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{
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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