import subprocess

def install_dependencies():
    subprocess.check_call(["python", "-m", "pip", "install", "--upgrade", "pip"])
    subprocess.check_call(["python", "-m", "pip", "install", "--upgrade", "huggingface_hub"])
    subprocess.check_call(["python", "-m", "pip", "install", "--upgrade", "streamlit"])
    subprocess.check_call(["python", "-m", "pip", "install", "--upgrade", "requests"])
    # subprocess.check_call(["python", "-m", "pip", "install", "--upgrade", "json"])
    # subprocess.check_call(["python", "-m", "pip", "install", "--upgrade", "os"])
    # subprocess.check_call(["python", "-m", "pip", "install", "--upgrade", "subprocess"])
    # other packages here
install_dependencies()

import streamlit as st
import requests
import json
import os
from huggingface_hub import InferenceClient




# ── Page config ──────────────────────────────────────────────────────────────
st.set_page_config(
    page_title="Weather Forecast",
    page_icon="🌤️",
    layout="wide",
)

# ── Custom CSS ────────────────────────────────────────────────────────────────
# with open('./style.css.txt', 'r') as file:
#     csstext = file.read()
#     #print(content)

# st.markdown(csstext, unsafe_allow_html=True)

def local_css(file_name):
    with open(file_name) as f:
        # st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
        st.markdown(f'{f.read()}', unsafe_allow_html=True)

# Call the function at the top of your app
# local_css("./style.css")
local_css("style.css")
st.title("RainyTrek")



# ── WMO weather code → emoji + label ─────────────────────────────────────────
WMO_CODES = {
    0: ("☀️", "Clear"),
    1: ("🌤️", "Mostly Clear"),
    2: ("⛅", "Partly Cloudy"),
    3: ("☁️", "Overcast"),
    45: ("🌫️", "Foggy"),
    48: ("🌫️", "Icy Fog"),
    51: ("🌦️", "Light Drizzle"),
    53: ("🌦️", "Drizzle"),
    55: ("🌧️", "Heavy Drizzle"),
    61: ("🌧️", "Light Rain"),
    63: ("🌧️", "Rain"),
    65: ("🌧️", "Heavy Rain"),
    71: ("🌨️", "Light Snow"),
    73: ("❄️", "Snow"),
    75: ("❄️", "Heavy Snow"),
    77: ("🌨️", "Snow Grains"),
    80: ("🌦️", "Rain Showers"),
    81: ("🌧️", "Heavy Showers"),
    82: ("⛈️", "Violent Showers"),
    85: ("🌨️", "Snow Showers"),
    86: ("🌨️", "Heavy Snow Showers"),
    95: ("⛈️", "Thunderstorm"),
    96: ("⛈️", "Thunderstorm + Hail"),
    99: ("⛈️", "Thunderstorm + Heavy Hail"),
}


def wmo_info(code):
    return WMO_CODES.get(code, ("🌡️", "Unknown"))


# ── Extract cities via HuggingFace LLM ───────────────────────────────────────
def extract_cities_with_llm(user_prompt: str) -> list[str]:
    """Use HF Inference API to extract city names from a natural-language prompt."""
    client = InferenceClient(
        # model="mistralai/Mistral-7B-Instruct-v0.3",
        # model="mistralai/Mistral-7B-Instruct-v0.2",
        # model="nvidia/Gemma-4-26B-A4B-NVFP4",
        # model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
        model="meta-llama/Llama-3.3-70B-Instruct",
        # token=st.secrets.get("HF_TOKEN", None),
        # token=st.secrets.get("rainytrek010526001read", None),
        # token=st.secrets.get("API_KEY", None),
        # token=os.getenv("API_KEY"),
        token=os.getenv("rainytrek010526001read"),
        provider="fireworks-ai"
    )

    system_prompt = (
        "You are a helpful assistant that extracts city names from user messages. "
        "Respond ONLY with a JSON array of city name strings. "
        "Example: [\"Paris\", \"Tokyo\", \"New York\"]. "
        "If no cities are mentioned, respond with []."
    )

    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": f"Extract all city names from this text:\n\n{user_prompt}"},
    ]

    response = client.chat_completion(messages=messages, max_tokens=256, temperature=0.1)
    
    raw = response.choices[0].message.content.strip()
    # # strip markdown fences if present
    # if raw.startswith("```"):
    #     raw = raw.split("```")[1]
    #     if raw.startswith("json"):
    #         raw = raw[4:]
    # raw = raw.strip()

    # cities = json.loads(raw)

    ## raw = [c.strip() for c in response["choices"][0]["message"]["content"].split("\"")  if len(c.strip()) > 1]
    
    cities = json.loads(raw)
    
    return [c.strip() for c in cities if isinstance(c, str) and c.strip()]


# ── Geocoding via Open-Meteo ──────────────────────────────────────────────────
def geocode_city(city: str) -> dict | None:
    url = "https://geocoding-api.open-meteo.com/v1/search"
    r = requests.get(url, params={"name": city, "count": 1, "language": "en", "format": "json"}, timeout=10)
    r.raise_for_status()
    results = r.json().get("results")
    if not results:
        return None
    loc = results[0]
    return {
        "name": loc.get("name", city),
        "country": loc.get("country", ""),
        "lat": loc["latitude"],
        "lon": loc["longitude"],
        "timezone": loc.get("timezone", "auto"),
    }


# ── Fetch 7-day forecast from Open-Meteo ─────────────────────────────────────
def fetch_forecast(lat: float, lon: float, timezone: str) -> dict:
    url = "https://api.open-meteo.com/v1/forecast"
    params = {
        "latitude": lat,
        "longitude": lon,
        "daily": [
            "weathercode",
            "temperature_2m_max",
            "temperature_2m_min",
            "precipitation_sum",
            "windspeed_10m_max",
        ],
        "current_weather": True,
        "timezone": timezone,
        "forecast_days": 7,
    }
    r = requests.get(url, params=params, timeout=10)
    r.raise_for_status()
    return r.json()


# ── Render a city weather card ────────────────────────────────────────────────
def render_city_card(loc: dict, forecast: dict):
    daily = forecast["daily"]
    current = forecast.get("current_weather", {})

    cur_temp = current.get("temperature", "—")
    cur_wind = current.get("windspeed", "—")
    cur_code = current.get("weathercode", 0)
    cur_icon, cur_label = wmo_info(cur_code)

    days_html = ""
    for i in range(len(daily["time"])):
        date = daily["time"][i]
        weekday = __import__("datetime").datetime.strptime(date, "%Y-%m-%d").strftime("%a")
        code = daily["weathercode"][i]
        icon, _ = wmo_info(code)
        tmax = daily["temperature_2m_max"][i]
        tmin = daily["temperature_2m_min"][i]
        precip = daily["precipitation_sum"][i]
        days_html += f"<div class=\"day-card\">"
        days_html += f"<div class=\"day-label\">{weekday}<br>{date[5:]}</div>"
        days_html += f"<div class=\"day-icon\">{icon}</div>"
        days_html += f"<div class=\"day-temp-max\">{tmax}°</div>"
        days_html += f"<div class=\"day-temp-min\">{tmin}°</div>"
        days_html += f"<div class=\"day-precip\">💧 {precip}mm</div>"
        days_html += f"</div>"
        

    st.markdown(f"""
    <div class="city-card">
        <div class="city-name">{cur_icon} {loc['name']}, {loc['country']}</div>
        <div class="city-coords">
            {loc['lat']:.4f}°N  {loc['lon']:.4f}°E
        </div>
        <div class="stat-row">
            <div class="stat-pill">Now <span>{cur_temp}°C</span></div>
            <div class="stat-pill">Wind <span>{cur_wind} km/h</span></div>
            <div class="stat-pill">{cur_label}</div>
        </div>
        <div class="weather-grid">
            {days_html}
        </div>
    </div>
    """, unsafe_allow_html=True)


# ── Main UI ───────────────────────────────────────────────────────────────────
st.markdown('<div class="main-title">🌤 Weather Forecast</div>', unsafe_allow_html=True)
# st.markdown('<div class="subtitle">powered by Open-Meteo · Mistral 7B · Hugging Face</div>', unsafe_allow_html=True)
st.markdown('<div class="subtitle">powered by Open-Meteo · Llama 70B · Hugging Face</div>', unsafe_allow_html=True)


user_input = st.text_input(
    label="Your question",
    placeholder='e.g. "What\'s the weather like in Paris and Tokyo this week?"',
    label_visibility="collapsed",
)

run = st.button("Get Forecast →", use_container_width=False)

if run and user_input.strip():
    with st.spinner("Asking the LLM to find cities…"):
        try:
            cities = extract_cities_with_llm(user_input)
        except Exception as e:
            st.markdown(f'<div class="error-box">⚠️ LLM error: {e}</div>', unsafe_allow_html=True)
            cities = []

    if not cities:
        st.markdown('<div class="error-box">what cities in particular should I look for?</div>', unsafe_allow_html=True)
    else:
        st.markdown(f'<div class="llm-box"><div class="llm-label">Cities detected by LLM</div>{" · ".join(cities)}</div>', unsafe_allow_html=True)

        for city in cities:
            with st.spinner(f"Fetching weather for {city}…"):
                try:
                    loc = geocode_city(city)
                    if not loc:
                        st.markdown(f'<div class="error-box">Could not geocode "{city}"</div>', unsafe_allow_html=True)
                        continue
                    forecast = fetch_forecast(loc["lat"], loc["lon"], loc["timezone"])
                    render_city_card(loc, forecast)
                except Exception as e:
                    st.markdown(f'<div class="error-box">⚠️ Error for {city}: {e}</div>', unsafe_allow_html=True)

elif run:
    st.markdown('<div class="error-box">Please enter a question first.</div>', unsafe_allow_html=True)

st.markdown("---")
st.markdown(
    '<div style="font-family: Space Mono, monospace; font-size: 0.65rem; color: #37474f; text-align:center;">'
    'Weather data: Open-Meteo (open-source, no API key needed) · LLM: Mistral-7B-Instruct via Hugging Face Inference API'
    '</div>',
    unsafe_allow_html=True,
)