{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import streamlit as st\n",
    "\n",
    "from streamlit_jupyter import StreamlitPatcher, tqdm\n",
    "\n",
    "StreamlitPatcher().jupyter()  # register streamlit with jupyter-compatible wrappers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# AI"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-13 14:11:32.399 \n",
      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
      "  command:\n",
      "\n",
      "    streamlit run /mnt/wsl/PHYSICALDRIVE1p1/@home/non/.venv/lib/python3.10/site-packages/ipykernel_launcher.py [ARGUMENTS]\n"
     ]
    }
   ],
   "source": [
    "import yaml\n",
    "from pathlib import Path\n",
    "from compliance_analysis import run_compliance_analysis_on_project, run_compliance_analysis_on_data, run_compliance_analysis_on_model\n",
    "\n",
    "# def process_files(files):\n",
    "#     results = []\n",
    "#     for file in files:\n",
    "#         with open(file.name, 'r') as f:\n",
    "#             content = f.read()\n",
    "#         if Path(file.name).name == \"project_cc.yaml\":\n",
    "#             project_cc_yaml = yaml.safe_load(content)\n",
    "#             msg = run_compliance_analysis_on_project(project_cc_yaml)\n",
    "#             results.append(msg)            \n",
    "#         # if Path(file.name).name == \"data_cc.yaml\":\n",
    "#         #     data_cc_yaml = yaml.safe_load(content)\n",
    "#         #     msg = run_compliance_analysis_on_data(data_cc_yaml)\n",
    "#         #     results.append(msg)        \n",
    "#         # if Path(file.name).name == \"model_cc.yaml\":\n",
    "#         #     model_cc_yaml = yaml.safe_load(content)\n",
    "#         #     msg = run_compliance_analysis_on_model(model_cc_yaml)\n",
    "#         #     results.append(msg)\n",
    "            \n",
    "#     return results\n",
    "\n",
    "import yaml\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "def process_files(files):\n",
    "    results = []\n",
    "    for file in files:\n",
    "        content = file.read().decode(\"utf-8\")\n",
    "        if Path(file.name).name == \"project_cc.yaml\":\n",
    "            project_cc_yaml = yaml.safe_load(content)\n",
    "            if project_cc_yaml:\n",
    "                msg = run_compliance_analysis_on_project(project_cc_yaml)\n",
    "                results.append(msg)            \n",
    "    return results\n",
    "\n",
    "def extract_properties(files):\n",
    "    properties = []\n",
    "    for file in files:\n",
    "        content = file.read().decode(\"utf-8\")\n",
    "        project_cc_yaml = yaml.safe_load(content)\n",
    "        if project_cc_yaml:\n",
    "            properties.extend([key for key in project_cc_yaml])\n",
    "    return properties\n",
    "\n",
    "def sentence_builder(keys):\n",
    "    return f\"Selected options: {', '.join(keys)}\"\n",
    "\n",
    "# Streamlit app\n",
    "st.title(\"AI\")\n",
    "\n",
    "uploaded_files = st.file_uploader(\"Upload YAML Files\", type=\"yaml\", accept_multiple_files=True)\n",
    "\n",
    "if uploaded_files:\n",
    "    # Process the files and display the output\n",
    "    if st.button(\"Process Files\"):\n",
    "        results = process_files(uploaded_files)\n",
    "        for result in results:\n",
    "            st.text(result)\n",
    "    \n",
    "    # Extract properties\n",
    "    properties = extract_properties(uploaded_files)\n",
    "    \n",
    "    # Create a DataFrame with properties and a checkbox column\n",
    "    df = pd.DataFrame({\n",
    "        \"Property\": properties,\n",
    "        \"Select\": [False] * len(properties)  # Default to unchecked\n",
    "    })\n",
    "\n",
    "    # Display DataFrame with checkboxes using st.column_config.CheckboxColumn\n",
    "    edited_df = st.data_editor(\n",
    "        df,\n",
    "        column_config={\n",
    "            \"Select\": st.column_config.CheckboxColumn(\"Select\"),\n",
    "        },\n",
    "        key=\"data_editor\"\n",
    "    )\n",
    "\n",
    "    # Get selected properties\n",
    "    selected_properties = edited_df[edited_df[\"Select\"]][\"Property\"].tolist()\n",
    "    \n",
    "    # Build the sentence based on selected properties\n",
    "    if selected_properties:\n",
    "        sentence = sentence_builder(selected_properties)\n",
    "        st.text(sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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