feat: 初始化香蕉叶病害智能识别系统

基于 ViT 视觉变换器实现香蕉叶片病害分类应用，支持 7 类病害识别。

  - app.py: Streamlit 前端应用，包含图片上传、病害识别、置信度图表展示及防治建议
  - pyproject.toml: 项目配置，声明 Python 3.14 及依赖（streamlit、transformers、torch、plotly 等）
  - uv.lock: 依赖版本锁定文件
  - Dockerfile: 基于 uv 镜像的容器化部署配置，含健康检查
  - justfile: 常用开发任务脚本（run、install、format、check 等）
  - main.py: 命令行入口
  - .doc/: 模型使用说明文档

  模型来源: Dmitry43243242/banana-disease-leaf-model (HuggingFace)

.gitignore

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+# 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

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+# 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"]

app.py

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+"""
+香蕉叶病害智能识别系统
+基于 ViT 视觉变换器的香蕉叶片病害分类应用
+"""
+
+import os
+
+import streamlit as st
+import plotly.graph_objects as go
+from PIL import Image
+
+# 国内 HuggingFace 镜像加速
+os.environ.setdefault("HF_ENDPOINT", "https://hf-mirror.com")
+
+from transformers import pipeline  # noqa: E402
+
+# ─── Disease Label Mapping ──────────────────────────────────────────────────
+# 模型输出 LABEL_0 ~ LABEL_6，映射为实际病害名称
+DISEASE_INFO = {
+    "LABEL_0": {
+        "name": "细菌性枯萎病",
+        "name_en": "Banana Bacterial Wilt (BBW)",
+        "color": "#e74c3c",
+        "description": "由细菌引起，叶片从边缘开始变黄枯萎，最终整株死亡。",
+        "advice": "及时清除病株，避免刀具交叉感染，使用无菌工具操作，加强田间排水。",
+    },
+    "LABEL_1": {
+        "name": "黑条斑病",
+        "name_en": "Black Sigatoka",
+        "color": "#8e44ad",
+        "description": "真菌性病害，叶片出现黑色条纹，严重时叶片大面积枯死。",
+        "advice": "选用抗病品种，合理密植保持通风，及时清除病叶，适时喷施杀菌剂。",
+    },
+    "LABEL_2": {
+        "name": "苞片花叶病毒病",
+        "name_en": "Bract Mosaic Virus (BBMV)",
+        "color": "#e67e22",
+        "description": "病毒性病害，苞片和叶片出现花叶状斑纹，影响果实品质。",
+        "advice": "使用无病种苗，防治传毒蚜虫，及时拔除病株，避免机械传播。",
+    },
+    "LABEL_3": {
+        "name": "健康叶片",
+        "name_en": "Healthy",
+        "color": "#27ae60",
+        "description": "叶片状态良好，无明显病害症状，颜色鲜绿。",
+        "advice": "继续保持良好的田间管理，合理施肥灌溉，定期巡检。",
+    },
+    "LABEL_4": {
+        "name": "花叶病毒病",
+        "name_en": "Banana Mosaic Virus",
+        "color": "#f39c12",
+        "description": "病毒性病害，叶片出现黄绿相间的花叶症状，植株矮化。",
+        "advice": "选用无毒组培苗，控制蚜虫等传毒媒介，拔除病株，工具消毒。",
+    },
+    "LABEL_5": {
+        "name": "巴拿马病（枯萎病）",
+        "name_en": "Panama Disease (Fusarium Wilt)",
+        "color": "#c0392b",
+        "description": "由尖孢镰刀菌引起，维管束变褐，叶片从下部开始黄化枯萎。",
+        "advice": "种植抗病品种，严格检疫，病区轮作或休耕，避免土壤传播。",
+    },
+    "LABEL_6": {
+        "name": "黄条斑病",
+        "name_en": "Yellow Sigatoka",
+        "color": "#d4ac0d",
+        "description": "真菌性病害，叶片出现黄色条纹，比黑条斑病症状较轻。",
+        "advice": "保持田间通风透光，清除病残叶，适时用药防治，合理施肥增强抗性。",
+    },
+}
+
+
+# ─── Page Config ────────────────────────────────────────────────────────────
+st.set_page_config(
+    page_title="香蕉叶病害识别",
+    page_icon="🍌",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+st.markdown("""
+<style>
+html, body, [class*="css"] {
+    font-family: "PingFang SC", "Microsoft YaHei", "Noto Sans SC", sans-serif;
+}
+</style>
+""", unsafe_allow_html=True)
+
+
+# ─── Model Loading ──────────────────────────────────────────────────────────
+@st.cache_resource
+def load_model():
+    """加载 HuggingFace 模型（首次运行自动下载，约 343MB）"""
+    classifier = pipeline(
+        "image-classification",
+        model="Dmitry43243242/banana-disease-leaf-model",
+    )
+    return classifier
+
+
+# ─── Sidebar ────────────────────────────────────────────────────────────────
+with st.sidebar:
+    st.header("🍌 香蕉叶病害识别")
+    st.caption("上传叶片照片，AI 帮你识别病害")
+    st.divider()
+
+    st.subheader("📷 上传图片")
+    uploaded_file = st.file_uploader(
+        "选择香蕉叶片图片",
+        type=["jpg", "jpeg", "png", "webp"],
+        help="支持 JPG、PNG、WebP 格式",
+    )
+
+    st.divider()
+    st.subheader("ℹ️ 使用说明")
+    st.markdown("""
+1. 上传一张香蕉叶片照片
+2. 系统自动识别病害类型
+3. 查看各类别置信度
+4. 获取防治建议
+
+**Tips:**
+- 图片越清晰，识别越准确
+- 尽量拍摄完整叶片
+- 病变部位清晰可见
+""")
+
+    st.divider()
+    st.subheader("📋 可识别病害")
+    for label_key, info in DISEASE_INFO.items():
+        st.markdown(
+            f"<span style='color:{info['color']}'>●</span> **{info['name']}** ({info['name_en']})",
+            unsafe_allow_html=True,
+        )
+
+
+# ─── Main Content ───────────────────────────────────────────────────────────
+st.title("🍌 香蕉叶病害智能识别系统")
+st.caption("基于 ViT 视觉变换器 · 7 类香蕉叶病害自动分类")
+
+# 加载模型
+with st.spinner("正在加载模型（首次运行需下载约 343MB）..."):
+    classifier = load_model()
+
+if uploaded_file is not None:
+    # 显示上传的图片
+    image = Image.open(uploaded_file).convert("RGB")
+
+    col_img, col_result = st.columns([1, 1])
+
+    with col_img:
+        st.subheader("🖼️ 上传图片")
+        st.image(image, use_container_width=True)
+
+    # 推理
+    with st.spinner("正在识别病害..."):
+        results = classifier(image)
+
+    # 解析结果
+    top_result = results[0]
+    top_label = top_result["label"]
+    top_score = top_result["score"]
+    disease = DISEASE_INFO.get(top_label, {
+        "name": top_label,
+        "name_en": top_label,
+        "color": "#95a5a6",
+        "description": "未知病害类型",
+        "advice": "建议咨询专业植保人员",
+    })
+
+    with col_result:
+        st.subheader("🔍 识别结果")
+        is_healthy = top_label == "LABEL_3"
+        if is_healthy:
+            st.success(f"**{disease['name']}** — 置信度 {top_score:.1%}")
+        else:
+            st.warning(f"**{disease['name']}** — 置信度 {top_score:.1%}")
+
+        st.markdown(f"**英文名**: {disease['name_en']}")
+        st.markdown(f"**症状描述**: {disease['description']}")
+
+    # ─── Confidence Bar Chart ────────────────────────────────────────────────
+    st.subheader("📊 各类别置信度")
+
+    labels = [r["label"] for r in results]
+    scores = [r["score"] for r in results]
+    display_names = [
+        f"{DISEASE_INFO.get(l, {}).get('name', l)}" for l in labels
+    ]
+    colors = [
+        DISEASE_INFO.get(l, {}).get("color", "#95a5a6") for l in labels
+    ]
+
+    fig = go.Figure()
+    fig.add_trace(go.Bar(
+        x=scores,
+        y=display_names,
+        orientation="h",
+        marker=dict(
+            color=colors,
+            opacity=0.85,
+            line=dict(color="rgba(0,0,0,0.08)", width=1),
+        ),
+        text=[f"{s:.1%}" for s in scores],
+        textposition="outside",
+        textfont=dict(color="#5a5a5a", size=11),
+    ))
+    fig.update_layout(
+        xaxis=dict(
+            title="置信度",
+            range=[0, 1.15],
+            tickformat=".0%",
+            color="#5a5a5a",
+            gridcolor="rgba(0,0,0,0.06)",
+        ),
+        yaxis=dict(color="#2c2c2c", 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=130, r=60),
+        height=320,
+        showlegend=False,
+    )
+    st.plotly_chart(fig, use_container_width=True)
+
+    # ─── Pie Chart ──────────────────────────────────────────────────────────
+    col_pie, col_advice = st.columns([1, 1])
+
+    with col_pie:
+        st.subheader("📈 置信度分布")
+        fig_pie = go.Figure()
+        fig_pie.add_trace(go.Pie(
+            labels=display_names,
+            values=scores,
+            marker=dict(colors=colors),
+            textinfo="label+percent",
+            textfont=dict(size=10),
+            hole=0.45,
+        ))
+        fig_pie.update_layout(
+            paper_bgcolor="rgba(0,0,0,0)",
+            font=dict(color="#2c2c2c", size=10),
+            margin=dict(t=10, b=10, l=10, r=10),
+            height=300,
+            showlegend=False,
+        )
+        st.plotly_chart(fig_pie, use_container_width=True)
+
+    # ─── Disease Advice ─────────────────────────────────────────────────────
+    with col_advice:
+        st.subheader("💡 防治建议")
+        if is_healthy:
+            st.success(f"叶片状态健康，继续保持良好的田间管理！")
+        else:
+            st.error(f"检测到 **{disease['name']}** 病害")
+        st.info(disease["advice"])
+
+        # 如果置信度不高，给出提示
+        if top_score < 0.6:
+            st.warning(
+                "⚠️ 置信度较低，建议：\n"
+                "- 确保图片清晰且病变部位可见\n"
+                "- 尝试拍摄完整叶片\n"
+                "- 可咨询专业植保人员确认"
+            )
+
+else:
+    # 无图片上传时的占位内容
+    st.info("👈 请在左侧上传一张香蕉叶片图片开始识别")
+
+    # 展示示例区域
+    st.subheader("📖 支持识别的病害类型")
+    cols = st.columns(4)
+    for i, (label_key, info) in enumerate(DISEASE_INFO.items()):
+        with cols[i % 4]:
+            with st.container(border=True):
+                st.markdown(
+                    f"<div style='color:{info['color']}; font-size:24px; font-weight:bold;'>●</div>",
+                    unsafe_allow_html=True,
+                )
+                st.markdown(f"**{info['name']}**")
+                st.caption(info["name_en"])
+
+st.divider()
+st.caption("香蕉叶病害智能识别系统 · 基于 ViT (Vision Transformer) · 仅供参考，确诊请咨询专业植保人员")

justfile

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+# Justfile for banana-disease-leaf 香蕉叶病害智能识别系统
+# 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 numpy torch transformers pillow
+
+# 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 banana-disease-leaf!")
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

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+[project]
+name = "banana-disease-leaf"
+version = "0.1.0"
+description = "香蕉叶病害智能识别系统 - 基于 ViT 视觉变换器的香蕉叶片病害分类应用"
+readme = "README.md"
+requires-python = ">=3.14"
+dependencies = [
+    "numpy>=2.3.5",
+    "plotly>=6.5.0",
+    "ruff>=0.14.8",
+    "streamlit==1.52.1",
+    "torch>=2.7.0",
+    "transformers>=4.52.0",
+    "pillow>=11.2.0",
+]
+
+[[tool.uv.index]]
+url = "https://mirrors.aliyun.com/pypi/simple"
+default = true