import os
import requests
import pandas as pd
import gradio as gr
from datetime import datetime, timedelta, timezone
from zoneinfo import ZoneInfo

# --- CONFIG ---
TOMORROW_API_KEY     = os.getenv("TOMORROW_API_KEY", "teKj9Rkys1UzWxKBEs36pAR8paCXnPW6")
METEOMATICS_USERNAME = os.getenv("METEO_USERNAME", "ptbukitteknologidigital_tudjuka_ricky")
METEOMATICS_PASSWORD = os.getenv("METEO_PASSWORD", "u5n25MwaviEG3O3v8q94")

DEFAULT_LAT = 0.46876
DEFAULT_LON = 116.16879
LOCAL_TZ = ZoneInfo("Asia/Kuala_Lumpur")  # ✅ changed from Asia/Jakarta to Asia/Kuala_Lumpur

MM_VAR_RAIN = "precip_1h:mm"
MM_VAR_PROB = "prob_precip_1h:p"
DEFAULT_MODEL = "ecmwf-ifs"

# --- Tomorrow.io fetch ---
def fetch_tomorrow(lat, lon, hours=24):
    if not TOMORROW_API_KEY:
        raise RuntimeError("Missing TOMORROW_API_KEY")

    url = "https://api.tomorrow.io/v4/weather/forecast"
    params = {
        "location": f"{lat},{lon}",
        "units": "metric",
        "fields": "rainIntensity,precipitationProbability",
        "apikey": TOMORROW_API_KEY,
    }
    r = requests.get(url, params=params, timeout=25)
    if r.status_code != 200:
        raise RuntimeError(f"Tomorrow.io API error: {r.status_code} {r.text}")

    data = r.json().get("timelines", {}).get("hourly", [])
    if not data:
        raise RuntimeError("Tomorrow.io: no hourly data returned")

    rows = []
    for p in data[:hours]:
        ts = pd.to_datetime(p["time"], utc=True).tz_convert(LOCAL_TZ)
        vals = p.get("values", {})
        rain = float(vals.get("rainIntensity", 0.0) or 0.0)
        prob = float(vals.get("precipitationProbability", 0.0) or 0.0)
        rows.append({"time": ts, "tio_rain": rain, "tio_prob": prob})
    return pd.DataFrame(rows)

# --- Meteomatics fetch ---
def fetch_meteomatics(lat, lon, hours=24):
    if not METEOMATICS_USERNAME or not METEOMATICS_PASSWORD:
        raise RuntimeError("Missing METEOMATICS credentials")

    start = datetime.now(timezone.utc).replace(minute=0, second=0, microsecond=0)
    end   = start + timedelta(hours=hours - 1)
    timepath = f"{start.isoformat().replace('+00:00','Z')}--{end.isoformat().replace('+00:00','Z')}:PT1H"
    url = f"https://api.meteomatics.com/{timepath}/{MM_VAR_RAIN},{MM_VAR_PROB}/{lat},{lon}/json"
    params = {"model": DEFAULT_MODEL}

    r = requests.get(url, auth=(METEOMATICS_USERNAME, METEOMATICS_PASSWORD), params=params, timeout=25)
    if r.status_code != 200:
        raise RuntimeError(f"Meteomatics API error: {r.status_code} {r.text}")

    js = r.json().get("data", [])
    if not js:
        raise RuntimeError("Meteomatics: no data returned")

    time_map = {}
    for entry in js:
        var = entry.get("parameter")
        for d in entry["coordinates"][0]["dates"]:
            ts = pd.to_datetime(d["date"], utc=True).tz_convert(LOCAL_TZ)
            val = float(d.get("value", 0.0) or 0.0)
            rec = time_map.setdefault(ts, {})
            if var == MM_VAR_RAIN:
                rec["mm_rain"] = val
            elif var == MM_VAR_PROB:
                rec["mm_prob"] = val

    df = pd.DataFrame([
        {"time": t, "mm_rain": rec.get("mm_rain", 0.0), "mm_prob": rec.get("mm_prob", 0.0)}
        for t, rec in sorted(time_map.items())
    ])
    return df

# --- Combine & table ---
def build_table(lat, lon):
    df_tio = fetch_tomorrow(lat, lon, 24)
    df_mm  = fetch_meteomatics(lat, lon, 24)
    df = pd.merge(df_tio, df_mm, on="time", how="outer").sort_values("time")

    table = pd.DataFrame({
        "Time (Asia/Kuala_Lumpur)": df["time"].dt.strftime("%Y-%m-%d %H:%M"),
        "Tomorrow.io Rain (mm/hr)": df["tio_rain"].round(3),
        "Tomorrow.io Prob (%)": df["tio_prob"].round(1),
        "Meteomatics Rain (mm/hr)": df["mm_rain"].round(3),
        "Meteomatics Prob (%)": df["mm_prob"].round(1),
    })
    return table

def run_table(lat, lon):
    try:
        table = build_table(lat, lon)
        info = f"<small>Location: {lat:.5f}, {lon:.5f} | Model: <b>{DEFAULT_MODEL}</b> | Timezone: <b>Asia/Kuala_Lumpur</b> | Units: <b>mm/hr</b> | Horizon: 24 hours</small>"
        return table, info
    except Exception as e:
        return None, f"<p><b>Error:</b> {e}</p>"

# --- Gradio UI ---
with gr.Blocks(title="Next 24 Hours — Tomorrow.io + Meteomatics Table (mm/hr)") as demo:
    gr.Markdown("## 🌧️ Next 24 Hours — **Tomorrow.io** & **Meteomatics** (All in mm/hr)\n"
                "Side-by-side hourly rain and probability forecasts for the next 24 hours.")

    with gr.Row():
        lat = gr.Number(label="Latitude", value=DEFAULT_LAT)
        lon = gr.Number(label="Longitude", value=DEFAULT_LON)

    btn = gr.Button("Fetch 24h Table")
    table_out = gr.Dataframe(interactive=False, wrap=True)
    info_out = gr.HTML()

    btn.click(run_table, inputs=[lat, lon], outputs=[table_out, info_out])

# if __name__ == "__main__":
#     os.environ["GRADIO_ANALYTICS_ENABLED"]="false"
#     os.environ["HF_HUB_DISABLE_TELEMETRY"]="1"
#     demo.launch(server_name="127.0.0.1", server_port=7861, show_error=True, inbrowser=False, debug=True)

if __name__ == "__main__":
    demo.launch()