"""AgroSense Streamlit chat UI.

Run:  streamlit run ui/app.py

Talks to the FastAPI backend if it's reachable; otherwise falls back to running
the RAG engine in-process so the UI works even without the API server.
"""
from __future__ import annotations

import os
import sys
from pathlib import Path

import requests
import streamlit as st

# Make the local package importable when run via `streamlit run ui/app.py`.
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))

API_URL = os.getenv("AGROSENSE_API_URL", "http://127.0.0.1:8000")

SAMPLE_QUERIES = [
    "My soil is sandy, rainfall 900 mm, I plan to grow maize. What fertilizer and pest steps?",
    "Give me the fertilizer schedule for aromatic crops like mint.",
    "How do I prevent disease in paddy on loamy soil?",
    "What pest management is recommended for cotton?",
]

st.set_page_config(page_title="AgroSense Advisor", page_icon="🌱", layout="centered")

# Increase the base font size by 3px (Streamlit's default root is 16px). Most text
# (incl. the rem-based tickers) scales off the root, so this enlarges the whole app.
st.markdown(
    "<style>html, body, [data-testid='stAppViewContainer']"
    " { font-size: 19px !important; }</style>",
    unsafe_allow_html=True,
)


def query_via_api(text: str, location: str | None, language: str,
                  include_prices: bool) -> dict | None:
    try:
        payload: dict = {"query": text, "language": language,
                         "include_prices": include_prices}
        if location:
            payload["location"] = location
        resp = requests.post(f"{API_URL}/query", json=payload, timeout=60)
        resp.raise_for_status()
        return resp.json()
    except Exception:
        return None


@st.cache_resource(show_spinner="Loading AgroSense knowledge base...")
def get_local_engine():
    from agrosense import RAGEngine
    return RAGEngine()


def query_local(text: str, location: str | None, language: str,
                include_prices: bool) -> dict:
    return get_local_engine().answer(
        text, location=location, language=language, include_prices=include_prices
    ).to_dict()


@st.cache_data(show_spinner=False, ttl=1800)
def get_satellite(location: str) -> dict | None:
    """Fetch satellite monitoring via the API, falling back to the local engine."""
    try:
        resp = requests.get(f"{API_URL}/satellite", params={"location": location}, timeout=40)
        resp.raise_for_status()
        data = resp.json()
        return data if data.get("available") else None
    except Exception:
        pass
    try:
        report = get_local_engine().get_satellite(location=location)
        return report.to_dict() if report else None
    except Exception:
        return None


@st.cache_data(show_spinner=False, ttl=1800)
def get_environment(location: str) -> dict | None:
    """Fetch the location environment profile via the API, falling back to engine."""
    try:
        resp = requests.get(f"{API_URL}/environment", params={"location": location}, timeout=40)
        resp.raise_for_status()
        data = resp.json()
        return data if data.get("available") else None
    except Exception:
        pass
    try:
        p = get_local_engine().get_environment(location=location)
        return p.to_dict() if p else None
    except Exception:
        return None


def _m(value, suffix=""):
    return f"{value}{suffix}" if value is not None else "—"


def render_environment(p: dict) -> None:
    sun, wind, aq = p.get("sunlight", {}), p.get("wind", {}), p.get("air_quality", {})
    gw, pollen = p.get("groundwater", {}), p.get("pollen", {})

    a, b, c = st.columns(3)
    a.metric("Latitude", _m(p.get("latitude")))
    b.metric("Longitude", _m(p.get("longitude")))
    c.metric("Altitude", _m(p.get("elevation_m"), " m"))

    # Wind: keep speed + bearing in the VALUE (not delta, which renders a ▲ arrow).
    wind_val = "—"
    if wind.get("speed_kmh") is not None:
        wind_val = f"{wind['speed_kmh']} km/h"
        if wind.get("direction_compass"):
            wind_val += f" {wind['direction_compass']}"
    d, e, f = st.columns(3)
    d.metric("Population", _m(p.get("population")))
    e.metric("Humidity", _m(p.get("humidity_pct"), " %"))
    f.metric("Wind", wind_val)
    if wind.get("direction_deg") is not None:
        f.caption(f"from {wind['direction_deg']}°")

    g, h, i = st.columns(3)
    g.metric("Sunshine today", _m(sun.get("sunshine_hours"), " h"))
    h.metric("UV index (max)", _m(sun.get("uv_index_max")))
    i.metric("Solar now", _m(sun.get("shortwave_wm2"), " W/m²"))

    # AQI: category in the VALUE, not delta.
    aqi_val = "—"
    if aq.get("us_aqi") is not None:
        aqi_val = str(aq["us_aqi"])
        if aq.get("category"):
            aqi_val += f" · {aq['category']}"
    j, k, l = st.columns(3)
    j.metric("Air quality (US AQI)", aqi_val)
    k.metric("PM2.5", _m(aq.get("pm2_5"), " µg/m³"))
    l.metric("PM10", _m(aq.get("pm10"), " µg/m³"))

    if pollen.get("available"):
        st.write("**Pollen** (grains/m³): "
                 + ", ".join(f"{k2}: {v}" for k2, v in pollen.get("values", {}).items()))
    else:
        st.caption("🌼 Pollen: " + pollen.get("note", "unavailable"))

    if gw.get("level_m") is not None:
        st.metric("Ground water table", f"{gw['level_m']} m below ground")
    else:
        st.metric("Ground water — soil moisture (3-9 cm)",
                  _m(gw.get("soil_moisture_m3m3"), " m³/m³"))
    st.caption("💧 " + gw.get("note", ""))
    st.caption(f"Sources: {', '.join(p.get('sources', []))}")


@st.cache_data(show_spinner=False, ttl=1800)
def get_hazards(location: str) -> dict | None:
    """Natural-hazard events + active fires near the location."""
    try:
        resp = requests.get(f"{API_URL}/hazards", params={"location": location}, timeout=40)
        resp.raise_for_status()
        data = resp.json()
        return data if data.get("available") else None
    except Exception:
        pass
    try:
        return get_local_engine().get_hazards(location=location)
    except Exception:
        return None


def render_hazards(rep: dict) -> None:
    events = rep.get("events", [])
    if events:
        st.markdown(f"**{len(events)} natural-hazard event(s) within range** "
                    f"(NASA EONET):")
        for e in events[:8]:
            dist = f" · {e['distance_km']} km away" if e.get("distance_km") is not None else ""
            st.warning(f"**{e['category']}** — {e['title']}{dist}")
    else:
        st.success("No active EONET hazard events within range.")
    fires = rep.get("fires")
    if fires is None:
        st.caption("🔥 Active fires: set AGROSENSE_FIRMS_MAP_KEY (free NASA FIRMS key) to enable.")
    elif not fires:
        st.caption("🔥 No active fire detections nearby (NASA FIRMS).")
    else:
        nearest = fires[0]
        st.error(f"🔥 {len(fires)} active fire detection(s) nearby (NASA FIRMS); "
                 f"nearest {nearest.get('distance_km')} km away "
                 f"(confidence {nearest.get('confidence')}, {nearest.get('acq_date')}).")


def render_consult_session(c: dict) -> None:
    exp = c.get("expert")
    st.markdown(f"**Consultation {c['id']}** · status: **{c['status']}** · "
                f"channel: {c['channel']}")
    if exp:
        st.success(f"👨‍⚕️ Assigned: **{exp['name']}** — {exp['specialization']}  \n"
                   f"{exp['region']} · speaks {', '.join(exp['languages'])}")
    else:
        st.info("Queued — awaiting an available expert.")
    st.caption(f"Shared with expert: {c.get('summary', '')}")
    notes = c.get("notifications") or []
    ok_channels = ", ".join(n["channel"] for n in notes if n.get("ok"))
    if ok_channels:
        st.caption(f"🔔 Expert notified via: {ok_channels} "
                   "(set AGROSENSE_NOTIFY_WEBHOOK / SMTP env for real delivery).")
    if c.get("room_url"):
        st.link_button("🎥 Join live video room", c["room_url"], width="stretch")
        st.caption("Public Jitsi room (may ask the first joiner to sign in as moderator).")
    st.markdown("**Conversation**")
    for m in c.get("messages", []):
        who = {"farmer": "🧑‍🌾 You", "expert": "👨‍⚕️ Expert", "system": "ℹ️ System"}.get(
            m["sender"], m["sender"])
        ts = f" ·  _{m['at']}_" if m.get("at") else ""
        st.markdown(f"- **{who}:** {m['text']}{ts}")


def render_consultation(c: dict) -> None:
    hs = c.get("health_status", "Inconclusive")
    box = {"Likely healthy": st.success, "Needs attention": st.warning}.get(hs, st.info)
    box(f"**Diagnosis:** {c.get('diagnosis', '—')}  \n"
        f"**Health:** {hs}  ·  **Severity:** {c.get('severity', 'n/a')}  ·  "
        f"confidence {round(c.get('confidence', 0) * 100)}% (basis: {c.get('diagnosis_basis')})")
    if c.get("weather_note"):
        st.warning("🌦️ Timing: " + c["weather_note"])
    st.markdown("**📋 Prescription**")
    for p in c.get("prescription", []):
        st.markdown(f"- **{p['category']}:** {p['instruction']}")
    if c.get("citations"):
        st.caption("Sources: " + ", ".join(c["citations"]))
    if c.get("follow_up"):
        st.markdown(f"**🔁 Follow-up:** {c['follow_up']}")
    st.caption("⚕️ " + c.get("disclaimer", ""))


def classify_image(image_bytes: bytes) -> dict:
    """Classify a plant/leaf image (disease + species), API then engine fallback."""
    try:
        files = {"file": ("upload.jpg", image_bytes, "image/jpeg")}
        resp = requests.post(f"{API_URL}/vision/classify", files=files,
                             params={"task": "all"}, timeout=60)
        resp.raise_for_status()
        return resp.json()
    except Exception:
        eng = get_local_engine()
        return {"disease": eng.predict_plant_disease(image_bytes),
                "plant": eng.predict_plant_species(image_bytes),
                "pest": eng.predict_pest(image_bytes)}


@st.cache_data(show_spinner=False, ttl=1800)
def get_advisories(location: str, crop: str | None, stage: str | None) -> dict | None:
    """Fetch fused decision advisories via the API, falling back to the engine."""
    params = {"location": location}
    if crop:
        params["crop"] = crop
    if stage:
        params["stage"] = stage
    try:
        resp = requests.get(f"{API_URL}/advisories", params=params, timeout=60)
        resp.raise_for_status()
        data = resp.json()
        return data if data.get("available") else None
    except Exception:
        pass
    try:
        rep = get_local_engine().get_fusion_advisories(location=location, crop=crop, stage=stage)
        return rep.to_dict() if rep else None
    except Exception:
        return None


@st.cache_data(show_spinner=False, ttl=900)
def get_planetary(location: str) -> dict | None:
    """Fetch planetary positions via the API, falling back to the engine."""
    try:
        resp = requests.get(f"{API_URL}/planetary", params={"location": location}, timeout=30)
        resp.raise_for_status()
        data = resp.json()
        return data if data.get("available") else None
    except Exception:
        pass
    try:
        rep = get_local_engine().get_planetary(location=location)
        return rep.to_dict() if rep else None
    except Exception:
        return None


def render_planetary(rep: dict) -> None:
    mp = rep.get("moon_phase", {})
    if mp:
        st.markdown(f"🌙 **Moon phase:** {mp.get('name', '—')} — "
                    f"{round(mp.get('illumination', 0) * 100)}% illuminated")
    rows = [
        {"Body": b["name"], "Altitude°": b["altitude_deg"], "Azimuth°": b["azimuth_deg"],
         "Dir": b["azimuth_compass"], "Visible": "✅" if b["above_horizon"] else "—"}
        for b in rep.get("bodies", [])
    ]
    if rows:
        st.dataframe(rows, hide_index=True)
    st.caption(f"{rep.get('utc_time')} · {rep.get('source')}")


def render_advisories(report: dict) -> None:
    advs = report.get("advisories", [])
    if not advs:
        st.success("No urgent signals — conditions look unremarkable right now.")
    for a in advs:
        msg = f"**{a['title']}** — {a['action']}  \n_Why: {a['rationale']}_"
        urgency = a.get("urgency")
        (st.error if urgency == "high" else st.warning if urgency == "medium"
         else st.info)(msg)
    img = report.get("imagery", {})
    if img.get("ndvi"):
        st.image(img["ndvi"], caption="NDVI context (MODIS)", width="stretch")
    st.caption(f"Source: {report.get('source')}")


def render_satellite(report: dict) -> None:
    st.markdown(f"**{report['location_name']}** · source: {report['source']}")
    c1, c2 = st.columns(2)
    with c1:
        st.image(report["imagery"]["true_color"],
                 caption=f"True color · {report['truecolor_date']}", width="stretch")
    with c2:
        st.image(report["imagery"]["ndvi"],
                 caption=f"NDVI (greener = denser vegetation) · {report['ndvi_date']}",
                 width="stretch")
    ac = report.get("agroclimate")
    if ac:
        m1, m2, m3 = st.columns(3)
        m1.metric("Avg solar", f"{ac['avg_solar_mj']} MJ/m²/d")
        m2.metric("Temp range", f"{ac['avg_tmin_c']}–{ac['avg_tmax_c']} °C")
        m3.metric(f"Rain ({ac['days']}d)", f"{ac['total_precip_mm']} mm")
        for note in ac.get("notes", []):
            st.info(note)

    nd = report.get("numeric_ndvi")
    if nd and nd.get("latest") is not None:
        st.markdown(f"**Field NDVI** · {nd['source']}")
        n1, n2, n3 = st.columns(3)
        n1.metric("Latest NDVI", nd["latest"], help=f"on {nd.get('latest_date')}")
        n2.metric("Mean NDVI", nd["mean"])
        n3.metric("Trend", str(nd.get("trend")))
        obs = nd.get("observations", [])
        if len(obs) >= 2:
            st.line_chart(
                {"date": [o["date"] for o in obs], "NDVI": [o["ndvi"] for o in obs]},
                x="date", y="NDVI",
            )
        for note in nd.get("notes", []):
            st.success(note)
    elif report.get("numeric_ndvi") is None:
        st.caption("Field-level NDVI not configured — set Earth Engine credentials to "
                   "enable Sentinel-2 (~10 m) NDVI. Showing MODIS imagery + agroclimate.")

    if report["imagery"].get("worldview"):
        st.markdown(f"[🌍 Open interactive view in NASA Worldview]({report['imagery']['worldview']})")


def answer_query(text: str, location: str | None, language: str,
                 include_prices: bool) -> tuple[dict, str]:
    result = query_via_api(text, location, language, include_prices)
    if result is not None:
        return result, "API"
    return query_local(text, location, language, include_prices), "in-process"


def render_prices(prices: dict) -> None:
    s = prices.get("summary") or {}
    if s:
        c1, c2, c3 = st.columns(3)
        c1.metric("Modal min", f"₹{s['modal_min']}")
        c2.metric("Modal avg", f"₹{s['modal_avg']}")
        c3.metric("Modal max", f"₹{s['modal_max']}")
    rows = [
        {"Market": r["market"], "State": r["state"], "Variety": r["variety"],
         "Min": r["min_price"], "Max": r["max_price"], "Modal": r["modal_price"],
         "Date": r["arrival_date"]}
        for r in prices.get("records", [])
    ]
    if rows:
        st.dataframe(rows, hide_index=True)
    for note in prices.get("notes", []):
        st.info(note)
    st.caption(f"Source: {prices.get('source')} (₹ per quintal)")


@st.cache_data(show_spinner=False, ttl=900)
def get_news_items(region: str, query: str | None) -> list[dict]:
    """Latest headlines for a region/topic via the API, falling back to the engine."""
    params: dict = {"limit": 15, "region": region}
    if query:
        params["query"] = query
    try:
        resp = requests.get(f"{API_URL}/news", params=params, timeout=20)
        resp.raise_for_status()
        return resp.json().get("items", [])
    except Exception:
        pass
    try:
        return [i.to_dict()
                for i in get_local_engine().get_news(query=query, region=region, limit=15)]
    except Exception:
        return []


@st.cache_data(show_spinner=False, ttl=900)
def get_current_weather(location: str) -> dict | None:
    """Local weather for the top-bar strip, via API then engine fallback."""
    try:
        r = requests.get(f"{API_URL}/weather", params={"location": location}, timeout=20)
        r.raise_for_status()
        data = r.json()
        return data if data.get("available") else None
    except Exception:
        pass
    try:
        wf = get_local_engine().get_weather(location=location)
        return wf.to_dict() if wf else None
    except Exception:
        return None


def render_weather_strip(w: dict) -> None:
    daily = (w.get("daily") or [{}])[0]
    bits = [f"🌤️ <b>{w.get('location_name', '')}</b>"]
    if w.get("current_temp_c") is not None:
        bits.append(f"{w['current_temp_c']}°C now")
    if w.get("current_humidity") is not None:
        bits.append(f"humidity {w['current_humidity']}%")
    if daily.get("tmin_c") is not None and daily.get("tmax_c") is not None:
        bits.append(f"today {daily['tmin_c']}–{daily['tmax_c']}°C")
    if daily.get("precip_mm") is not None:
        prob = daily.get("precip_prob")
        bits.append(f"rain {daily['precip_mm']}mm"
                    + (f" ({prob}%)" if prob is not None else ""))
    st.markdown(
        "<div style='background:#10324a;color:#e8f4ff;padding:7px 14px;border-radius:8px;"
        "font-size:0.9rem;margin-top:2px;'>" + " &nbsp;·&nbsp; ".join(bits) + "</div>",
        unsafe_allow_html=True,
    )
    advs = w.get("advisories") or []
    if advs:
        st.caption("⚠️ " + advs[0])


@st.cache_data(show_spinner=False, ttl=900)
def get_commodities() -> list[dict]:
    """Commodity prices for the ticker, via API then engine fallback."""
    try:
        resp = requests.get(f"{API_URL}/commodities", timeout=20)
        resp.raise_for_status()
        return resp.json().get("items", [])
    except Exception:
        pass
    try:
        return get_local_engine().get_commodities()
    except Exception:
        return []


def render_commodities_ticker() -> None:
    items = get_commodities()
    if not items:
        return
    chips = []
    for c in items:
        if c.get("price") is None:
            chips.append(f"<span class='cmd'>{c['name']}: <i>n/a</i></span>")
            continue
        chg = c.get("change_pct")
        if chg is None:
            delta = ""
        else:
            color = "#7CFC9A" if chg >= 0 else "#FF8A8A"
            arrow = "▲" if chg >= 0 else "▼"
            delta = f" <span style='color:{color}'>{arrow}{abs(chg)}%</span>"
        chips.append(
            f"<span class='cmd'>{c['name']}: {c.get('currency','')}{c['price']}"
            f"/{c['unit']}{delta}</span>")
    strip = "　　".join(chips)
    st.markdown(
        f"""<div class="cmd-wrap"><div class="cmd-ticker">💹 Commodities:　{strip}</div></div>
        <style>
        .cmd-wrap{{width:100%;overflow:hidden;box-sizing:border-box;background:#2a210e;
          border-radius:8px;margin:4px 0;padding:6px 0;}}
        .cmd-ticker{{display:inline-block;white-space:nowrap;padding-left:100%;
          animation:cmdticker 110s linear infinite;}}
        .cmd-ticker:hover{{animation-play-state:paused;}}
        .cmd-ticker .cmd{{color:#ffe9b8;font-size:0.9rem;margin:0 0.4rem;}}
        @keyframes cmdticker{{0%{{transform:translateX(0)}}100%{{transform:translateX(-100%)}}}}
        </style>""",
        unsafe_allow_html=True,
    )
    st.caption("Gold/Silver/Oil/Coffee: Yahoo Finance. Arecanut/Coconut: Agmarknet "
               "(needs a data.gov.in key).")


def render_top_bar() -> None:
    from agrosense.calendars import datetime_header
    from agrosense.news import LOCALES, TOPICS
    h = datetime_header()
    st.markdown(
        f"""<div style="background:#0e3b2e;color:#eaffea;padding:8px 14px;border-radius:8px;
        display:flex;justify-content:space-between;flex-wrap:wrap;font-size:0.9rem;">
        <span>📅 <b>{h['gregorian']}</b></span>
        <span>🪔 {h['indian_national']}{('  ·  🌙 ' + h['lunar_day']) if h.get('lunar_day') else ''}{('  ·  ⭐ ' + h['panchang']['nakshatra']) if h.get('panchang') else ''}</span>
        <span>🕐 <b>{h['ist_time']} IST</b></span></div>""",
        unsafe_allow_html=True,
    )
    c1, c2 = st.columns(2)
    region = c1.selectbox("News region", options=list(LOCALES.keys()),
                          format_func=lambda k: LOCALES[k][3], index=0, key="news_region")
    topic = c2.selectbox("News topic", options=list(TOPICS.keys()), index=0, key="news_topic")
    items = get_news_items(region, TOPICS[topic])
    if items:
        ticker = "　•　".join(
            f'<a href="{i["link"]}" target="_blank">{i["title"]}</a>' for i in items)
        st.markdown(
            f"""<div class="agro-ticker-wrap"><div class="agro-ticker">
            📰 Google News:　{ticker}</div></div>
            <style>
            .agro-ticker-wrap{{width:100%;overflow:hidden;box-sizing:border-box;
              background:#142b22;border-radius:8px;margin:6px 0 4px 0;padding:6px 0;}}
            .agro-ticker{{display:inline-block;white-space:nowrap;padding-left:100%;
              animation:agroticker 160s linear infinite;}}
            .agro-ticker:hover{{animation-play-state:paused;}}
            .agro-ticker a{{color:#ffd;text-decoration:none;font-size:0.88rem;}}
            .agro-ticker a:hover{{text-decoration:underline;}}
            @keyframes agroticker{{0%{{transform:translateX(0)}}100%{{transform:translateX(-100%)}}}}
            </style>""",
            unsafe_allow_html=True,
        )
    # Commodity prices ticker (slow scroll, directly below the news scroll).
    render_commodities_ticker()

    st.caption("Time updates on refresh. Headlines: Google News top stories.")


render_top_bar()

st.title("🌱 AgroSense")
st.caption("RAG-based agriculture farming advisor — grounded, cited answers (offline POC)")

with st.sidebar:
    st.subheader("About")
    st.write(
        "Answers are composed **only** from the AgroSense knowledge base and shown "
        "with source citations. No external LLM is required in this POC."
    )
    st.divider()
    st.subheader("📍 Location (optional)")
    location = st.text_input(
        "Place name", placeholder="e.g. Belagavi", key="location",
        help="Drives local weather (top bar), satellite, advisories, environment, planets.",
    ).strip() or None
    st.caption("Leave blank to skip weather. Requires internet.")
    show_satellite = st.checkbox(
        "🛰️ Show satellite monitoring", value=False,
        help="MODIS true-color + NDVI imagery (NASA GIBS) and agroclimate (NASA POWER).",
        disabled=location is None,
    )
    show_advisories = st.checkbox(
        "🧭 Show decision advisories", value=False,
        help="Fuse weather + satellite + NDVI into prioritized, actionable advisories.",
        disabled=location is None,
    )
    adv_crop = st.text_input("Crop (for advisories)", value="",
                             placeholder="e.g. Tomato", disabled=location is None).strip() or None
    adv_stage = st.selectbox(
        "Growth stage", options=["(any)", "seedling", "vegetative", "flowering", "maturity"],
        index=0, disabled=location is None,
        help="Crop + stage tune the advisory thresholds and urgencies.")
    adv_stage = None if adv_stage == "(any)" else adv_stage
    show_environment = st.checkbox(
        "🌍 Show location environment", value=False,
        help="Altitude, population, humidity, sunlight, wind, air quality, pollen, groundwater.",
        disabled=location is None,
    )
    show_planetary = st.checkbox(
        "🪐 Show planetary positions", value=False,
        help="Sun, Moon (phase) and planets — altitude/azimuth for the current time.",
        disabled=location is None,
    )
    show_hazards = st.checkbox(
        "⚠️ Show hazards & fires", value=False,
        help="NASA EONET natural-hazard events + NASA FIRMS active fires near the location.",
        disabled=location is None,
    )
    st.divider()
    st.subheader("🌿 Plant image diagnosis")
    plant_image = st.file_uploader("Upload a leaf/plant photo",
                                   type=["jpg", "jpeg", "png"], key="plant_img")
    st.divider()
    st.subheader("🩺 Plant telemedicine")
    tele_crop = st.text_input("Crop", key="tele_crop", placeholder="e.g. Tomato")
    tele_symptoms = st.text_area("Describe symptoms", key="tele_symptoms",
                                 placeholder="e.g. yellow spots spreading on lower leaves")
    run_consult = st.button("🩺 Get consultation", width="stretch")
    st.divider()
    st.subheader("👨‍⚕️ Live agri-doctor")
    farmer_name = st.text_input("Your name", value="Farmer", key="farmer_name")
    consult_channel = st.selectbox("Channel", ["video", "chat", "phone"], key="consult_channel")
    consult_lang = st.selectbox(
        "Preferred language",
        ["(any)", "English", "Hindi", "Kannada", "Telugu", "Tamil", "Gujarati", "Urdu"],
        key="consult_lang")
    request_consult_btn = st.button("👨‍⚕️ Request live consultation", width="stretch")
    st.divider()
    st.subheader("🌐 Language")
    from agrosense.translation import SUPPORTED_LANGUAGES
    lang_code = st.selectbox(
        "Answer language", options=list(SUPPORTED_LANGUAGES.keys()),
        format_func=lambda c: SUPPORTED_LANGUAGES[c], index=0,
        help="Non-English requires a translation backend (argostranslate / deep-translator).",
    )
    st.divider()
    st.subheader("💰 Market prices")
    include_prices = st.checkbox(
        "Attach mandi prices to answers", value=False,
        help="Live Agmarknet prices for the crop in your question. Needs a free data.gov.in key.",
    )
    st.divider()
    st.subheader("Try a sample")
    for q in SAMPLE_QUERIES:
        if st.button(q, width="stretch"):
            st.session_state["pending"] = q

# Local weather — now shown with the location panels (not the top bar).
if location:
    with st.expander(f"🌤️ Local weather — {location}", expanded=True):
        w = get_current_weather(location)
        if w:
            render_weather_strip(w)
        else:
            st.warning("Local weather unavailable (offline or location not found).")

if show_environment and location:
    with st.expander(f"🌍 Location & environment — {location}", expanded=True):
        with st.spinner("Fetching environment profile..."):
            envp = get_environment(location)
        if envp:
            render_environment(envp)
        else:
            st.warning("Environment data unavailable (offline or location not found).")

if show_advisories and location:
    label = location + (f" · {adv_crop}" if adv_crop else "") + (f" · {adv_stage}" if adv_stage else "")
    with st.expander(f"🧭 Decision advisories — {label}", expanded=True):
        with st.spinner("Fusing weather + satellite signals..."):
            adv = get_advisories(location, adv_crop, adv_stage)
        if adv:
            render_advisories(adv)
        else:
            st.warning("Advisories unavailable (offline or location not found).")

if run_consult:
    img_bytes = plant_image.getvalue() if plant_image is not None else None
    with st.spinner("Preparing your plant consultation..."):
        st.session_state["consultation"] = get_local_engine().plant_consultation(
            crop=tele_crop or None, symptoms=tele_symptoms or None,
            image_bytes=img_bytes, location=location)
if st.session_state.get("consultation"):
    with st.expander("🩺 Plant telemedicine consultation", expanded=True):
        render_consultation(st.session_state["consultation"])

if request_consult_btn:
    with st.spinner("Connecting you to an agri-doctor..."):
        st.session_state["consult_session"] = get_local_engine().request_live_consult(
            farmer_name=farmer_name or "Farmer", crop=tele_crop or None,
            symptoms=tele_symptoms or None, channel=consult_channel,
            language=None if consult_lang == "(any)" else consult_lang)
if st.session_state.get("consult_session"):
    cs = st.session_state["consult_session"]
    with st.expander(f"👨‍⚕️ Live agri-doctor consultation — {cs['id']}", expanded=True):
        render_consult_session(cs)
        msg = st.text_input("Message your agri-doctor", key="consult_msg_input")
        if st.button("Send message", key="consult_send") and msg:
            st.session_state["consult_session"] = get_local_engine().add_consult_message(
                cs["id"], "farmer", msg)
            st.rerun()

if plant_image is not None:
    with st.expander("🌿 Plant image diagnosis", expanded=True):
        img_bytes = plant_image.getvalue()
        col_img, col_res = st.columns([1, 2])
        with col_img:
            st.image(img_bytes, caption="Uploaded image", width="stretch")
        with col_res:
            with st.spinner("Analyzing image..."):
                result = classify_image(img_bytes)
            dis = result.get("disease", {})
            pl = result.get("plant", {})
            pest = result.get("pest", {})
            st.markdown(f"**Disease/health:** {dis.get('label', '—')} "
                        f"({round(dis.get('confidence', 0) * 100)}%)  \n"
                        f"<small>{dis.get('note', '')}</small>", unsafe_allow_html=True)
            st.markdown(f"**Plant ID:** {pl.get('label', '—')} "
                        f"({round(pl.get('confidence', 0) * 100)}%)  \n"
                        f"<small>{pl.get('note', '')}</small>", unsafe_allow_html=True)
            st.markdown(f"**Pest ID:** {pest.get('label', '—')} "
                        f"({round(pest.get('confidence', 0) * 100)}%)  \n"
                        f"<small>{pest.get('note', '')}</small>", unsafe_allow_html=True)
            st.caption(f"Backends — disease: {dis.get('backend')}, plant: {pl.get('backend')}, "
                       f"pest: {pest.get('backend')}")

if show_hazards and location:
    with st.expander(f"⚠️ Hazards & fires — {location}", expanded=True):
        with st.spinner("Checking NASA EONET / FIRMS..."):
            hz = get_hazards(location)
        if hz:
            render_hazards(hz)
        else:
            st.warning("Hazard data unavailable (offline or location not found).")

if show_planetary and location:
    with st.expander(f"🪐 Planetary positions — {location}", expanded=True):
        with st.spinner("Computing sky positions..."):
            pl = get_planetary(location)
        if pl:
            render_planetary(pl)
        else:
            st.warning("Planetary data unavailable (location not found).")

if show_satellite and location:
    with st.expander(f"🛰️ Satellite monitoring — {location}", expanded=True):
        with st.spinner("Fetching satellite imagery & agroclimate..."):
            report = get_satellite(location)
        if report:
            render_satellite(report)
        else:
            st.warning("Satellite data unavailable (offline or location not found).")

if "history" not in st.session_state:
    st.session_state["history"] = []

for turn in st.session_state["history"]:
    with st.chat_message(turn["role"]):
        st.markdown(turn["content"])

prompt = st.chat_input("Ask about crops, fertilizer, disease, or pests...")
if "pending" in st.session_state and not prompt:
    prompt = st.session_state.pop("pending")

if prompt:
    st.session_state["history"].append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)

    with st.chat_message("assistant"):
        with st.spinner("Retrieving and composing a grounded answer..."):
            result, mode = answer_query(prompt, location, lang_code, include_prices)
        st.markdown(result["answer"])
        if lang_code != "en" and not result.get("translation_backend"):
            st.warning("Translation backend unavailable — showing the English answer. "
                       "Install argostranslate or deep-translator to enable.")
        prices = result.get("prices")
        if prices:
            with st.expander(f"💰 Market prices — {prices.get('commodity')}", expanded=True):
                render_prices(prices)
        elif include_prices:
            st.caption("Market prices unavailable — set a data.gov.in key "
                       "(AGROSENSE_DATAGOV_API_KEY) to enable.")
        meta = result.get("backends", {})
        st.caption(
            f"via {mode} · {result.get('latency_ms', 0)} ms · "
            f"lang: {result.get('language')} · "
            f"embeddings: {meta.get('embedding')} · vector: {meta.get('vector_store')}"
        )
    st.session_state["history"].append({"role": "assistant", "content": result["answer"]})