feat: 初始化 YieldSmart 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。

  新增文件：
  - app.py: Streamlit 主应用，包含产量预测模型、多作物数据库、
    雷达图/敏感性分析可视化、作物推荐排行及智能建议面板
  - main.py: 入口文件
  - pyproject.toml: 项目配置（Python 3.14+，依赖 streamlit/plotly/pandas/numpy）
  - Dockerfile: 基于 uv 镜像的容器化部署配置
  - justfile: 任务自动化（运行/格式化/检查/清理）
  - .gitignore: Python/IDE/缓存忽略规则

.gitignore

@@ -0,0 +1,29 @@
+# Python
+__pycache__/
+*.py[cod]
+*.egg-info/
+dist/
+build/
+
+# Virtual environment
+.venv/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Streamlit
+.streamlit_cache/
+
+# Ruff
+.ruff_cache/
+
+# UV
+*.lock
+
+# OS
+.DS_Store
+Thumbs.db
+/.doc/

Dockerfile

@@ -0,0 +1,43 @@
+# 使用 uv 官方镜像作为基础镜像（已包含 Python 3.14 和 uv）
+FROM 172.16.102.3:30648/astral-sh/uv:python3.14-bookworm
+
+# 设置工作目录
+WORKDIR /app
+
+# 配置 apt 使用阿里云镜像源
+RUN sed -i 's/httpredir.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources
+
+# 安装系统依赖
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# 复制项目配置文件和锁定文件
+COPY pyproject.toml justfile uv.lock ./
+
+# 配置 uv 使用阿里云镜像源（通过环境变量）
+ENV UV_INDEX_URL=https://mirrors.aliyun.com/pypi/simple/
+
+# 安装 Python 依赖（使用 uv，锁定版本）
+RUN uv sync --frozen --no-dev
+
+# 复制应用代码和其他文件
+COPY . .
+
+# 暴露 Streamlit 默认端口
+EXPOSE 8000
+
+# 设置环境变量
+ENV STREAMLIT_SERVER_PORT=8000 \
+    STREAMLIT_SERVER_ADDRESS=0.0.0.0 \
+    STREAMLIT_SERVER_ENABLE_XSRF_PROTECTION=false \
+    STREAMLIT_SERVER_ENABLE_CORS=false \
+    STREAMLIT_SERVER_HEADLESS=true \
+    STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
+
+# 健康检查
+HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
+    CMD curl -f http://localhost:8000/_stcore/health || exit 1
+
+# 运行 Streamlit 应用
+CMD ["uv", "run", "streamlit", "run", "app.py", "--server.port=8000", "--server.address=0.0.0.0"]

README.md

@@ -1,3 +1,89 @@
-# yield-smart-app
+# YieldSmart 农业智能决策系统
 
-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。
+
+## 功能特性
+
+- 🌾 多作物产量预测（水稻、小麦、玉米、大豆、油菜、棉花）
+- 📊 影响因子雷达图可视化
+- 🏅 作物推荐智能排行
+- 📈 产量敏感性分析（氮肥/降雨量）
+- 💡 智能种植建议与环境匹配评估
+
+## 技术栈
+
+- Python 3.14+
+- Streamlit 1.52.1
+- Plotly 6.5.0
+- Pandas 2.3.3
+- NumPy 2.3.5
+
+## 快速开始
+
+### 使用 uv（推荐）
+
+```bash
+# 安装依赖
+uv sync
+
+# 运行应用
+uv run streamlit run app.py
+```
+
+### 使用传统方式
+
+```bash
+# 创建虚拟环境
+python -m venv .venv
+source .venv/bin/activate
+
+# 安装依赖
+pip install -r requirements.txt
+
+# 运行应用
+streamlit run app.py
+```
+
+## 项目结构
+
+```
+YieldSmart/
+├── app.py              # 主应用文件（Streamlit）
+├── main.py             # 入口文件
+├── pyproject.toml      # 项目配置
+├── justfile            # 任务自动化
+├── Dockerfile          # Docker 配置
+└── README.md           # 项目文档
+```
+
+## 使用 just
+
+项目使用 justfile 进行任务管理：
+
+```bash
+# 查看所有可用命令
+just --list
+
+# 运行应用
+just run
+
+# 代码格式化
+just format
+
+# 代码检查
+just check
+```
+
+## Docker 部署
+
+```bash
+# 构建镜像
+docker build -t yieldsmart .
+
+# 运行容器
+docker run -p 8000:8000 yieldsmart
+```
+
+## 许可证
+
+MIT License

app.py

@@ -0,0 +1,587 @@
+"""
+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 pandas as pd
+import plotly.graph_objects as go
+import plotly.express as px
+from plotly.subplots import make_subplots
+
+# ─── Page Config ────────────────────────────────────────────────────────────
+st.set_page_config(
+    page_title="农业智能决策系统",
+    page_icon="🌾",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+# ─── Custom CSS ──────────────────────────────────────────────────────────────
+st.markdown("""
+<style>
+@import url('https://fonts.googleapis.com/css2?family=Noto+Serif+SC:wght@400;600;700&family=JetBrains+Mono:wght@400;500&display=swap');
+
+:root {
+    --bg-dark: #0a1628;
+    --bg-card: #0f2040;
+    --accent-green: #4ade80;
+    --accent-gold: #f59e0b;
+    --accent-blue: #38bdf8;
+    --text-primary: #e2e8f0;
+    --text-muted: #64748b;
+    --border: rgba(74, 222, 128, 0.2);
+}
+
+html, body, [class*="css"] {
+    font-family: 'Noto Serif SC', serif;
+    background-color: var(--bg-dark);
+    color: var(--text-primary);
+}
+
+.stApp {
+    background: linear-gradient(135deg, #0a1628 0%, #0d1f3c 50%, #091520 100%);
+}
+
+/* Sidebar */
+[data-testid="stSidebar"] {
+    background: linear-gradient(180deg, #0f2040 0%, #0a1628 100%);
+    border-right: 1px solid var(--border);
+}
+[data-testid="stSidebar"] .stSlider label,
+[data-testid="stSidebar"] .stNumberInput label,
+[data-testid="stSidebar"] .stSelectbox label {
+    color: var(--accent-green) !important;
+    font-size: 0.82rem;
+    font-family: 'JetBrains Mono', monospace;
+    letter-spacing: 0.05em;
+}
+
+/* Metric cards */
+.metric-card {
+    background: linear-gradient(135deg, #0f2040, #132b55);
+    border: 1px solid var(--border);
+    border-radius: 12px;
+    padding: 20px 24px;
+    text-align: center;
+    position: relative;
+    overflow: hidden;
+}
+.metric-card::before {
+    content: '';
+    position: absolute;
+    top: 0; left: 0; right: 0;
+    height: 2px;
+    background: linear-gradient(90deg, var(--accent-green), var(--accent-blue));
+}
+.metric-value {
+    font-family: 'JetBrains Mono', monospace;
+    font-size: 2.2rem;
+    font-weight: 700;
+    color: var(--accent-green);
+    line-height: 1;
+}
+.metric-unit {
+    font-size: 0.85rem;
+    color: var(--text-muted);
+    margin-top: 4px;
+}
+.metric-label {
+    font-size: 0.9rem;
+    color: var(--text-primary);
+    margin-top: 8px;
+}
+
+/* Section headers */
+.section-header {
+    font-size: 0.75rem;
+    font-family: 'JetBrains Mono', monospace;
+    letter-spacing: 0.15em;
+    color: var(--accent-gold);
+    text-transform: uppercase;
+    border-bottom: 1px solid rgba(245,158,11,0.3);
+    padding-bottom: 6px;
+    margin-bottom: 16px;
+    margin-top: 24px;
+}
+
+/* Crop badge */
+.crop-badge {
+    display: inline-block;
+    background: linear-gradient(135deg, rgba(74,222,128,0.15), rgba(56,189,248,0.15));
+    border: 1px solid var(--accent-green);
+    border-radius: 6px;
+    padding: 4px 12px;
+    font-family: 'JetBrains Mono', monospace;
+    font-size: 0.78rem;
+    color: var(--accent-green);
+    margin: 3px;
+}
+
+/* Recommendation card */
+.rec-card {
+    background: linear-gradient(135deg, rgba(74,222,128,0.08), rgba(56,189,248,0.05));
+    border: 1px solid rgba(74,222,128,0.3);
+    border-radius: 12px;
+    padding: 18px 22px;
+    margin: 10px 0;
+}
+.rec-rank {
+    font-family: 'JetBrains Mono', monospace;
+    font-size: 1.5rem;
+    color: var(--accent-gold);
+    font-weight: 700;
+}
+.rec-crop {
+    font-size: 1.1rem;
+    color: var(--text-primary);
+    font-weight: 600;
+}
+.rec-score {
+    font-family: 'JetBrains Mono', monospace;
+    font-size: 0.85rem;
+    color: var(--accent-blue);
+}
+
+/* Hero title */
+.hero-title {
+    font-size: 2rem;
+    font-weight: 700;
+    background: linear-gradient(135deg, var(--accent-green), var(--accent-blue));
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;
+    line-height: 1.2;
+}
+.hero-sub {
+    font-family: 'JetBrains Mono', monospace;
+    font-size: 0.8rem;
+    color: var(--text-muted);
+    letter-spacing: 0.1em;
+    margin-top: 4px;
+}
+
+/* Alert boxes */
+.alert-good {
+    background: rgba(74,222,128,0.1);
+    border-left: 3px solid var(--accent-green);
+    border-radius: 0 8px 8px 0;
+    padding: 12px 16px;
+    margin: 8px 0;
+    font-size: 0.9rem;
+}
+.alert-warn {
+    background: rgba(245,158,11,0.1);
+    border-left: 3px solid var(--accent-gold);
+    border-radius: 0 8px 8px 0;
+    padding: 12px 16px;
+    margin: 8px 0;
+    font-size: 0.9rem;
+}
+
+/* Override streamlit slider colors */
+.stSlider [data-baseweb="slider"] [data-testid="stTickBarMin"],
+.stSlider [data-baseweb="slider"] [data-testid="stTickBarMax"] {
+    color: var(--text-muted);
+}
+</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": "#4ade80"
+    },
+    "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": "#f59e0b"
+    },
+    "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": "#fbbf24"
+    },
+    "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": "#a78bfa"
+    },
+    "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": "#f97316"
+    },
+    "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": "#e2e8f0"
+    },
+}
+
+
+# ─── 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.markdown('<div class="hero-title">🌾 农业决策</div>', unsafe_allow_html=True)
+    st.markdown('<div class="hero-sub">SMART FARMING SYSTEM v2.0</div>', unsafe_allow_html=True)
+    st.markdown("---")
+
+    st.markdown('<div class="section-header">🧪 土壤参数</div>', unsafe_allow_html=True)
+    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.markdown('<div class="section-header">🌦 气象数据</div>', unsafe_allow_html=True)
+    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.markdown('<div class="section-header">🌱 种植参数</div>', unsafe_allow_html=True)
+    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.markdown('<div class="section-header">🎯 目标作物</div>', unsafe_allow_html=True)
+    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.markdown(f"""
+<div style="display:flex; align-items:baseline; gap:16px; margin-bottom:4px;">
+  <div class="hero-title">农业智能决策系统</div>
+</div>
+<div class="hero-sub">YIELD = f(SOIL · WEATHER · PESTICIDE) &nbsp;|&nbsp; 基于多因子 Cobb-Douglas 产量模型</div>
+""", unsafe_allow_html=True)
+
+st.markdown("<br>", unsafe_allow_html=True)
+
+# KPI row
+k1, k2, k3, k4 = st.columns(4)
+with k1:
+    st.markdown(f"""
+    <div class="metric-card">
+        <div class="metric-value">{yph:,.0f}</div>
+        <div class="metric-unit">kg / 公顷</div>
+        <div class="metric-label">{CROPS[selected_crop]['emoji']} {selected_crop} 单产</div>
+    </div>""", unsafe_allow_html=True)
+with k2:
+    st.markdown(f"""
+    <div class="metric-card">
+        <div class="metric-value">{ytotal/1000:,.1f}</div>
+        <div class="metric-unit">吨 / 总产量</div>
+        <div class="metric-label">📦 {area:.0f} 公顷总产</div>
+    </div>""", unsafe_allow_html=True)
+with k3:
+    overall = np.mean(list(factors.values()))
+    st.markdown(f"""
+    <div class="metric-card">
+        <div class="metric-value">{overall*100:.1f}%</div>
+        <div class="metric-unit">综合适宜度</div>
+        <div class="metric-label">🎯 环境匹配指数</div>
+    </div>""", unsafe_allow_html=True)
+with k4:
+    st.markdown(f"""
+    <div class="metric-card">
+        <div class="metric-value">{best_crop['emoji']}</div>
+        <div class="metric-unit">{best_crop['crop']} ({best_crop['score']*100:.0f}%)</div>
+        <div class="metric-label">🏆 最优推荐作物</div>
+    </div>""", unsafe_allow_html=True)
+
+st.markdown("<br>", unsafe_allow_html=True)
+
+# ─── Charts Row ──────────────────────────────────────────────────────────────
+col_left, col_right = st.columns([3, 2])
+
+with col_left:
+    st.markdown('<div class="section-header">📊 影响因子雷达图</div>', unsafe_allow_html=True)
+
+    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(74,222,128,0.15)',
+        line=dict(color='#4ade80', 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(255,255,255,0.1)', 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='#64748b', size=9),
+                            gridcolor='rgba(255,255,255,0.06)'),
+            angularaxis=dict(tickfont=dict(color='#e2e8f0', size=11),
+                             gridcolor='rgba(255,255,255,0.08)'),
+        ),
+        paper_bgcolor='rgba(0,0,0,0)',
+        plot_bgcolor='rgba(0,0,0,0)',
+        font=dict(color='#e2e8f0'),
+        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.markdown('<div class="section-header">🏅 作物推荐排行</div>', unsafe_allow_html=True)
+    for i, r in enumerate(rankings[:4]):
+        rank_icons = ["🥇", "🥈", "🥉", "4️⃣"]
+        bar_width  = int(r['score'] * 100)
+        bar_color  = r['color']
+        st.markdown(f"""
+        <div class="rec-card" style="margin-bottom:8px;">
+          <div style="display:flex; justify-content:space-between; align-items:center;">
+            <div>
+              <span class="rec-rank">{rank_icons[i]}</span>
+              <span class="rec-crop" style="margin-left:8px;">{r['emoji']} {r['crop']}</span>
+            </div>
+            <span class="rec-score">{r['score']*100:.1f}%</span>
+          </div>
+          <div style="background:rgba(255,255,255,0.06); border-radius:4px; height:4px; margin-top:8px; overflow:hidden;">
+            <div style="width:{bar_width}%; height:100%; background:{bar_color}; border-radius:4px;"></div>
+          </div>
+          <div style="font-size:0.78rem; color:#64748b; margin-top:4px; font-family:'JetBrains Mono',monospace;">
+            {r['yield_ha']:,.0f} kg/ha &nbsp;·&nbsp; 总产 {r['total_yield']/1000:,.1f} 吨
+          </div>
+        </div>""", unsafe_allow_html=True)
+
+# ─── Sensitivity Analysis ─────────────────────────────────────────────────────
+st.markdown('<div class="section-header">📈 产量敏感性分析</div>', unsafe_allow_html=True)
+
+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='#4ade80', width=2.5),
+        fill='tozeroy', fillcolor='rgba(74,222,128,0.08)',
+        name='产量'
+    ))
+    fig_N.add_vline(x=N, line=dict(color='#f59e0b', width=1.5, dash='dot'),
+                    annotation_text=f"当前 {N}", annotation_font_color='#f59e0b')
+    fig_N.update_layout(
+        title=dict(text="氮肥用量 vs 产量", font=dict(color='#94a3b8', size=12)),
+        xaxis=dict(title="氮 N (mg/kg)", color='#64748b', gridcolor='rgba(255,255,255,0.05)'),
+        yaxis=dict(title="产量 (kg/ha)", color='#64748b', gridcolor='rgba(255,255,255,0.05)'),
+        paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)',
+        font=dict(color='#e2e8f0', 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='#38bdf8', width=2.5),
+        fill='tozeroy', fillcolor='rgba(56,189,248,0.08)',
+        name='产量'
+    ))
+    fig_R.add_vline(x=rainfall, line=dict(color='#f59e0b', width=1.5, dash='dot'),
+                    annotation_text=f"当前 {rainfall}mm", annotation_font_color='#f59e0b')
+    fig_R.update_layout(
+        title=dict(text="月降雨量 vs 产量", font=dict(color='#94a3b8', size=12)),
+        xaxis=dict(title="降雨量 (mm/月)", color='#64748b', gridcolor='rgba(255,255,255,0.05)'),
+        yaxis=dict(title="产量 (kg/ha)", color='#64748b', gridcolor='rgba(255,255,255,0.05)'),
+        paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)',
+        font=dict(color='#e2e8f0', 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.markdown('<div class="section-header">🌐 全作物产量对比</div>', unsafe_allow_html=True)
+
+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(255,255,255,0.2)', width=1)),
+    text=[f"{y:,.0f}" for y in crop_yields],
+    textposition='outside',
+    textfont=dict(color='#94a3b8', size=10, family='JetBrains Mono'),
+))
+fig_bar.update_layout(
+    xaxis=dict(color='#64748b', gridcolor='rgba(255,255,255,0.04)'),
+    yaxis=dict(title="预期产量 (kg/ha)", color='#64748b', gridcolor='rgba(255,255,255,0.05)'),
+    paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)',
+    font=dict(color='#e2e8f0', size=11),
+    margin=dict(t=20, b=30, l=60, r=20), height=240,
+    showlegend=False,
+)
+st.plotly_chart(fig_bar, use_container_width=True)
+
+# ─── Advisory Panel ───────────────────────────────────────────────────────────
+st.markdown('<div class="section-header">💡 智能建议</div>', unsafe_allow_html=True)
+adv1, adv2 = st.columns(2)
+
+with adv1:
+    crop_opt = CROPS[selected_crop]["optimal"]
+    advisories = []
+
+    if not (crop_opt["ph"][0] <= ph <= crop_opt["ph"][1]):
+        advisories.append(("warn", 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:
+        advisories.append(("good", f"土壤 pH {ph} 处于 {selected_crop} 适宜范围内 ✓"))
+
+    if N < crop_opt["N"][0]:
+        advisories.append(("warn", 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]:
+        advisories.append(("warn", f"氮肥过量（{N} mg/kg），可能造成徒长，建议减施"))
+    else:
+        advisories.append(("good", f"氮肥水平 {N} mg/kg 适宜 ✓"))
+
+    if rainfall < crop_opt["rainfall"][0]:
+        advisories.append(("warn", 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]:
+        advisories.append(("warn", f"降雨量偏多，注意防涝排水"))
+    else:
+        advisories.append(("good", f"降雨量 {rainfall}mm 适合 {selected_crop} 生长 ✓"))
+
+    for typ, msg in advisories:
+        css_class = "alert-good" if typ == "good" else "alert-warn"
+        st.markdown(f'<div class="{css_class}">{msg}</div>', unsafe_allow_html=True)
+
+with adv2:
+    st.markdown(f"""
+    <div class="rec-card">
+      <div style="font-size:0.82rem; color:#64748b; font-family:'JetBrains Mono',monospace; margin-bottom:12px;">
+        当前环境参数下适宜种植：
+      </div>
+    """, unsafe_allow_html=True)
+    badges = "".join([
+        f'<span class="crop-badge">{r["emoji"]} {r["crop"]} {r["score"]*100:.0f}%</span>'
+        for r in rankings if r['score'] > 0.6
+    ])
+    st.markdown(f'{badges}</div>', unsafe_allow_html=True)
+
+    st.markdown(f"""
+    <div style="margin-top:16px; font-size:0.85rem; color:#94a3b8; line-height:1.7;">
+    <b style="color:#4ade80;">最优方案：</b>{best_crop['emoji']} {best_crop['crop']}<br>
+    预期单产：<span style="font-family:'JetBrains Mono',monospace; color:#38bdf8;">{best_crop['yield_ha']:,.0f} kg/ha</span><br>
+    {area:.0f}公顷总产：<span style="font-family:'JetBrains Mono',monospace; color:#38bdf8;">{best_crop['total_yield']/1000:,.1f} 吨</span>
+    </div>
+    """, unsafe_allow_html=True)
+
+# ─── Footer ───────────────────────────────────────────────────────────────────
+st.markdown("<br>", unsafe_allow_html=True)
+st.markdown("""
+<div style="text-align:center; font-family:'JetBrains Mono',monospace; font-size:0.72rem;
+            color:#334155; padding:16px; border-top:1px solid rgba(74,222,128,0.1);">
+  YIELD = f(Soil, Weather, Pesticide) &nbsp;|&nbsp; Cobb-Douglas Multi-Factor Model &nbsp;|&nbsp; 农业智能决策系统
+</div>
+""", unsafe_allow_html=True)

justfile

@@ -0,0 +1,30 @@
+# Justfile for YieldSmart 农业智能决策系统
+# Use `just <command>` to run tasks
+
+# Default task: show available commands
+default:
+    just --list
+
+# Run the Streamlit app
+run:
+    uv run streamlit run app.py
+
+# Install dependencies
+install:
+    uv add streamlit ruff plotly pandas numpy
+
+# Format code with ruff
+format:
+    uv run ruff format .
+
+# Check code with ruff
+check:
+    uv run ruff check .
+
+# Run all checks and formatting
+lint:
+    just format && just check
+
+# Clean up cache files
+clean:
+    rm -rf __pycache__ .ruff_cache .streamlit_cache

main.py

@@ -0,0 +1,6 @@
+def main():
+    print("Hello from YieldSmart!")
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -0,0 +1,17 @@
+[project]
+name = "yieldsmart"
+version = "0.1.0"
+description = "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"
+readme = "README.md"
+requires-python = ">=3.14"
+dependencies = [
+    "numpy>=2.3.5",
+    "pandas>=2.3.3",
+    "plotly>=6.5.0",
+    "ruff>=0.14.8",
+    "streamlit>=1.52.1",
+]
+
+[[tool.uv.index]]
+url = "https://mirrors.aliyun.com/pypi/simple"
+default = true