From 68119df128d01f870a239799b3aab06232f7d655 Mon Sep 17 00:00:00 2001 From: RYDE-WORK Date: Mon, 26 Jan 2026 10:33:50 +0800 Subject: [PATCH] Update app.py --- app/app.py | 143 +++++++++++++++++++++++++++-------------------------- 1 file changed, 72 insertions(+), 71 deletions(-) diff --git a/app/app.py b/app/app.py index 6b3a093..4ecb698 100644 --- a/app/app.py +++ b/app/app.py @@ -27,13 +27,13 @@ AVAILABLE_ORGANS = [ ] 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)", + "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 ============ @@ -146,19 +146,20 @@ def format_results_dataframe(results: dict) -> pd.DataFrame: 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"], + # f"{target_organ}分布": f"{f['target_biodist']*100:.2f}%", + f"{target_organ}分布": f"{f['target_biodist']*100:.8f}%", + "阳离子脂质/mRNA比例": f["cationic_lipid_to_mrna_ratio"], + "阳离子脂质(mol)比例": f["cationic_lipid_mol_ratio"], + "磷脂(mol)比例": f["phospholipid_mol_ratio"], + "胆固醇(mol)比例": f["cholesterol_mol_ratio"], + "PEG脂质(mol)比例": 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}" + row[f"{organ}分布"] = f"{value*100:.2f}%" rows.append(row) return pd.DataFrame(rows) @@ -184,7 +185,7 @@ def main(): # ========== 侧边栏 ========== with st.sidebar: - st.header("⚙️ 参数设置") + # st.header("⚙️ 参数设置") # API 状态 if api_online: @@ -193,7 +194,7 @@ def main(): 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() + # st.divider() # SMILES 输入 st.subheader("🔬 分子结构") @@ -206,18 +207,18 @@ def main(): ) # 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() + # 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() + # st.divider() # 目标器官选择 st.subheader("🎯 目标器官") @@ -228,7 +229,7 @@ def main(): index=0, ) - st.divider() + # st.divider() # 高级选项 with st.expander("🔧 高级选项"): @@ -294,8 +295,8 @@ def main(): with col2: best_score = results["formulations"][0]["target_biodist"] st.metric( - "最优 Biodistribution", - f"{best_score:.4f}", + "最优分布", + f"{best_score*100:.2f}%", ) with col3: @@ -333,61 +334,61 @@ def main(): ) # 详细信息 - with st.expander("🔍 查看最优配方详情"): - best = results["formulations"][0] + # with st.expander("🔍 查看最优配方详情"): + # best = results["formulations"][0] - col1, col2 = st.columns(2) + # 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 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) + # 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 + # 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 + # 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 + # ### 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 + # 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 + # ### 约束条件 - mol 比例之和 = 1 (阳离子脂质 + 磷脂 + 胆固醇 + PEG 脂质) - """) + # mol 比例之和 = 1 (阳离子脂质 + 磷脂 + 胆固醇 + PEG 脂质) + # """) if __name__ == "__main__":