.dockerignore

@@ -1,75 +0,0 @@
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-*.so
-.Python
-*.egg-info/
-.eggs/
-dist/
-build/
-*.egg
-
-# Virtual environments
-.venv/
-venv/
-ENV/
-env/
-.pixi/
-
-# IDE
-.idea/
-.vscode/
-*.swp
-*.swo
-.cursor/
-
-# Git
-.git/
-.gitignore
-
-# Data (不需要打包到镜像)
-data/
-!data/.gitkeep
-
-# Notebooks
-notebooks/
-*.ipynb
-
-# Documentation
-docs/
-
-# Reports
-reports/
-
-# References
-references/
-
-# Scripts (训练脚本不需要)
-scripts/
-
-# Lock files
-pixi.lock
-
-# Tests
-tests/
-.pytest_cache/
-
-# Logs
-*.log
-logs/
-
-# Temporary files
-*.tmp
-*.temp
-.DS_Store
-
-# Models (will be mounted as volume or copied explicitly)
-# Note: models/final/ is copied in Dockerfile
-models/finetune_cv/
-models/pretrain_cv/
-models/mpnn/
-models/*.pt
-models/*.json
-!models/final/
-

Dockerfile

@@ -1,63 +0,0 @@
-# LNP-ML Docker Image
-# 多阶段构建，支持 API 和 Streamlit 两种服务
-
-FROM python:3.8-slim AS base
-
-# 设置环境变量
-ENV PYTHONUNBUFFERED=1 \
-    PYTHONDONTWRITEBYTECODE=1 \
-    PIP_NO_CACHE_DIR=1 \
-    PIP_DISABLE_PIP_VERSION_CHECK=1
-
-WORKDIR /app
-
-# 安装系统依赖
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential \
-    libxrender1 \
-    libxext6 \
-    curl \
-    && rm -rf /var/lib/apt/lists/*
-
-# 复制依赖文件
-COPY requirements.txt .
-
-# 安装 Python 依赖
-RUN pip install --upgrade pip && \
-    pip install -r requirements.txt
-
-# 复制项目代码
-COPY pyproject.toml .
-COPY README.md .
-COPY LICENSE .
-COPY lnp_ml/ ./lnp_ml/
-COPY app/ ./app/
-
-# 安装项目包
-RUN pip install -e .
-
-# 复制模型文件
-COPY models/final/ ./models/final/
-
-# ============ API 服务 ============
-FROM base AS api
-
-EXPOSE 8000
-
-ENV MODEL_PATH=/app/models/final/model.pt
-
-CMD ["uvicorn", "app.api:app", "--host", "0.0.0.0", "--port", "8000"]
-
-# ============ Streamlit 服务 ============
-FROM base AS streamlit
-
-EXPOSE 8501
-
-# Streamlit 配置
-ENV STREAMLIT_SERVER_PORT=8501 \
-    STREAMLIT_SERVER_ADDRESS=0.0.0.0 \
-    STREAMLIT_SERVER_HEADLESS=true \
-    STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
-
-CMD ["streamlit", "run", "app/app.py"]
-

Makefile

@@ -200,48 +200,6 @@ serve:
 	@echo "然后访问: http://localhost:8501"
 
 
-#################################################################################
-# DOCKER COMMANDS                                                               #
-#################################################################################
-
-## Build Docker images
-.PHONY: docker-build
-docker-build:
-	docker compose build
-
-## Start all services with Docker Compose
-.PHONY: docker-up
-docker-up:
-	docker compose up -d
-
-## Stop all Docker services
-.PHONY: docker-down
-docker-down:
-	docker compose down
-
-## View Docker logs
-.PHONY: docker-logs
-docker-logs:
-	docker compose logs -f
-
-## Build and start all services
-.PHONY: docker-serve
-docker-serve: docker-build docker-up
-	@echo ""
-	@echo "🚀 服务已启动!"
-	@echo "   - API:       http://localhost:8000"
-	@echo "   - Web 应用:  http://localhost:8501"
-	@echo ""
-	@echo "查看日志: make docker-logs"
-	@echo "停止服务: make docker-down"
-
-## Clean Docker resources (images, volumes, etc.)
-.PHONY: docker-clean
-docker-clean:
-	docker compose down -v --rmi local
-	docker system prune -f
-
-
 #################################################################################
 # Self Documenting Commands                                                     #
 #################################################################################

app/app.py

@@ -3,13 +3,9 @@ Streamlit 配方优化交互界面
 
 启动应用:
     streamlit run app/app.py
-
-Docker 环境变量:
-    API_URL: API 服务地址 (默认: http://localhost:8000)
 """
 
 import io
-import os
 from datetime import datetime
 
 import httpx
@@ -18,8 +14,7 @@ import streamlit as st
 
 # ============ 配置 ============
 
-# 从环境变量读取 API 地址，支持 Docker 环境
+API_URL = "http://localhost:8000"
-API_URL = os.environ.get("API_URL", "http://localhost:8000")
 
 AVAILABLE_ORGANS = [
     "liver",
@@ -32,13 +27,13 @@ AVAILABLE_ORGANS = [
 ]
 
 ORGAN_LABELS = {
-    "liver": "肝脏 (Liver)",
+    "liver": "🫀 肝脏 (Liver)",
-    "spleen": "脾脏 (Spleen)",
+    "spleen": "🟣 脾脏 (Spleen)",
-    "lung": "肺 (Lung)",
+    "lung": "🫁 肺 (Lung)",
-    "heart": "心脏 (Heart)",
+    "heart": "❤️ 心脏 (Heart)",
-    "kidney": "肾脏 (Kidney)",
+    "kidney": "🫘 肾脏 (Kidney)",
-    "muscle": "肌肉 (Muscle)",
+    "muscle": "💪 肌肉 (Muscle)",
-    "lymph_nodes": "淋巴结 (Lymph Nodes)",
+    "lymph_nodes": "🔵 淋巴结 (Lymph Nodes)",
 }
 
 # ============ 页面配置 ============
@@ -151,20 +146,19 @@ def format_results_dataframe(results: dict) -> pd.DataFrame:
     for f in formulations:
         row = {
             "排名": f["rank"],
-            # f"{target_organ}分布": f"{f['target_biodist']*100:.2f}%",
+            f"Biodist_{target_organ}": f"{f['target_biodist']:.4f}",
-            f"{target_organ}分布": f"{f['target_biodist']*100:.8f}%",
+            "阳离子脂质/mRNA": f["cationic_lipid_to_mrna_ratio"],
-            "阳离子脂质/mRNA比例": f["cationic_lipid_to_mrna_ratio"],
+            "阳离子脂质(mol)": f["cationic_lipid_mol_ratio"],
-            "阳离子脂质(mol)比例": f["cationic_lipid_mol_ratio"],
+            "磷脂(mol)": f["phospholipid_mol_ratio"],
-            "磷脂(mol)比例": f["phospholipid_mol_ratio"],
+            "胆固醇(mol)": f["cholesterol_mol_ratio"],
-            "胆固醇(mol)比例": f["cholesterol_mol_ratio"],
+            "PEG脂质(mol)": f["peg_lipid_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"{organ}分布"] = f"{value*100:.2f}%"
+                row[f"Biodist_{organ}"] = f"{value:.4f}"
         rows.append(row)
     
     return pd.DataFrame(rows)
@@ -190,7 +184,7 @@ 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:
@@ -199,7 +193,7 @@ 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")
@@ -212,18 +206,18 @@ def main():
         )
         
         # 示例 SMILES
-        # with st.expander("📋 示例 SMILES"):
+        with st.expander("📋 示例 SMILES"):
-        #     example_smiles = {
+            example_smiles = {
-        #         "DLin-MC3-DMA": "CC(C)=CCCC(C)=CCCC(C)=CCN(C)CCCCCCCCOC(=O)CCCCCCC/C=C\\CCCCCCCC",
+                "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",
+                "简单胺": "CC(C)NCCNC(C)C",
-        #         "长链胺": "CCCCCCCCCCCCNCCNCCCCCCCCCCCC",
+                "长链胺": "CCCCCCCCCCCCNCCNCCCCCCCCCCCC",
-        #     }
+            }
-        #     for name, smi in example_smiles.items():
+            for name, smi in example_smiles.items():
-        #         if st.button(f"使用 {name}", key=f"example_{name}"):
+                if st.button(f"使用 {name}", key=f"example_{name}"):
-        #             st.session_state["smiles_input"] = smi
+                    st.session_state["smiles_input"] = smi
-        #             st.rerun()
+                    st.rerun()
         
-        # st.divider()
+        st.divider()
         
         # 目标器官选择
         st.subheader("🎯 目标器官")
@@ -234,7 +228,7 @@ def main():
             index=0,
         )
         
-        # st.divider()
+        st.divider()
         
         # 高级选项
         with st.expander("🔧 高级选项"):
@@ -300,8 +294,8 @@ def main():
         with col2:
             best_score = results["formulations"][0]["target_biodist"]
             st.metric(
-                "最优分布",
+                "最优 Biodistribution",
-                f"{best_score*100:.2f}%",
+                f"{best_score:.4f}",
             )
         
         with col3:
@@ -339,61 +333,61 @@ def main():
         )
         
         # 详细信息
-        # with st.expander("🔍 查看最优配方详情"):
+        with st.expander("🔍 查看最优配方详情"):
-        #     best = results["formulations"][0]
+            best = results["formulations"][0]
             
-        #     col1, col2 = st.columns(2)
+            col1, col2 = st.columns(2)
             
-        #     with col1:
+            with col1:
-        #         st.markdown("**配方参数**")
+                st.markdown("**配方参数**")
-        #         st.json({
+                st.json({
-        #             "阳离子脂质/mRNA 比例": best["cationic_lipid_to_mrna_ratio"],
+                    "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"],
+                    "阳离子脂质 (mol%)": best["cationic_lipid_mol_ratio"],
-        #             "磷脂 (mol%)": best["phospholipid_mol_ratio"],
+                    "磷脂 (mol%)": best["phospholipid_mol_ratio"],
-        #             "胆固醇 (mol%)": best["cholesterol_mol_ratio"],
+                    "胆固醇 (mol%)": best["cholesterol_mol_ratio"],
-        #             "PEG 脂质 (mol%)": best["peg_lipid_mol_ratio"],
+                    "PEG 脂质 (mol%)": best["peg_lipid_mol_ratio"],
-        #             "辅助脂质": best["helper_lipid"],
+                    "辅助脂质": best["helper_lipid"],
-        #             "给药途径": best["route"],
+                    "给药途径": best["route"],
-        #         })
+                })
             
-        #     with col2:
+            with col2:
-        #         st.markdown("**各器官 Biodistribution 预测**")
+                st.markdown("**各器官 Biodistribution 预测**")
-        #         biodist_df = pd.DataFrame([
+                biodist_df = pd.DataFrame([
-        #             {"器官": ORGAN_LABELS.get(k, k), "Biodistribution": f"{v:.4f}"}
+                    {"器官": ORGAN_LABELS.get(k, k), "Biodistribution": f"{v:.4f}"}
-        #             for k, v in best["all_biodist"].items()
+                    for k, v in best["all_biodist"].items()
-        #         ])
+                ])
-        #         st.dataframe(biodist_df, hide_index=True, use_container_width=True)
+                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"):
+        with st.expander("📖 使用说明"):
-        #     st.markdown("""
+            st.markdown("""
-        #     ### 如何使用
+            ### 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
+            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
+            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
+            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
+            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
+            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:
+            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/mRNA 比例**: 0.05 - 0.30
-        #     - **阳离子脂质 mol 比例**: 0.05 - 0.80
+            - **阳离子脂质 mol 比例**: 0.05 - 0.80
-        #     - **磷脂 mol 比例**: 0.00 - 0.80
+            - **磷脂 mol 比例**: 0.00 - 0.80
-        #     - **胆固醇 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
+            - **PEG 脂质 mol 比例**: 0.00 - 0.05
-        #     - **辅助脂质**: DOPE / DSPC / DOTAP
+            - **辅助脂质**: DOPE / DSPC / DOTAP
-        #     - **给药途径**: 静脉注射 / 肌肉注射
+            - **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__":

docker-compose-gpu.yml

@@ -1,54 +0,0 @@
-services:
-  # FastAPI 后端服务
-  api:
-    build:
-      context: .
-      dockerfile: Dockerfile
-      target: api
-    container_name: lnp-api
-    environment:
-      - MODEL_PATH=/app/models/final/model.pt
-    volumes:
-      # 挂载模型目录以便更新模型
-      - ./models/final:/app/models/final:ro
-      - ./models/mpnn:/app/models/mpnn:ro
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:8000/"]
-      interval: 30s
-      timeout: 10s
-      retries: 3
-      start_period: 60s
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-
-  # Streamlit 前端服务
-  streamlit:
-    build:
-      context: .
-      dockerfile: Dockerfile
-      target: streamlit
-    container_name: lnp-streamlit
-    ports:
-      - "8501:8501"
-    environment:
-      - API_URL=http://api:8000
-    depends_on:
-      api:
-        condition: service_started
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:8501/_stcore/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 3
-      start_period: 30s
-
-networks:
-  default:
-    name: lnp-network

docker-compose.yml

@@ -1,49 +0,0 @@
-services:
-  # FastAPI 后端服务
-  api:
-    build:
-      context: .
-      dockerfile: Dockerfile
-      target: api
-    container_name: lnp-api
-    environment:
-      - MODEL_PATH=/app/models/final/model.pt
-      - DEVICE=cpu
-    volumes:
-      # 挂载模型目录以便更新模型
-      - ./models/final:/app/models/final:ro
-      - ./models/mpnn:/app/models/mpnn:ro
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:8000/"]
-      interval: 30s
-      timeout: 10s
-      retries: 3
-      start_period: 60s
-
-  # Streamlit 前端服务
-  streamlit:
-    build:
-      context: .
-      dockerfile: Dockerfile
-      target: streamlit
-    container_name: lnp-streamlit
-    ports:
-      - "8501:8501"
-    environment:
-      - API_URL=http://api:8000
-    depends_on:
-      api:
-        condition: service_started
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:8501/_stcore/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 3
-      start_period: 30s
-
-networks:
-  default:
-    name: lnp-network
-

requirements.txt

@@ -1,20 +0,0 @@
-# 严格遵循 pixi.toml 的依赖版本
-# 注意: lnp_ml 本地包在 Dockerfile 中单独安装
-
-# conda dependencies (in pixi.toml [dependencies])
-loguru
-tqdm
-typer
-
-# pypi dependencies (in pixi.toml [pypi-dependencies])
-chemprop==1.7.0
-setuptools
-pandas>=2.0.3,<3
-openpyxl>=3.1.5,<4
-python-dotenv>=1.0.1,<2
-pyarrow>=17.0.0,<18
-fastparquet>=2024.2.0,<2025
-fastapi>=0.124.4,<0.125
-streamlit>=1.40.1,<2
-httpx>=0.28.1,<0.29
-uvicorn>=0.33.0,<0.34