feat: 初始化病虫害以图搜图应用

- 基于 CLIP 模型实现图片相似度搜索（app.py / main.py）
  - 新增 Streamlit 可视化交互界面
  - 新增 pyproject.toml、justfile、Dockerfile 项目配置
  - 补充完整 README 文档（功能介绍、快速开始、Docker 部署）
  - 新增 .gitignore

.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/

Dockerfile

@@ -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"]

README.md

@@ -1,3 +1,80 @@
-# pest-image-search
+# pest-image-search 病虫害以图搜图
 
-病虫害以图搜图
+基于 CLIP 视觉模型的病虫害图片相似度搜索应用。上传病虫害患处图片，系统自动提取视觉特征并检索知识库中最相似的病虫害类型，提供症状描述与防治建议。
+
+## 功能特性
+
+- 🖼️ 支持上传本地图片、输入图片 URL、选择示例图片三种查询方式
+- 🧠 基于 `openai/clip-vit-base-patch32` 本地视觉模型提取图像特征
+- 📊 相似度可视化条形图
+- 🏷️ 覆盖水稻、小麦、玉米、大豆、番茄、黄瓜等常见作物的病虫害知识库
+- 💡 智能推荐最可能的病虫害及防治方案
+
+## 技术栈
+
+- Python 3.14+
+- Streamlit 1.52.1
+- Plotly 6.5.0
+- Transformers 4.51.3 + PyTorch 2.7.0 (CLIP 模型)
+- Pillow、NumPy、Requests
+
+## 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 just
+
+```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 pest-image-search .
+
+# 运行容器
+docker run -p 8000:8000 pest-image-search
+```
+
+## 项目结构
+
+```
+pest-image-search/
+├── 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
+```
+
+## 使用说明
+
+1. 首次启动时会自动下载 CLIP 模型（约 300MB），请保持网络畅通
+2. 加载完成后自动构建病虫害图片索引
+3. 上传或选择查询图片后点击「开始搜索」，即可获得 Top-K 相似病虫害结果
+4. 结果仅供参考，实际防治请结合田间情况或咨询农业专家
+
+## 许可证
+
+MIT License

app.py

@@ -0,0 +1,599 @@
+"""
+病虫害以图搜图
+基于 CLIP 本地模型的图片 Embedding 相似度搜索
+"""
+
+from __future__ import annotations
+
+import io
+import os
+from dataclasses import dataclass
+from typing import Literal
+
+import numpy as np
+import plotly.graph_objects as go
+import requests
+import streamlit as st
+from PIL import Image
+from transformers import CLIPModel, CLIPProcessor
+
+# ─── Page Config ────────────────────────────────────────────────────────────
+st.set_page_config(
+    page_title="病虫害以图搜图",
+    page_icon="🌿",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+# ─── Custom CSS ──────────────────────────────────────────────────────────────
+st.markdown("""
+<style>
+:root {
+    --soil: #7c5e42;
+    --leaf: #4a7c59;
+    --leaf-light: #6b9e75;
+    --wheat: #d4a574;
+    --cream: #faf8f3;
+    --paper: #ffffff;
+    --ink: #2c2c2c;
+    --ink-muted: #5a5a5a;
+    --border: #e5e0d5;
+    --shadow: rgba(0,0,0,0.04);
+    --danger: #c45c4a;
+    --danger-light: #f5eae8;
+}
+
+html, body, [class*="css"] {
+    font-family: "PingFang SC", "Microsoft YaHei", "Noto Sans SC", sans-serif;
+    color: var(--ink);
+}
+
+.stApp {
+    background: var(--cream);
+}
+
+/* Sidebar */
+[data-testid="stSidebar"] {
+    background: #f5f2eb;
+    border-right: 1px solid var(--border);
+}
+[data-testid="stSidebar"] .stSlider label,
+[data-testid="stSidebar"] .stNumberInput label,
+[data-testid="stSidebar"] .stSelectbox label,
+[data-testid="stSidebar"] .stTextInput label,
+[data-testid="stSidebar"] .stFileUploader label {
+    color: var(--soil) !important;
+    font-size: 0.85rem;
+    font-weight: 500;
+}
+
+/* Metric / hero cards */
+.metric-card {
+    background: var(--paper);
+    border: 1px solid var(--border);
+    border-radius: 14px;
+    padding: 20px 18px;
+    text-align: center;
+    box-shadow: 0 2px 10px var(--shadow);
+}
+.metric-value {
+    font-size: 1.9rem;
+    font-weight: 700;
+    color: var(--leaf);
+    line-height: 1.1;
+}
+.metric-unit {
+    font-size: 0.8rem;
+    color: var(--ink-muted);
+    margin-top: 4px;
+}
+.metric-label {
+    font-size: 0.9rem;
+    color: var(--ink);
+    margin-top: 8px;
+    font-weight: 500;
+}
+
+/* Section headers */
+.section-header {
+    font-size: 0.95rem;
+    font-weight: 600;
+    color: var(--soil);
+    padding-bottom: 8px;
+    margin-bottom: 14px;
+    margin-top: 22px;
+    border-bottom: 1px solid var(--border);
+}
+
+/* Result cards */
+.result-card {
+    background: var(--paper);
+    border: 1px solid var(--border);
+    border-radius: 14px;
+    padding: 16px;
+    margin-bottom: 14px;
+    box-shadow: 0 1px 6px var(--shadow);
+}
+.result-rank {
+    width: 28px;
+    height: 28px;
+    border-radius: 50%;
+    background: var(--wheat);
+    color: #fff;
+    font-size: 0.85rem;
+    font-weight: 700;
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    margin-right: 10px;
+}
+.result-name {
+    font-size: 1.05rem;
+    font-weight: 600;
+    color: var(--ink);
+}
+.result-score {
+    font-size: 0.9rem;
+    color: var(--leaf);
+    font-weight: 600;
+}
+
+/* Tags */
+.tag {
+    display: inline-block;
+    background: #f3f6f3;
+    border: 1px solid var(--leaf-light);
+    border-radius: 999px;
+    padding: 3px 10px;
+    font-size: 0.78rem;
+    color: var(--leaf);
+    margin: 3px 3px 3px 0;
+}
+.tag-warn {
+    background: var(--danger-light);
+    border-color: var(--danger);
+    color: var(--danger);
+}
+
+/* Hero */
+.hero-title {
+    font-size: 1.6rem;
+    font-weight: 700;
+    color: var(--soil);
+    line-height: 1.2;
+}
+.hero-sub {
+    font-size: 0.85rem;
+    color: var(--ink-muted);
+    margin-top: 4px;
+}
+
+/* Sidebar title */
+.sidebar-title {
+    font-size: 1.15rem;
+    font-weight: 700;
+    color: var(--soil);
+    margin-bottom: 2px;
+}
+.sidebar-sub {
+    font-size: 0.75rem;
+    color: var(--ink-muted);
+    margin-bottom: 12px;
+}
+
+/* Info panel */
+.info-panel {
+    background: var(--paper);
+    border: 1px solid var(--border);
+    border-radius: 12px;
+    padding: 14px 16px;
+    font-size: 0.88rem;
+    color: var(--ink-muted);
+    line-height: 1.7;
+}
+
+/* Streamlit overrides */
+.stButton > button {
+    border-radius: 10px !important;
+    background: var(--leaf) !important;
+    border: none !important;
+    color: #fff !important;
+}
+.stButton > button:hover {
+    background: var(--leaf-light) !important;
+}
+
+/* Radio horizontal */
+.stRadio [role="radiogroup"] {
+    gap: 8px;
+}
+</style>
+""", unsafe_allow_html=True)
+
+
+# ─── Knowledge Base ──────────────────────────────────────────────────────────
+@dataclass(frozen=True)
+class PestItem:
+    name: str
+    url: str
+    symptoms: str
+    treatment: str
+    crop: str
+    category: Literal["病害", "虫害"]
+
+
+PEST_KNOWLEDGE: list[PestItem] = [
+    PestItem(
+        name="水稻稻瘟病",
+        url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151854_dc9667cf_%E6%B0%B4%E7%A8%BB%E7%A8%BB%E7%98%9F%E7%97%851.jpeg",
+        symptoms="叶片出现梭形或纺锤形病斑，中央灰白色，边缘褐色，严重时病斑连片导致叶片枯死",
+        treatment="选用抗病品种，合理施肥避免偏施氮肥，发病初期喷施三环唑或稻瘟灵",
+        crop="水稻",
+        category="病害",
+    ),
+    PestItem(
+        name="水稻纹枯病",
+        url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_152022_9f3124ab_%E6%B0%B4%E7%A8%BB%E7%BA%B9%E6%9E%AF%E7%97%851.jpeg",
+        symptoms="叶鞘和叶片上出现云纹状灰绿色至灰褐色病斑，后期病斑边缘褐色、中央灰白色",
+        treatment="合理密植，科学管水，发病初期喷施井冈霉素或噻呋酰胺",
+        crop="水稻",
+        category="病害",
+    ),
+    PestItem(
+        name="水稻胡麻叶斑病",
+        url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151936_41fdb1dc_%E6%B0%B4%E7%A8%BB%E8%83%A1%E9%BA%BB%E5%8F%B6%E6%96%91%E7%97%851.jpeg",
+        symptoms="叶片上出现暗褐色芝麻粒大小的椭圆形病斑，病斑周围有黄色晕圈",
+        treatment="增施硅肥和钾肥提高抗病力，喷施丙环唑或咪鲜胺防治",
+        crop="水稻",
+        category="病害",
+    ),
+    PestItem(
+        name="小麦锈病",
+        url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_153814_3e175ca3_%E5%B0%8F%E9%BA%A6%E9%94%88%E7%97%851.jpeg",
+        symptoms="叶片和叶鞘上出现铁锈色粉状疱疹（夏孢子堆），后期变为黑色冬孢子堆",
+        treatment="种植抗锈品种，发病初期喷施三唑酮或烯唑醇，注意轮作",
+        crop="小麦",
+        category="病害",
+    ),
+    PestItem(
+        name="小麦赤霉病",
+        url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_152112_2e1f530e_%E5%B0%8F%E9%BA%A6%E8%B5%A4%E9%9C%89%E7%97%851.jpeg",
+        symptoms="穗部小穗发病，颖壳上出现水浸状褐色斑，后期产生粉红色霉层",
+        treatment="选用抗病品种，齐穗至扬花初期喷施多菌灵或戊唑醇",
+        crop="小麦",
+        category="病害",
+    ),
+    PestItem(
+        name="玉米大斑病",
+        url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_153911_ee5a72be_%E7%8E%89%E7%B1%B3%E5%A4%A7%E6%96%91%E7%97%851.jpeg",
+        symptoms="叶片上出现灰绿色水浸状斑点，扩展为长梭形灰褐色大型病斑",
+        treatment="种植抗病品种，适时早播，发病初期喷施多菌灵或代森锰锌",
+        crop="玉米",
+        category="病害",
+    ),
+    PestItem(
+        name="玉米小斑病",
+        url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_154001_e31a0103_%E7%8E%89%E7%B1%B3%E5%B0%8F%E6%96%91%E7%97%851.jpeg",
+        symptoms="叶片上出现椭圆形黄褐色小病斑，有2-3圈同心轮纹，边缘紫褐色",
+        treatment="轮作倒茬，清除病残体，喷施百菌清或甲基托布津",
+        crop="玉米",
+        category="病害",
+    ),
+    PestItem(
+        name="玉米螟",
+        url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_153938_8be05006_%E7%8E%89%E7%B1%B3%E8%9E%9F1.jpeg",
+        symptoms="幼虫蛀食茎秆和穗轴，茎秆上有蛀孔，孔口有虫粪，造成茎秆折断",
+        treatment="心叶期撒施白僵菌颗粒剂，释放赤眼蜂生物防治，大喇叭口期灌心",
+        crop="玉米",
+        category="虫害",
+    ),
+    PestItem(
+        name="稻飞虱",
+        url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151643_db5e1d36_%E7%A8%BB%E9%A3%9E%E8%99%AB1.jpeg",
+        symptoms="稻株基部聚集大量褐色或白色小型飞虫，受害稻株发黄矮缩，严重时枯死倒伏",
+        treatment="合理施肥避免贪青晚熟，选用吡蚜酮或烯啶虫胺防治，保护利用天敌",
+        crop="水稻",
+        category="虫害",
+    ),
+    PestItem(
+        name="大豆蚜虫",
+        url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151549_d9cf327b_%E5%A4%A7%E8%B1%86%E8%9A%9C%E8%99%AB1.jpeg",
+        symptoms="嫩叶和茎尖聚集大量绿色或黄色蚜虫，叶片卷缩变形，植株矮化",
+        treatment="保护瓢虫等天敌，百株蚜量达1000头时喷施吡虫啉或啶虫脒",
+        crop="大豆",
+        category="虫害",
+    ),
+    PestItem(
+        name="番茄晚疫病",
+        url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151705_3dd8baab_%E7%95%AA%E8%8C%84%E6%99%9A%E7%96%AB%E7%97%851.jpeg",
+        symptoms="叶片出现水浸状暗绿色不规则病斑，潮湿时叶背面产生白色霉层，果实变褐硬化",
+        treatment="控制温湿度，及时通风降湿，发病初期喷施甲霜灵锰锌或霜脲氰",
+        crop="番茄",
+        category="病害",
+    ),
+    PestItem(
+        name="黄瓜霜霉病",
+        url="https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151804_7be515fa_%E9%BB%84%E7%93%9C%E9%9C%9C%E9%9C%89%E7%97%851.jpeg",
+        symptoms="叶片正面出现黄色多角形病斑，叶背面潮湿时产生灰黑色霉层",
+        treatment="选用抗病品种，膜下滴灌降低湿度，喷施百菌清或霜霉威盐酸盐",
+        crop="黄瓜",
+        category="病害",
+    ),
+]
+
+EXAMPLE_IMAGES: list[tuple[str, str]] = [
+    (
+        "水稻稻瘟病",
+        "https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151914_4f5b8fef_%E6%B0%B4%E7%A8%BB%E7%A8%BB%E7%98%9F%E7%97%852.jpeg",
+    ),
+    (
+        "番茄晚疫病",
+        "https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_151726_a8f31320_%E7%95%AA%E8%8C%84%E6%99%9A%E7%96%AB%E7%97%852.jpeg",
+    ),
+    (
+        "小麦锈病",
+        "https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_153837_e8ae9f43_%E5%B0%8F%E9%BA%A6%E9%94%88%E7%97%852.jpeg",
+    ),
+    (
+        "水稻纹枯病",
+        "https://minio.dev.maimaiag.com/crop-prod-bucket/field_photo/20260410_152050_77d568b1_%E6%B0%B4%E7%A8%BB%E7%BA%B9%E6%9E%AF%E7%97%852.jpeg",
+    ),
+]
+
+
+# ─── CLIP Embedder ───────────────────────────────────────────────────────────
+class CLIPEmbedder:
+    MODEL_NAME = "openai/clip-vit-base-patch32"
+
+    def __init__(self) -> None:
+        self._processor: CLIPProcessor | None = None
+        self._model: CLIPModel | None = None
+
+    def _load(self) -> tuple[CLIPProcessor, CLIPModel]:
+        if self._processor is None or self._model is None:
+            with st.spinner("首次启动正在加载 CLIP 模型，请稍候..."):
+                self._processor = CLIPProcessor.from_pretrained(self.MODEL_NAME)
+                self._model = CLIPModel.from_pretrained(self.MODEL_NAME)
+        return self._processor, self._model
+
+    def embed(self, image: Image.Image) -> np.ndarray:
+        processor, model = self._load()
+        inputs = processor(images=image, return_tensors="pt")
+        image_features = model.get_image_features(**inputs)
+        vec = image_features.detach().cpu().numpy().flatten()
+        norm = np.linalg.norm(vec)
+        if norm == 0:
+            return vec
+        return vec / norm
+
+
+@st.cache_resource(show_spinner=False)
+def get_embedder() -> CLIPEmbedder:
+    return CLIPEmbedder()
+
+
+# ─── Utilities ───────────────────────────────────────────────────────────────
+def load_image(source: str | io.BytesIO) -> Image.Image | None:
+    try:
+        if isinstance(source, str):
+            resp = requests.get(source, timeout=30)
+            resp.raise_for_status()
+            return Image.open(io.BytesIO(resp.content)).convert("RGB")
+        return Image.open(source).convert("RGB")
+    except Exception as e:
+        st.error(f"图片加载失败: {e}")
+        return None
+
+
+def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
+    return float(np.dot(a, b))
+
+
+@st.cache_data(show_spinner=False)
+def build_index() -> tuple[list[dict], list[str], list[str]]:
+    embedder = get_embedder()
+    items, succeeded, failed = [], [], []
+    progress = st.progress(0, text="正在构建病虫害图片索引...")
+    total = len(PEST_KNOWLEDGE)
+    for i, pest in enumerate(PEST_KNOWLEDGE):
+        img = load_image(pest.url)
+        if img is None:
+            failed.append(pest.name)
+            progress.progress((i + 1) / total, text=f"索引构建中 ({i + 1}/{total})...")
+            continue
+        try:
+            embedding = embedder.embed(img)
+            items.append({
+                "name": pest.name,
+                "url": pest.url,
+                "embedding": embedding,
+                "symptoms": pest.symptoms,
+                "treatment": pest.treatment,
+                "crop": pest.crop,
+                "category": pest.category,
+            })
+            succeeded.append(pest.name)
+        except Exception:
+            failed.append(pest.name)
+        progress.progress((i + 1) / total, text=f"索引构建中 ({i + 1}/{total})...")
+    progress.empty()
+    return items, succeeded, failed
+
+
+# ─── Sidebar ─────────────────────────────────────────────────────────────────
+with st.sidebar:
+    st.markdown('<div class="sidebar-title">🌿 病虫害以图搜图</div>', unsafe_allow_html=True)
+    st.markdown('<div class="sidebar-sub">上传图片，智能识别相似病虫害</div>', unsafe_allow_html=True)
+    st.markdown("<hr style='border:none;border-top:1px solid var(--border);margin:12px 0;'>", unsafe_allow_html=True)
+
+    st.markdown('<div class="section-header" style="margin-top:0">🖼️ 输入方式</div>', unsafe_allow_html=True)
+    input_mode = st.radio("", ["上传本地图片", "输入图片 URL", "选择示例图片"], label_visibility="collapsed")
+
+    query_source = None
+    query_url = ""
+
+    if input_mode == "上传本地图片":
+        uploaded = st.file_uploader("选择图片", type=["jpg", "jpeg", "png", "webp"])
+        if uploaded is not None:
+            query_source = io.BytesIO(uploaded.getvalue())
+            query_url = ""
+    elif input_mode == "输入图片 URL":
+        query_url = st.text_input("图片 URL", placeholder="https://example.com/image.jpg")
+        if query_url.strip():
+            query_source = query_url.strip()
+    else:
+        st.markdown('<div style="font-size:0.8rem;color:#7a7a7a;margin-bottom:6px;">点击选择示例</div>', unsafe_allow_html=True)
+        cols = st.columns(2)
+        for idx, (name, url) in enumerate(EXAMPLE_IMAGES):
+            with cols[idx % 2]:
+                if st.button(name, key=f"ex_{name}"):
+                    st.session_state.query_url = url
+        if "query_url" in st.session_state:
+            query_url = st.session_state.query_url
+            query_source = query_url
+            st.image(query_url, use_container_width=True)
+
+    st.markdown('<div class="section-header">⚙️ 搜索设置</div>', unsafe_allow_html=True)
+    top_k = st.slider("返回条数", 1, min(12, len(PEST_KNOWLEDGE)), 5)
+
+    st.markdown("<br>", unsafe_allow_html=True)
+    search_clicked = st.button("开始搜索", type="primary", use_container_width=True)
+
+    st.markdown("<hr style='border:none;border-top:1px solid var(--border);margin:12px 0;'>", unsafe_allow_html=True)
+    st.markdown("""
+    <div class="info-panel">
+      <b>使用说明</b><br>
+      1. 上传病虫害患处图片<br>
+      2. 系统自动提取图像特征<br>
+      3. 与知识库比对返回相似结果<br>
+      4. 参考症状与防治建议
+    </div>
+    """, unsafe_allow_html=True)
+
+# ─── Build Index ─────────────────────────────────────────────────────────────
+index_items, succeeded, failed = build_index()
+
+# ─── Main Layout ─────────────────────────────────────────────────────────────
+st.markdown("""
+<div style="display:flex; align-items:baseline; gap:12px; margin-bottom:4px;">
+  <div class="hero-title">病虫害以图搜图</div>
+</div>
+<div class="hero-sub">基于 CLIP 视觉模型的病虫害相似度检索与防治建议</div>
+""", unsafe_allow_html=True)
+
+# Status badges
+badges = []
+if succeeded:
+    badges.append(f'<span class="tag">📚 知识库 {len(succeeded)} 种</span>')
+if failed:
+    badges.append(f'<span class="tag tag-warn">⚠️ 索引失败 {len(failed)} 种</span>')
+if badges:
+    st.markdown(f"<div style='margin-top:8px;'>{''.join(badges)}</div>", unsafe_allow_html=True)
+
+st.markdown("<br>", unsafe_allow_html=True)
+
+# ─── Search Logic ────────────────────────────────────────────────────────────
+if search_clicked and query_source is not None and index_items:
+    query_img = load_image(query_source)
+    if query_img is not None:
+        col_query, col_preview = st.columns([1, 3])
+        with col_query:
+            st.markdown('<div class="section-header" style="margin-top:0">🔍 查询图片</div>', unsafe_allow_html=True)
+            st.image(query_img, use_container_width=True)
+
+        with col_preview:
+            st.markdown('<div class="section-header" style="margin-top:0">⏳ 正在分析...</div>', unsafe_allow_html=True)
+            progress = st.progress(0, text="提取图像特征...")
+
+            embedder = get_embedder()
+            query_embedding = embedder.embed(query_img)
+            progress.progress(50, text="比对知识库...")
+
+            scores = []
+            for item in index_items:
+                sim = cosine_similarity(query_embedding, item["embedding"])
+                scores.append((sim, item))
+            scores.sort(key=lambda x: x[0], reverse=True)
+            results = scores[:top_k]
+            progress.progress(100, text="搜索完成")
+            progress.empty()
+
+            st.markdown(f'<div class="section-header" style="margin-top:0">🏆 搜索结果（Top-{len(results)}）</div>', unsafe_allow_html=True)
+
+            # Similarity bar chart
+            names = [f"{r[1]['name']}" for r in results]
+            sims = [r[0] * 100 for r in results]
+            colors = ["#c45c4a" if r[1]["category"] == "虫害" else "#4a7c59" for r in results]
+
+            fig_bar = go.Figure()
+            fig_bar.add_trace(go.Bar(
+                x=sims,
+                y=names,
+                orientation="h",
+                marker=dict(color=colors, opacity=0.85, line=dict(color="rgba(0,0,0,0.08)", width=1)),
+                text=[f"{s:.1f}%" for s in sims],
+                textposition="outside",
+                textfont=dict(color="#5a5a5a", size=10),
+            ))
+            fig_bar.update_layout(
+                xaxis=dict(title="相似度 (%)", color="#5a5a5a", gridcolor="rgba(0,0,0,0.06)", range=[0, 105]),
+                yaxis=dict(color="#5a5a5a", gridcolor="rgba(0,0,0,0.04)", autorange="reversed"),
+                paper_bgcolor="rgba(0,0,0,0)",
+                plot_bgcolor="rgba(0,0,0,0)",
+                font=dict(color="#2c2c2c", size=11),
+                margin=dict(t=10, b=30, l=80, r=50),
+                height=160 + len(results) * 34,
+                showlegend=False,
+            )
+            st.plotly_chart(fig_bar, use_container_width=True)
+
+        # Result cards below
+        st.markdown('<div class="section-header">📋 详细结果</div>', unsafe_allow_html=True)
+        for rank, (sim, item) in enumerate(results, 1):
+            with st.container():
+                st.markdown(f"""
+                <div class="result-card">
+                  <div style="display:flex; gap:14px; align-items:flex-start;">
+                    <div style="flex:0 0 140px;">
+                      <img src="{item['url']}" style="width:100%; border-radius:10px; border:1px solid var(--border);">
+                    </div>
+                    <div style="flex:1;">
+                      <div style="display:flex; align-items:center; margin-bottom:8px;">
+                        <span class="result-rank">{rank}</span>
+                        <span class="result-name">{item['name']}</span>
+                        <span style="margin-left:auto;" class="result-score">相似度 {sim*100:.1f}%</span>
+                      </div>
+                      <div style="margin-bottom:8px;">
+                        <span class="tag">{item['crop']}</span>
+                        <span class="tag{' tag-warn' if item['category'] == '虫害' else ''}">{item['category']}</span>
+                      </div>
+                      <div style="font-size:0.88rem; color:var(--ink); line-height:1.6;">
+                        <b>症状：</b>{item['symptoms']}<br>
+                        <b>防治：</b>{item['treatment']}
+                      </div>
+                    </div>
+                  </div>
+                </div>
+                """, unsafe_allow_html=True)
+
+        # Advisory summary
+        if results:
+            best = results[0][1]
+            st.markdown('<div class="section-header">💡 初步建议</div>', unsafe_allow_html=True)
+            st.markdown(f"""
+            <div class="info-panel" style="border-left:3px solid var(--leaf-light); border-radius:0 12px 12px 0;">
+              系统判断该图片与 <b>{best['name']}</b>（{best['crop']}{best['category']}）最为相似，相似度 <b>{results[0][0]*100:.1f}%</b>。<br>
+              建议结合田间实际情况进一步确认，参考防治方案：<b>{best['treatment']}</b>
+            </div>
+            """, unsafe_allow_html=True)
+
+elif search_clicked and not index_items:
+    st.warning("知识库索引为空，请检查网络连接后刷新页面重试。")
+
+# ─── Footer ───────────────────────────────────────────────────────────────────
+st.markdown("<br>", unsafe_allow_html=True)
+st.markdown("""
+<div style="text-align:center; font-size:0.78rem; color:#aaa; padding:14px; border-top:1px solid #e5e0d5;">
+  病虫害以图搜图 · 基于 CLIP 视觉模型 · 结果仅供参考，请结合田间实际情况判断
+</div>
+""", unsafe_allow_html=True)

justfile

@@ -0,0 +1,30 @@
+# Justfile for pest-image-search 病虫害以图搜图
+# 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 requests pillow numpy torch transformers
+
+# 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 +1,7 @@
+def main():
+    print("Hello from pest-image-search!")
+    print("Run with: uv run streamlit run app.py")
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -0,0 +1,20 @@
+[project]
+name = "pest-image-search"
+version = "0.1.0"
+description = "病虫害以图搜图 — 基于图片 Embedding 的相似度搜索"
+readme = "README.md"
+requires-python = ">=3.14"
+dependencies = [
+    "numpy>=2.3.5",
+    "pillow>=11.2.1",
+    "plotly>=6.5.0",
+    "requests>=2.32.3",
+    "ruff>=0.14.8",
+    "streamlit==1.52.1",
+    "torch>=2.7.0",
+    "transformers>=4.51.3",
+]
+
+[[tool.uv.index]]
+url = "https://mirrors.aliyun.com/pypi/simple"
+default = true