From 75e1dcb0ebd8a2a4d7be28b32747051231203f5f Mon Sep 17 00:00:00 2001 From: RYDE-WORK Date: Mon, 26 Jan 2026 00:09:21 +0800 Subject: [PATCH] Add UI --- Makefile | 19 ++ app/api.py | 244 ++++++++++++++++++++++++ app/app.py | 395 +++++++++++++++++++++++++++++++++++++++ pixi.lock | 533 ++++++++++++++++++++++++++++++++++++++++++++++++++++- pixi.toml | 4 + 5 files changed, 1190 insertions(+), 5 deletions(-) create mode 100644 app/api.py create mode 100644 app/app.py diff --git a/Makefile b/Makefile index 3183091..8d7182b 100644 --- a/Makefile +++ b/Makefile @@ -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 # diff --git a/app/api.py b/app/api.py new file mode 100644 index 0000000..61af646 --- /dev/null +++ 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, + ) + diff --git a/app/app.py b/app/app.py new file mode 100644 index 0000000..6b3a093 --- /dev/null +++ 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(""" + +""", 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('

🧬 LNP 配方优化系统

', unsafe_allow_html=True) + st.markdown('

基于深度学习的脂质纳米颗粒配方智能优选

', 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() + diff --git a/pixi.lock b/pixi.lock index a0ccf55..e3f6800 100644 --- 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 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda - pypi: https://files.pythonhosted.org/packages/64/88/c7083fc61120ab661c5d0b82cb77079fc1429d3f913a456c1c82cf4658f7/alabaster-0.7.13-py3-none-any.whl + - pypi: https://files.pythonhosted.org/packages/9b/52/4a86a4fa1cc2aae79137cc9510b7080c3e5aede2310d14fae5486feec7f7/altair-5.4.1-py3-none-any.whl + - pypi: https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl + - 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