{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "e4rVoXBpRMwI"
   },
   "outputs": [],
   "source": [
    "# If you are using Colab for free, we highly recommend you activate the T4 GPU\n",
    "# hardware accelerator. Our models are designed to run with at least 16GB\n",
    "# of RAM, activating T4 will grant the notebook 16GB of GDDR6 RAM as opposed\n",
    "# to the ~13GB Colab gives automatically.\n",
    "# To activate T4:\n",
    "# 1. click on the \"Runtime\" tab\n",
    "# 2. click on \"Change runtime type\"\n",
    "# 3. select T4 GPU under \"Hardware Accelerator\"\n",
    "# NOTE: there is a weekly usage limit on using T4 for free"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "9TasXeh9ntUx",
    "outputId": "0673f3a8-126d-47e7-e2ec-1eb0943343de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: psycopg-binary, psycopg, pgvector, jmespath, dnspython, colorama, pymongo, botocore, s3transfer, boto3, llmware\n",
      "Successfully installed boto3-1.34.124 botocore-1.34.124 colorama-0.4.6 dnspython-2.6.1 jmespath-1.0.1 llmware-0.3.0 pgvector-0.2.4 psycopg-3.1.17 psycopg-binary-3.1.17 pymongo-4.7.3 s3transfer-0.10.1\n"
     ]
    }
   ],
   "source": [
    "!pip install llmware"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DNym3TAGnw9N"
   },
   "outputs": [],
   "source": [
    "from llmware.agents import LLMfx\n",
    "\n",
    "earnings_transcripts = [\n",
    "    \"This is one of the best quarters we can remember for the industrial sector with significant growth across the \"\n",
    "    \"board in new order volume, as well as price increases in excess of inflation.  We continue to see very strong \"\n",
    "    \"demand, especially in Asia and Europe. Accordingly, we remain bullish on the tier 1 suppliers and would be \"\n",
    "    \"accumulating more stock on any dips. \",\n",
    "\n",
    "    \"Not the worst results, but overall we view as negative signals on the direction of the economy, and the likely \"\n",
    "    \"short-term trajectory for the telecom sector, and especially larger market leaders, including AT&T, Comcast, and\"\n",
    "    \"Deutsche Telekom.\",\n",
    "\n",
    "    \"This quarter was a disaster for Tesla, with falling order volume, increased costs and supply, and negative \"\n",
    "    \"guidance for future growth forecasts in 2024 and beyond.\",\n",
    "\n",
    "    \"On balance, this was an average result, with earnings in line with expectations and no big surprises to either \"\n",
    "    \"the positive or the negative.\"\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kwIXabPGn2cK"
   },
   "outputs": [],
   "source": [
    "def get_one_sentiment_classification(text):\n",
    "\n",
    "    \"\"\"This example shows a basic use to get a sentiment classification and use the output programmatically. \"\"\"\n",
    "\n",
    "    #   simple basic use to get the sentiment on a single piece of text\n",
    "    agent = LLMfx(verbose=True)\n",
    "    agent.load_tool(\"sentiment\")\n",
    "    sentiment = agent.sentiment(text)\n",
    "\n",
    "    #   look at the output\n",
    "    print(\"sentiment: \", sentiment)\n",
    "    for keys, values in sentiment.items():\n",
    "        print(f\"{keys}-{values}\")\n",
    "\n",
    "    #   two key attributes of the sentiment output dictionary\n",
    "    sentiment_value = sentiment[\"llm_response\"][\"sentiment\"]\n",
    "    confidence_level = sentiment[\"confidence_score\"]\n",
    "\n",
    "    #   use the sentiment classification as a 'if...then' decision point in a process\n",
    "    if \"positive\" in sentiment_value:\n",
    "        print(\"sentiment is positive .... will take 'positive' analysis path ...\", sentiment_value)\n",
    "\n",
    "    if \"positive\" in sentiment_value and confidence_level > 0.8:\n",
    "        print(\"sentiment is positive with high confidence ... \", sentiment_value, confidence_level)\n",
    "\n",
    "    return sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-YqsnaLBn6SK"
   },
   "outputs": [],
   "source": [
    "def review_batch_earning_transcripts():\n",
    "\n",
    "    \"\"\" This example highlights how to review multiple earnings transcripts and iterate through a batch\n",
    "    using the load_work mechanism. \"\"\"\n",
    "\n",
    "    agent = LLMfx()\n",
    "    agent.load_tool(\"sentiment\")\n",
    "\n",
    "    #   iterating through a larger list of samples\n",
    "    #   note: load_work method is a flexible input mechanism - pass a string, list, dictionary or combination, and\n",
    "    #   it will 'package' as iterable units of processing work for the agent\n",
    "\n",
    "    agent.load_work(earnings_transcripts)\n",
    "\n",
    "    while True:\n",
    "        output = agent.sentiment()\n",
    "        # print(\"update: test - output - \", output)\n",
    "        if not agent.increment_work_iteration():\n",
    "            break\n",
    "\n",
    "    response_output = agent.response_list\n",
    "\n",
    "    agent.clear_work()\n",
    "    agent.clear_state()\n",
    "\n",
    "    return response_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 765,
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    },
    "id": "RK8KQ6g_n9a7",
    "outputId": "52f96443-5719-41dc-dea4-c7d9256ba2f4"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "update: Launching LLMfx process\n",
      "step - \t1 - \tcreating object - ready to start processing.\n",
      "step - \t2 - \tloading tool - sentiment\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
      "You will be able to reuse this secret in all of your notebooks.\n",
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      "step - \t3 - \texecuting function call - deploying - sentiment \n",
      "step - \t4 - \tloading new processing text - 1 new entries\n",
      "step - \t5 - \texecuting function call - getting response - sentiment\n",
      "\t\t\t\t -- llm_response - {'sentiment': ['positive']}\n",
      "\t\t\t\t -- output type - dict\n",
      "\t\t\t\t -- usage - {'input': 98, 'output': 9, 'total': 107, 'metric': 'tokens', 'processing_time': 0.717214822769165, 'type': 'dict'}\n"
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      "step - \t6 - \tanalyzing response - sentiment\n",
      "\t\t\t\t -- confidence score - 0.98\n",
      "\t\t\t\t -- analyzing response - \u001b[32msent\u001b[32miment\u001b[32m':\u001b[32m ['\u001b[32mpos\u001b[32mitive\u001b[32m']\u001b[32m}\u001b[39m\n",
      "\t\t\t\t -- analyzing response - {'choice_1': ['pos', 0.98, 1066], 'choice_2': ['neut', 0.018, 17821], 'choice_3': ['negative', 0.001, 22198]}\n",
      "\t\t\t\t -- analyzing response - [('positive', 1066, 0.98), ('neutral', 17821, 0.018), ('negative', 22198, 0.001)]\n",
      "sentiment:  {'llm_response': {'sentiment': ['positive']}, 'dict_output': True, 'confidence_score': 0.98, 'choices': [('positive', 1066, 0.98), ('neutral', 17821, 0.018), ('negative', 22198, 0.001)]}\n",
      "llm_response-{'sentiment': ['positive']}\n",
      "dict_output-True\n",
      "confidence_score-0.98\n",
      "choices-[('positive', 1066, 0.98), ('neutral', 17821, 0.018), ('negative', 22198, 0.001)]\n",
      "sentiment is positive .... will take 'positive' analysis path ... ['positive']\n",
      "sentiment is positive with high confidence ...  ['positive'] 0.98\n"
     ]
    }
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    "if __name__ == \"__main__\":\n",
    "\n",
    "    #   first - quick illustration of getting a sentiment classification\n",
    "    #   and using in an \"if...then\"\n",
    "    sentiment = get_one_sentiment_classification(earnings_transcripts[0])\n",
    "\n",
    "    #   second - iterate thru a batch of transcripts and apply a sentiment classification\n",
    "    # response_output = review_batch_earning_transcripts()"
   ]
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