mirror of
https://github.com/RYDE-WORK/lnp_ml.git
synced 2026-03-21 09:36:32 +08:00
402 lines
12 KiB
Python
402 lines
12 KiB
Python
"""
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Streamlit 配方优化交互界面
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启动应用:
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streamlit run app/app.py
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Docker 环境变量:
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API_URL: API 服务地址 (默认: http://localhost:8000)
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"""
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import io
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import os
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from datetime import datetime
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import httpx
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import pandas as pd
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import streamlit as st
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# ============ 配置 ============
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# 从环境变量读取 API 地址，支持 Docker 环境
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API_URL = os.environ.get("API_URL", "http://localhost:8000")
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AVAILABLE_ORGANS = [
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"liver",
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"spleen",
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"lung",
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"heart",
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"kidney",
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"muscle",
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"lymph_nodes",
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]
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ORGAN_LABELS = {
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"liver": "肝脏 (Liver)",
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"spleen": "脾脏 (Spleen)",
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"lung": "肺 (Lung)",
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"heart": "心脏 (Heart)",
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"kidney": "肾脏 (Kidney)",
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"muscle": "肌肉 (Muscle)",
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"lymph_nodes": "淋巴结 (Lymph Nodes)",
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}
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# ============ 页面配置 ============
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st.set_page_config(
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page_title="LNP 配方优化",
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page_icon="🧬",
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layout="wide",
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initial_sidebar_state="expanded",
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)
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# ============ 自定义样式 ============
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st.markdown("""
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<style>
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/* 主标题样式 */
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.main-title {
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font-size: 2.5rem;
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font-weight: 700;
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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text-align: center;
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margin-bottom: 0.5rem;
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}
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/* 副标题样式 */
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.sub-title {
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font-size: 1.1rem;
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color: #6c757d;
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text-align: center;
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margin-bottom: 2rem;
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}
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/* 结果卡片 */
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.result-card {
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background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
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border-radius: 12px;
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padding: 1.5rem;
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margin-bottom: 1rem;
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}
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/* 指标高亮 */
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.metric-highlight {
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font-size: 2rem;
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font-weight: 700;
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color: #667eea;
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}
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/* 侧边栏样式 */
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.sidebar-section {
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background: #f8f9fa;
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border-radius: 8px;
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padding: 1rem;
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margin-bottom: 1rem;
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}
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/* 状态指示器 */
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.status-online {
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color: #28a745;
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font-weight: 600;
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}
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.status-offline {
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color: #dc3545;
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font-weight: 600;
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}
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/* 表格样式优化 */
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.dataframe {
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font-size: 0.85rem;
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}
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</style>
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""", unsafe_allow_html=True)
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# ============ 辅助函数 ============
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def check_api_status() -> bool:
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"""检查 API 状态"""
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try:
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with httpx.Client(timeout=5) as client:
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response = client.get(f"{API_URL}/")
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return response.status_code == 200
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except:
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return False
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def call_optimize_api(smiles: str, organ: str, top_k: int = 20) -> dict:
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"""调用优化 API"""
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with httpx.Client(timeout=300) as client: # 5 分钟超时
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response = client.post(
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f"{API_URL}/optimize",
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json={
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"smiles": smiles,
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"organ": organ,
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"top_k": top_k,
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},
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)
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response.raise_for_status()
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return response.json()
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def format_results_dataframe(results: dict) -> pd.DataFrame:
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"""将 API 结果转换为 DataFrame"""
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formulations = results["formulations"]
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target_organ = results["target_organ"]
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rows = []
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for f in formulations:
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row = {
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"排名": f["rank"],
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# f"{target_organ}分布": f"{f['target_biodist']*100:.2f}%",
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f"{target_organ}分布": f"{f['target_biodist']*100:.8f}%",
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"阳离子脂质/mRNA比例": f["cationic_lipid_to_mrna_ratio"],
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"阳离子脂质(mol)比例": f["cationic_lipid_mol_ratio"],
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"磷脂(mol)比例": f["phospholipid_mol_ratio"],
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"胆固醇(mol)比例": f["cholesterol_mol_ratio"],
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"PEG脂质(mol)比例": f["peg_lipid_mol_ratio"],
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"辅助脂质": f["helper_lipid"],
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"给药途径": f["route"],
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}
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# 添加其他器官的 biodist
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for organ, value in f["all_biodist"].items():
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if organ != target_organ:
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row[f"{organ}分布"] = f"{value*100:.2f}%"
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rows.append(row)
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return pd.DataFrame(rows)
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def create_export_csv(df: pd.DataFrame, smiles: str, organ: str) -> str:
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"""创建导出用的 CSV 内容"""
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# 添加元信息
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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"
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csv_content = df.to_csv(index=False)
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return meta_info + csv_content
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# ============ 主界面 ============
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def main():
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# 标题
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st.markdown('<h1 class="main-title">🧬 LNP 配方优化系统</h1>', unsafe_allow_html=True)
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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)
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# 检查 API 状态
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api_online = check_api_status()
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# ========== 侧边栏 ==========
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with st.sidebar:
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# st.header("⚙️ 参数设置")
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# API 状态
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if api_online:
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st.success("🟢 API 服务在线")
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else:
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st.error("🔴 API 服务离线")
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st.info("请先启动 API 服务:\n```\nuvicorn app.api:app --port 8000\n```")
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# st.divider()
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# SMILES 输入
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st.subheader("🔬 分子结构")
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smiles_input = st.text_area(
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"输入阳离子脂质 SMILES",
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value="",
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height=100,
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placeholder="例如: CC(C)NCCNC(C)C",
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help="输入阳离子脂质的 SMILES 字符串",
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)
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# 示例 SMILES
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# with st.expander("📋 示例 SMILES"):
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# example_smiles = {
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# "DLin-MC3-DMA": "CC(C)=CCCC(C)=CCCC(C)=CCN(C)CCCCCCCCOC(=O)CCCCCCC/C=C\\CCCCCCCC",
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# "简单胺": "CC(C)NCCNC(C)C",
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# "长链胺": "CCCCCCCCCCCCNCCNCCCCCCCCCCCC",
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# }
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# for name, smi in example_smiles.items():
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# if st.button(f"使用 {name}", key=f"example_{name}"):
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# st.session_state["smiles_input"] = smi
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# st.rerun()
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# st.divider()
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# 目标器官选择
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st.subheader("🎯 目标器官")
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selected_organ = st.selectbox(
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"选择优化目标器官",
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options=AVAILABLE_ORGANS,
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format_func=lambda x: ORGAN_LABELS.get(x, x),
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index=0,
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)
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# st.divider()
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# 高级选项
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with st.expander("🔧 高级选项"):
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top_k = st.slider(
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"返回配方数量",
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min_value=5,
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max_value=50,
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value=20,
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step=5,
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)
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st.divider()
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# 优化按钮
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optimize_button = st.button(
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"🚀 开始配方优选",
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type="primary",
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use_container_width=True,
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disabled=not api_online or not smiles_input.strip(),
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)
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# ========== 主内容区 ==========
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# 使用 session state 存储结果
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if "results" not in st.session_state:
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st.session_state["results"] = None
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if "results_df" not in st.session_state:
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st.session_state["results_df"] = None
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# 执行优化
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if optimize_button and smiles_input.strip():
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with st.spinner("🔄 正在优化配方，请稍候..."):
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try:
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results = call_optimize_api(
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smiles=smiles_input.strip(),
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organ=selected_organ,
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top_k=top_k,
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)
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st.session_state["results"] = results
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st.session_state["results_df"] = format_results_dataframe(results)
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st.session_state["smiles_used"] = smiles_input.strip()
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st.session_state["organ_used"] = selected_organ
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st.success("✅ 优化完成！")
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except httpx.RequestError as e:
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st.error(f"❌ API 请求失败: {e}")
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except Exception as e:
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st.error(f"❌ 发生错误: {e}")
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# 显示结果
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if st.session_state["results"] is not None:
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results = st.session_state["results"]
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df = st.session_state["results_df"]
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# 结果概览
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col1, col2, col3 = st.columns(3)
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with col1:
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st.metric(
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"目标器官",
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ORGAN_LABELS.get(results["target_organ"], results["target_organ"]).split(" ")[0],
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)
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with col2:
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best_score = results["formulations"][0]["target_biodist"]
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st.metric(
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"最优分布",
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f"{best_score*100:.2f}%",
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)
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with col3:
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st.metric(
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"优选配方数",
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len(results["formulations"]),
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)
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st.divider()
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# 结果表格
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st.subheader("📊 优选配方列表")
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# 导出按钮行
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col_export, col_spacer = st.columns([1, 4])
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with col_export:
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csv_content = create_export_csv(
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df,
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st.session_state.get("smiles_used", ""),
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st.session_state.get("organ_used", ""),
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)
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st.download_button(
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label="📥 导出 CSV",
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data=csv_content,
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file_name=f"lnp_optimization_{results['target_organ']}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
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mime="text/csv",
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)
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# 显示表格
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st.dataframe(
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df,
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use_container_width=True,
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hide_index=True,
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height=600,
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)
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# 详细信息
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# with st.expander("🔍 查看最优配方详情"):
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# best = results["formulations"][0]
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# col1, col2 = st.columns(2)
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# with col1:
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# st.markdown("**配方参数**")
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# st.json({
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# "阳离子脂质/mRNA 比例": best["cationic_lipid_to_mrna_ratio"],
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# "阳离子脂质 (mol%)": best["cationic_lipid_mol_ratio"],
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# "磷脂 (mol%)": best["phospholipid_mol_ratio"],
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# "胆固醇 (mol%)": best["cholesterol_mol_ratio"],
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# "PEG 脂质 (mol%)": best["peg_lipid_mol_ratio"],
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# "辅助脂质": best["helper_lipid"],
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# "给药途径": best["route"],
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# })
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# with col2:
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# st.markdown("**各器官 Biodistribution 预测**")
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# biodist_df = pd.DataFrame([
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# {"器官": ORGAN_LABELS.get(k, k), "Biodistribution": f"{v:.4f}"}
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# for k, v in best["all_biodist"].items()
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# ])
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# st.dataframe(biodist_df, hide_index=True, use_container_width=True)
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else:
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# 欢迎信息
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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」")
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# 使用说明
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# with st.expander("📖 使用说明"):
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# st.markdown("""
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# ### 如何使用
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# 1. **输入 SMILES**: 在左侧输入框中输入阳离子脂质的 SMILES 字符串
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# 2. **选择目标器官**: 选择您希望优化的器官靶向
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# 3. **点击优选**: 系统将自动搜索最优配方组合
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# 4. **查看结果**: 右侧将显示 Top-20 优选配方
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# 5. **导出数据**: 点击导出按钮将结果保存为 CSV 文件
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# ### 优化参数
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# 系统会优化以下配方参数:
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# - **阳离子脂质/mRNA 比例**: 0.05 - 0.30
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# - **阳离子脂质 mol 比例**: 0.05 - 0.80
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# - **磷脂 mol 比例**: 0.00 - 0.80
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# - **胆固醇 mol 比例**: 0.00 - 0.80
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# - **PEG 脂质 mol 比例**: 0.00 - 0.05
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# - **辅助脂质**: DOPE / DSPC / DOTAP
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# - **给药途径**: 静脉注射 / 肌肉注射
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# ### 约束条件
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# mol 比例之和 = 1 (阳离子脂质 + 磷脂 + 胆固醇 + PEG 脂质)
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# """)
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if __name__ == "__main__":
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main()
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