app.py

import os
import io
import json
from datetime import datetime, timedelta, timezone

import requests
import pandas as pd
import matplotlib.pyplot as plt
import gradio as gr


# -----------------------------
# 時區：台北 (UTC+8)
# -----------------------------
TAIPEI_TZ = timezone(timedelta(hours=8))

def now_taipei():
    return datetime.now(TAIPEI_TZ)

def fmt_dt(dt: datetime) -> str:
    return dt.strftime("%Y-%m-%dT%H:%M:%S")


# -----------------------------
# 快速時間範圍
# -----------------------------
def set_time_range(hours: int | None = None, days: int | None = None):
    """
    依台北時間回傳 (timeFrom, timeTo) 字串（yyyy-MM-ddTHH:mm:ss）
    """
    now = now_taipei()
    if hours is not None:
        t_from = now - timedelta(hours=hours)
    elif days is not None:
        t_from = now - timedelta(days=days)
    else:
        t_from = now - timedelta(days=3)  # 預設最近 3 天
    return fmt_dt(t_from), fmt_dt(now)


# -----------------------------
# 呼叫 CWA E-A0015-001 API
# -----------------------------
API_URL = "https://opendata.cwa.gov.tw/api/v1/rest/datastore/E-A0015-001"

def fetch_reports(time_from: str, time_to: str) -> dict:
    """
    以環境變數 CWA_API_KEY 做授權參數呼叫 API，回傳 JSON 物件（dict）
    """
    api_key = os.getenv("CWA_API_KEY", "").strip()
    if not api_key:
        raise RuntimeError("環境變數 CWA_API_KEY 未設定。請在 Space Secrets 設定。")

    params = {
        "Authorization": api_key,
        "timeFrom": time_from,
        "timeTo": time_to,
        # 其餘參數保持預設；排序改由本地處理，以避免介面差異
    }
    resp = requests.get(API_URL, params=params, timeout=30)
    resp.raise_for_status()
    return resp.json()


# -----------------------------
# 解析 JSON → pandas.DataFrame
# 只保留必要欄位（不含 area / station 等）
# -----------------------------
def safe_get(d, *keys, default=None):
    cur = d
    for k in keys:
        if isinstance(cur, dict) and k in cur:
            cur = cur[k]
        else:
            return default
    return cur

def parse_ea0015(json_obj: dict) -> pd.DataFrame:
    """
    嘗試容錯各種大小寫與路徑差異，輸出欄位：
    OriginTime, Lat, Lon, Depth_km, Magnitude, Location, ReportURL
    """
    records = json_obj.get("records") or json_obj.get("Records") or {}
    quakes = (
        records.get("earthquake") or
        records.get("Earthquake") or
        records.get("data") or
        []
    )

    rows = []
    for q in quakes:
        origin = safe_get(q, "originTime") or safe_get(q, "OriginTime")
        # 經緯度 & 深度
        lat = (
            safe_get(q, "epicenter", "epicenterLat") or
            safe_get(q, "Epicenter", "EpicenterLat")
        )
        lon = (
            safe_get(q, "epicenter", "epicenterLon") or
            safe_get(q, "Epicenter", "EpicenterLon")
        )
        depth = (
            safe_get(q, "depth") or
            safe_get(q, "Depth")
        )
        # 規模
        mag = (
            safe_get(q, "magnitude", "magnitudeValue") or
            safe_get(q, "Magnitude", "MagnitudeValue") or
            safe_get(q, "magnitude", "magnitude") or
            safe_get(q, "Magnitude", "Magnitude")
        )
        # 位置與連結
        loc = (
            safe_get(q, "epicenter", "location") or
            safe_get(q, "Epicenter", "Location")
        )
        url = safe_get(q, "reportURL") or safe_get(q, "ReportURL")

        rows.append({
            "OriginTime": origin,
            "Lat": _to_float(lat),
            "Lon": _to_float(lon),
            "Depth_km": _to_float(depth),
            "Magnitude": _to_float(mag),
            "Location": loc,
            "ReportURL": url,
        })

    df = pd.DataFrame(rows)

    # 轉時間、排序（預設：OriginTime 由新到舊）
    if not df.empty:
        df["OriginTime"] = pd.to_datetime(df["OriginTime"], errors="coerce")
        df = df.sort_values("OriginTime", ascending=False, na_position="last").reset_index(drop=True)

    return df

def _to_float(x):
    try:
        if x is None or x == "":
            return None
        return float(str(x).strip())
    except Exception:
        return None


# -----------------------------
# 視覺化：趨勢圖、台灣地圖
# -----------------------------
TAIWAN_BBOX = (119, 123, 21, 26)  # lon_min, lon_max, lat_min, lat_max

def plot_trend(df: pd.DataFrame) -> bytes | None:
    """時間-規模散點圖，輸出 PNG bytes"""
    if df.empty:
        return None
    fig, ax = plt.subplots(figsize=(6, 3.8))
    ax.scatter(df["OriginTime"], df["Magnitude"])
    ax.set_xlabel("Origin Time (Taipei)")
    ax.set_ylabel("Magnitude")
    ax.grid(True, linestyle="--", alpha=0.4)
    fig.autofmt_xdate()

    buf = io.BytesIO()
    fig.savefig(buf, format="png", dpi=160, bbox_inches="tight")
    plt.close(fig)
    buf.seek(0)
    return buf.getvalue()

def plot_taiwan_map(df: pd.DataFrame) -> bytes | None:
    """
    基礎台灣範圍框圖（非海岸線），用散點展示震央；
    以規模對應 marker 大小，附簡易圖例。
    """
    if df.empty:
        return None

    lon_min, lon_max, lat_min, lat_max = TAIWAN_BBOX
    fig, ax = plt.subplots(figsize=(6, 6))

    # 邊框
    ax.set_xlim(lon_min, lon_max)
    ax.set_ylim(lat_min, lat_max)
    ax.set_xlabel("Longitude (°E)")
    ax.set_ylabel("Latitude (°N)")
    ax.set_title("Epicenters in Taiwan Region (119–123E, 21–26N)")

    # 散點：大小反映規模
    mags = df["Magnitude"].fillna(0)
    sizes = (mags.clip(lower=0) + 2.0) ** 3  # 調整一下讓差異更明顯
    ax.scatter(df["Lon"], df["Lat"], s=sizes, alpha=0.6, edgecolor="black")

    # 簡單圖例：M3/4/5/6 對應大小
    for m in [3, 4, 5, 6]:
        ax.scatter([], [], s=((m + 2.0) ** 3), alpha=0.6, edgecolor="black", label=f"M {m}")
    ax.legend(title="Magnitude", loc="upper right", frameon=True)

    ax.grid(True, linestyle="--", alpha=0.3)

    buf = io.BytesIO()
    fig.savefig(buf, format="png", dpi=160, bbox_inches="tight")
    plt.close(fig)
    buf.seek(0)
    return buf.getvalue()


# -----------------------------
# 主流程：查詢 + 輸出
# -----------------------------
def query_and_render(time_from: str, time_to: str, sort_order: str):
    """
    1) 取資料 → 2) 轉成 DataFrame → 3) 依排序輸出 → 4) 回傳表格、圖、CSV
    Gradio 會接收回傳值更新元件。
    """
    try:
        raw = fetch_reports(time_from, time_to)
    except Exception as e:
        return gr.update(value=f"查詢錯誤：{e}"), None, None, None

    try:
        df = parse_ea0015(raw)
        if df.empty:
            return "（查無資料）", None, None, None

        # 排序（本地）
        if sort_order == "OriginTime (舊→新)":
            df = df.sort_values("OriginTime", ascending=True, na_position="last").reset_index(drop=True)
        else:
            df = df.sort_values("OriginTime", ascending=False, na_position="last").reset_index(drop=True)

        # 表格（轉為 markdown 簡表，手機閱讀較清楚）
        md = df_to_markdown(df)

        # 圖
        trend_png = plot_trend(df)
        map_png = plot_taiwan_map(df)

        # 下載 CSV（以 bytes 形式回傳給 DownloadButton）
        csv_bytes = df.to_csv(index=False).encode("utf-8-sig")

        return md, trend_png, map_png, csv_bytes
    except Exception as e:
        return gr.update(value=f"解析錯誤：{e}"), None, None, None


def df_to_markdown(df: pd.DataFrame, top_n: int = 100) -> str:
    """
    將前 top_n 筆轉為 markdown 表（欄位：OriginTime, Magnitude, Depth_km, Lat, Lon, Location, ReportURL）
    """
    if df.empty:
        return "（查無資料）"

    show_cols = ["OriginTime", "Magnitude", "Depth_km", "Lat", "Lon", "Location", "ReportURL"]
    exist_cols = [c for c in show_cols if c in df.columns]
    slim = df[exist_cols].head(top_n).copy()

    # 時間顯示（台北時區）
    if "OriginTime" in slim.columns:
        slim["OriginTime"] = slim["OriginTime"].dt.tz_convert(TAIPEI_TZ).dt.strftime("%Y-%m-%d %H:%M:%S %Z")

    return slim.to_markdown(index=False)


# -----------------------------
# Gradio 介面
# -----------------------------
default_from, default_to = set_time_range(days=3)

with gr.Blocks(fill_height=True, analytics_enabled=False) as demo:
    gr.Markdown(
        """
# CWA 顯著有感地震報告 (E-A0015-001)
此 Space **只使用環境變數 `CWA_API_KEY` 作為授權**。  
預設查詢 **最近 3 天（台北時間）**。手機版為單欄顯示。
        """
    )

    # 單欄（手機友好）
    with gr.Column():
        with gr.Row():
            time_from = gr.Textbox(label="timeFrom yyyy-MM-ddThh:mm:ss", value=default_from, scale=1)
        with gr.Row():
            time_to   = gr.Textbox(label="timeTo yyyy-MM-ddThh:mm:ss", value=default_to, scale=1)

        # 快速時間範圍（已移除「最近6小時」）
        with gr.Row():
            btn_12h = gr.Button("最近 12 小時")
            btn_24h = gr.Button("最近 24 小時")
            btn_3d  = gr.Button("最近 3 天")
            btn_5d  = gr.Button("最近 5 天")

        # 排序（在地端）
        sort_dd = gr.Dropdown(
            choices=["OriginTime (新→舊)", "OriginTime (舊→新)"],
            value="OriginTime (新→舊)",
            label="排序（本地）"
        )

        run_btn = gr.Button("查詢", variant="primary")

        # 輸出
        table_out = gr.Markdown("（尚未查詢）")
        trend_out = gr.Image(label="趨勢圖（時間-規模）", type="numpy")
        map_out   = gr.Image(label="台灣範圍圖（119–123E, 21–26N）", type="numpy")
        dl_btn    = gr.DownloadButton(label="下載 CSV", file_name="CWA_E-A0015-001.csv")

    # 綁定：快速鍵 → 更新時間欄位
    btn_12h.click(lambda: set_time_range(hours=12), outputs=[time_from, time_to])
    btn_24h.click(lambda: set_time_range(hours=24), outputs=[time_from, time_to])
    btn_3d.click(lambda: set_time_range(days=3), outputs=[time_from, time_to])
    btn_5d.click(lambda: set_time_range(days=5), outputs=[time_from, time_to])

    # 綁定：查詢 → 表格 / 圖 / CSV
    def _on_query(tfrom, tto, sort_sel):
        md, trend_png, map_png, csv_bytes = query_and_render(tfrom, tto, sort_sel)
        # DownloadButton 需要返回 bytes-like 物件
        return md, trend_png, map_png, csv_bytes

    run_btn.click(
        _on_query,
        inputs=[time_from, time_to, sort_dd],
        outputs=[table_out, trend_out, map_out, dl_btn]
    )

# 注意：Hugging Face Spaces 會自動呼叫 demo.launch()
if __name__ == "__main__":
    demo.launch()