Data_flagging.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Automated data flagging\n",
        "\n",
        "Copyright &copy; 2024 Praneeth Vadlapati"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ckSIx9bYgtMV"
      },
      "outputs": [],
      "source": [
        "import io\n",
        "import os\n",
        "import time\n",
        "from urllib.parse import urlparse\n",
        "\n",
        "from datasets import load_dataset\n",
        "import pandas as pd\n",
        "import replicate\n",
        "# import requests\n",
        "from tavily import TavilyClient\n",
        "\n",
        "for key in ['TAVILY_API_KEY', 'GROQ_API_KEY']:\n",
        "\tif not os.getenv(key):\n",
        "\t\traise Exception(f'{key} not found in environment or .env file')\n",
        "\n",
        "from common_functions import get_filename, dataset_name, latest_dump_name, \\\n",
        "\t\t\t\tflags_list, safe_flag, harm_categories, \\\n",
        "\t\t\t\tprint_progress, print_error, get_bot_response, \\\n",
        "\t\t\t\tis_not_na, print_modelname\n",
        "\n",
        "# Whether to process existing file or fetch data into a new file\n",
        "PROCESS_EXISTING_FILE = True"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Collect the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "New data file: New_data - 0.full.csv\n",
            "Flagged data file: New_data - 0.flagged.csv\n"
          ]
        },
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>id</th>\n",
              "      <th>url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>We want to know how to best serve you. Please ...</td>\n",
              "      <td>&lt;urn:uuid:faff9b64-041c-4b98-8be4-7ff2a02e4b8d&gt;</td>\n",
              "      <td>http://38.paulosimoes.net/forms/feedback</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Architectural Control Committee Policies and F...</td>\n",
              "      <td>&lt;urn:uuid:77695799-0774-42a1-8eaa-5efbe154c4e0&gt;</td>\n",
              "      <td>http://aberdeencreekfl.com/ACCBusiness/Procedu...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                text  \\\n",
              "0  We want to know how to best serve you. Please ...   \n",
              "1  Architectural Control Committee Policies and F...   \n",
              "\n",
              "                                                id  \\\n",
              "0  <urn:uuid:faff9b64-041c-4b98-8be4-7ff2a02e4b8d>   \n",
              "1  <urn:uuid:77695799-0774-42a1-8eaa-5efbe154c4e0>   \n",
              "\n",
              "                                                 url  \n",
              "0           http://38.paulosimoes.net/forms/feedback  \n",
              "1  http://aberdeencreekfl.com/ACCBusiness/Procedu...  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_data_filename = None\n",
        "skip_index = 0\n",
        "last_existing_file_index = -1\n",
        "for index in range(1000):\n",
        "\tnew_data_filename = get_filename(index)\n",
        "\tif os.path.exists(new_data_filename) and os.stat(new_data_filename).st_size > 0:\n",
        "\t\tlast_existing_file_index = index\n",
        "\t\tskip_index += len(pd.read_csv(new_data_filename))\n",
        "\telse:\n",
        "\t\tflagged_data_filename = get_filename(index, 'flagged')\n",
        "\t\tfiltered_data_filename = get_filename(index, 'filtered')\n",
        "\t\tshort_text_filename = get_filename(index, 'shortened')\n",
        "\t\tbreak  # found file that doesn't exist or is empty\n",
        "\n",
        "if PROCESS_EXISTING_FILE and last_existing_file_index != -1:\n",
        "\tindex = last_existing_file_index\n",
        "\tnew_data_filename = get_filename(index)\n",
        "\tflagged_data_filename = get_filename(index, 'flagged')\n",
        "\tfiltered_data_filename = get_filename(index, 'filtered')\n",
        "\tshort_text_filename = get_filename(index, 'shortened')\n",
        "\tfull_df = pd.read_csv(new_data_filename)\n",
        "else:  # if the file exists, load it\n",
        "\tstart_time = time.time()\n",
        "\tdataset = load_dataset(dataset_name, name=latest_dump_name, split='train', streaming=True)\n",
        "\tif skip_index:\n",
        "\t\tdataset = dataset.skip(skip_index)\n",
        "\n",
        "\tlimit = 100\n",
        "\tdata = []\n",
        "\tcurr_size = 0\n",
        "\tfor i, item in enumerate(dataset):\n",
        "\t\t# print(i)\n",
        "\t\tif curr_size >= limit:\n",
        "\t\t\tbreak\n",
        "\t\tdata.append(item)\n",
        "\t\tprint_progress()\n",
        "\t\tcurr_size += 1\n",
        "\n",
        "\ttime_min, time_sec = divmod(time.time() - start_time, 60)\n",
        "\tprint(f'Time taken: {time_min:.0f} min {time_sec:.0f} sec')\n",
        "\n",
        "\tfull_df = pd.DataFrame(data)\n",
        "\tfull_df = full_df[full_df['language'] == 'en']  # drop rows with language not 'en'\n",
        "\tif not full_df.empty:\n",
        "\t\tfull_df.to_csv(new_data_filename, index=False)\n",
        "\n",
        "print('New data file:', os.path.basename(new_data_filename))\n",
        "print('Flagged data file:', os.path.basename(flagged_data_filename))\n",
        "full_df.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Flagging the data:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Essential functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Emulating LlamaGuard using LLM\n",
        "categories_str = '\\n'.join([f'{code}: {category_label}' for code, category_label in harm_categories.items()])\n",
        "categories_codes = list(harm_categories.keys())\n",
        "\n",
        "def check_unsafe_LLM(text, user_prompt='', max_retries=3):\n",
        "\tinstruction_prompt = (\n",
        "\t\t'Classify the following text based on predefined categories related to content safety. \\n'\n",
        "\t\t'Respond with `safe` if the text does not fall into any unsafe categories. \\n'\n",
        "\t\t'If the text is unsafe, respond with `unsafe` followed by the relevant category code(s) like S1.\\n\\n'\n",
        "\t\tf'Categories:\\n{categories_str}\\n\\n'\n",
        "\t\tf'Text to classify:\\n{text}\\n{user_prompt} \\n'\n",
        "\t\tf'Respond in `backticks` like `safe`, `unsafe\\n{categories_codes[0]}`. \\n'\n",
        "\t)\n",
        "\tmessages = [{'role': 'user', 'content': instruction_prompt}]\n",
        "\n",
        "\tfor _ in range(max_retries):\n",
        "\t\ttry:\n",
        "\t\t\tresponse = get_bot_response(messages, process_backticks=True)\n",
        "\t\t\tif 'unsafe' in response:\n",
        "\t\t\t\tresponse = response.replace('unsafe', '').strip()  # Extract code if unsafe\n",
        "\t\t\t\treturn response or True\n",
        "\t\t\telse:\n",
        "\t\t\t\tif not response:\n",
        "\t\t\t\t\traise Exception('No response')\n",
        "\t\t\t\treturn safe_flag\n",
        "\t\texcept Exception as e:\n",
        "\t\t\tprint_error(e)\n",
        "\t\t\treturn None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "replicate_not_working = True\n",
        "\n",
        "def is_unsafe(bot_response, user_prompt='', max_retries=3):\n",
        "\t'Ask LlamaGuard if a text is safe'\n",
        "\tglobal replicate_not_working\n",
        "\tif replicate_not_working:\n",
        "\t\tcheck_unsafe_LLM(bot_response, user_prompt, max_retries=max_retries)\n",
        "\tfor _ in range(max_retries):\n",
        "\t\ttry:\n",
        "\t\t\tif not len(bot_response):\n",
        "\t\t\t\treturn True\n",
        "\n",
        "\t\t\toutput = replicate.run(\n",
        "\t\t\t\t'meta/meta-llama-guard-2-8b:b063023ee937f28e922982abdbf97b041ffe34ad3b35a53d33e1d74bb19b36c4',\n",
        "\t\t\t\tinput={ 'prompt': user_prompt, 'assistant': bot_response },\n",
        "\t\t\t)\n",
        "\t\t\tif isinstance(output, str):\n",
        "\t\t\t\tif output == safe_flag:\n",
        "\t\t\t\t\treturn False\n",
        "\t\t\t\telse:\n",
        "\t\t\t\t\toutput = output.replace('unsafe', '').strip()\n",
        "\t\t\t\t\treturn output  # returns reason like 'S5', 'S6', etc.\n",
        "\t\t\telse:\n",
        "\t\t\t\traise Exception('Invalid output')\n",
        "\t\texcept Exception as e:\n",
        "\t\t\treplicate_not_working = True\n",
        "\t\t\tprint(f'Error: {e}. Retrying')\n",
        "\n",
        "flagged_df = None\n",
        "\n",
        "# if the file exists, load it\n",
        "if os.path.exists(flagged_data_filename):\n",
        "\tflagged_df = pd.read_csv(flagged_data_filename)\n",
        "\n",
        "\n",
        "if flagged_df is None or flagged_df.empty:\n",
        "\t# if flagged_df is None or flagged_df.empty:\n",
        "\tflagged_df = full_df.copy()\n",
        "\tflagged_df = flagged_df.dropna(subset=['text'])  # Filter out rows with empty text\n",
        "\t# columns_to_keep = ['id', 'url', 'date', 'text', \n",
        "\t# \t\t\t\t\t'text_unsafe', 'domain_unsafe',\n",
        "\t# \t\t\t\t\t'domain_unindexed', 'flags']\n",
        "\t# # Keep only the columns in `columns_to_keep` that are also in `flagged_df`\n",
        "\t# available_columns = [col for col in columns_to_keep if col in flagged_df.columns]\n",
        "\t# flagged_df = flagged_df[available_columns]\n",
        "\n",
        "\n",
        "def save_flagged_df(df=flagged_df):\n",
        "\tdf.to_csv(flagged_data_filename, index=False)\n",
        "\n",
        "\n",
        "tavily = TavilyClient(api_key=os.getenv('TAVILY_API_KEY'))\n",
        "\n",
        "def is_indexed(domain, max_retries=3):\n",
        "\t'Search online to check if a domain is indexed'\n",
        "\tfor _ in range(max_retries):\n",
        "\t\ttry:\n",
        "\t\t\tresult = tavily.search(query=f'site:{domain}')\n",
        "\t\t\tif not result or 'results' not in result:\n",
        "\t\t\t\traise Exception('No results')\n",
        "\t\t\tindexed = len(result['results']) > 0\n",
        "\t\t\tif indexed is not None:\n",
        "\t\t\t\treturn True if indexed else False\n",
        "\t\texcept Exception as e:\n",
        "\t\t\tprint(f'Error: {e}. Retrying')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Flagging unsafe text\n",
        "\n",
        "(Typically takes 8 minutes for 100 rows)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time taken: 0.0005154609680175781\n",
            "Flagged data size: (100, 9)\n"
          ]
        },
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>url</th>\n",
              "      <th>text</th>\n",
              "      <th>text_unsafe</th>\n",
              "      <th>domain_unsafe</th>\n",
              "      <th>domain_unindexed</th>\n",
              "      <th>flags</th>\n",
              "      <th>flag_reason</th>\n",
              "      <th>flags_singlerow</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>&lt;urn:uuid:faff9b64-041c-4b98-8be4-7ff2a02e4b8d&gt;</td>\n",
              "      <td>http://38.paulosimoes.net/forms/feedback</td>\n",
              "      <td>We want to know how to best serve you. Please ...</td>\n",
              "      <td>safe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sensitive_topic,unusable</td>\n",
              "      <td>Report discrimination, harassment, and sexual ...</td>\n",
              "      <td>safe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>&lt;urn:uuid:77695799-0774-42a1-8eaa-5efbe154c4e0&gt;</td>\n",
              "      <td>http://aberdeencreekfl.com/ACCBusiness/Procedu...</td>\n",
              "      <td>Architectural Control Committee Policies and F...</td>\n",
              "      <td>safe</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>safe</td>\n",
              "      <td>Useful knowledge</td>\n",
              "      <td>safe</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                id  \\\n",
              "0  <urn:uuid:faff9b64-041c-4b98-8be4-7ff2a02e4b8d>   \n",
              "1  <urn:uuid:77695799-0774-42a1-8eaa-5efbe154c4e0>   \n",
              "\n",
              "                                                 url  \\\n",
              "0           http://38.paulosimoes.net/forms/feedback   \n",
              "1  http://aberdeencreekfl.com/ACCBusiness/Procedu...   \n",
              "\n",
              "                                                text text_unsafe  \\\n",
              "0  We want to know how to best serve you. Please ...        safe   \n",
              "1  Architectural Control Committee Policies and F...        safe   \n",
              "\n",
              "  domain_unsafe domain_unindexed                     flags  \\\n",
              "0           NaN              NaN  sensitive_topic,unusable   \n",
              "1          True              NaN                      safe   \n",
              "\n",
              "                                         flag_reason flags_singlerow  \n",
              "0  Report discrimination, harassment, and sexual ...            safe  \n",
              "1                                   Useful knowledge            safe  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def flag_unsafe_text(flagged_df):\n",
        "\tif 'text_unsafe' not in flagged_df.columns:\n",
        "\t\tflagged_df['text_unsafe'] = None\n",
        "\n",
        "\t# Identify rows with empty 'text_unsafe' value\n",
        "\tindices = flagged_df[flagged_df['text_unsafe'].isna()].index\n",
        "\tfor i in indices:\n",
        "\t\tflagged_df.loc[i, 'text_unsafe'] = is_unsafe(flagged_df.loc[i, 'text']) or safe_flag\n",
        "\t\tprint_progress()\n",
        "\n",
        "\tif not indices.empty:\n",
        "\t\tsave_flagged_df()\n",
        "\n",
        "start_time = time.time()\n",
        "flag_unsafe_text(flagged_df)\n",
        "print('Time taken:', time.time() - start_time)\n",
        "print(f'Flagged data size: {flagged_df.shape}')\n",
        "flagged_df.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Flagging unsafe domains\n",
        "\n",
        "(Typically takes 5 minutes for 100 rows)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Time taken: 0.0010066032409667969\n",
            "Flagged data size: (100, 9)\n"
          ]
        },
        {
          "data": {
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>url</th>\n",
              "      <th>text</th>\n",
              "      <th>text_unsafe</th>\n",
              "      <th>domain_unsafe</th>\n",
              "      <th>domain_unindexed</th>\n",
              "      <th>flags</th>\n",
              "      <th>flag_reason</th>\n",
              "      <th>flags_singlerow</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>&lt;urn:uuid:faff9b64-041c-4b98-8be4-7ff2a02e4b8d&gt;</td>\n",
              "      <td>http://38.paulosimoes.net/forms/feedback</td>\n",
              "      <td>We want to know how to best serve you. Please ...</td>\n",
              "      <td>safe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sensitive_topic,unusable</td>\n",
              "      <td>Report discrimination, harassment, and sexual ...</td>\n",
              "      <td>safe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>&lt;urn:uuid:77695799-0774-42a1-8eaa-5efbe154c4e0&gt;</td>\n",
              "      <td>http://aberdeencreekfl.com/ACCBusiness/Procedu...</td>\n",
              "      <td>Architectural Control Committee Policies and F...</td>\n",
              "      <td>safe</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>safe</td>\n",
              "      <td>Useful knowledge</td>\n",
              "      <td>safe</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                id  \\\n",
              "0  <urn:uuid:faff9b64-041c-4b98-8be4-7ff2a02e4b8d>   \n",
              "1  <urn:uuid:77695799-0774-42a1-8eaa-5efbe154c4e0>   \n",
              "\n",
              "                                                 url  \\\n",
              "0           http://38.paulosimoes.net/forms/feedback   \n",
              "1  http://aberdeencreekfl.com/ACCBusiness/Procedu...   \n",
              "\n",
              "                                                text text_unsafe  \\\n",
              "0  We want to know how to best serve you. Please ...        safe   \n",
              "1  Architectural Control Committee Policies and F...        safe   \n",
              "\n",
              "  domain_unsafe domain_unindexed                     flags  \\\n",
              "0           NaN              NaN  sensitive_topic,unusable   \n",
              "1          True              NaN                      safe   \n",
              "\n",
              "                                         flag_reason flags_singlerow  \n",
              "0  Report discrimination, harassment, and sexual ...            safe  \n",
              "1                                   Useful knowledge            safe  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# use tavily api to find whether google indexes the page or not\n",
        "\n",
        "def get_main_domain(domain):\n",
        "\t'Sometimes, only subdomains are provided. This function returns the main domain.'\n",
        "\t'Example: news.example.com -> example.com, www.example.co.uk -> example.co.uk, www.example.ac.ir -> example.ac.ir'\n",
        "\tparts = domain.split('.')\n",
        "\t# Handle cases with ccTLDs or multi-part TLDs (like co.uk, ac.ir)\n",
        "\tif len(parts) >= 3 and (parts[-2] in ['co', 'ac'] or len(parts[-1]) == 2):\n",
        "\t\treturn '.'.join(parts[-3:])\n",
        "\telse:\n",
        "\t\treturn '.'.join(parts[-2:])\n",
        "\n",
        "\n",
        "def flag_unsafe_domains(flagged_df):\n",
        "\t'Flag domains that are unsafe or unindexed, as unsafe domains ' \\\n",
        "    'are less likely to be indexed by a search engine'\n",
        "\turls = flagged_df['url'].dropna().unique()\n",
        "\tdomains = [urlparse(url).netloc for url in urls]\n",
        "\t# get main domains from subdomains\n",
        "\tdomains = [get_main_domain(domain) for domain in domains]\n",
        "\tdomains = list(set(domains))  # remove duplicates\n",
        "\n",
        "\tif 'domain_unsafe' not in flagged_df.columns:\n",
        "\t\tflagged_df['domain_unsafe'] = None\n",
        "\n",
        "\t\tunsafe_domains = set()\n",
        "\t\tfor domain in domains:\n",
        "\t\t\tif is_unsafe(domain):\n",
        "\t\t\t\tunsafe_domains.add(domain)\n",
        "\t\t\t\tprint_progress()\n",
        "\t\tprint(f'Unsafe domains: {unsafe_domains}')\n",
        "\n",
        "\t\tfor i, row in flagged_df.iterrows():\n",
        "\t\t\tif row['domain_unsafe'] is None:\n",
        "\t\t\t\tdomain = urlparse(row['url']).netloc\n",
        "\t\t\t\tflagged_df.loc[i, 'domain_unsafe'] = domain in unsafe_domains\n",
        "\n",
        "\t\tsave_flagged_df()\n",
        "\n",
        "\tif 'domain_unindexed' not in flagged_df.columns:\n",
        "\t\tflagged_df['domain_unindexed'] = None\n",
        "\n",
        "\t\tunindexed_domains = set()\n",
        "\t\tfor domain in domains:\n",
        "\t\t\tif not is_indexed(domain):\n",
        "\t\t\t\tunindexed_domains.add(domain)\n",
        "\t\t\t\tprint_progress()\n",
        "\t\tprint(f'Unindexed domains: {unindexed_domains}')\n",
        "\n",
        "\t\tfor i, row in flagged_df.iterrows():\n",
        "\t\t\tif row['domain_unindexed'] is None:\n",
        "\t\t\t\tdomain = urlparse(row['url']).netloc\n",
        "\t\t\t\tflagged_df.loc[i, 'domain_unindexed'] = domain in unindexed_domains\n",
        "\n",
        "\t\tsave_flagged_df()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "flag_unsafe_domains(flagged_df)\n",
        "print('Time taken:', time.time() - start_time)\n",
        "print(f'Flagged data size: {flagged_df.shape}')\n",
        "flagged_df.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Flagging unwanted rows like Sensitive Topics and Biased data\n",
        "\n",
        "(Typically takes 5-10 minutes for 100 rows)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: None\n",
            "Total chunks: 0\n",
            "Time taken: 0.0007460117340087891\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>url</th>\n",
              "      <th>text</th>\n",
              "      <th>text_unsafe</th>\n",
              "      <th>domain_unsafe</th>\n",
              "      <th>domain_unindexed</th>\n",
              "      <th>flags</th>\n",
              "      <th>flag_reason</th>\n",
              "      <th>flags_singlerow</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>&lt;urn:uuid:faff9b64-041c-4b98-8be4-7ff2a02e4b8d&gt;</td>\n",
              "      <td>http://38.paulosimoes.net/forms/feedback</td>\n",
              "      <td>We want to know how to best serve you. Please ...</td>\n",
              "      <td>safe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sensitive_topic,unusable</td>\n",
              "      <td>Report discrimination, harassment, and sexual ...</td>\n",
              "      <td>safe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>&lt;urn:uuid:77695799-0774-42a1-8eaa-5efbe154c4e0&gt;</td>\n",
              "      <td>http://aberdeencreekfl.com/ACCBusiness/Procedu...</td>\n",
              "      <td>Architectural Control Committee Policies and F...</td>\n",
              "      <td>safe</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>safe</td>\n",
              "      <td>Useful knowledge</td>\n",
              "      <td>safe</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                id  \\\n",
              "0  <urn:uuid:faff9b64-041c-4b98-8be4-7ff2a02e4b8d>   \n",
              "1  <urn:uuid:77695799-0774-42a1-8eaa-5efbe154c4e0>   \n",
              "\n",
              "                                                 url  \\\n",
              "0           http://38.paulosimoes.net/forms/feedback   \n",
              "1  http://aberdeencreekfl.com/ACCBusiness/Procedu...   \n",
              "\n",
              "                                                text text_unsafe  \\\n",
              "0  We want to know how to best serve you. Please ...        safe   \n",
              "1  Architectural Control Committee Policies and F...        safe   \n",
              "\n",
              "  domain_unsafe domain_unindexed                     flags  \\\n",
              "0           NaN              NaN  sensitive_topic,unusable   \n",
              "1          True              NaN                      safe   \n",
              "\n",
              "                                         flag_reason flags_singlerow  \n",
              "0  Report discrimination, harassment, and sexual ...            safe  \n",
              "1                                   Useful knowledge            safe  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "flags_to_detect = ', '.join(flags_list[:-1])\n",
        "flag_df_columns = ['id', 'flags', 'flag_reason']\n",
        "flag_df_columns_str = ','.join(flag_df_columns)\n",
        "unusable_df_columns = ['id', 'unusable_flag', 'unusable_flag_reason']\n",
        "unusable_df_columns_str = ','.join(unusable_df_columns)\n",
        "\n",
        "def flag_chunk_using_LLM(df, indices_list, max_retries=3):\n",
        "\tcsv_text = df[['id', 'text']].iloc[indices_list].to_csv(index=False)\n",
        "\tprompt = (\n",
        "\t\t'You are a content moderator. The text below will be used to fine-tune LLMs. '\n",
        "\t\tf'Fill `flags` column that contains one or more of flags to detect: `{flags_to_detect}`. \\n'\n",
        "\t\t'If you flag a row, fill `flag_reason` column with a very short reason for flag choices. \\n'\n",
        "\t\tf'Return only error-free CSV text back in triple backticks and no other text, like '\n",
        "\t\tf'```csv\\n{flag_df_columns_str}\\n<urn:uuid:aaaa>,\"safe\",\"safe\"\\n'\n",
        "  \t\t'<urn:uuid:aaab>,\"scam,spam\",\"Suggests potential manipulation, and crimes\"\\n``` \\n'\n",
        "\t\tf'Input data: ```csv\\n{csv_text}\\n```. \\n '\n",
        "\t\tf'Columns: `{flag_df_columns_str}`'\n",
        "\t)\n",
        "\tflag_result_df = None\n",
        "\tfor _ in range(max_retries):\n",
        "\t\ttry:\n",
        "\t\t\tresponse = get_bot_response(messages=[{ 'role': 'user', 'content': prompt }],\n",
        "\t\t\t\t\t\t\t\t\t\tprocess_backticks=True)\n",
        "\t\t\tdf = pd.read_csv(io.StringIO(response))\n",
        "\t\t\tdf = df[flag_df_columns]\n",
        "\t\t\tif df.empty:\n",
        "\t\t\t\tflag_result_df = None  # retry\n",
        "\t\t\telse:\n",
        "\t\t\t\tflag_result_df = df\n",
        "\t\t\t\tbreak\n",
        "\t\texcept Exception as e:\n",
        "\t\t\t# print('Exception in flagging chunk using LLM:', e)\n",
        "\t\t\tprint_error(e)\n",
        "\n",
        "\tunusable_flagging_prompt = (\n",
        "\t\t'You are a content moderator. The text below will be used to fine-tune LLMs. '\n",
        "\t\t'Use \"unusable\" as the flag if text does not convey new information/knowledge, or mark as \"safe\". \\n'\n",
        "\t\t'Return csv text back in triple backticks. \\n '\n",
        "\t\tf'Output columns (to be strictly followed): `{unusable_df_columns_str}` \\n'\n",
        "\t\tf'Return only csv response and no other text like ```csv\\n{unusable_df_columns_str}\\n'\n",
        "  \t\t'<urn:uuid:aaaa>,\"unusable\",\"No useful/new info\"\\n<urn:uuid:aaab>,\"safe\",\"Useful knowledge\"\\n``` \\n'\n",
        "\t\tf'Input data: ```\\n{csv_text}\\n```. \\n '\n",
        "\t\tf'Columns: `{unusable_df_columns_str}`'\n",
        "\t)\n",
        "\tunusable_flag_df = None\n",
        "\tfor _ in range(max_retries):\n",
        "\t\ttry:\n",
        "\t\t\tresponse = get_bot_response(messages=[{ 'role': 'user', 'content': unusable_flagging_prompt }],\n",
        "\t\t\t\t\t\t\t\t\t\tprocess_backticks=True)\n",
        "\t\t\tdf = pd.read_csv(io.StringIO(response))\n",
        "\t\t\tdf = df[unusable_df_columns]\n",
        "\t\t\tif df.empty:\n",
        "\t\t\t\tunusable_flag_df = None\n",
        "\t\t\telse:\n",
        "\t\t\t\tunusable_flag_df = df\n",
        "\t\t\t\tbreak\n",
        "\t\texcept Exception as e:\n",
        "\t\t\tprint_error(e)\n",
        "\t\t\t# print('Exception in unusable-flagging', e)\n",
        "\n",
        "\t# Merge the two dataframes\n",
        "\tif flag_result_df is not None and unusable_flag_df is not None:\n",
        "\t\tdf_combined = pd.merge(flag_result_df, unusable_flag_df, on='id', how='left')\n",
        "\t\t# Combine unusable_flag value using a comma\n",
        "\t\tdf_combined['flags'] = df_combined.apply(\n",
        "\t\t\tlambda row: f\"{row['flags']},{row['unusable_flag']}\"\n",
        "\t\t\t\tif is_not_na(row['flags']) and is_not_na(row['unusable_flag'])\n",
        "\t\t\t\telse row['unusable_flag'] if is_not_na(row['unusable_flag'])\n",
        "\t\t\t\telse row['flags'] if is_not_na(row['flags'])\n",
        "\t\t\t\telse None,\n",
        "\t\t\taxis=1\n",
        "\t\t)\n",
        "\t\tdf_combined['flag_reason'] = df_combined.apply(\n",
        "\t\t\tlambda row: f\"{row['flag_reason']}. {row['unusable_flag_reason']}\"\n",
        "\t\t\t\tif is_not_na(row['flag_reason']) and is_not_na(row['unusable_flag_reason'])\n",
        "\t\t\t\telse row['flag_reason'] if is_not_na(row['flag_reason'])\n",
        "\t\t\t\telse row['unusable_flag_reason'] if is_not_na(row['unusable_flag_reason'])\n",
        "\t\t\t\telse None,\n",
        "\t\t\taxis=1\n",
        "\t\t)\n",
        "\telif flag_result_df is not None:\n",
        "\t\tdf_combined = flag_result_df\n",
        "\telif unusable_flag_df is not None:\n",
        "\t\tdf_combined = unusable_flag_df.rename({'unusable_flag': 'flags'})\n",
        "\t\tdf_combined['flag_reason'] = None\n",
        "\telse:\n",
        "\t\traise Exception('Bot response failed for both undesirable flagging and unusable flagging')\n",
        "\n",
        "\treturn df_combined[flag_df_columns]\n",
        "\n",
        "\n",
        "def flag_with_LLM(flagged_df):\n",
        "\tif 'flags' not in flagged_df.columns:\n",
        "\t\tflagged_df['flags'] = None\n",
        "\tif 'flag_reason' not in flagged_df.columns:\n",
        "\t\tflagged_df['flag_reason'] = None\n",
        "\n",
        "\t# split indices into chunks\n",
        "\tchunk_size = 3\n",
        "\tnoflags_indices = flagged_df[flagged_df['flags'].isna() |\n",
        "\t\t\t\t\t\t\t  flagged_df['flag_reason'].isna()].index\n",
        "\tchunks = [noflags_indices[i:i + chunk_size] for i in range(0, len(noflags_indices), chunk_size)]\n",
        "\tprint(f'Total chunks: {len(chunks)}')\n",
        "\n",
        "\tfor chunk in chunks:\n",
        "\t\ttry:\n",
        "\t\t\tnew_flags_df = flag_chunk_using_LLM(flagged_df, indices_list=chunk)\n",
        "\t\t\tnew_flags_df['flags'] = new_flags_df['flags'].fillna(safe_flag)\n",
        "\t\t\t# for each id in flags_df, update the flags column in flagged_df\n",
        "\t\t\t# for all listed indices, mark as safe\n",
        "\t\t\tfor i in chunk:\n",
        "\t\t\t\tflagged_df.loc[i, 'flags'] = safe_flag\n",
        "\t\t\tfor i, row in new_flags_df.iterrows():\n",
        "\t\t\t\tflagged_df.loc[flagged_df['id'] == row['id'], 'flags'] = row['flags']\n",
        "\t\t\t\tflagged_df.loc[flagged_df['id'] == row['id'], 'flag_reason'] = row['flag_reason']\n",
        "\t\t\tprint_progress()\n",
        "\t\t\tsave_flagged_df()\n",
        "\t\texcept Exception as e:\n",
        "\t\t\tprint('Exception in flag with LLM:', e)\n",
        "\t\t\tprint_error(e)\n",
        "\n",
        "\t# Experimenting by flagging only 1 row at a time\n",
        "\t# if 'flags_singlerow' not in flagged_df.columns:\n",
        "\t# \tflagged_df['flags_singlerow'] = None\n",
        "\t# \tprint('Created column `flags_singlerow`')\n",
        "\t# print('Processing single row flags for:', flagged_df['flags_singlerow'].isna().sum())\n",
        "\t# for i, row in flagged_df.iterrows():\n",
        "\t# \ttry:\n",
        "\t# \t\tif pd.isna(row['flags_singlerow']):\n",
        "\t# \t\t\tnew_flags_df = flag_chunk_using_LLM(flagged_df, indices_list=[i])\n",
        "\t# \t\t\tif not new_flags_df.empty:\n",
        "\t# \t\t\t\tnew_flags_df['flags'] = new_flags_df['flags'].fillna(safe_flag)\n",
        "\t# \t\t\t\tflagged_df.loc[i, 'flags_singlerow'] = new_flags_df.loc[0, 'flags']\n",
        "\t# \t\t\tprint_progress()\n",
        "\t# \t\t\tsave_flagged_df()\n",
        "\t# \texcept Exception as e:\n",
        "\t# \t\tprint_error(e)\n",
        "\n",
        "print_modelname()\n",
        "start_time = time.time()\n",
        "flag_with_LLM(flagged_df)\n",
        "print('Time taken:', time.time() - start_time)\n",
        "flagged_df.head(2)"
      ]
    }
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