os.getenv("GEMINI_MODEL", "gemini-2.5-flash"),
            contents=prompt,
        )
        return response.text or make_rule_based_brief(comparison, summary)
    except Exception:
        return make_rule_based_brief(comparison, summary)


def extract_json_object(text: str) -> dict[str, Any] | None:
    try:
        return json.loads(text)
    except json.JSONDecodeError:
        pass
    match = re.search(r"\{.*\}", text, flags=re.DOTALL)
    if not match:
        return None
    try:
        return json.loads(match.group(0))
    except json.JSONDecodeError:
        return None


def fallback_action(user_message: str) -> dict[str, Any]:
    text = user_message.lower()
    horizon_match = re.search(r"(?:next|for)\s+(\d+)\s*(?:day|days)?", text)
    horizon = clamp_horizon(horizon_match.group(1) if horizon_match else None)

    if any(word in text for word in ["rain", "precip", "precipitation", "shower"]):
        variable = "rain"
    elif "wind" in text:
        variable = "wind"
    elif any(word in text for word in ["temperature", "temp", "hot", "cold", "high", "low"]):
        variable = "temperature"
    else:
        variable = "all"

    if any(word in text for word in ["season", "seasonal", "cycle", "cyclic", "monthly", "summer", "winter"]):
        tool = "analyze_seasonality"
    elif any(word in text for word in ["trend", "past", "historical", "history", "30 years", "long-term"]):
        tool = "analyze_historical_trend"
    elif any(word in text for word in ["model", "onnx", "confidence", "method", "how", "source", "data"]):
        tool = "explain_model"
    elif any(word in text for word in ["compare", "difference", "versus", "vs"]):
        tool = "compare_model_vs_open_meteo"
    elif horizon_match or any(word in text for word in ["predict", "forecast", "next"]):
        tool = "forecast_next_days"
    else:
        tool = "show_dashboard_view"

    return {"tool": tool, "variable": variable, "horizon_days": horizon, "reason": "fallback parser"}


def infer_action(user_message: str) -> dict[str, Any]:
    if not has_gemini_key():
        return fallback_action(user_message)
    try:
        from google import genai

        client = genai.Client()
        prompt = f"{AGENT_SYSTEM_PROMPT}\n\nUser request: {user_message}"
        response = client.models.generate_content(
            model=os.getenv("GEMINI_MODEL", "gemini-2.5-flash"),
            contents=prompt,
        )
        action = extract_json_object(response.text or "")
        if action and "tool" in action:
            action["variable"] = normalize_variable(action.get("variable"))
            action["horizon_days"] = clamp_horizon(action.get("horizon_days"))
            return action
    except Exception:
        pass
    return fallback_action(user_message)


def deterministic_agent_response(action: dict[str, Any], tool_result: dict[str, Any]) -> str:
    tool = tool_result.get("tool", action.get("tool"))
    summary = tool_result.get("summary", {})
    variable = tool_result.get("variable", action.get("variable", "all"))

    if tool == "forecast_next_days":
        return (
            f"Showing the next {tool_result['horizon_days']} days for {variable}. "
            "Open-Meteo is shown beside the local model. Note: ONNX directly predicts five-day max temperature; "
            "longer horizons, rain, wind, and low temperature use historical seasonal statistics."
        )
    if tool == "compare_model_vs_open_meteo":
        return f"Model vs Open-Meteo comparison for {variable}: {json.dumps(summary.get('average_absolute_differences', {}))}."
    if tool == "analyze_historical_trend":
        return (
            f"Historical {variable} trend from {summary['history_start']} to {summary['history_end']}: "
            f"the recent five-year mean is {summary['last_5_year_mean']} {summary['unit']}, "
            f"versus {summary['first_5_year_mean']} {summary['unit']} in the first five years. "
            f"Change: {summary['change_last_vs_first']} {summary['unit']}."
        )
    if tool == "analyze_seasonality":
        return (
            f"Seasonality for {variable}: peak month is {summary['peak_month']} "
            f"at {summary['peak_value']} {summary['unit']}; lowest month is {summary['low_month']} "
            f"at {summary['low_value']} {summary['unit']}. Seasonal amplitude is "
            f"{summary['seasonal_amplitude']} {summary['unit']}."
        )
    if tool == "explain_model":
        return " ".join(str(value) for value in summary.values())
    if tool == "show_dashboard_view":
        return f"{variable.title()} is available in {summary['view']}."
    return "I handled the request with the weather dashboard tools."


def summarize_agent_response(user_message: str, action: dict[str, Any], tool_result: dict[str, Any]) -> str:
    if not has_gemini_key():
        return deterministic_agent_response(action, tool_result)
    try:
        from google import genai

        safe_result = {key: value for key, value in tool_result.items() if key != "comparison"}
        prompt = AGENT_RESPONSE_PROMPT.format(
            user_message=user_message,
            tool_result=json.dumps(safe_result, default=str),
        )
        client = genai.Client()
        response = client.models.generate_content(
            model=os.getenv("GEMINI_MODEL", "gemini-2.5-flash"),
            contents=prompt,
        )
        return response.text or deterministic_agent_response(action, tool_result)
    except Exception:
        return deterministic_agent_response(action, tool_result)


def run_weather_agent(user_message: str) -> AgentRun:
    action = infer_action(user_message)
    tool_result = run_tool(action)
    response = summarize_agent_response(user_message, action, tool_result)
    comparison = tool_result.get("comparison")
    if not isinstance(comparison, pd.DataFrame):
        comparison = None
    return AgentRun(
        response=response,
        action=action,
        tool_result=tool_result,
        comparison=comparison,
    )