Add UI

2026-03-21 09:36:32 +08:00 · 2026-01-26 00:09:21 +08:00 · 2026-01-26 00:09:21 +08:00 · 75e1dcb0eb
commit 75e1dcb0eb
parent 982e98cced
5 changed files with 1190 additions and 5 deletions
--- a/19
+++ b/19
@ -180,6 +180,25 @@ test: requirements
 optimize: requirements
 	$(PYTHON_INTERPRETER) -m app.optimize --smiles "$(SMILES)" --organ $(ORGAN) $(DEVICE_FLAG)
 ## Start FastAPI backend server (port 8000)
 .PHONY: api
 api: requirements
 	uvicorn app.api:app --host 0.0.0.0 --port 8000 --reload
 ## Start Streamlit frontend app (port 8501)
 .PHONY: webapp
 webapp: requirements
 	streamlit run app/app.py --server.port 8501
 ## Start both API and webapp (run in separate terminals)
 .PHONY: serve
 serve:
 	@echo "请在两个终端分别运行:"
 	@echo "  终端 1: make api"
 	@echo "  终端 2: make webapp"
 	@echo ""
 	@echo "然后访问: http://localhost:8501"
 #################################################################################
 # Self Documenting Commands                                                     #
--- a/app/api.py
+++ b/app/api.py
@ -0,0 +1,244 @@
 """
 FastAPI 配方优化 API
 启动服务:
    uvicorn app.api:app --host 0.0.0.0 --port 8000 --reload
 """
 import os
 from pathlib import Path
 from typing import List, Dict, Optional
 from contextlib import asynccontextmanager
 import torch
 from fastapi import FastAPI, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel, Field
 from loguru import logger
 from lnp_ml.config import MODELS_DIR
 from lnp_ml.modeling.predict import load_model
 from app.optimize import (
    optimize,
    format_results,
    AVAILABLE_ORGANS,
    TARGET_BIODIST,
 )
 # ============ Pydantic Models ============
 class OptimizeRequest(BaseModel):
    """优化请求"""
    smiles: str = Field(..., description="Cationic lipid SMILES string")
    organ: str = Field(..., description="Target organ for optimization")
    top_k: int = Field(default=20, ge=1, le=100, description="Number of top formulations")
    class Config:
        json_schema_extra = {
            "example": {
                "smiles": "CC(C)NCCNC(C)C",
                "organ": "liver",
                "top_k": 20
            }
        }
 class FormulationResult(BaseModel):
    """单个配方结果"""
    rank: int
    target_biodist: float
    cationic_lipid_to_mrna_ratio: float
    cationic_lipid_mol_ratio: float
    phospholipid_mol_ratio: float
    cholesterol_mol_ratio: float
    peg_lipid_mol_ratio: float
    helper_lipid: str
    route: str
    all_biodist: Dict[str, float]
 class OptimizeResponse(BaseModel):
    """优化响应"""
    smiles: str
    target_organ: str
    formulations: List[FormulationResult]
    message: str
 class HealthResponse(BaseModel):
    """健康检查响应"""
    status: str
    model_loaded: bool
    device: str
    available_organs: List[str]
 # ============ Global State ============
 class ModelState:
    """模型状态管理"""
    model = None
    device = None
    model_path = None
 state = ModelState()
 # ============ Lifespan ============
@asynccontextmanager
 async def lifespan(app: FastAPI):
    """应用生命周期管理：启动时加载模型"""
    # Startup
    logger.info("Starting API server...")
    # 确定设备
    if torch.cuda.is_available():
        device_str = "cuda"
    elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
        device_str = "mps"
    else:
        device_str = "cpu"
    # 可通过环境变量覆盖
    device_str = os.environ.get("DEVICE", device_str)
    state.device = torch.device(device_str)
    logger.info(f"Using device: {state.device}")
    # 加载模型
    model_path = Path(os.environ.get("MODEL_PATH", MODELS_DIR / "final" / "model.pt"))
    state.model_path = model_path
    logger.info(f"Loading model from {model_path}...")
    try:
        state.model = load_model(model_path, state.device)
        logger.success("Model loaded successfully!")
    except Exception as e:
        logger.error(f"Failed to load model: {e}")
        raise
    yield
    # Shutdown
    logger.info("Shutting down API server...")
    state.model = None
    torch.cuda.empty_cache() if torch.cuda.is_available() else None
 # ============ FastAPI App ============
 app = FastAPI(
    title="LNP 配方优化 API",
    description="基于深度学习的 LNP 纳米颗粒配方优化服务",
    version="1.0.0",
    lifespan=lifespan,
 )
 # CORS 配置
 app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
 )
 # ============ Endpoints ============
@app.get("/", response_model=HealthResponse)
 async def health_check():
    """健康检查"""
    return HealthResponse(
        status="healthy" if state.model is not None else "model_not_loaded",
        model_loaded=state.model is not None,
        device=str(state.device),
        available_organs=AVAILABLE_ORGANS,
    )
@app.get("/organs", response_model=List[str])
 async def get_available_organs():
    """获取可用的目标器官列表"""
    return AVAILABLE_ORGANS
@app.post("/optimize", response_model=OptimizeResponse)
 async def optimize_formulation(request: OptimizeRequest):
    """
    执行配方优化
    通过迭代式 Grid Search 寻找最大化目标器官 Biodistribution 的最优配方。
    """
    # 验证模型状态
    if state.model is None:
        raise HTTPException(status_code=503, detail="Model not loaded")
    # 验证器官
    if request.organ not in AVAILABLE_ORGANS:
        raise HTTPException(
            status_code=400,
            detail=f"Invalid organ: {request.organ}. Available: {AVAILABLE_ORGANS}"
        )
    # 验证 SMILES
    if not request.smiles or len(request.smiles.strip()) == 0:
        raise HTTPException(status_code=400, detail="SMILES string cannot be empty")
    logger.info(f"Optimization request: organ={request.organ}, smiles={request.smiles[:50]}...")
    try:
        # 执行优化
        results = optimize(
            smiles=request.smiles,
            organ=request.organ,
            model=state.model,
            device=state.device,
            top_k=request.top_k,
            batch_size=256,
        )
        # 转换结果
        formulations = []
        for i, f in enumerate(results):
            formulations.append(FormulationResult(
                rank=i + 1,
                target_biodist=f.get_biodist(request.organ),
                cationic_lipid_to_mrna_ratio=f.cationic_lipid_to_mrna_ratio,
                cationic_lipid_mol_ratio=f.cationic_lipid_mol_ratio,
                phospholipid_mol_ratio=f.phospholipid_mol_ratio,
                cholesterol_mol_ratio=f.cholesterol_mol_ratio,
                peg_lipid_mol_ratio=f.peg_lipid_mol_ratio,
                helper_lipid=f.helper_lipid,
                route=f.route,
                all_biodist={
                    col.replace("Biodistribution_", ""): f.biodist_predictions.get(col, 0.0)
                    for col in TARGET_BIODIST
                },
            ))
        logger.success(f"Optimization completed: {len(formulations)} formulations")
        return OptimizeResponse(
            smiles=request.smiles,
            target_organ=request.organ,
            formulations=formulations,
            message=f"Successfully found top {len(formulations)} formulations for {request.organ}",
        )
    except Exception as e:
        logger.error(f"Optimization failed: {e}")
        raise HTTPException(status_code=500, detail=str(e))
 if __name__ == "__main__":
    import uvicorn
    uvicorn.run(
        "app.api:app",
        host="0.0.0.0",
        port=8000,
        reload=True,
    )
--- a/app/app.py
+++ b/app/app.py
@ -0,0 +1,395 @@
 """
 Streamlit 配方优化交互界面
 启动应用:
    streamlit run app/app.py
 """
 import io
 from datetime import datetime
 import httpx
 import pandas as pd
 import streamlit as st
 # ============ 配置 ============
 API_URL = "http://localhost:8000"
 AVAILABLE_ORGANS = [
    "liver",
    "spleen", 
    "lung",
    "heart",
    "kidney",
    "muscle",
    "lymph_nodes",
 ]
 ORGAN_LABELS = {
    "liver": "🫀 肝脏 (Liver)",
    "spleen": "🟣 脾脏 (Spleen)",
    "lung": "🫁 肺 (Lung)",
    "heart": "❤️ 心脏 (Heart)",
    "kidney": "🫘 肾脏 (Kidney)",
    "muscle": "💪 肌肉 (Muscle)",
    "lymph_nodes": "🔵 淋巴结 (Lymph Nodes)",
 }
 # ============ 页面配置 ============
 st.set_page_config(
    page_title="LNP 配方优化",
    page_icon="🧬",
    layout="wide",
    initial_sidebar_state="expanded",
 )
 # ============ 自定义样式 ============
 st.markdown("""
 <style>
    /* 主标题样式 */
    .main-title {
        font-size: 2.5rem;
        font-weight: 700;
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        text-align: center;
        margin-bottom: 0.5rem;
    }
    /* 副标题样式 */
    .sub-title {
        font-size: 1.1rem;
        color: #6c757d;
        text-align: center;
        margin-bottom: 2rem;
    }
    /* 结果卡片 */
    .result-card {
        background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
        border-radius: 12px;
        padding: 1.5rem;
        margin-bottom: 1rem;
    }
    /* 指标高亮 */
    .metric-highlight {
        font-size: 2rem;
        font-weight: 700;
        color: #667eea;
    }
    /* 侧边栏样式 */
    .sidebar-section {
        background: #f8f9fa;
        border-radius: 8px;
        padding: 1rem;
        margin-bottom: 1rem;
    }
    /* 状态指示器 */
    .status-online {
        color: #28a745;
        font-weight: 600;
    }
    .status-offline {
        color: #dc3545;
        font-weight: 600;
    }
    /* 表格样式优化 */
    .dataframe {
        font-size: 0.85rem;
    }
 </style>
 """, unsafe_allow_html=True)
 # ============ 辅助函数 ============
 def check_api_status() -> bool:
    """检查 API 状态"""
    try:
        with httpx.Client(timeout=5) as client:
            response = client.get(f"{API_URL}/")
            return response.status_code == 200
    except:
        return False
 def call_optimize_api(smiles: str, organ: str, top_k: int = 20) -> dict:
    """调用优化 API"""
    with httpx.Client(timeout=300) as client:  # 5 分钟超时
        response = client.post(
            f"{API_URL}/optimize",
            json={
                "smiles": smiles,
                "organ": organ,
                "top_k": top_k,
            },
        )
        response.raise_for_status()
        return response.json()
 def format_results_dataframe(results: dict) -> pd.DataFrame:
    """将 API 结果转换为 DataFrame"""
    formulations = results["formulations"]
    target_organ = results["target_organ"]
    rows = []
    for f in formulations:
        row = {
            "排名": f["rank"],
            f"Biodist_{target_organ}": f"{f['target_biodist']:.4f}",
            "阳离子脂质/mRNA": f["cationic_lipid_to_mrna_ratio"],
            "阳离子脂质(mol)": f["cationic_lipid_mol_ratio"],
            "磷脂(mol)": f["phospholipid_mol_ratio"],
            "胆固醇(mol)": f["cholesterol_mol_ratio"],
            "PEG脂质(mol)": f["peg_lipid_mol_ratio"],
            "辅助脂质": f["helper_lipid"],
            "给药途径": f["route"],
        }
        # 添加其他器官的 biodist
        for organ, value in f["all_biodist"].items():
            if organ != target_organ:
                row[f"Biodist_{organ}"] = f"{value:.4f}"
        rows.append(row)
    return pd.DataFrame(rows)
 def create_export_csv(df: pd.DataFrame, smiles: str, organ: str) -> str:
    """创建导出用的 CSV 内容"""
    # 添加元信息
    meta_info = f"# LNP 配方优化结果\n# SMILES: {smiles}\n# 目标器官: {organ}\n# 导出时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
    csv_content = df.to_csv(index=False)
    return meta_info + csv_content
 # ============ 主界面 ============
 def main():
    # 标题
    st.markdown('<h1 class="main-title">🧬 LNP 配方优化系统</h1>', unsafe_allow_html=True)
    st.markdown('<p class="sub-title">基于深度学习的脂质纳米颗粒配方智能优选</p>', unsafe_allow_html=True)
    # 检查 API 状态
    api_online = check_api_status()
    # ========== 侧边栏 ==========
    with st.sidebar:
        st.header("⚙️ 参数设置")
        # API 状态
        if api_online:
            st.success("🟢 API 服务在线")
        else:
            st.error("🔴 API 服务离线")
            st.info("请先启动 API 服务:\n```\nuvicorn app.api:app --port 8000\n```")
        st.divider()
        # SMILES 输入
        st.subheader("🔬 分子结构")
        smiles_input = st.text_area(
            "输入阳离子脂质 SMILES",
            value="",
            height=100,
            placeholder="例如: CC(C)NCCNC(C)C",
            help="输入阳离子脂质的 SMILES 字符串",
        )
        # 示例 SMILES
        with st.expander("📋 示例 SMILES"):
            example_smiles = {
                "DLin-MC3-DMA": "CC(C)=CCCC(C)=CCCC(C)=CCN(C)CCCCCCCCOC(=O)CCCCCCC/C=C\\CCCCCCCC",
                "简单胺": "CC(C)NCCNC(C)C",
                "长链胺": "CCCCCCCCCCCCNCCNCCCCCCCCCCCC",
            }
            for name, smi in example_smiles.items():
                if st.button(f"使用 {name}", key=f"example_{name}"):
                    st.session_state["smiles_input"] = smi
                    st.rerun()
        st.divider()
        # 目标器官选择
        st.subheader("🎯 目标器官")
        selected_organ = st.selectbox(
            "选择优化目标器官",
            options=AVAILABLE_ORGANS,
            format_func=lambda x: ORGAN_LABELS.get(x, x),
            index=0,
        )
        st.divider()
        # 高级选项
        with st.expander("🔧 高级选项"):
            top_k = st.slider(
                "返回配方数量",
                min_value=5,
                max_value=50,
                value=20,
                step=5,
            )
        st.divider()
        # 优化按钮
        optimize_button = st.button(
            "🚀 开始配方优选",
            type="primary",
            use_container_width=True,
            disabled=not api_online or not smiles_input.strip(),
        )
    # ========== 主内容区 ==========
    # 使用 session state 存储结果
    if "results" not in st.session_state:
        st.session_state["results"] = None
    if "results_df" not in st.session_state:
        st.session_state["results_df"] = None
    # 执行优化
    if optimize_button and smiles_input.strip():
        with st.spinner("🔄 正在优化配方，请稍候..."):
            try:
                results = call_optimize_api(
                    smiles=smiles_input.strip(),
                    organ=selected_organ,
                    top_k=top_k,
                )
                st.session_state["results"] = results
                st.session_state["results_df"] = format_results_dataframe(results)
                st.session_state["smiles_used"] = smiles_input.strip()
                st.session_state["organ_used"] = selected_organ
                st.success("✅ 优化完成！")
            except httpx.RequestError as e:
                st.error(f"❌ API 请求失败: {e}")
            except Exception as e:
                st.error(f"❌ 发生错误: {e}")
    # 显示结果
    if st.session_state["results"] is not None:
        results = st.session_state["results"]
        df = st.session_state["results_df"]
        # 结果概览
        col1, col2, col3 = st.columns(3)
        with col1:
            st.metric(
                "目标器官",
                ORGAN_LABELS.get(results["target_organ"], results["target_organ"]).split(" ")[0],
            )
        with col2:
            best_score = results["formulations"][0]["target_biodist"]
            st.metric(
                "最优 Biodistribution",
                f"{best_score:.4f}",
            )
        with col3:
            st.metric(
                "优选配方数",
                len(results["formulations"]),
            )
        st.divider()
        # 结果表格
        st.subheader("📊 优选配方列表")
        # 导出按钮行
        col_export, col_spacer = st.columns([1, 4])
        with col_export:
            csv_content = create_export_csv(
                df,
                st.session_state.get("smiles_used", ""),
                st.session_state.get("organ_used", ""),
            )
            st.download_button(
                label="📥 导出 CSV",
                data=csv_content,
                file_name=f"lnp_optimization_{results['target_organ']}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
                mime="text/csv",
            )
        # 显示表格
        st.dataframe(
            df,
            use_container_width=True,
            hide_index=True,
            height=600,
        )
        # 详细信息
        with st.expander("🔍 查看最优配方详情"):
            best = results["formulations"][0]
            col1, col2 = st.columns(2)
            with col1:
                st.markdown("**配方参数**")
                st.json({
                    "阳离子脂质/mRNA 比例": best["cationic_lipid_to_mrna_ratio"],
                    "阳离子脂质 (mol%)": best["cationic_lipid_mol_ratio"],
                    "磷脂 (mol%)": best["phospholipid_mol_ratio"],
                    "胆固醇 (mol%)": best["cholesterol_mol_ratio"],
                    "PEG 脂质 (mol%)": best["peg_lipid_mol_ratio"],
                    "辅助脂质": best["helper_lipid"],
                    "给药途径": best["route"],
                })
            with col2:
                st.markdown("**各器官 Biodistribution 预测**")
                biodist_df = pd.DataFrame([
                    {"器官": ORGAN_LABELS.get(k, k), "Biodistribution": f"{v:.4f}"}
                    for k, v in best["all_biodist"].items()
                ])
                st.dataframe(biodist_df, hide_index=True, use_container_width=True)
    else:
        # 欢迎信息
        st.info("👈 请在左侧输入 SMILES 并选择目标器官，然后点击「开始配方优选」")
        # 使用说明
        with st.expander("📖 使用说明"):
            st.markdown("""
            ### 如何使用
            1. **输入 SMILES**: 在左侧输入框中输入阳离子脂质的 SMILES 字符串
            2. **选择目标器官**: 选择您希望优化的器官靶向
            3. **点击优选**: 系统将自动搜索最优配方组合
            4. **查看结果**: 右侧将显示 Top-20 优选配方
            5. **导出数据**: 点击导出按钮将结果保存为 CSV 文件
            ### 优化参数
            系统会优化以下配方参数:
            - **阳离子脂质/mRNA 比例**: 0.05 - 0.30
            - **阳离子脂质 mol 比例**: 0.05 - 0.80
            - **磷脂 mol 比例**: 0.00 - 0.80
            - **胆固醇 mol 比例**: 0.00 - 0.80
            - **PEG 脂质 mol 比例**: 0.00 - 0.05
            - **辅助脂质**: DOPE / DSPC / DOTAP
            - **给药途径**: 静脉注射 / 肌肉注射
            ### 约束条件
            mol 比例之和 = 1 (阳离子脂质 + 磷脂 + 胆固醇 + PEG 脂质)
            """)
 if __name__ == "__main__":
    main()
--- a/pixi.lock
+++ b/pixi.lock
@ -53,7 +53,14 @@ environments:
      - conda: https://conda.anaconda.org/conda-forge/linux-64/xz-tools-5.8.1-hb9d3cd8_2.conda
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