"""
农业智能决策系统
基于多因子 Cobb-Douglas 产量模型的作物种植决策支持应用
"""

import streamlit as st
import numpy as np
import plotly.graph_objects as go

# ─── Page Config ────────────────────────────────────────────────────────────
st.set_page_config(
    page_title="种植决策助手",
    page_icon="🌾",
    layout="wide",
    initial_sidebar_state="expanded",
)

# ─── Minimal CSS ─────────────────────────────────────────────────────────────
st.markdown("""
<style>
html, body, [class*="css"] {
    font-family: "PingFang SC", "Microsoft YaHei", "Noto Sans SC", sans-serif;
}
</style>
""", unsafe_allow_html=True)


# ─── Crop Database ───────────────────────────────────────────────────────────
CROPS = {
    "水稻": {
        "emoji": "🌾",
        "optimal": {"ph": (6.0, 7.0), "N": (80, 120), "P": (30, 60), "K": (40, 80),
                    "rainfall": (150, 250), "temp": (22, 30)},
        "base_yield": 7500,  # kg/ha
        "color": "#4a7c59"
    },
    "小麦": {
        "emoji": "🌿",
        "optimal": {"ph": (6.0, 7.5), "N": (60, 100), "P": (20, 50), "K": (30, 60),
                    "rainfall": (60, 120), "temp": (15, 22)},
        "base_yield": 6000,
        "color": "#c69c5d"
    },
    "玉米": {
        "emoji": "🌽",
        "optimal": {"ph": (5.8, 7.0), "N": (100, 150), "P": (40, 70), "K": (60, 100),
                    "rainfall": (100, 180), "temp": (20, 28)},
        "base_yield": 8500,
        "color": "#e8a93f"
    },
    "大豆": {
        "emoji": "🫘",
        "optimal": {"ph": (6.0, 7.0), "N": (20, 50), "P": (30, 60), "K": (40, 80),
                    "rainfall": (80, 150), "temp": (18, 26)},
        "base_yield": 3500,
        "color": "#8b7cb3"
    },
    "油菜": {
        "emoji": "🌻",
        "optimal": {"ph": (6.0, 7.5), "N": (80, 130), "P": (30, 60), "K": (50, 90),
                    "rainfall": (80, 130), "temp": (15, 20)},
        "base_yield": 3000,
        "color": "#d97836"
    },
    "棉花": {
        "emoji": "☁️",
        "optimal": {"ph": (6.0, 8.0), "N": (60, 100), "P": (20, 45), "K": (40, 70),
                    "rainfall": (70, 120), "temp": (25, 32)},
        "base_yield": 4500,
        "color": "#5a6b7c"
    },
}


# ─── Yield Model ─────────────────────────────────────────────────────────────
def compute_factor(value, optimal_low, optimal_high, penalty=0.5):
    """Score 0-1: 1 if in optimal range, decays outside."""
    mid = (optimal_low + optimal_high) / 2
    width = (optimal_high - optimal_low) / 2 + 1e-9
    if optimal_low <= value <= optimal_high:
        return 1.0
    dist = min(abs(value - optimal_low), abs(value - optimal_high))
    return max(0.0, 1.0 - penalty * (dist / width))


def predict_yield(crop_name, ph, N, P, K, rainfall, temp, pesticide, area):
    crop = CROPS[crop_name]
    opt = crop["optimal"]

    f_ph       = compute_factor(ph, *opt["ph"], penalty=0.6)
    f_N        = compute_factor(N, *opt["N"], penalty=0.4)
    f_P        = compute_factor(P, *opt["P"], penalty=0.4)
    f_K        = compute_factor(K, *opt["K"], penalty=0.4)
    f_rain     = compute_factor(rainfall, *opt["rainfall"], penalty=0.5)
    f_temp     = compute_factor(temp, *opt["temp"], penalty=0.7)
    f_pest     = 0.5 + 0.5 * min(pesticide / 100, 1.0)

    # Cobb-Douglas style yield function
    nutrient_idx = (f_N * f_P * f_K) ** (1/3)
    soil_idx     = f_ph
    climate_idx  = (f_rain * f_temp) ** 0.5

    total_factor = soil_idx ** 0.2 * nutrient_idx ** 0.4 * climate_idx ** 0.3 * f_pest ** 0.1

    yield_per_ha = crop["base_yield"] * total_factor
    total_yield  = yield_per_ha * area

    factors = {
        "土壤pH": f_ph, "氮(N)": f_N, "磷(P)": f_P,
        "钾(K)": f_K, "降雨量": f_rain, "温度": f_temp, "农药": f_pest
    }
    return yield_per_ha, total_yield, factors


def rank_crops(ph, N, P, K, rainfall, temp, pesticide, area):
    results = []
    for name in CROPS:
        yph, ytotal, factors = predict_yield(name, ph, N, P, K, rainfall, temp, pesticide, area)
        score = np.mean(list(factors.values()))
        results.append({
            "crop": name,
            "emoji": CROPS[name]["emoji"],
            "yield_ha": yph,
            "total_yield": ytotal,
            "score": score,
            "color": CROPS[name]["color"],
            "factors": factors
        })
    results.sort(key=lambda x: x["score"], reverse=True)
    return results


# ─── Sidebar Inputs ──────────────────────────────────────────────────────────
with st.sidebar:
    st.header("🌾 种植决策助手")
    st.caption("根据土壤和气候，推荐适宜作物")
    st.divider()

    st.subheader("🧪 土壤参数")
    col1, col2 = st.columns(2)
    with col1:
        ph = st.slider("pH 值", 4.0, 9.0, 6.5, 0.1)
        N  = st.slider("氮 N (mg/kg)", 0, 200, 90, 5)
    with col2:
        P  = st.slider("磷 P (mg/kg)", 0, 100, 45, 5)
        K  = st.slider("钾 K (mg/kg)", 0, 150, 60, 5)

    st.subheader("🌦 气象数据")
    col3, col4 = st.columns(2)
    with col3:
        rainfall = st.slider("降雨量 (mm/月)", 0, 400, 120, 10)
    with col4:
        temp = st.slider("温度 (°C)", 0, 45, 22, 1)

    st.subheader("🌱 种植参数")
    area      = st.number_input("种植面积 (公顷)", 0.1, 10000.0, 100.0, 10.0)
    pesticide = st.slider("农药用量 (kg/ha)", 0, 200, 50, 5)

    st.subheader("🎯 目标作物")
    selected_crop = st.selectbox(
        "选择分析作物",
        list(CROPS.keys()),
        format_func=lambda x: f"{CROPS[x]['emoji']} {x}"
    )


# ─── Compute ──────────────────────────────────────────────────────────────────
yph, ytotal, factors = predict_yield(selected_crop, ph, N, P, K, rainfall, temp, pesticide, area)
rankings = rank_crops(ph, N, P, K, rainfall, temp, pesticide, area)
best_crop = rankings[0]


# ─── Main Layout ─────────────────────────────────────────────────────────────
st.title("🌾 种植决策助手")
st.caption("输入土壤与气象条件，获得作物产量预测与种植建议")

# KPI row
overall = np.mean(list(factors.values()))
k1, k2, k3, k4 = st.columns(4)
k1.metric(f"{CROPS[selected_crop]['emoji']} {selected_crop} 单产", f"{yph:,.0f} kg/ha")
k2.metric(f"📦 {area:.0f} 公顷总产", f"{ytotal/1000:,.1f} 吨")
k3.metric("🎯 环境匹配指数", f"{overall*100:.1f}%")
k4.metric("🏆 最优推荐作物", f"{best_crop['emoji']} {best_crop['crop']}", f"匹配度 {best_crop['score']*100:.0f}%")

# ─── Charts Row ──────────────────────────────────────────────────────────────
col_left, col_right = st.columns([3, 2])

with col_left:
    st.subheader("📊 影响因子雷达图")

    factor_names = list(factors.keys())
    factor_vals  = [round(v * 100, 1) for v in factors.values()]
    factor_names_closed = factor_names + [factor_names[0]]
    factor_vals_closed  = factor_vals + [factor_vals[0]]

    fig_radar = go.Figure()
    fig_radar.add_trace(go.Scatterpolar(
        r=factor_vals_closed,
        theta=factor_names_closed,
        fill='toself',
        fillcolor='rgba(74, 124, 89, 0.15)',
        line=dict(color='#4a7c59', width=2),
        name=selected_crop,
    ))
    fig_radar.add_trace(go.Scatterpolar(
        r=[100]*len(factor_names_closed),
        theta=factor_names_closed,
        line=dict(color='rgba(0,0,0,0.15)', width=1, dash='dot'),
        mode='lines',
        name='理想值',
    ))
    fig_radar.update_layout(
        polar=dict(
            bgcolor='rgba(0,0,0,0)',
            radialaxis=dict(range=[0, 100], showticklabels=True,
                            tickfont=dict(color='#5a5a5a', size=9),
                            gridcolor='rgba(0,0,0,0.08)'),
            angularaxis=dict(tickfont=dict(color='#2c2c2c', size=11),
                             gridcolor='rgba(0,0,0,0.1)'),
        ),
        paper_bgcolor='rgba(0,0,0,0)',
        plot_bgcolor='rgba(0,0,0,0)',
        font=dict(color='#2c2c2c'),
        legend=dict(orientation='h', y=-0.12, font=dict(size=10)),
        margin=dict(t=20, b=40, l=40, r=40),
        height=320,
    )
    st.plotly_chart(fig_radar, use_container_width=True)

with col_right:
    st.subheader("🏅 作物推荐排行")
    for i, r in enumerate(rankings[:4]):
        rank_icons = ["🥇", "🥈", "🥉", "4️⃣"]
        with st.container(border=True):
            c1, c2 = st.columns([3, 1])
            c1.markdown(f"**{rank_icons[i]} {r['emoji']} {r['crop']}**")
            c2.markdown(f"<div style='text-align:right; color:#4a7c59; font-weight:600;'>{r['score']*100:.1f}%</div>", unsafe_allow_html=True)
            st.progress(int(r['score'] * 100), text=f"{r['yield_ha']:,.0f} kg/ha · 总产 {r['total_yield']/1000:,.1f} 吨")

# ─── Sensitivity Analysis ─────────────────────────────────────────────────────
st.subheader("📈 产量敏感性分析")

sa_col1, sa_col2 = st.columns(2)

with sa_col1:
    N_range = np.linspace(0, 200, 60)
    y_N = [predict_yield(selected_crop, ph, n, P, K, rainfall, temp, pesticide, 1)[0] for n in N_range]

    fig_N = go.Figure()
    fig_N.add_trace(go.Scatter(
        x=N_range, y=y_N,
        mode='lines', line=dict(color='#4a7c59', width=2.5),
        fill='tozeroy', fillcolor='rgba(74, 124, 89, 0.08)',
        name='产量'
    ))
    fig_N.add_vline(x=N, line=dict(color='#d4a574', width=1.5, dash='dot'),
                    annotation_text=f"当前 {N}", annotation_font_color='#7c5e42')
    fig_N.update_layout(
        title=dict(text="氮肥用量 vs 产量", font=dict(color='#5a5a5a', size=12)),
        xaxis=dict(title="氮 N (mg/kg)", color='#5a5a5a', gridcolor='rgba(0,0,0,0.06)'),
        yaxis=dict(title="产量 (kg/ha)", color='#5a5a5a', gridcolor='rgba(0,0,0,0.06)'),
        paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)',
        font=dict(color='#2c2c2c', size=10),
        margin=dict(t=36, b=36, l=50, r=20), height=220,
        showlegend=False,
    )
    st.plotly_chart(fig_N, use_container_width=True)

with sa_col2:
    rain_range = np.linspace(0, 400, 60)
    y_rain = [predict_yield(selected_crop, ph, N, P, K, r, temp, pesticide, 1)[0] for r in rain_range]

    fig_R = go.Figure()
    fig_R.add_trace(go.Scatter(
        x=rain_range, y=y_rain,
        mode='lines', line=dict(color='#5a8f9e', width=2.5),
        fill='tozeroy', fillcolor='rgba(90, 143, 158, 0.08)',
        name='产量'
    ))
    fig_R.add_vline(x=rainfall, line=dict(color='#d4a574', width=1.5, dash='dot'),
                    annotation_text=f"当前 {rainfall}mm", annotation_font_color='#7c5e42')
    fig_R.update_layout(
        title=dict(text="月降雨量 vs 产量", font=dict(color='#5a5a5a', size=12)),
        xaxis=dict(title="降雨量 (mm/月)", color='#5a5a5a', gridcolor='rgba(0,0,0,0.06)'),
        yaxis=dict(title="产量 (kg/ha)", color='#5a5a5a', gridcolor='rgba(0,0,0,0.06)'),
        paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)',
        font=dict(color='#2c2c2c', size=10),
        margin=dict(t=36, b=36, l=50, r=20), height=220,
        showlegend=False,
    )
    st.plotly_chart(fig_R, use_container_width=True)

# ─── All Crops Comparison Bar Chart ───────────────────────────────────────────
st.subheader("🌐 全作物产量对比")

crop_names   = [f"{r['emoji']} {r['crop']}" for r in rankings]
crop_yields  = [r['yield_ha'] for r in rankings]
crop_colors  = [r['color'] for r in rankings]

fig_bar = go.Figure()
fig_bar.add_trace(go.Bar(
    x=crop_names, y=crop_yields,
    marker=dict(color=crop_colors, opacity=0.85,
                line=dict(color='rgba(0,0,0,0.08)', width=1)),
    text=[f"{y:,.0f}" for y in crop_yields],
    textposition='outside',
    textfont=dict(color='#5a5a5a', size=10),
))
fig_bar.update_layout(
    xaxis=dict(color='#5a5a5a', gridcolor='rgba(0,0,0,0.04)'),
    yaxis=dict(title="预期产量 (kg/ha)", color='#5a5a5a', gridcolor='rgba(0,0,0,0.06)'),
    paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)',
    font=dict(color='#2c2c2c', size=11),
    margin=dict(t=20, b=30, l=60, r=20), height=260,
    showlegend=False,
)
st.plotly_chart(fig_bar, use_container_width=True)

# ─── Advisory Panel ───────────────────────────────────────────────────────────
st.subheader("💡 种植建议")
adv1, adv2 = st.columns(2)

with adv1:
    crop_opt = CROPS[selected_crop]["optimal"]

    if not (crop_opt["ph"][0] <= ph <= crop_opt["ph"][1]):
        st.warning(f"pH {ph} 偏离 {selected_crop} 适宜范围 {crop_opt['ph']}，建议{'施石灰' if ph < crop_opt['ph'][0] else '施硫磺'}调节")
    else:
        st.success(f"土壤 pH {ph} 处于 {selected_crop} 适宜范围内")

    if N < crop_opt["N"][0]:
        st.warning(f"氮肥不足（{N} vs 建议 {crop_opt['N'][0]}-{crop_opt['N'][1]} mg/kg），建议追施尿素")
    elif N > crop_opt["N"][1]:
        st.warning(f"氮肥过量（{N} mg/kg），可能造成徒长，建议减施")
    else:
        st.success(f"氮肥水平 {N} mg/kg 适宜")

    if rainfall < crop_opt["rainfall"][0]:
        st.warning(f"降雨量不足，建议增加灌溉（缺水 {crop_opt['rainfall'][0]-rainfall} mm）")
    elif rainfall > crop_opt["rainfall"][1]:
        st.warning(f"降雨量偏多，注意防涝排水")
    else:
        st.success(f"降雨量 {rainfall}mm 适合 {selected_crop} 生长")

with adv2:
    suitable = [r for r in rankings if r['score'] > 0.6]
    if suitable:
        st.info("当前环境参数下适宜种植：" + "、".join([f"{r['emoji']} {r['crop']} ({r['score']*100:.0f}%)" for r in suitable]))
    else:
        st.info("当前环境参数下暂无特别适宜的作物")

    with st.container(border=True):
        st.markdown(f"**最优方案：{best_crop['emoji']} {best_crop['crop']}**")
        st.markdown(f"- 预期单产：**{best_crop['yield_ha']:,.0f} kg/ha**")
        st.markdown(f"- {area:.0f} 公顷总产：**{best_crop['total_yield']/1000:,.1f} 吨**")

st.divider()
st.caption("种植决策助手 · 基于 Cobb-Douglas 多因子产量模型 · 仅供参考")