{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "499abdf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# 设置 Hugging Face 镜像\n",
    "os.environ[\"HF_ENDPOINT\"] = \"https://hf-mirror.com\"\n",
    "\n",
    "# 如果是完全离线模式\n",
    "# os.environ[\"HF_HUB_OFFLINE\"] = \"1\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "08edf2a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "183e4df62da84c9389f36d6e011f6be0",
       "version_major": 2,
       "version_minor": 1
      },
      "text/plain": [
       "EmbeddingAtlasWidget()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from embedding_atlas.widget import EmbeddingAtlasWidget  \n",
    "import pandas as pd   \n",
    "  \n",
    "df = pd.read_csv('data/drugbank_pre_filtered_mordred_qed_id_selfies.csv')    \n",
    "  \n",
    "# 计算投影  \n",
    "from embedding_atlas.projection import compute_text_projection  \n",
    "compute_text_projection(df, text=\"smiles\",  \n",
    "    x=\"projection_x\", y=\"projection_y\", neighbors=\"neighbors\"  \n",
    ")  \n",
    "  \n",
    "# 创建并显示 widget  \n",
    "widget = EmbeddingAtlasWidget(df, text=\"smiles\",  \n",
    "    x=\"projection_x\", y=\"projection_y\", neighbors=\"neighbors\"  \n",
    ")  \n",
    "  \n",
    "# 确保在单独的单元格中显示  \n",
    "widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be723d90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b67dc99218614138bf6657716f3739f8",
       "version_major": 2,
       "version_minor": 1
      },
      "text/plain": [
       "EmbeddingAtlasWidget()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd  \n",
    "from embedding_atlas.projection import compute_text_projection  \n",
    "from embedding_atlas.widget import EmbeddingAtlasWidget  \n",
    "  \n",
    "# 读取您的数据  \n",
    "df = pd.read_csv('data/drugbank_pre_filtered_mordred_qed_id_selfies.csv')  \n",
    "  \n",
    "# 计算 SMILES 的文本嵌入和 2D 投影  \n",
    "compute_text_projection(  \n",
    "    df,   \n",
    "    text=\"smiles\",        # 使用 SMILES 列作为文本输入  \n",
    "    x=\"projection_x\",     # X 坐标列名  \n",
    "    y=\"projection_y\",     # Y 坐标列名  \n",
    "    neighbors=\"neighbors\" # 最近邻列名  \n",
    ")  \n",
    "\n",
    "# 在 Jupyter 中显示可视化  \n",
    "EmbeddingAtlasWidget(df, text=\"smiles\",   \n",
    "                    x=\"projection_x\", y=\"projection_y\",   \n",
    "                    neighbors=\"neighbors\")\n",
    "  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e3bb412",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}