refactor: 精简 UI 自定义样式，使用 Streamlit 原生组件替代 HTML 卡片

- 将大量自定义 CSS（主题色、卡片、徽章、alert 等）精简为仅保留字体设置 - sidebar 标题/副标题/分隔线改用 st.header、st.caption、st.divider - KPI 指标卡片改用 st.metric - 作物推荐排行改用 st.container + st.progress - 种植建议的 alert 框改用 st.warning / st.success / st.info - 删除自定义 HTML footer，改用 st.caption
refactor: 整体 UI 主题改版为浅色自然风格，并优化文案与清理代码
2026-04-14 17:35:42 +08:00 · 2026-04-14 11:24:08 +08:00 · 2026-04-13 17:51:36 +08:00 · 2026-04-13 17:45:17 +08:00 · 2026-04-13 17:45:17 +08:00 · 2026-04-13 17:45:17 +08:00
8 changed files with 1222 additions and 2 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1,26 @@
+# Python
+__pycache__/
+*.py[cod]
+*.egg-info/
+dist/
+build/
+
+# Virtual environment
+.venv/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Streamlit
+.streamlit_cache/
+
+# Ruff
+.ruff_cache/
+
+# OS
+.DS_Store
+Thumbs.db
+/.doc/
--- a/44
+++ b/44
@@ -0,0 +1,44 @@
+# 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/ \
+    UV_HTTP_TIMEOUT=300
+
+# 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"]
--- a/README.md
+++ b/README.md
@@ -1,3 +1,89 @@
-# yield-smart-app
+# yield-smart-app 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
+
+```
+yield-smart-app/
+├── 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 yield-smart-app .
+
+# 运行容器
+docker run -p 8000:8000 yield-smart-app
+```
+
+## 许可证
+
+MIT License
--- a/app.py
+++ b/app.py
@@ -0,0 +1,358 @@
+"""
+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")
--- a/30
+++ b/30
@@ -0,0 +1,30 @@
+# Justfile for yield-smart-app 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
--- a/main.py
+++ b/main.py
@@ -0,0 +1,6 @@
+def main():
+    print("Hello from yield-smart-app!")
+
+
+if __name__ == "__main__":
+    main()
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -0,0 +1,17 @@
+[project]
+name = "yield-smart-app"
+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
--- a/uv.lock
+++ b/uv.lock
@@ -0,0 +1,653 @@
+version = 1
+revision = 3
+requires-python = ">=3.14"
+resolution-markers = [
+    "sys_platform == 'win32'",
+    "sys_platform == 'emscripten'",
+    "sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
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Author	SHA1	Message	Date
zhenghu	e3ac2beb7a	refactor: 精简 UI 自定义样式，使用 Streamlit 原生组件替代 HTML 卡片 - 将大量自定义 CSS（主题色、卡片、徽章、alert 等）精简为仅保留字体设置 - sidebar 标题/副标题/分隔线改用 st.header、st.caption、st.divider - KPI 指标卡片改用 st.metric - 作物推荐排行改用 st.container + st.progress - 种植建议的 alert 框改用 st.warning / st.success / st.info - 删除自定义 HTML footer，改用 st.caption	2026-04-14 17:35:42 +08:00
zhenghu	6af6a10636	refactor: 整体 UI 主题改版为浅色自然风格，并优化文案与清理代码 - 将页面主题从深色科技风改为浅色自然风（土壤棕/叶绿/麦穗黄配色） - 页面标题与文案统一为"种植决策助手"，提升可读性 - 移除未使用的导入（pandas、plotly.express、make_subplots） - 更新作物颜色配置以适配新主题 - 调整 Plotly 图表样式（背景、轴线、网格、标注线颜色）适配浅色模式 - 新增 Streamlit 按钮的圆角与悬停样式覆盖 - 删除多余的伪元素装饰代码，精简 CSS	2026-04-14 11:24:08 +08:00
zhenghu	1c97a065a7	chore: 将项目名称统一为 yield-smart-app - 更新 pyproject.toml 中的包名 - 同步 uv.lock 中的包名 - 更新 README.md 中的项目目录和 Docker 镜像名称 - 更新 justfile 和 main.py 中的引用名称	2026-04-13 17:51:36 +08:00
zhenghu	3e34770e64	chore: 将 uv HTTP 超时时间从 120 秒调整为 300 秒	2026-04-13 17:45:17 +08:00
zhenghu	efa844d32f	fix: 统一服务端口为 8000，固定 streamlit 版本并同步依赖锁定 - 将 Dockerfile/HealthCheck/CMD 中的服务端口从 8001 统一改为 8000 - 同步更新 README.md 中的 docker run 端口映射 - pyproject.toml 中 streamlit 版本从 >=1.52.1 固定为 ==1.52.1 - 同步 uv.lock：streamlit 降级锁定至 1.52.1，同步更新 pandas、protobuf、cachetools 等依赖版本	2026-04-13 17:45:17 +08:00
zhenghu	942db700be	fix: 移除 Google Fonts 外部依赖，增加本地字体回退方案 - 删除 Google Fonts 的 @import 远程加载（Noto Serif SC、JetBrains Mono），避免网络请求延迟 - 为正文字体增加 PingFang SC、Microsoft YaHei 等本地中文字体回退 - 为等宽字体增加 Fira Code、Source Code Pro 等本地回退	2026-04-13 17:45:17 +08:00
zhenghu	c3185cea33	fix: 增加 uv HTTP 超时时间配置，避免依赖安装超时失败	2026-04-13 17:45:17 +08:00
zhenghu	c50bf68612	chore: 同步 uv.lock 包名为连字符格式（yieldsmart → yield-smart）	2026-04-13 17:45:17 +08:00
zhenghu	f049e4b13d	chore: 规范化项目名称，使用连字符分隔格式（yieldsmart → yield-smart）	2026-04-13 17:45:17 +08:00
zhenghu	817c3c75df	chore: 将 uv.lock 纳入版本管理 - 从 .gitignore 中移除对 *.lock 文件的忽略规则 - 添加 uv.lock 锁文件以确保依赖版本一致性	2026-04-13 17:45:17 +08:00
zhenghu	bf1b8235d7	chore: 调整 Streamlit 服务端口从 8000 到 8001 - 更新 Dockerfile 中 EXPOSE、ENV、HEALTHCHECK、CMD 的端口号 - 同步更新 README.md 中的 docker run 示例端口映射	2026-04-13 17:45:17 +08:00
zhenghu	9cb70267b6	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	2026-04-13 17:45:14 +08:00