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Update app.py
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RYDE-WORK
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app/app.py
143
app/app.py
@ -27,13 +27,13 @@ AVAILABLE_ORGANS = [
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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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"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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@ -146,19 +146,20 @@ def format_results_dataframe(results: dict) -> pd.DataFrame:
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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"Biodist_{target_organ}": f"{f['target_biodist']:.4f}",
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"阳离子脂质/mRNA": f["cationic_lipid_to_mrna_ratio"],
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"阳离子脂质(mol)": f["cationic_lipid_mol_ratio"],
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"磷脂(mol)": f["phospholipid_mol_ratio"],
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"胆固醇(mol)": f["cholesterol_mol_ratio"],
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"PEG脂质(mol)": f["peg_lipid_mol_ratio"],
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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"Biodist_{organ}"] = f"{value:.4f}"
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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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@ -184,7 +185,7 @@ def main():
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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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# st.header("⚙️ 参数设置")
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# API 状态
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if api_online:
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@ -193,7 +194,7 @@ def main():
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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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# st.divider()
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# SMILES 输入
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st.subheader("🔬 分子结构")
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@ -206,18 +207,18 @@ def main():
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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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# 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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# 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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@ -228,7 +229,7 @@ def main():
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index=0,
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)
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st.divider()
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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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@ -294,8 +295,8 @@ def main():
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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 Biodistribution",
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f"{best_score:.4f}",
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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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@ -333,61 +334,61 @@ def main():
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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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# 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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# 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 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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# 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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# 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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# 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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# ### 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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# 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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# ### 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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# 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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