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app.py

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+
+# app.py (upgraded)
+# Gradio app for Hugging Face Spaces: CWA 顯著有感地震報告 (E-A0015-001) — with map & filters
+import os
+import json
+import tempfile
+from datetime import datetime, timedelta
+from typing import List, Dict, Any, Tuple
+
+import gradio as gr
+import pandas as pd
+import requests
+import folium
+
+BASE_URL = "https://opendata.cwa.gov.tw/api/v1/rest/datastore/E-A0015-001"
+AREAS = ["宜蘭縣","花蓮縣","臺東縣","澎湖縣","金門縣","連江縣","臺北市","新北市","桃園市","臺中市","臺南市","高雄市","基隆市","新竹縣","新竹市","苗栗縣","彰化縣","南投縣","雲林縣","嘉義縣","嘉義市","屏東縣"]
+
+# 台灣震度等級轉序值（用於過濾與排序）
+INT_ORDER = {
+    "0": 0.0, "1": 1.0, "2": 2.0, "3": 3.0, "4": 4.0,
+    "5弱": 5.0, "5-": 5.0, "5+": 5.3, "5強": 5.3,
+    "6弱": 6.0, "6-": 6.0, "6+": 6.3, "6強": 6.3,
+    "7": 7.0
+}
+
+INT_FILTER_CHOICES = ["0","1","2","3","4","5弱","5強","6弱","6強","7"]
+
+def intensity_to_order(s: str) -> float:
+    if s is None:
+        return -1.0
+    s = str(s).strip()
+    # 常見寫法容錯
+    s = s.replace("級","").replace(" ", "")
+    return INT_ORDER.get(s, -1.0)
+
+def validate_iso(dt: str) -> str:
+    if not dt:
+        return ""
+    try:
+        datetime.strptime(dt, "%Y-%m-%dT%H:%M:%S")
+        return dt
+    except ValueError:
+        raise gr.Error("時間格式需為 yyyy-MM-ddThh:mm:ss")
+
+def build_params(auth: str, limit: int|None, offset: int|None, fmt: str, areas: List[str], stations: List[str], sort: str|None, timeFrom: str|None, timeTo: str|None):
+    params = []
+    if auth:
+        params.append(("Authorization", auth))
+    elif os.getenv("CWA_API_KEY"):
+        params.append(("Authorization", os.getenv("CWA_API_KEY")))
+    else:
+        raise gr.Error("缺少授權碼：請在左側輸入 Authorization，或於 Spaces Secrets 設定 CWA_API_KEY。")
+
+    if limit is not None: params.append(("limit", str(limit)))
+    if offset is not None: params.append(("offset", str(offset)))
+    if fmt: params.append(("format", fmt))
+    for a in (areas or []):
+        params.append(("AreaName", a))
+    for s in (stations or []):
+        params.append(("StationName", s))
+    if sort: params.append(("sort", sort))
+    if timeFrom: params.append(("timeFrom", timeFrom))
+    if timeTo: params.append(("timeTo", timeTo))
+    return params
+
+def http_get(url: str, params: List[Tuple[str,str]]) -> Dict[str, Any]:
+    sess = requests.Session()
+    resp = sess.get(url, params=params, timeout=(5, 20))
+    resp.raise_for_status()
+    if "application/json" in resp.headers.get("Content-Type","").lower() or resp.text.strip().startswith("{"):
+        return resp.json()
+    else:
+        return {"raw": resp.text}
+
+def extract_records(payload: Dict[str, Any]):
+    recs = payload.get("records")
+    if isinstance(recs, dict):
+        for k, v in recs.items():
+            if isinstance(v, list):
+                return v
+    result = payload.get("result")
+    if isinstance(result, dict) and isinstance(result.get("records"), list):
+        return result["records"]
+    for key in ("Earthquake","earthquakes","data","items"):
+        v = payload.get(key)
+        if isinstance(v, list):
+            return v
+    return []
+
+def flatten_row(row: Dict[str, Any]) -> Dict[str, Any]:
+    out = {}
+    for key in ("EarthquakeNo","ReportImageURI","Web","ReportColor","ReportContent"):
+        if key in row:
+            out[key] = row.get(key)
+    eqi = row.get("EarthquakeInfo")
+    if isinstance(eqi, dict):
+        out["OriginTime"] = eqi.get("OriginTime")
+        out["Depth_km"] = eqi.get("FocalDepth")
+        mag = eqi.get("EarthquakeMagnitude") or {}
+        if isinstance(mag, dict):
+            out["Magnitude"] = mag.get("MagnitudeValue")
+            out["MagnitudeType"] = mag.get("MagnitudeType")
+        epic = eqi.get("Epicenter") or {}
+        if isinstance(epic, dict):
+            out["Epicenter"] = epic.get("Location")
+            out["EpicenterLon"] = epic.get("EpicenterLongitude")
+            out["EpicenterLat"] = epic.get("EpicenterLatitude")
+    for k in ("OriginTime","originTime","Time"):
+        if k in row and "OriginTime" not in out:
+            out["OriginTime"] = row.get(k)
+    for k in ("Depth","depth","FocalDepth"):
+        if k in row and "Depth_km" not in out:
+            out["Depth_km"] = row.get(k)
+    for k in ("Magnitude","mag"):
+        if k in row and "Magnitude" not in out:
+            out["Magnitude"] = row.get(k)
+    maxint = row.get("Intensity") or row.get("ShakingArea")
+    if isinstance(maxint, dict):
+        out["MaxIntensity"] = maxint.get("MaxIntensity")
+    return out
+
+def df_with_types(df: pd.DataFrame) -> pd.DataFrame:
+    if "OriginTime" in df.columns:
+        try:
+            df["OriginTime"] = pd.to_datetime(df["OriginTime"], format="%Y-%m-%dT%H:%M:%S", errors="coerce")
+        except Exception:
+            pass
+    if "Magnitude" in df.columns:
+        df["Magnitude"] = pd.to_numeric(df["Magnitude"], errors="coerce")
+    if "Depth_km" in df.columns:
+        df["Depth_km"] = pd.to_numeric(df["Depth_km"], errors="coerce")
+    if "MaxIntensity" in df.columns:
+        df["MaxIntensityOrder"] = df["MaxIntensity"].map(intensity_to_order)
+    return df
+
+def make_map(df: pd.DataFrame) -> str:
+    # 如果沒有經緯度，就回傳空字串
+    if not {"EpicenterLat","EpicenterLon"}.issubset(df.columns):
+        return "<p>沒有可用的經緯度資料。</p>"
+    valid = df.dropna(subset=["EpicenterLat","EpicenterLon"]).copy()
+    if valid.empty:
+        return "<p>沒有可用的經緯度資料。</p>"
+    try:
+        valid["EpicenterLat"] = pd.to_numeric(valid["EpicenterLat"], errors="coerce")
+        valid["EpicenterLon"] = pd.to_numeric(valid["EpicenterLon"], errors="coerce")
+    except Exception:
+        pass
+    valid = valid.dropna(subset=["EpicenterLat","EpicenterLon"])
+    if valid.empty:
+        return "<p>沒有可用的經緯度資料。</p>"
+
+    # 中心點：取平均
+    center_lat = valid["EpicenterLat"].mean()
+    center_lon = valid["EpicenterLon"].mean()
+
+    m = folium.Map(location=[center_lat, center_lon], zoom_start=6, tiles="OpenStreetMap")
+
+    for _, r in valid.iterrows():
+        lat = float(r["EpicenterLat"])
+        lon = float(r["EpicenterLon"])
+        mag = r.get("Magnitude", None)
+        itx = r.get("MaxIntensity", "")
+        # 半徑：與規模近似對應
+        radius = 4.0
+        try:
+            if pd.notna(mag):
+                radius = max(4.0, min(20.0, float(mag) * 2.5))
+        except Exception:
+            pass
+        # 顏色：依規模粗略分層
+        color = "#2c7bb6"  # default
+        try:
+            if mag is not None and float(mag) >= 6.0:
+                color = "#d7191c"
+            elif mag is not None and float(mag) >= 5.0:
+                color = "#fdae61"
+            elif mag is not None and float(mag) >= 4.0:
+                color = "#abd9e9"
+        except Exception:
+            pass
+        popup = folium.Popup(html=f"<b>時間</b>: {r.get('OriginTime','')}<br>"
+                                  f"<b>震央</b>: {r.get('Epicenter','')}<br>"
+                                  f"<b>規模</b>: {mag}<br>"
+                                  f"<b>深度</b>: {r.get('Depth_km','')} km<br>"
+                                  f"<b>最大震度</b>: {itx}", max_width=320)
+        folium.CircleMarker(location=[lat, lon], radius=radius, color=color, fill=True, fill_opacity=0.7, popup=popup).add_to(m)
+
+    # 直接輸出為 HTML iframe
+    return m._repr_html_()
+
+def fetch(auth, time_from, time_to, limit, offset, fmt, sel_areas, stations, sort, min_intensity):
+    time_from = validate_iso(time_from) if time_from else None
+    time_to = validate_iso(time_to) if time_to else None
+    params = build_params(auth=auth, limit=limit, offset=offset, fmt=fmt, areas=sel_areas, stations=stations, sort=sort, timeFrom=time_from, timeTo=time_to)
+    payload = http_get(BASE_URL, params)
+    records = extract_records(payload)
+    flat = [flatten_row(r) for r in records]
+
+    df = pd.DataFrame(flat)
+    df = df_with_types(df)
+
+    # 依最大震度濾掉較小的
+    if min_intensity:
+        thr = intensity_to_order(min_intensity)
+        if "MaxIntensityOrder" in df.columns and thr >= 0:
+            df = df[df["MaxIntensityOrder"] >= thr]
+
+    # 排序：如果使用者指定 OriginTime 升冪，否則預設降冪（若 API 已處理仍保險再排序）
+    if "OriginTime" in df.columns:
+        ascending = True if sort == "OriginTime" else False
+        df = df.sort_values("OriginTime", ascending=ascending)
+
+    # 輸出檔案
+    tmpdir = tempfile.mkdtemp(prefix="cwa_")
+    csv_path = os.path.join(tmpdir, "cwa_quake.csv")
+    json_path = os.path.join(tmpdir, "raw.json")
+    df.to_csv(csv_path, index=False, encoding="utf-8")
+    with open(json_path, "w", encoding="utf-8") as f:
+        json.dump(payload, f, ensure_ascii=False, indent=2)
+
+    # 摘要
+    total = len(df)
+    earliest = latest = ""
+    if total and "OriginTime" in df.columns:
+        earliest_row = df.iloc[0]
+        latest_row = df.iloc[-1]
+        def fmt_row(r):
+            ot = r.get("OriginTime","")
+            if isinstance(ot, pd.Timestamp):
+                ot = ot.strftime("%Y-%m-%dT%H:%M:%S")
+            return f"{ot} | {r.get('Epicenter','')} | M{r.get('Magnitude','')} | 深{r.get('Depth_km','')}km | 震度{r.get('MaxIntensity','')}"
+        earliest = "最早: " + fmt_row(earliest_row)
+        latest = "最新: " + fmt_row(latest_row)
+
+    summary = f"取得筆數: {total}\n{earliest}\n{latest}"
+
+    # 產生地圖
+    html_map = make_map(df)
+
+    # 顯示表格：把時間欄轉回字串以便顯示
+    if "OriginTime" in df.columns:
+        df["OriginTime"] = df["OriginTime"].astype(str)
+
+    return df, summary, csv_path, json_path, html_map
+
+def last_24h(auth, limit, offset, fmt, sel_areas, stations, sort, min_intensity):
+    # 以 UTC+08:00（台北��為常見需求；伺服器可能是 UTC，這裡用當前系統時間
+    now = datetime.now()
+    tf = (now - timedelta(days=1)).strftime("%Y-%m-%dT%H:%M:%S")
+    tt = now.strftime("%Y-%m-%dT%H:%M:%S")
+    return fetch(auth, tf, tt, limit, offset, fmt, sel_areas, stations, sort, min_intensity)
+
+with gr.Blocks(title="CWA 顯著有感地震報告 E-A0015-001") as demo:
+    gr.Markdown("# CWA 顯著有感地震報告 (E-A0015-001)")
+    gr.Markdown("左側輸入授權與查詢條件。Authorization 可留空並改用環境變數 **CWA_API_KEY**（建議在 Spaces Secrets 設定）。")
+    with gr.Row():
+        with gr.Column(scale=1):
+            auth = gr.Textbox(label="Authorization（可留空改用 CWA_API_KEY）", type="password", placeholder="留空則使用環境變數 CWA_API_KEY")
+            time_from = gr.Textbox(label="timeFrom yyyy-MM-ddThh:mm:ss", placeholder="例如 2025-08-01T00:00:00")
+            time_to = gr.Textbox(label="timeTo yyyy-MM-ddThh:mm:ss", placeholder="例如 2025-08-10T23:59:59")
+            areas = gr.CheckboxGroup(choices=AREAS, label="AreaName（可複選）")
+            stations = gr.Textbox(label="StationName（以逗號分隔，可留空）", placeholder="例如：台北、花蓮...")
+            sort = gr.Dropdown(choices=[None, "OriginTime"], value=None, label="sort（預設降冪；選 OriginTime 會升冪）")
+            min_intensity = gr.Dropdown(choices=INT_FILTER_CHOICES, value=None, label="最小最大震度（過濾）", info="例如選 4 僅顯示最大震度≥4 的事件")
+            with gr.Row():
+                limit = gr.Number(label="limit（筆數上限）", precision=0)
+                offset = gr.Number(label="offset（起始偏移）", precision=0, value=0)
+            fmt = gr.Radio(choices=["JSON","XML"], value="JSON", label="回傳格式")
+            with gr.Row():
+                run_btn = gr.Button("查詢", variant="primary")
+                last24_btn = gr.Button("最近 24 小時", variant="secondary")
+        with gr.Column(scale=2):
+            out_df = gr.Dataframe(label="查詢結果（扁平化）", interactive=False, wrap=True, datatype="str")
+            out_summary = gr.Textbox(label="摘要", interactive=False)
+            out_csv = gr.File(label="下載 CSV")
+            out_json = gr.File(label="下載原始 JSON")
+            out_map = gr.HTML(label="震央地圖")
+    def on_click(auth, time_from, time_to, limit, offset, fmt, areas_sel, stations_txt, sort, min_intensity):
+        stations_list = []
+        if stations_txt:
+            stations_list = [s.strip() for s in stations_txt.split(",") if s.strip()]
+        df, summary, csv_path, json_path, html_map = fetch(auth, time_from, time_to, int(limit) if limit is not None else None, int(offset) if offset is not None else None, fmt, areas_sel, stations_list, sort, min_intensity)
+        return df, summary, csv_path, json_path, html_map
+    def on_last24(auth, limit, offset, fmt, areas_sel, stations_txt, sort, min_intensity):
+        stations_list = []
+        if stations_txt:
+            stations_list = [s.strip() for s in stations_txt.split(",") if s.strip()]
+        df, summary, csv_path, json_path, html_map = last_24h(auth, int(limit) if limit is not None else None, int(offset) if offset is not None else None, fmt, areas_sel, stations_list, sort, min_intensity)
+        return df, summary, csv_path, json_path, html_map
+    run_btn.click(on_click, inputs=[auth, time_from, time_to, limit, offset, fmt, areas, stations, sort, min_intensity], outputs=[out_df, out_summary, out_csv, out_json, out_map])
+    last24_btn.click(on_last24, inputs=[auth, limit, offset, fmt, areas, stations, sort, min_intensity], outputs=[out_df, out_summary, out_csv, out_json, out_map])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+
+gradio>=4.44.0
+pandas>=2.2.0
+requests>=2.31.0
+python-dateutil>=2.8.2
+folium>=0.16.0
+branca>=0.7.2