project_research.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OpenAI Intent Classifier\n",
    "*This Notebook explores the ATIS dataset and implements a zero- or few-shot intent classifier based on a fine-tuned GPT3 model, that can be used to provide inferencing service via an HTTP Service.*\n",
    "\n",
    "[GitHub repo](https://github.com/alexdjulin/openai-intent-classifier)  \n",
    "[Kaggle Notebook](https://www.kaggle.com/code/alexandredj/intent-classifier-based-on-fine-tuned-gpt3#OpenAI-Intent-Classifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modules installation\n",
    "To run the notebook code below, you need the following python modules.  \n",
    "Create a virtual environment if needed first, before running the install cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Couldn't find program: 'false'\n"
     ]
    }
   ],
   "source": [
    "%%script false\n",
    "\n",
    "!pip install pandas\n",
    "!pip install numpy  # should come with pandas\n",
    "!pip install matplotlib\n",
    "!pip install scikit-learn\n",
    "!pip install seaborn\n",
    "!pip install openai\n",
    "!pip install python-dotenv\n",
    "!pip install kaggle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import zipfile\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from random import randint\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import seaborn as sns\n",
    "from openai import OpenAI\n",
    "\n",
    "# load environment variables from .env file\n",
    "import dotenv\n",
    "dotenv.load_dotenv()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API-KEY\n",
    "To run the OpenAI calls below, you need a valid [OpenAI API key](https://platform.openai.com/api-keys). Create an .env file at the root an add your key (see .env-example)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset Review\n",
    "Let's look at the [ATIS Airline Travel Information System](https://www.kaggle.com/datasets/hassanamin/atis-airlinetravelinformationsystem) dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset URL: https://www.kaggle.com/datasets/hassanamin/atis-airlinetravelinformationsystem\n",
      "License(s): GPL-2.0\n",
      "atis-airlinetravelinformationsystem.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
     ]
    }
   ],
   "source": [
    "!kaggle datasets download -d hassanamin/atis-airlinetravelinformationsystem\n",
    "\n",
    "atis_path = 'atis_dataset'\n",
    "zip_file = 'atis-airlinetravelinformationsystem.zip'\n",
    "# unzip dataset\n",
    "with zipfile.ZipFile(zip_file, 'r') as zip_ref:\n",
    "    zip_ref.extractall(atis_path)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train samples: 4834\n",
      "Test samples: 800\n",
      "Total samples: 5634\n"
     ]
    }
   ],
   "source": [
    "atis_train_csv = os.path.join(atis_path, 'atis_intents_train.csv')\n",
    "atis_test_csv = os.path.join(atis_path, 'atis_intents_test.csv')\n",
    "\n",
    "atis_train_df = pd.read_csv(atis_train_csv, names=['intent', 'message'])\n",
    "atis_test_df = pd.read_csv(atis_test_csv, names=['intent', 'message'])\n",
    "\n",
    "print('Train samples:', len(atis_train_df))\n",
    "print('Test samples:', len(atis_test_df))\n",
    "total_samples = len(atis_train_df) + len(atis_test_df)\n",
    "print('Total samples:', total_samples)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the first 10 rows of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ i want to fly from boston at 838 am and arrive in denver at 1110 in the morning] --> [atis_flight]\n",
      "[ what flights are available from pittsburgh to baltimore on thursday morning] --> [atis_flight]\n",
      "[ what is the arrival time in san francisco for the 755 am flight leaving washington] --> [atis_flight_time]\n",
      "[ cheapest airfare from tacoma to orlando] --> [atis_airfare]\n",
      "[ round trip fares from pittsburgh to philadelphia under 1000 dollars] --> [atis_airfare]\n",
      "[ i need a flight tomorrow from columbus to minneapolis] --> [atis_flight]\n",
      "[ what kind of aircraft is used on a flight from cleveland to dallas] --> [atis_aircraft]\n",
      "[ show me the flights from pittsburgh to los angeles on thursday] --> [atis_flight]\n",
      "[ all flights from boston to washington] --> [atis_flight]\n",
      "[ what kind of ground transportation is available in denver] --> [atis_ground_service]\n"
     ]
    }
   ],
   "source": [
    "def print_df_head(df, samples=10):\n",
    "    for i in range(min(samples, len(df))):\n",
    "        print(f\"[{df['message'][i]}] --> [{df['intent'][i]}]\")\n",
    "\n",
    "print_df_head(atis_train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fix dataset issues\n",
    "There are two minor issues we need to fix:\n",
    "- There seem to be useless spaces at the beginning of all messages, let's remove them.\n",
    "- All intents are prefixed with 'atis_', let's get rid of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[i want to fly from boston at 838 am and arrive in denver at 1110 in the morning] --> [flight]\n",
      "[what flights are available from pittsburgh to baltimore on thursday morning] --> [flight]\n",
      "[what is the arrival time in san francisco for the 755 am flight leaving washington] --> [flight_time]\n",
      "[cheapest airfare from tacoma to orlando] --> [airfare]\n",
      "[round trip fares from pittsburgh to philadelphia under 1000 dollars] --> [airfare]\n",
      "[i need a flight tomorrow from columbus to minneapolis] --> [flight]\n",
      "[what kind of aircraft is used on a flight from cleveland to dallas] --> [aircraft]\n",
      "[show me the flights from pittsburgh to los angeles on thursday] --> [flight]\n",
      "[all flights from boston to washington] --> [flight]\n",
      "[what kind of ground transportation is available in denver] --> [ground_service]\n"
     ]
    }
   ],
   "source": [
    "# Strip spaces around the message\n",
    "atis_train_df['message'] = atis_train_df['message'].apply(lambda x: x.strip())\n",
    "atis_test_df['message'] = atis_test_df['message'].apply(lambda x: x.strip())\n",
    "\n",
    "# Remove atis_ prefix\n",
    "atis_train_df['intent'] = atis_train_df['intent'].apply(lambda x: x.replace('atis_', ''))\n",
    "atis_test_df['intent'] = atis_test_df['intent'].apply(lambda x: x.replace('atis_', ''))\n",
    "\n",
    "for i, message in enumerate(range(10)):\n",
    "    print(f\"[{atis_train_df['message'][message]}] --> [{atis_train_df['intent'][message]}]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Train, Validation and Test sets\n",
    "It would be nice to have a validation and a test set. The first one for training/fine-tuning, the second one to have unseed data to test our final model.\n",
    "- The rename our test set into validation (conventional name)\n",
    "- Let's take 500 samples from the train set and create a test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set: 4284 samples, 76%\n",
      "Validation set: 800 samples, 14%\n",
      "Test set: 550 samples, 10%\n"
     ]
    }
   ],
   "source": [
    "# create validation set\n",
    "val_df = atis_test_df.copy()\n",
    "\n",
    "# shuffel train set\n",
    "atis_train_df = atis_train_df.sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "# cresate train and test set\n",
    "samples = 550\n",
    "train_df, test_df = atis_train_df[:-samples], atis_train_df[-samples:]\n",
    "\n",
    "# reset indexes\n",
    "train_df.reset_index(drop=True, inplace=True)\n",
    "test_df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "print(f\"Training set: {len(train_df)} samples, {len(train_df)/total_samples*100:.0f}%\")\n",
    "print(f\"Validation set: {len(val_df)} samples, {len(val_df)/total_samples*100:.0f}%\")\n",
    "print(f\"Test set: {len(test_df)} samples, {len(test_df)/total_samples*100:.0f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get a random message and intent from a set\n",
    "For quick testing, let's create a method that returns a random message from a set passed as argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " i'd like to find a flight between dallas and philadelphia --> flight\n"
     ]
    }
   ],
   "source": [
    "def get_random_message_intent(df=None):\n",
    "\n",
    "    if df is None:\n",
    "        df = test_df\n",
    "    \n",
    "    index = randint(0, len(df))\n",
    "    message = df['message'][index]\n",
    "    intent = df['intent'][index]\n",
    "    \n",
    "    return message, intent\n",
    "\n",
    "message, intent = get_random_message_intent()\n",
    "print(f\"{message} --> {intent}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Review dataset intents\n",
    "\n",
    "If you look at the distribution of messages per intent (see lists and confusion matrix below), you can see that the dataset is highly unbalanced, with most messages corresponding to the *flight* intent. This will restrain our training methods. Most usual Machine Learning or Deep Learning training methods won't provide us with good results, as they require large and even dataset samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 Train intents: {'flight', 'airfare', 'abbreviation', 'airline', 'flight_time', 'aircraft', 'quantity', 'ground_service'}\n",
      "8 Validation intents: {'flight', 'airfare', 'abbreviation', 'airline', 'flight_time', 'aircraft', 'quantity', 'ground_service'}\n",
      "8 Test intents: {'flight', 'airfare', 'abbreviation', 'airline', 'flight_time', 'aircraft', 'quantity', 'ground_service'}\n",
      "TOTAL: 8 unique intents: ['flight', 'airfare', 'abbreviation', 'airline', 'flight_time', 'aircraft', 'quantity', 'ground_service']\n"
     ]
    }
   ],
   "source": [
    "train_intents = list_intents(train_df)\n",
    "val_intents = list_intents(val_df)\n",
    "test_intents = list_intents(test_df)\n",
    "intents_list = list(train_intents.union(val_intents).union(test_intents))\n",
    "\n",
    "print(f'{len(train_intents)} Train intents:', train_intents)\n",
    "print(f'{len(val_intents)} Validation intents:', val_intents)\n",
    "print(f'{len(test_intents)} Test intents:', test_intents)\n",
    "print(f'TOTAL: {len(intents_list)} unique intents:', intents_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of messages per intent in the training set:\n",
      "intent\n",
      "flight            3244\n",
      "airfare            376\n",
      "ground_service     232\n",
      "airline            135\n",
      "abbreviation       128\n",
      "aircraft            73\n",
      "quantity            48\n",
      "flight_time         48\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Number of messages per intent in the validation set:\n",
      "intent\n",
      "flight            632\n",
      "airfare            48\n",
      "airline            38\n",
      "ground_service     36\n",
      "abbreviation       33\n",
      "aircraft            9\n",
      "quantity            3\n",
      "flight_time         1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Number of messages per intent in the test set:\n",
      "intent\n",
      "flight            422\n",
      "airfare            47\n",
      "ground_service     23\n",
      "airline            22\n",
      "abbreviation       19\n",
      "aircraft            8\n",
      "flight_time         6\n",
      "quantity            3\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Number of messages per intent\n",
    "train_intent_counts = train_df['intent'].value_counts()\n",
    "val_intent_counts = val_df['intent'].value_counts()\n",
    "test_intent_counts = test_df['intent'].value_counts()\n",
    "\n",
    "print(\"\\nNumber of messages per intent in the training set:\")\n",
    "print(train_intent_counts)\n",
    "print()\n",
    "print(\"Number of messages per intent in the validation set:\")\n",
    "print(val_intent_counts)\n",
    "print()\n",
    "print(\"Number of messages per intent in the test set:\")\n",
    "print(test_intent_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Message lengths / Tokens\n",
    "\n",
    "Let's have a look at messages length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set message length:\n",
      "count    4284.000000\n",
      "mean       65.179972\n",
      "std        23.506792\n",
      "min         7.000000\n",
      "25%        50.000000\n",
      "50%        63.000000\n",
      "75%        78.000000\n",
      "max       260.000000\n",
      "Name: message, dtype: float64\n",
      "\n",
      "Validatoin set message length:\n",
      "count    800.000000\n",
      "mean      58.938750\n",
      "std       21.486789\n",
      "min       12.000000\n",
      "25%       46.000000\n",
      "50%       56.500000\n",
      "75%       71.000000\n",
      "max      175.000000\n",
      "Name: message, dtype: float64\n",
      "\n",
      "Test set message length:\n",
      "count    550.000000\n",
      "mean      63.685455\n",
      "std       22.432842\n",
      "min       11.000000\n",
      "25%       49.000000\n",
      "50%       61.000000\n",
      "75%       76.000000\n",
      "max      170.000000\n",
      "Name: message, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check message length\n",
    "train_df_len = train_df['message'].apply(len)\n",
    "val_df_len = val_df['message'].apply(len)\n",
    "test_df_len = test_df['message'].apply(len)\n",
    "\n",
    "# print message length statistics\n",
    "print(\"Train set message length:\")\n",
    "print(train_df_len.describe())\n",
    "print()\n",
    "print(\"Validatoin set message length:\")\n",
    "print(val_df_len.describe())\n",
    "print()\n",
    "print(\"Test set message length:\")\n",
    "print(test_df_len.describe())\n",
    "\n",
    "# plot message length distribution\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(train_df_len + val_df_len + test_df_len, bins=50, color='blue', kde=True)\n",
    "plt.title('Message Length Distribution')\n",
    "plt.xlabel('Message Length')\n",
    "plt.ylabel('Count')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average message length of our 3 datasets seems to be around 185 characters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train a Machine Learning model on the dataset\n",
    "Let's train a simple Regression model on our dataset. This can be useful to visualise its structure, using the Confusion Matrix for instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                precision    recall  f1-score   support\n",
      "\n",
      "  abbreviation       1.00      1.00      1.00        33\n",
      "      aircraft       0.89      0.89      0.89         9\n",
      "       airfare       0.95      0.88      0.91        48\n",
      "       airline       1.00      0.74      0.85        38\n",
      "        flight       0.97      0.99      0.98       632\n",
      "   flight_time       0.00      0.00      0.00         1\n",
      "ground_service       1.00      1.00      1.00        36\n",
      "      quantity       0.38      1.00      0.55         3\n",
      "\n",
      "      accuracy                           0.97       800\n",
      "     macro avg       0.77      0.81      0.77       800\n",
      "  weighted avg       0.97      0.97      0.97       800\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train, X_val, y_train, y_val = train_df['message'], val_df['message'], train_df['intent'], val_df['intent']\n",
    "\n",
    "# Vectorize the text data\n",
    "vectorizer = TfidfVectorizer()\n",
    "X_train_tfidf = vectorizer.fit_transform(X_train)\n",
    "X_val_tfidf = vectorizer.transform(X_val)\n",
    "\n",
    "# Train a ML classifier\n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(X_train_tfidf, y_train)\n",
    "\n",
    "# Evaluate the classifier\n",
    "y_pred = classifier.predict(X_val_tfidf)\n",
    "\n",
    "# Calculate the confusion matrix\n",
    "conf_matrix = confusion_matrix(y_val, y_pred, labels=classifier.classes_)\n",
    "\n",
    "# Display the confusion matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=classifier.classes_, yticklabels=classifier.classes_)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "# Print the classification report for more detailed metrics\n",
    "print(classification_report(y_val, y_pred, zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As said above, the ATIS dataset is highly unbalanced, with a large amount of messages in some classes like *flight*, *airfare* or *ground_service* but way fewer in others, like *flight_time*, *quantity* or *aircraft*. The f1-score goes over 90% on these classes but is much lower or simply 0 on low-sample classes.\n",
    "\n",
    "Therefore, a zero- or few-shot solution would indeed give us the best results. A Machine Learning approach like above or a Deep Learning solution (LSTM/GRU/Conv1D) would give us poor result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Model\n",
    "Let's settle on a LLM and configure it to perform intent-classifing tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After running some prediction tests, I decided to build the classifier on the lastest version of GPT3: `gpt-3.5-turbo-0125`, to give results better suited to our use case.\n",
    "\n",
    "### Benefits\n",
    "\n",
    "- GPT3 offers great classification performances out of the box. The model predicts the correct intent out of the list most of the time, without any prior knowledge of the dataset. It works with raw text data and can parse and understand multiple languages. No need to tokenize the input, create embeddings or run through a translation step first.\n",
    "  \n",
    "- The model is running in the cloud, no need to download and run it locally, thus drastically saving on disk space (GPT3 is about 350 GB).\n",
    "  \n",
    "- GPT3 can be fine-tuned on the ATIS dataset. The operation happens via the online interface on OpenAI's GPUs. No need for a strong CUDA/GPU hardware locally.\n",
    "  \n",
    "- GPT3 completion algorithm offers a JSON option, ensuring that we will always get an answer in this format. It worked during all my tests.\n",
    "  \n",
    "- The latency when sending a request to OpenAI and getting back the answer is very short, especially for a cloud-based model. I never had to wait more than 1 second, even on very long prompts.\n",
    "  \n",
    "- Using a GPT model makes it very easy to swap to another model. Since they all run in the cloud, we can upgrade our model just by changing the name id. This could even happen in real time during a conversation (customer is changing subject, we load a model better suited to the new context).\n",
    "\n",
    "- Expanding or replacing the list of valid intents to chose from is very easy and can be done in the prompt directly, without the need of re-training the model.\n",
    "\n",
    "- The context window of 16k tokens ensures that we can input large amount of text and long conversations. Of course, we could implement an additional summarizing step if prompts get too long.\n",
    " \n",
    "- The costs of using and fine-tuning GPT3 are still much lower than for GPT4 models, for similar results considering our use-case.\n",
    "  \n",
    "- Finally, on a personal note, I find GPT models easy to use and I already had past experience working with them.\n",
    "\n",
    "### Bottlenecks / Drawbacks\n",
    "\n",
    "- The main drawback building our intent classifyier on a GPT model is obviously that we depends on OpenAI services. Any technical problem on their side would impact us. We are also impacted by any change in the API they would implement or models they would deprecate overtime.   \n",
    "\n",
    "- Regarding the costs, we are not on a free and open-source solution. Scaling it up to target a large amount of customers could eventually becomes expensive, even if we consider the current Pricing, which is very low ($0.50 input / $1.50 output for 1M tokens).\n",
    "\n",
    "- Regarding the fine-tuning possibilities, we are limited by OpenAI's API and restrictions. While we do have access to multiple hyperparameters, the fine-tuning operation is running on OpenAI's servers and out of our hands. It is difficult to assess exactly how it is happening and what PEFT methods are used, considering the size and complexity of the GPT models. The interest in fine-tuning our own model most probably depends on the dataset and use-case. Also let's keep in mind that fine-tuning a model is much more expensive than using it ($8.00 training, $3.00 input usage and $6.00 output usage for 1M tokens).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Desired Input / Output\n",
    "We want to build a HTTP infering service to send a message to our model and get the resulting intents. We should therefore use the JSON format for all data exchange, so our flask application can handle them properly.\n",
    "\n",
    "**INPUT:**  \n",
    "\n",
    "```json\n",
    "POST /intent\n",
    "Content-Type: application/json\n",
    "{\n",
    "    \"text\": \"find me a flight that flies from memphis to tacoma\"\n",
    "}\n",
    "```\n",
    "\n",
    "**OUTPUT:**  \n",
    "\n",
    "```json\n",
    "{\n",
    "    \"intent\": \"flight\"\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using OpenAI GPT3\n",
    "Let's have a look at GPT3 from OpenAI and how it performs on intent classification tasks.\n",
    "\n",
    "We will use the latest version, `gpt-3.5-turbo-0125`, as it can be fine-tuned too.\n",
    "\n",
    "[API documentation](https://platform.openai.com/docs/guides/text-generation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpt_model = 'gpt-3.5-turbo-0125'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create our prompt and completion method call \n",
    "\n",
    "We need an efficient and cost-effective prompt to ensure that our model understands the following specifications:  \n",
    "- **Describe the task**: We are performing intent classification\n",
    "  \n",
    "- **List the valid intents**: The predicted intent should be picked from the list only\n",
    "  \n",
    "- **Describe the output**: The argument `response_format={\"type\": \"json_object\"}` along with the word *JSON* specified in the prompt ensures that our model will format all answers as such\n",
    "  \n",
    "- **Examples**: Give at least one user / assistant example to help the model\n",
    "  \n",
    "- **Add current task**: Finish with the current message we want our model to predict the intent on\n",
    "\n",
    "- **Handle confusing or out-of-context messages**: If the message is confusing, the model should still try to understand what the customer is asking for. However, if the message makes no sense or is out of context, we should skip the prediction and return an empty list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create client to interact with openai API\n",
    "client = OpenAI()\n",
    "\n",
    "def get_intent_predictions(message, intents, model, verbose=False):\n",
    "\n",
    "    prompt = [\n",
    "        {\n",
    "            \"role\": \"system\", \n",
    "            \"content\": f\"\"\"\n",
    "            Role: You are a helpful intent classifier for virtual assistant interactions.\n",
    "            Context: The following dialog involves a user interacting with a virtual assistant in the context of Airline Travel Information.\n",
    "            Predefined Intents: You should pick the corresponding intent from the list: {intents}.\n",
    "            If the message does not make sense, try to guess what the customer is asking for.\n",
    "            If the message is empty or out of context, return an empty string.\n",
    "            Output: You output the most probable prediction intent using JSON as follows.\n",
    "            \"\"\"\n",
    "        },\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": json.dumps({\"text\": \"find me a flight that flies from memphis to tacoma\"})\n",
    "        },\n",
    "        {\n",
    "            \"role\": \"assistant\",\n",
    "            \"content\": json.dumps({\"intent\": \"flight\"})\n",
    "        },\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": json.dumps({\"text\": message})\n",
    "        }\n",
    "    ]\n",
    "\n",
    "    response = client.chat.completions.create(\n",
    "        model=model,\n",
    "        response_format={\"type\": \"json_object\"},\n",
    "        messages=prompt,\n",
    "        max_tokens=100,\n",
    "        temperature=0,\n",
    "        top_p=1,\n",
    "        frequency_penalty=0.5,\n",
    "        presence_penalty=0,\n",
    "    )\n",
    "\n",
    "    # print tokens / debug information\n",
    "    if verbose:\n",
    "        print('Intents:', response.choices[0].message.content)\n",
    "        print('Tokens:', response.usage)\n",
    "        print()\n",
    "\n",
    "    return response.choices[0].message.content\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test model completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intents: {\"intent\": \"airfare\"}\n",
      "Tokens: CompletionUsage(completion_tokens=7, prompt_tokens=194, total_tokens=201)\n",
      "\n",
      "MESSAGE:  list all round trip fares from pittsburgh to washington dc | INTENT: airfare\n",
      "OUTPUT: {'intent': 'airfare'}\n"
     ]
    }
   ],
   "source": [
    "message, intent = get_random_message_intent()\n",
    "\n",
    "response = get_intent_predictions(message, intents_list, model=gpt_model, verbose=True)\n",
    "\n",
    "data = json.loads(response)\n",
    "openai_intent = data['intent']\n",
    "\n",
    "print(f\"MESSAGE: {message} | INTENT: {intent}\")\n",
    "print(f\"OUTPUT: {data}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our completion call requires around **200 tokens**, which should be no issue for gpt-3.5-turbo 16k-token context window, even on long conversations.\n",
    "\n",
    "Regarding the costs, that's about **$0,0001** for the prompt and **$0,00001** for the completion tokens, which is very low, even if we scale up our solution and target a large amount of customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test model with additional intents\n",
    "What if we want to expand our list of intents? Or even replace it completely?  \n",
    "We can easily add or replace intents inside our prompt directly, as below. Our GPT model will consider them when generating the output list. This is one major benefit of using a LLM as intent classifier, whereas we should re-train the model on a large amount of matching samples, if we were to use a ML or DL approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MESSAGE: I want to cancel my flight to NYC due to a strike.\n",
      "OUTPUT: {'intent': 'strike'}\n",
      "MESSAGE: Can I get a refund for my flight to Paris CDG, the plane was 3 hours late?\n",
      "OUTPUT: {'intent': 'refund'}\n"
     ]
    }
   ],
   "source": [
    "# Let's add two new intents to the list and send a corresponding message\n",
    "expanded_intents_list = intents_list + ['strike', 'refund']  # mandatory addition for French airlines :)\n",
    "messages = ['I want to cancel my flight to NYC due to a strike.', 'Can I get a refund for my flight to Paris CDG, the plane was 3 hours late?']\n",
    "\n",
    "for message in messages:\n",
    "    response = get_intent_predictions(message, expanded_intents_list, model=gpt_model)\n",
    "    data = json.loads(response)\n",
    "    openai_intent = data['intent']\n",
    "    print(f\"MESSAGE: {message}\")\n",
    "    print(f\"OUTPUT: {data}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assess the model results\n",
    "\n",
    "Let's see how our model performs on 100 random messages from our test set and their corresponding true intent label.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPT model: gpt-3.5-turbo-0125\n",
      "Predictions: 100 | Successful Prediction: 82 (82%) | Failed Prediction: 18 (18%)\r"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False Prediction Examples:\n",
      "1.  what is the latest flight leaving las vegas for ontario | TRUE: flight | PREDICTED: flight_time\n",
      "2.  which airline offers the cheapest rate going from dallas to baltimore on july fourth | TRUE: airline | PREDICTED: airfare\n",
      "3.  list the earliest flights from st. paul to houston | TRUE: flight | PREDICTED: flight_time\n",
      "4.  i need a flight to atlanta this afternoon | TRUE: flight | PREDICTED: flight_time\n",
      "5.  the most expensive flight between boston and philadelphia | TRUE: flight | PREDICTED: airfare\n",
      "6.  what do you have from philadelphia to dallas on saturday morning | TRUE: flight | PREDICTED: flight_time\n",
      "7.  what is the earliest flight from boston to atlanta | TRUE: flight | PREDICTED: flight_time\n",
      "8.  all flights before 10 am boston denver | TRUE: flight | PREDICTED: flight_time\n",
      "9.  show me the cheapest flights from dallas to baltimore | TRUE: flight | PREDICTED: airfare\n",
      "10.  does flight dl 1083 from philadelphia to denver fly on saturdays | TRUE: flight | PREDICTED: flight_time\n",
      "11.  what morning flights do you have between oakland and denver | TRUE: flight | PREDICTED: flight_time\n",
      "12.  give me morning flights from charlotte to baltimore | TRUE: flight | PREDICTED: flight_time\n",
      "13.  i would like to fly from boston to oakland what airlines fly from boston to oakland | TRUE: flight | PREDICTED: airline\n",
      "14.  what are the morning flights in the next two days from nashville to tacoma | TRUE: flight | PREDICTED: flight_time\n",
      "15.  show me the economy flights from baltimore to dallas | TRUE: flight | PREDICTED: airfare\n",
      "16.  what are the early weekday flights from san francisco to pittsburgh | TRUE: flight | PREDICTED: flight_time\n",
      "17.  is there a flight tomorrow morning from columbus to nashville | TRUE: flight | PREDICTED: flight_time\n",
      "18.  does delta fly from atlanta to san francisco | TRUE: flight | PREDICTED: airline\n"
     ]
    }
   ],
   "source": [
    "def asses_model_results(model, samples=100):\n",
    "        \n",
    "    true_label_found = 0\n",
    "    true_label_not_found = 0\n",
    "    pred_label_plot = []\n",
    "    index_list = []\n",
    "    false_predictions = {}\n",
    "    \n",
    "    df = test_df.copy()\n",
    "\n",
    "    for p in range(samples):\n",
    "        \n",
    "        # make sure we don't get the same message twice\n",
    "        while True:\n",
    "            index = randint(0, len(df))\n",
    "            if index not in index_list:\n",
    "                index_list.append(index)\n",
    "                break\n",
    "\n",
    "        message, true_intent = df['message'][index], df['intent'][index]\n",
    "\n",
    "        response = get_intent_predictions(message, intents_list, model=model)\n",
    "        data = json.loads(response)\n",
    "\n",
    "        # get predicted intents\n",
    "        pred_intent = data.get('intent', '')\n",
    "\n",
    "        # log result\n",
    "        if true_intent == pred_intent:\n",
    "            true_label_found += 1\n",
    "        else:\n",
    "            true_label_not_found += 1\n",
    "            false_predictions[message] = {'true_intent': true_intent, 'pred_intent': pred_intent}\n",
    "        \n",
    "        # store prediction success for plotting\n",
    "        success_rate = true_label_found/(p+1)*100\n",
    "        failure_rate = true_label_not_found/(p+1)*100\n",
    "\n",
    "        pred_label_plot.append(success_rate)\n",
    "\n",
    "        print(f\"Predictions: {p+1} | \"\n",
    "            f\"Successful Prediction: {true_label_found} ({success_rate:.0f}%) | \"\n",
    "            f\"Failed Prediction: {true_label_not_found} ({failure_rate:.0f}%)\", end='\\r')\n",
    "        \n",
    "        # remove message from the dataset to avoid duplicate predictions\n",
    "        df = df[df['message'] != message]\n",
    "\n",
    "    # plot the results\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(pred_label_plot)\n",
    "    plt.ylim(0, 100)\n",
    "    plt.title('Percentage of Successful Predictions')\n",
    "    plt.xlabel('Number of Predictions')\n",
    "    plt.ylabel('Percentage of Success')\n",
    "    plt.show()\n",
    "\n",
    "    # let's look at some of the failed predictions\n",
    "    if false_predictions:\n",
    "        print('False Prediction Examples:')\n",
    "        for i, (message, data) in enumerate(false_predictions.items()):\n",
    "            print(f\"{i+1}. {message} | TRUE: {data['true_intent']} | PREDICTED: {data['pred_intent']}\")\n",
    "\n",
    "print('GPT model:', gpt_model)\n",
    "asses_model_results(model=gpt_model, samples=100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results:  \n",
    "\n",
    "```\n",
    "Predictions: 100\n",
    "Successful Prediction: 82 (82%)\n",
    "Failed Prediction: 18 (18%)\n",
    "```\n",
    "GPT3 is doing a pretty good job already, predicting the true label about 80% of the cases already. Moreover, if we look at the list of true Vs. predicted intents, the error are quite forgivable. Most of the time, GPT3 predicts *flight_time* instead of *flight*, when both answers are definitely valid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-tuning GPT3 to better match our intents\n",
    "\n",
    "Let's fine-tune GPT3 on the ATIS dataset and see if we can get better predictions.\n",
    "\n",
    "Not all GPT models are available for fine-tuning. This is why we picked `gpt-3.5-turbo-0125`, which is the flagship model of the GPT3 family and also the recommended one for fine-tuning.  \n",
    "\n",
    "To reduce training time and [costs](https://openai.com/api/pricing/), I limited this operation to 1000 training and 100 validation samples.\n",
    "\n",
    "[API documentation](https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the training data\n",
    "\n",
    "The OpenAI API requires data stored in jsonl format, as follows. My first task was to write down the ATIS dataset samples to jsonl files.\n",
    "\n",
    "```jsonl\n",
    "{\"messages\": [\n",
    "    {\"role\": \"system\", \"content\": \"Marv is a factual chatbot that is also sarcastic.\"}, \n",
    "    {\"role\": \"user\", \"content\": \"What's the capital of France?\"}, \n",
    "    {\"role\": \"assistant\", \"content\": \"Paris\", \"weight\": 0}, \n",
    "    {\"role\": \"user\", \"content\": \"Can you be more sarcastic?\"}, \n",
    "    {\"role\": \"assistant\", \"content\": \"Paris, as if everyone doesn't know that already.\", \"weight\": 1}\n",
    "]}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create fine_tuning directory if needed\n",
    "os.makedirs('fine_tuning', exist_ok=True)\n",
    "\n",
    "# shuffle train and test dataframes\n",
    "train_df = train_df.sample(frac=1).reset_index(drop=True)\n",
    "test_df = test_df.sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "system_prompt = \"You are an intent classifier for virtual assistant interactions, which predicts the most probable intent from an input message.\"\n",
    "\n",
    "def create_jsonl_file(df, jsonl_file, samples=None):\n",
    "\n",
    "    if samples is None:\n",
    "        samples = len(df)\n",
    "\n",
    "    # deletes the file if it already exists\n",
    "    if os.path.exists(jsonl_file):\n",
    "        os.remove(jsonl_file)\n",
    "\n",
    "    # browse through the dataset and write the messages to the jsonl file\n",
    "    for i in range(samples):\n",
    "        message = df['message'][i]\n",
    "        intent = df['intent'][i]\n",
    "        message_json = json.dumps(\n",
    "            {\n",
    "                \"messages\": [\n",
    "                    {\n",
    "                        \"role\": \"system\", \"content\": system_prompt\n",
    "                    }, \n",
    "                    {\n",
    "                        \"role\": \"user\", \"content\": message\n",
    "                    }, \n",
    "                    {\n",
    "                        \"role\": \"assistant\", \"content\": json.dumps({\"intent\": intent})\n",
    "                    }\n",
    "                ]\n",
    "            }\n",
    "        )\n",
    "        \n",
    "        # writes the message to the jsonl file\n",
    "        with open(jsonl_file, 'a') as file:\n",
    "            file.write(message_json)\n",
    "            file.write('\\n')\n",
    "\n",
    "train_jsonl_file = \"fine_tuning/train.jsonl\"\n",
    "val_jsonl_file = \"fine_tuning/val.jsonl\"\n",
    "\n",
    "create_jsonl_file(train_df, train_jsonl_file, samples=1000)\n",
    "create_jsonl_file(test_df, val_jsonl_file, samples=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upload our training and testing files\n",
    "\n",
    "Both jsonl files need to be sent to OpenAI for fine-tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FileObject(id='file-2NWr5q7FRxyxonmlsBLPzxgJ', bytes=339833, created_at=1717668320, filename='train.jsonl', object='file', purpose='fine-tune', status='processed', status_details=None)\n",
      "FileObject(id='file-rlMj2sHwpo5ZMqTHSHAe9aQH', bytes=33826, created_at=1717668321, filename='val.jsonl', object='file', purpose='fine-tune', status='processed', status_details=None)\n"
     ]
    }
   ],
   "source": [
    "train_file = client.files.create(\n",
    "    file=open(train_jsonl_file, \"rb\"), \n",
    "    purpose=\"fine-tune\"\n",
    "    )\n",
    "\n",
    "val_file = client.files.create(\n",
    "    file=open(val_jsonl_file, \"rb\"), \n",
    "    purpose=\"fine-tune\"\n",
    "    )\n",
    "\n",
    "print(train_file)\n",
    "print(val_file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create fine-tuning job\n",
    "I used the default hyperparameters, which should be fine for our use-case.\n",
    "\n",
    "[API documenation](https://platform.openai.com/docs/api-reference/fine-tuning/create)\n",
    "\n",
    "[Fine-tuning UI](https://platform.openai.com/finetune)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create job. It will appear in the OpenAI web interface\n",
    "response = client.fine_tuning.jobs.create(\n",
    "    model=\"gpt-3.5-turbo-0125\",\n",
    "    training_file=train_file.id,\n",
    "    validation_file=val_file.id,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check our job's metadata and make sure it's running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JOB ID: ftjob-HsuSn9e6UnNtiZp0rteTavyD\n",
      "JOB Status: succeeded\n"
     ]
    }
   ],
   "source": [
    "job_id = 'ftjob-HsuSn9e6UnNtiZp0rteTavyD'\n",
    "job = client.fine_tuning.jobs.retrieve(job_id)\n",
    "print('JOB ID:', job.id)\n",
    "print('JOB Status:', job.status)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyse the fine-tuned model\n",
    "OpenAI offers multiple metrics during training to assess how robust our new model is.   \n",
    "This is my fine-tuned version. The Validation Loss went down pretty quickly. The process took about **1 hour** to train and used **162,420 tokens** for a total cost of **$1.30**."
   ]
  },
  {
   "attachments": {
    "image-4.png": {
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gSms0Twpo3HnHfW5grs2bN7lyISmVJUs2Ux3jY8eOW5Uq59gbrw+1USM/cutg2NAx1v2JZ1xw+UxSvnwlVydd2asKAojaj3/bly5derv++pvcwIM66Bc9R6+78477w9Zh1aCJPXv2jdE21cZ0pUmrllfbE4/3SpXax8WKlXD/bt++LexBuKjutbLlFHDVAIopsXPnLnciQ9nxugw/lNpnnsD32rt3n/f/k+cy/SzdEyeOW48ez9k7I8a69vXeuxO8bX6otW59XYLbnAIhft+gQGpCkrPu1K+ovIgCUioDpX5Bf7qtad2f6O0CsQnRybvu3Xu7NuB/ttpApsBtURtQW1Cb0PJU+xP9mz9/QVd7v3evF2PV39frej7dx9q2uSHG6/Q5VSqfYw899Jhrg3FR36g+Un2lXqvvpxN7V13VNlYdY1HA1N9WFLQUfRdXvsXrL7t27ZHgukuOpOwPgul1/V9L+HUHDxxw/xbInz/O+VdplMyZM7l2o8C7Txmnw0e8ZcpGL1euvFs+V1xxtV17zfXuhJayw0MD8ZLU7UDlyrRf0na3Kok193+Y8Z1t3Lje7Su0z1A70nsr6HrZZS2sw023u/7pjz8W208/zQ28KvX3B8Gflz17dlu1aqU7sSAqRaOT4Kqv3bnTvXbbrXclavsKpXXsr2+1GbWB+Nx8cyc3IG64Nt2kyaXuZERi5kPLMzgQrzI7fiA+WHL6olMpuX1Rcmm53X/fo24cgOLefszf7+pf3df0W27p7KaFSsvLUlc1ap727dvn9r3hqE9Yv369u63seJ38Su6+vGjR4u7fhF+3O1V+AwAAAACnwn8yGK9B2fwB2l54obfLCNWfbvvT/T8/M0hZYsHTV61a7qYrgyx4uv4efOgu7+AgZVlcumT/mtbXuSCD5s0f9E0DxY0aNdxlaSp4Fh8d8O3e/U/0oHYaXEuDxalOrgbXanNtO/c8zavm2Z//UaOGucBIXAPwpXbGZ3BAatTID+2+Lo9a4cJF7M8/l9hr/V8ImzWbVFmzZnEHxwqUNL7wEhc0VmBCFKxRQPHxx3umykFuj6e6RS+rxLavlAgeSFLrS8tLAYZ69Rq6kgqffvqRG7TR/8zQ9afvr4NlBY/0PL+N6eBWQbLQwQ+DB01UG1GtZrUvvVYDnirTsFy5itEZqSmRIUNUGZiDh+LOStYB+qFDUUFHf1DG5Jo7b5bdcedNduddN9uvvy4ITD2pXr0Grr1q+1i8eGFgapRsXtuSypXPcQHjk0GUjC740+ba9u5qj+BAZEoldd3p9k033mr58uW3FSv+sv6vv+jWXfBgeArI3tyhU/R8KgP9jjs7RLcff5BSBYqyZMli06Z/HaOfCG7TzZq1ctmRCuIFD1ypP/VHf/75u9dH7batW2Ju4x1u6uTa0LFjR23OnJnW7bH7Y8yj2t7VV7d1JxbCUd8Y3Pe9MUBZ3VvduqjrzXdwEFLt+ZKLVcs4syt1okE1NX+aV789n122nMvOPN2sXbvatdUSJUrZ5Ze3DEw9SQHK8+vWd4E+BbIUSA/222+/urrrKtukK2BuaH+LO1GlK676v/5yrEC8JHU7UOkXBcaPHDls69eFD+7FRYG7AQNfcdm3RYsWc/sO7UOGD/vAbu14pwuAz58/z41LEjyvqbE/UBaz6m1rv+V/ntqdMt9ff+PF6M/TiQBdkaEBdLVf18mdPs+94oL+OvmmbVTbcGrS1Qtq0xkyZIyxnatNazvUyQEtr+DtPBw9FhqIV238cJLSF53q31OSnL4oOb8Vffp9pfajq3mmfPWZWyZ6nr+f1O8wjemgEyOhktqvn0rpAydv1b4PHjwUmBqbri4Tbds6+ZXcfXliX6ftS1L6GwAAAAA4FciMT6Nq167rLi1XIEV1ZHUQrMCDgvA6sBs5aph3ULg/8OzwdMA9cuRQN8iVDpyU6fhyv762bNmfLjCiYJqCA2mRAkPK4CtRIubggMmVPVsOl70oGuxPAxnqANcPTugAW5/3bx7kpiYdLCtQ/vwLvWzc+NHuYDU+v/y6wJWHiDqojWpjep0OcAsXLmwNGlwYeKZZk4uaumWl5Thw0GvuUnu1L71WA57OnBVVa10DoSqolVyq0axgUlqhAO71193kAnjr1q1xWbnBNGijqJzA/AXzXIBHwRcFdxXk1TqpVatO9Emw1JKUdRc1cHIxF7zs1fsJFzjUutOf+ol333vbBXwSU3tX24wyo1VOIa5gmfowbcv6PL8P80X1R8+55bR8xbLA1Kh2U61a1GfPmDnNBVs3bFgXPY86+aO2p4zLCxs1cc8LFdr3qTzJpEkTXGBMAVNlg/uOHz9qixf/aot/+9UNBqhMZtG8fvHlZBdU0nfImzefm3468TPHFRBv26a9CxgryKcgqzKAn+rR12Wsap3HRaVoPpk4we2LFLBWzfax40a75RpOUrcDv8Z8clWoUMl9J71vKL2vMu9VDz9YpPYHaic6gV64UMxxTlTWpXfv7m57U5kYBWIVcFU5EJ3YfKXfYG9d9Ik1GG5yaJ+ugTi1ztWmg7dztWlth/q+uursr+Unt7tQwYF4ZUGPGj0izkC8Lyl90amWnL4oudRm6tdv5LaB98e86wLp/j7Y30/Onz/XnVSsVauumx4qrS5LjX+QVNm8bS05+/K09hsAAAAASC0E40+x0Cz0uLLM/UuBd+3a5bIQQ82dO9u2bNkUuBfe6tWrbMHCnwL3ouiA/Kef57jASt68+V0t8dQQWm4h+C+hchvBy0TZe8riW7lyucvc63T7PamSrf77ksXewXYfGzPmPRegVlBBB7h+cOLjT8a7QJ0Oouudf0HgVSepDny476bMxrRIg5lpbAAFGRKikzo/zvohcO+kH36Y5gI2ypotG6ibq0CP32Y0yG5ojWaZMWO6W64KeirrOFRwuYXgP2WZKtvUt8+bL2VGpwXaHlUGQ1njCqZNnPRRjGCO6IqSwW/2txHvvG2vvvp89LJXcPfV1/qa6m7rJFit2nXjPAkWnImZmKsmkrLuRNnNCkhVrlzVZRCHrgOV51CQW32PX1YgLuEG7wu1a+cOF3jUSZlHH+nuaiAHly/SNhga1K1UqarXdrK75axgeii1uSVLfnO3S5c+O2xWsU5ELVgYs66yglrKcleQMXgwTwXnFPR68cXedujQQZepel+XR+zllwbYs8+86L1/VK3nnAlciZQcid0fhNLrHno44depjSoYqKsCFPhTKZXX+w+JcQXS9z98Zzt2bAu8Ijb1vwqca0wK1RxXCaD27W+ODmaHSmg7UN8evB3sP3AgzsB+QpQBrlJsCpwrs/mll55x+xAF1t9863V3IqJkybNilfZI6f5Agq8Y0ecNH/6m2+ZUSqRLl4djlYD5e+1qt2+7+56O9r/HH3InOJYvX+o+R8tD22TXR590+9JwgrPJ48sW14mHwt46Ut8wfdq3YZettqtHHr3HjZ0R7nGd2PAD8bpiYcKH7ydYVz+pfVFKJXY/4ktOX5RcZ5et4PpRbXN3dO4Sdj4VrJdCgZNjwZK7LDXmybvvjI/1WZqmx1KD+sikOuAt2+Tsy9PSbwAAAAAgNRGMT6P8S4HTpTvhHSAGJgbRQaNq8sYnrst6VQpAB9iqI5tQwO1U0/dSFl+/V56zVatWuEuyr2hxVeDR5NP7Knj8+RcTXZZtqK+++twNEKaAk8pjnO4UNPMvE0/YCe/gX/XPY9Iy2xrI5lMpCcmTJ58LMIhquIazfv0627Mnqn5raNmLpDp2LKome9Z4svEUjNVl8OLXyE5N5b320PXR7la6dFlXrmPY8DfDBqEV7J016/tYg/KJgnyz58xwJ8EK5C+YaifBkrLuJEeOmIOwxkXbQf58+QP3wotr/QebNv0bFzxScF9X+2ig0MGD3rWhQ953J7I0sGVoUFdBbwWbFKTctSt8xvb6DWtd1qlfAiGllIn/7DP9bMAbw13wTFmnOgGTnCzQtEalZvr07eG1v5m2b99eF5DUSQn1dyPeecumTfvaaxdRNdt37NwReFUUlQHqeEtn1w8riP7B2JGuzEftWnXjHHckoe3gx9kxt4Pt27d694966zKzlShZKvDMhCnYfUWLK93rZsz8zp7r85T9umiha/sKrM6cOd2e7vmYLVmy2M3/tde2iz4Jlpj9gcohJXZ/oM/7zluOKoukEx86adeiedz7LQXSNchkz16P2z333uquYNA6UV+W0prlRYsUdfO9f/9eWxdHTe+EqP0rEK/fIFq+Gug53LqOKWl90amWnL4oufLly+vWQWKoT479OyztLsvjXv+hPkTtQeWe4qLfCqJtW6VnkrsvT+zr/N8lkfgNAAAAAKQ2gvFplH/Ac+JEOu+gJzAxiA6ENMhXWhGa4Rn8F5rhW7ZsOevV8wV7+61R7hL9cBS0WRbIui5QoECc2YKJpc9UFmVcJQeCD3LTZ4gddAiuAx/8p0xmJE1ovWD/T1mmyjYNpsxWUU3ncBnQonrYynDU1QBbt6V8fIFgjRtfYo8+2t2VdtGVKMr4DReI1+cr07Jhw7jrivsnwdKn97bpdMkvy5EawtVmDv1LbJZ2fJQV/WSPR2zyZxNt06aNpjEMFOBTqR+V6GjX7mZ76cUBcdZ+PxWUMa1M+LPPVqbpCVu37m8XHB02bLC98kpfl2V/utM4EgMHvuLGQuhw87XW8dbrrGu3Li5grjrauXPncic//O1NdBJKgXiVXdFVHQMG9rMvvpjkssbVjlVeI3QMhORsBwqQ6ioIBS/jy5zW5xQrVixwz6xypSqWK1ced3Jg7pzZgakxKUiuKyQU/FcGsv/+idkfbAucbPL3B3Xr1re+fV+zNwe/a/XOb+imhVJ7VwkrKVkycSVnNI8ffTTWrSOdwFQ5lX+bxpRQaZp582a7fvXcc2uGHXPg35SU/Yj8G31RXOPuBP/Fd5VDUgWPHxP8p2l6LDXo5JX6Cp1E0L43HG2rJUpEnWBQCSydoEvuvnzTpg3u34RflycivwEAAACASCAYn0atWrnCDhw4YHnz5nUZaqE0kKSy7+ITVyaRsg+V7aaD0eDgy6ly+LBK5OQzDa5XpXLVwNTYlLmfGhQ4UfkN1f+Nq+SADh4VrJG0kFmVIWPsOqnZsyurOXZd5JRTQCJ2xnTwMlH2qijb3h8oTQfH4ajOv4JkOjA+fORIYGryrFr1lwuk6YRMuNIDUrlSVZcVp/kKVzYnuVSaw88K1gCDKmuhLONwlGWpv5YtW8dZgsbf7pQpePhI3APfJU3i151omxeVdzlVlOWuchiPdr3Xbru9nWlAQp3UmDN3lgvqqB1d3uyKwLPN9u7b67LelcGaN2+ewNSYShQv5d7Xz7pMiaaXXOrWsU62KFP5f48/aG+/PSAqk3bL5sCzTk8KbN12611eO74jMCW2BvUbuisAFHzWYJ+ik0933f2A28esXfu3jXjnTRcsli+//NTGTxjj1pFqv1/v9au+Lvc+muTtQIG6NWuiStlUrlzF6z/Cn3itW6e+PdO7n7391khXSkjrP0o6r79M/MmtxO4PCgb6N39/oG1H09RWqlat5qaFo+8WTP1Ir57Pu3URlx07drhtQY57/WZKbHSB5kNev5DTSsYRLE2IrgBSXXON17Dkj9/ctnh5s1axSu/ElLS+6N+Q1L4oufzSS8lfB2l3WerEnE6eqc+oUb1WYGpMVapU9ZZlIZdQsiJQgz+5+/J/8zcAAAAAECkE41MoXNA0Nfy+ZJFt2LDWZQvq8un27W9xB/O6jFp1fzvcdLt3sJY98OzwdOCsbL5gOphTUEPvu2vXDluzZnXgkdjiyspPKWWBrfk76nPLl69sjS+82N0Opu+qzEzRgV9KThpoWSpbSt+5fv2GYYMiyvorVqy4OyBX7eHUl7iAkX9JdskwAanzzjvfDfqX2tSOGjZq7NpGsMaNL3aDHyrotmr1CjdNA2P6baZixSpWoUJldzvYhRc2cctYJTH++CNqMMzkmr/gJxcEVDDo4osvi7Xu1MYbNGjsMkoVNNRAnCml5XD/fY/aVVe1dcEbZYeqBEa4cha+xYt/cQGD4sVKuGz6UJrvBvUvdG1wy5aN9tdfKR8kUJKy7kT10XWSRLXjw5XDUABVZVouuqhprPdMKmUdP/Lw49bz6edjBPH8MiYDBvSzRYH1paxG39KlS7y2s98FPcPNo9qcHwxVEFfvlxI6cSR/e+3Hr2/uK168uNfnxt/PpmUqlaLtRv1euIxfZYdXrnyOa7tz585y27ds3rzRvvvua2+bWmNvvf16rOWigPznX3zqSr1MnXpyUM/FixcmuB1c0CD2djBr1g/usxXAa3f9jbHanl7XsuXVrr1HBdyWuXIw+iz1iXXqxD5hLdpfKuCuz9Pr9uz9x+0PtmzdnOD+oHjxEjH2BzpRsWnTJpdNrTInVbzlFkptU6+Tbdu3W/PLW9mVV7bx9nOV7MEHHoszE18n11WLX9vm+nj6mcTQfOoqLy2rJhdfGnY71nbV/7W37JZbOod9XCV+dAWQAsrjxo1yy7pQIW/dtLs5zn4hqX1ROJH6PZXcvii8hPflf/zxm+3cuTPedaDtQyfKwg3amxrLMlL0+23p0j9doP2cc6rHGtNH83y51+61HJUVv2DhfDc9ufvyqNdtPqW/AQAAAIBIy5A3X/5egdv/SUUKF/UOhKOy4+bO/THBQVF9devW8w6KilvRosXt6JEjts47QFHNV2X1nVP1XNu1e6d3IB+VhRpMjz/37Et2c4dO1uba9i57fXGYbFu9dvOmjS7olDt3XqtUsYq1anmNXX1VWxdUSZ8+venydmWPb9i43ubMOTnQYVS94xLuMQUH9h/Y7w5SVPP6zju6WJUq1dxB//Tp39ovv0QdKAUrULCQ1axxnuXJk8f7jsW8g6+1tmvXThfYqF69pjvISky96Pjs3r3batSoZbly5XLfMY/3HVevWWHKllSA5K4773cD72mZ6vJqBX1CJWVZKsio4LGCPbVrnW979/7jagIrE+7GGztai+ZXuoM91amf8OEHYdddcuig+vy69d0yU4bcps0b3AGpDlgrV67mfUdvmreefTqwVAmFfPnyWbZs2d0Af7qKQJmt9etf4A44VQ9eGdrLA1mswfQ5DS9o7LLE4nqOz2/7yuTUgH+aF5XoUAmJS5s2tzZt2psGYVUASgEZv4atyhdo3amUQbVq1d0VHGpfWpaaT32+2qcC1JMnf+xek1z6TAXNFPjS+2twTwWr1B7VTvR5RYtGDaqqgRfVVuPif18tw7iWjR+0qeG1fy1ntb3hIwa7dhgfrVdlCWuZaF71Pn//vdrbRg9Ft2fVx9ay/eLLydHZgqFUu1iDFqs9K3s9eLsOltx1p/6tZo3arvSI6qRn89rJ2nVr7NChQ25gQ10JUKdOPa/fqGQrVi63cCVaml7SzA3+rAzEcNubr6rXD+rEoUoLKCCqZbN9+3Y3noACKgoUN6jfyPVTf/65xJ30EPUt6qtUrsP9ef3AunVrXbvTstSAnRpUVYEelUwJ3n78vk/t47vvvglMjRLX+q9Xr6HLAFfZBb2nPkv9UrNmraxtmxvcNiUrVy23RYsWuttxSeyy8SW2Dwul1z37jPe6m+N/nQYA1/rWtqP9ktqHTiqpT4nZ7y230e+/E6PfU9/z7dQpblmGo1rsM2ZMi1HbOjHbwVlnxd4ONm7a4Nqk+j8FJ8/12qb6Zw0sq3bZuVMXNzCq9lvffjvF5sydaap5X8PbH+kzSpUsbdXOqe4yuhU4zpIli/d5jezuux6IDr4uXDjfvvn2S/cdtX6Tuj/Q3wnvPy1HnSiqXr2WZfTa0pq/V5kGYFWg8Jabo8r67N271yZOGm8zZ37v9ednu5rgmk/Nk8Zi0D5bg3Rq22jjtbHWV1/n7StyuLY8Zsw7MZapTwOWPv54L7e+L2p8ide/LnLbUijNp76/BoTV59bw1v/efXvce+t76zfEtde0c+voqLe/1TpUYFXCtV+t/3Tp07n303ai76x170tuXxQsub+nEiu5fVGwpOzL9R3zeM/TSRgNRK8yP1u3bXbP9z+vdevr3IkwLRsNqi+psSxTym8Dob8rgym5oXr12tHb+SHvt6hO0qvfvu22u7ztsrbbf86YOc2men2IaF6Tsy+Pel3mVPkNAAAAAKQV6cqULRd1FPYfooCXaoQmZPXqFXHWBFc2UOdO97iD8lAKYL30ch+XQRRKB5ePdXvSBZNEtZvjq82swMRNN97qDkCUAagDVB3QTZw0wTug02Xj5ezn+fNi1GV/+OEnrM5557uDKV1Wr6BCMB14L1gwz/q//rLLfAulg8uHHuwWPYBbqITmObGUGXaDy/gPn4mm7C8FM4aPeCvsfCZlWeo7dbr9bmvU6KKg8gYxKaAcLgs0pZR92PSSy13AJlToulPg6MEHuoUtQaQD/Tx58rr30aX2fg3Y5pe3tPbtO7oD3fiEtmdd8v3AA129dZzeBbEUNAilA9xwA5YqsHH9dTe5AFIo/9L0V17t62r/p5TWXcdbOlmTJpeFXYbKeJ0wYYy3PD4LTInibwcJUXatvzwV8FKJjcRQkOCNN/qZX59YwW21sbjKR+lz9Bn6rLgE902hbSNYStadTuYp4K+gYTihy9P/rHDrOli4fi+hbVzCbXcKWD1wfzcXrArXByW0zqPaejdvysndm/89FNgJ3n7i68vVB+lEhQJgEvy+id3uJK59QlL3Bz69rlvXJ12AV+J7XULbqr7T4Ddfj/fKj6RI7nag7Vw16HUFkE7mhdK+T1nMatP+/kD95V13PmClSp0Vtp2IgoI6ifL2kDei+6OU7A9UduaKK652JzzDUdtUbf2PPh7n7usk9h133OfaZVyfJTpRpn1dXKWwgvum+H5j+NQ21Ubjap/an2gcgODv5/c/oe0p+DeB+pW3hwyIPpGfkr7Il9zfU0mR3L4oWFL25VpmXe59yH23cM8Pt59MjWWZFEnpw0K/n66uUCA83DrTNjffe/4bA/rF+O2W3H15cl8HAAAApFX/ycx4P/MnIVHZlV8H7sWkwMWGjeusSOFiLuta2ZZy6NBBd4n/7NkzTJlvoXQg2LBh4+jASEIZlDrw+vHHH+zTyR/Zx97B/cSJE+zrb76wHTu2W5OLLnXB2UWLF8bI2PSzQ5cu+9O+nDLZBfQVyFewQqUApn8/1YYMHRQ2wC06SFz4y89uHpVJpmwxvVbBEAUgf/U+66+/lgaenXxr1qyyhQt/dlnxygTXAaE+RxlwOhge88G73vf+2M1POElZlnqPBQt+su3btrrsKgXX/CzznTt32A8zptmgwa96B/6pX4dVl02fOHHctRUtSwWa9LkaeFDzHHx1gtrciuV/ueCSvp8OQtWO5s+f663Lz+y88+rGyuwtX76iVfMO4v02GJfQ9uxn4SmwOGrUCDvuzWOB/AXdenCXwa9aae+8OyTs1RMrVy63X39Z4DIvdTCu1+g7qV1O/e4bGzZskMtkTg1ad5oHradiXrtWW9Zy8dvJqNHDbMbM6YFnn+RvBwnRfPvLUxmMFSvGLr0TjoKKwVfTbNmy2bUxBemUMehvNyp1oeU1ctQw++abkyU9wgnum+LLTEzJulMQToGcPHnzuSxdf7vTFQ7LVyyz994daj/O/iHw7JjZmvFR3zDrxxlen3UyW1fb+Lx5P1qmzFksb5487j3U/tWXqExWXNudAuAzZ/1gx7w+Kni70bJc5vU9ofPoS05mfHBfrpMAaltahpp3BXj++ON3t42pRnLw+yZ2u5Nwy0aSuj/wuddd0NjrxxJ+XbhtVctfdcq/m/aNty8Y4Lat1JLc7UDbuWp3qw8J3s5Prov37ZOJ42PsD7Q+pnnf4cDBA25fpXXkrw/ti9euXW3jx4+2D8aOdG3Kl5L9gdqD/lRWxl+eou1nmbfPHTJ0oNd2v3fTRMta28CC+T+5/bXGSlFA0d8OtB6mev3yoEGvuOz8uAT3TXG1p2Bq43+vXeXatf8bRd9PV3+oXxk2fHCsEzB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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image-4.png](attachment:image-4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check our list of available models\n",
    "\n",
    "Our fine-tuned model id (and checkpoint versions) should now appears in the list of available models.\n",
    "\n",
    "Fine-tuned models start with the `ft:{base-model}` prefix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ft:gpt-3.5-turbo-0125:personal::9S3O96TD\n",
      "ft:gpt-3.5-turbo-0125:personal::9S3O9Jww:ckpt-step-1000\n",
      "ft:gpt-3.5-turbo-0125:personal::9S3O9Q9R:ckpt-step-500\n",
      "ft:gpt-3.5-turbo-0125:personal::9TSgV6sR:ckpt-step-1000\n",
      "ft:gpt-3.5-turbo-0125:personal::9TSgVvbd:ckpt-step-500\n",
      "ft:gpt-3.5-turbo-0125:personal::9TSgWFtt\n",
      "ft:gpt-3.5-turbo-0125:personal::9X5C853S:ckpt-step-1000\n",
      "ft:gpt-3.5-turbo-0125:personal::9X5C8I1k:ckpt-step-500\n",
      "ft:gpt-3.5-turbo-0125:personal::9X5C8XVA\n"
     ]
    }
   ],
   "source": [
    "available_models = [model.id for model in client.models.list()]\n",
    "for model in sorted(available_models):\n",
    "    if model.startswith('ft:'):\n",
    "        print(model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpt_model = 'ft:gpt-3.5-turbo-0125:personal::9X5C8XVA'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test our fine-tuned model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our fine-tuned model now outputs a list of 3 intents as expected, after training it on the augmented dataset.  \n",
    "As a reminder, it was only outputing 1 intent when trained on the original ATIS dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Message:  show flights from burbank to st. louis on monday | Intent: flight\n",
      "INPUT: {\"text\": \" show flights from burbank to st. louis on monday\"}\n",
      "OUPTUT: {'intent': 'flight'}\n"
     ]
    }
   ],
   "source": [
    "message, intent = get_random_message_intent(val_df)\n",
    "message_json = json.dumps({\"text\": message})\n",
    "\n",
    "response = get_intent_predictions(message_json, intents_list, model=gpt_model)\n",
    "\n",
    "data = json.loads(response)\n",
    "\n",
    "print(f\"Message: {message} | Intent: {intent}\")\n",
    "print(f\"INPUT: {message_json}\")\n",
    "print(f\"OUPTUT: {data}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test model with additional intents\n",
    "Again, let's make sure that our model is flexible and can adapt to new intents added to the prompt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MESSAGE: I am affraid to get sick in the plane\n",
      "OUTPUT: {'intent': 'air_sickness'}\n",
      "MESSAGE: Do you have some painkillers for the flight?\n",
      "OUTPUT: {'intent': 'medicine'}\n"
     ]
    }
   ],
   "source": [
    "# add new intents to the list\n",
    "expanded_intents_list = intents_list + ['air_sickness', 'medicine']\n",
    "\n",
    "# create a new message using the new intents\n",
    "messages = ['I am affraid to get sick in the plane', 'Do you have some painkillers for the flight?']\n",
    "\n",
    "for message in messages:\n",
    "    response = get_intent_predictions(message, expanded_intents_list, model=gpt_model)\n",
    "    data = json.loads(response)\n",
    "    openai_intent = data['intent']\n",
    "    print(f\"MESSAGE: {message}\")\n",
    "    print(f\"OUTPUT: {data}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run another 100 predictions with it and compare it with the base model\n",
    "\n",
    "To finish, I want to see if my fine-tuned model improved during the fine-tuning step. Let's run again 100 predictions on unseen messages from the test set and see if it predict the right intent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPT model: ft:gpt-3.5-turbo-0125:personal::9X5C8XVA\n",
      "Predictions: 100 | Successful Prediction: 100 (100%) | Failed Prediction: 0 (0%)\r"
     ]
    },
    {
     "data": {
      "image/png": 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pU61cuVIeHh6qUKGCYmNjtXTpUi1fvlzz58/Xu+++qxEjRmj06NF3rMNms2U4Z+71vZcrV+4PH8knpf++pM29Tz75JMNw6upj26wqM9/fgQMH6vHHH9eiRYu0YsUKDR8+XOPHj9eaNWtUvXr17CoVACzrz/FfBADAPStTpoyMMQoPD9cDDzxw12Ns2bJFycnJ6Y7M39zn66+/Vv369e86+N/qdmH0m2++0blz57RgwQI1bNjQ0X7z3fKl32/kJv1+07smTZo42m/cuKGjR4/qwQcfdKp/165datasmcunF6e9TmxsbLojrLGxsY717pL2B5qTJ09K+t9Rzvj4eKd+tx4dLVy4sPz9/bVnz547jl+mTJlM9cns/sqTJ4+aNWumZs2aafLkyRo3bpxefvllrV271hGMfX199dRTT+mpp55SUlKS2rZtq7Fjx2rYsGF3fNxZgQIFMryk4tb3nl3SbixYpEiRO4b+tDmRdmT+ZrGxsXd8jcx+jq7M47CwMP34449KTU11OtqedqnJ3c7hMmXKaMiQIRoyZIgOHTqkatWq6Y033tCnn356V+MBwJ8J17QDACRJbdu2lYeHh0aPHp3uiKQxRufOnfvDMdq1a6ezZ8/qnXfeSbcubcyOHTsqJSVFr7zySro+N27cSBcoMyPtmde3bpt2lO/m95OUlKR3333XqV+tWrUUFBSk999/Xzdu3HC0z5kzJ91p+B07dtSJEyf0/vvvp6vj6tWrjjuDZ6RWrVoqUqSIZsyY4fS4s2XLlmn//v1q1arVH7zTjK1duzbDo8hfffWVpP+dju/v769ChQpp/fr1Tv1u3R958uRRmzZttGTJEm3bti3duGmv1a5dO8ep+Lfrk9n9df78+XTrq1WrJkmOfXXrHPTy8lLFihVljFFycnK67W9WpkwZHThwQGfOnHG07dq1y+nu9NmpZcuW8vf317hx4zKsPa3OokWLqlq1avroo48cl3NIv18Tv2/fvju+RmY/x9t9fzLy6KOPKi4uTnPnznW03bhxQ1OnTpWfn5/jkXuZdeXKFV27ds2prUyZMsqfP3+mH6EIAH92HGkHAEj6/RflV199VcOGDXM87ix//vw6cuSIFi5cqN69e+v555+/4xhdu3bVxx9/rMGDB+v777/Xww8/rMuXL+vrr79W37591bp1azVq1Eh9+vTR+PHjtXPnTrVo0UJ58+bVoUOHNG/ePL399ttq3769S7VXq1ZNHh4emjhxohISEmS329W0aVM99NBDKlCggKKjo9W/f3/ZbDZ98skn6QKul5eXRo0apeeee05NmzZVx44ddfToUc2ePVtlypRxOhL5zDPP6PPPP9ff/vY3rV27VvXr11dKSooOHDigzz//XCtWrLjtJQh58+bVxIkT1b17dzVq1EidO3d2PPKtVKlSGjRokEvvO81zzz2nK1eu6Mknn1RERISSkpL03Xffae7cuSpVqpTTDQOfffZZTZgwQc8++6xq1aql9evX6+DBg+nGHDdunFauXKlGjRo5HtV28uRJzZs3Txs2bFBgYKBeeOEFffHFF+rQoYN69OihmjVr6vz581q8eLFmzJihqlWrZnp/jRkzRuvXr1erVq0UFham06dP691331Xx4sXVoEEDSVKLFi0UEhKi+vXrKzg4WPv379c777yjVq1aKX/+/HfcRz169NDkyZPVsmVL9ezZU6dPn9aMGTNUqVKldPc6yA7+/v6aPn26nnnmGdWoUUOdOnVS4cKFdezYMf33v/9V/fr1HX/8Gj9+vFq1aqUGDRqoR48eOn/+vON59ZcuXbrj62Tmc7zd9yej+0707t1b7733nrp166bt27erVKlS+uKLL7Rx40a99dZbf/g53OrgwYNq1qyZOnbsqIoVK8rT01MLFy7UqVOn1KlTJ5fGAoA/rRy4Yz0AIJu58viw+fPnmwYNGhhfX1/j6+trIiIiTExMjImNjXX0adSo0W0fvXXlyhXz8ssvm/DwcJM3b14TEhJi2rdvbw4fPuzUb+bMmaZmzZrGx8fH5M+f31SpUsUMHTrU/Pbbb44+YWFhplWrVuleI6PHd73//vumdOnSxsPDw+nxVRs3bjT16tUzPj4+JjQ01PE4NGXwiKspU6aYsLAwY7fbTZ06dczGjRtNzZo1zV/+8henfklJSWbixImmUqVKxm63mwIFCpiaNWua0aNHm4SEhD/axWbu3LmmevXqxm63m4IFC5ouXbqYX3/91amPK5/ZsmXLTI8ePUxERITx8/MzXl5epmzZsua5554zp06dcup75coV07NnTxMQEGDy589vOnbsaE6fPp3hY85++eUX07VrV1O4cGFjt9tN6dKlTUxMjLl+/bqjz7lz50y/fv1MsWLFjJeXlylevLiJjo42Z8+edWl/rV692rRu3dqEhoYaLy8vExoaajp37mwOHjzoGOe9994zDRs2NEFBQcZut5syZcqYF154wWmf3+6Rb8YY8+mnn5rSpUsbLy8vU61aNbNixYp7fuTbHz2q7I8+x7Vr15qWLVuagIAA4+3tbcqUKWO6detmtm3b5tRv/vz5pkKFCsZut5uKFSuaBQsWZLr2zHyOt/v+ZPRdO3XqlOnevbspVKiQ8fLyMlWqVEn3GME77Z+bazx79qyJiYkxERERxtfX1wQEBJi6deuazz//POMdCgC5kM2YTNzJBwCAXCg1NVWFCxdW27ZtMzy9GwAAIKtxTTsAAJKuXbuW7rT5jz/+WOfPn1fjxo1zpigAAJDrcaQdAAD9fqf5QYMGqUOHDgoKCtIPP/ygDz74QBUqVND27dvTPZMeAAAgO3AjOgAAJJUqVUolSpTQlClTdP78eRUsWFBdu3bVhAkTCOwAACDH5Ojp8evXr9fjjz+u0NBQ2Ww2LVq0yGm9MUYjRoxQ0aJF5ePjo6ioqHTPKT1//ry6dOkif39/BQYGqmfPnn94J1UAAG5VqlQpLV68WHFxcUpKSlJcXJw+/PDDDO+gDQAAkF1yNLRfvnxZVatW1bRp0zJcP2nSJE2ZMkUzZszQli1b5Ovrq5YtWzo9z7NLly7au3evVq1apaVLl2r9+vXq3bt3dr0FAAAAAACyjGWuabfZbFq4cKHatGkj6fej7KGhoRoyZIjjucAJCQkKDg7W7Nmz1alTJ+3fv18VK1bU1q1bHc/EXb58uR599FH9+uuvCg0Nzam3AwAAAADAPbPsNe1HjhxRXFycoqKiHG0BAQGqW7euNm3apE6dOmnTpk0KDAx0BHZJioqKUp48ebRlyxY9+eSTGY59/fp1Xb9+3bGcmpqq8+fPKygoSDabLeveFAAAAAAA+v1A9cWLFxUaGqo8eW5/ErxlQ3tcXJwkKTg42Kk9ODjYsS4uLi7dtYaenp4qWLCgo09Gxo8fr9GjR7u5YgAAAAAAXHP8+HEVL178tustG9qz0rBhwzR48GDHckJCgkqWLKnjx4/L398/Byu7PWOMrian5HQZAAAAAGB5Pnk9LH8WdWJiokqUKKH8+fPfsZ9lQ3tISIgk6dSpUypatKij/dSpU6pWrZqjz+nTp522u3Hjhs6fP+/YPiN2u112uz1du7+/v2VDuyQF5HQBAAAAAAC3+qM/LuTo3ePvJDw8XCEhIVq9erWjLTExUVu2bFFkZKQkKTIyUvHx8dq+fbujz5o1a5Samqq6detme80AAAAAALhTjh5pv3Tpkn766SfH8pEjR7Rz504VLFhQJUuW1MCBA/Xqq6+qXLlyCg8P1/DhwxUaGuq4w3yFChX0l7/8Rb169dKMGTOUnJysfv36qVOnTtw5HgAAAABw38vR0L5t2zY1adLEsZx2nXl0dLRmz56toUOH6vLly+rdu7fi4+PVoEEDLV++XN7e3o5t5syZo379+qlZs2bKkyeP2rVrpylTpmT7ewEAAAAAwN0s85z2nJSYmKiAgAAlJCRY+pp2AAAAAMCfQ2ZzqGWvaQcAAAAAILcjtAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIsitAMAAAAAYFGEdgAAAAAALIrQDgAAAACARRHaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCiCO0AAAAAAFgUoR0AAAAAAIuydGhPSUnR8OHDFR4eLh8fH5UpU0avvPKKjDGOPsYYjRgxQkWLFpWPj4+ioqJ06NChHKwaAAAAAAD3sHRonzhxoqZPn6533nlH+/fv18SJEzVp0iRNnTrV0WfSpEmaMmWKZsyYoS1btsjX11ctW7bUtWvXcrByAAAAAADunc3cfNjaYh577DEFBwfrgw8+cLS1a9dOPj4++vTTT2WMUWhoqIYMGaLnn39ekpSQkKDg4GDNnj1bnTp1ytTrJCYmKiAgQAkJCfL398+S9wIAAAAAQJrM5lBLH2l/6KGHtHr1ah08eFCStGvXLm3YsEGPPPKIJOnIkSOKi4tTVFSUY5uAgADVrVtXmzZtuu24169fV2JiotMPAAAAAABW45nTBdzJSy+9pMTEREVERMjDw0MpKSkaO3asunTpIkmKi4uTJAUHBzttFxwc7FiXkfHjx2v06NFZVzgAAAAAAG5g6SPtn3/+uebMmaPPPvtMP/zwgz766CO9/vrr+uijj+5p3GHDhikhIcHxc/z4cTdVDAAAAACA+1j6SPsLL7ygl156yXFtepUqVfTLL79o/Pjxio6OVkhIiCTp1KlTKlq0qGO7U6dOqVq1arcd1263y263Z2ntAAAAAADcK0sfab9y5Yry5HEu0cPDQ6mpqZKk8PBwhYSEaPXq1Y71iYmJ2rJliyIjI7O1VgAAAAAA3M3SR9off/xxjR07ViVLllSlSpW0Y8cOTZ48WT169JAk2Ww2DRw4UK+++qrKlSun8PBwDR8+XKGhoWrTpk3OFg8AAAAAwD2ydGifOnWqhg8frr59++r06dMKDQ1Vnz59NGLECEefoUOH6vLly+rdu7fi4+PVoEEDLV++XN7e3jlYOQAAAAAA987Sz2nPLjynHQAAAACQnf4Uz2kHAAAAACA3I7QDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAou45tCcmJmrRokXav3+/O+oBAAAAAAD/z+XQ3rFjR73zzjuSpKtXr6pWrVrq2LGjHnzwQc2fP9/tBQIAAAAAkFu5HNrXr1+vhx9+WJK0cOFCGWMUHx+vKVOm6NVXX3V7gQAAAAAA5FYuh/aEhAQVLFhQkrR8+XK1a9dO+fLlU6tWrXTo0CG3FwgAAAAAQG7lcmgvUaKENm3apMuXL2v58uVq0aKFJOnChQvy9vZ2e4EAAAAAAORWnq5uMHDgQHXp0kV+fn4KCwtT48aNJf1+2nyVKlXcXR8AAAAAALmWy6G9b9++qlOnjo4fP67mzZsrT57fD9aXLl2aa9oBAAAAAHAjmzHG3MsAKSkp2r17t8LCwlSgQAF31ZWtEhMTFRAQoISEBPn7++d0OQAAAACAP7nM5lCXr2kfOHCgPvjgA0m/B/ZGjRqpRo0aKlGihL755pu7LhgAAAAAADhzObR/8cUXqlq1qiRpyZIlOnLkiA4cOKBBgwbp5ZdfdnuBAAAAAADkVi6H9rNnzyokJESS9NVXX6lDhw564IEH1KNHD+3evdvtBQIAAAAAkFu5HNqDg4O1b98+paSkaPny5WrevLkk6cqVK/Lw8HB7gQAAAAAA5FYu3z2+e/fu6tixo4oWLSqbzaaoqChJ0pYtWxQREeH2AgEAAAAAyK1cDu2jRo1S5cqVdfz4cXXo0EF2u12S5OHhoZdeesntBQIAAAAAkFvd0yPfrl27Jm9vb3fWkyN45BsAAAAAIDtl2SPfUlJS9Morr6hYsWLy8/PTzz//LEkaPny441FwAAAAAADg3rkc2seOHavZs2dr0qRJ8vLycrRXrlxZ//rXv9xaHAAAAAAAuZnLof3jjz/WzJkz1aVLF6e7xVetWlUHDhxwa3EAAAAAAORmLof2EydOqGzZsunaU1NTlZyc7JaiAAAAAADAXYT2ihUr6ttvv03X/sUXX6h69epuKQoAAAAAANzFI99GjBih6OhonThxQqmpqVqwYIFiY2P18ccfa+nSpVlRIwAAAAAAuZLLR9pbt26tJUuW6Ouvv5avr69GjBih/fv3a8mSJWrevHlW1AgAAAAAQK50T89p/7PgOe0AAAAAgOyUZc9p37p1q7Zs2ZKufcuWLdq2bZurwwEAAAAAgNtwObTHxMTo+PHj6dpPnDihmJgYtxQFAAAAAADuIrTv27dPNWrUSNdevXp17du3zy1FAQAAAACAuwjtdrtdp06dStd+8uRJeXq6fDN6AAAAAABwGy6H9hYtWmjYsGFKSEhwtMXHx+sf//hHltw9/sSJE3r66acVFBQkHx8fValSxenaeWOMRowYoaJFi8rHx0dRUVE6dOiQ2+sAAAAAACC7uRzaX3/9dR0/flxhYWFq0qSJmjRpovDwcMXFxemNN95wa3EXLlxQ/fr1lTdvXi1btkz79u3TG2+8oQIFCjj6TJo0SVOmTNGMGTO0ZcsW+fr6qmXLlrp27ZpbawEAAAAAILvd1SPfLl++rDlz5mjXrl3y8fHRgw8+qM6dOytv3rxuLe6ll17Sxo0b9e2332a43hij0NBQDRkyRM8//7wkKSEhQcHBwZo9e7Y6deqUqdfhkW8AAAAAgOyU2Rxq6ee0V6xYUS1bttSvv/6qdevWqVixYurbt6969eolSfr5559VpkwZ7dixQ9WqVXNs16hRI1WrVk1vv/12huNev35d169fdywnJiaqRIkShHYAAAAAQLbIsue0jx8/Xh9++GG69g8//FATJ050dbg7+vnnnzV9+nSVK1dOK1as0N///nf1799fH330kSQpLi5OkhQcHOy0XXBwsGNdRsaPH6+AgADHT4kSJdxaNwAAAAAA7uByaH/vvfcUERGRrr1SpUqaMWOGW4pKk5qaqho1amjcuHGqXr26evfurV69et3z66TdSC/tJ6PnzgMAAAAAkNNcDu1xcXEqWrRouvbChQvr5MmTbikqTdGiRVWxYkWntgoVKujYsWOSpJCQEElK9wi6U6dOOdZlxG63y9/f3+kHAAAAAACrcTm0lyhRQhs3bkzXvnHjRoWGhrqlqDT169dXbGysU9vBgwcVFhYmSQoPD1dISIhWr17tWJ+YmKgtW7YoMjLSrbUAAAAAAJDdPF3doFevXho4cKCSk5PVtGlTSdLq1as1dOhQDRkyxK3FDRo0SA899JDGjRunjh076vvvv9fMmTM1c+ZMSZLNZtPAgQP16quvqly5cgoPD9fw4cMVGhqqNm3auLUWAAAAAACym8uh/YUXXtC5c+fUt29fJSUlSZK8vb314osvatiwYW4trnbt2lq4cKGGDRumMWPGKDw8XG+99Za6dOni6DN06FBdvnxZvXv3Vnx8vBo0aKDly5fL29vbrbUAAAAAAJDd7vqRb5cuXdL+/fvl4+OjcuXKyW63u7u2bMNz2gEAAAAA2SmzOdTlI+1p/Pz8VLt27bvdHAAAAAAA/AGXQ3uTJk1ks9luu37NmjX3VBAAAAAAAPidy6G9WrVqTsvJycnauXOn9uzZo+joaHfVBQAAAABArudyaH/zzTczbB81apQuXbp0zwUBAAAAAIDfufyc9tt5+umn9eGHH7prOAAAAAAAcj23hfZNmzbxmDUAAAAAANzI5dPj27Zt67RsjNHJkye1bds2DR8+3G2FAQAAAACQ27kc2gMCApyW8+TJo/Lly2vMmDFq0aKF2woDAAAAACC3czm0z5o1KyvqAAAAAAAAt3A5tN/s2rVrmjt3ri5fvqzmzZurXLly7qoLAAAAAIBcL9OhffDgwUpOTtbUqVMlSUlJSapXr5727dunfPnyaejQoVq1apUiIyOzrFgAAAAAAHKTTN89fuXKlWrevLljec6cOTp27JgOHTqkCxcuqEOHDnr11VezpEgAAAAAAHKjTIf2Y8eOqWLFio7llStXqn379goLC5PNZtOAAQO0Y8eOLCkSAAAAAIDcKNOhPU+ePDLGOJY3b96sevXqOZYDAwN14cIF91YHAAAAAEAulunQXqFCBS1ZskSStHfvXh07dkxNmjRxrP/ll18UHBzs/goBAAAAAMilMn0juqFDh6pTp07673//q7179+rRRx9VeHi4Y/1XX32lOnXqZEmRAAAAAADkRpk+0v7kk0/qq6++0oMPPqhBgwZp7ty5Tuvz5cunvn37ur1AAAAAAAByK5u5+UL1XCoxMVEBAQFKSEiQv79/TpcDAAAAAPiTy2wOzfSRdgAAAAAAkL0I7QAAAAAAWBShHQAAAAAAi8pUaF+8eLGSk5OzuhYAAAAAAHCTTIX2J598UvHx8ZIkDw8PnT59OitrAgAAAAAAymRoL1y4sDZv3ixJMsbIZrNlaVEAAAAAAEDyzEynv/3tb2rdurVsNptsNptCQkJu2zclJcVtxQEAAAAAkJtlKrSPGjVKnTp10k8//aQnnnhCs2bNUmBgYBaXBgAAAABA7pap0C5JERERioiI0MiRI9WhQwfly5cvK+sCAAAAACDXsxljzN1seObMGcXGxkqSypcvr8KFC7u1sOyUmJiogIAAJSQkyN/fP6fLAQAAAAD8yWU2h7r8nPYrV66oR48eCg0NVcOGDdWwYUOFhoaqZ8+eunLlyj0VDQAAAAAA/sfl0D5o0CCtW7dOixcvVnx8vOLj4/Xll19q3bp1GjJkSFbUCAAAAABAruTy6fGFChXSF198ocaNGzu1r127Vh07dtSZM2fcWV+24PR4AAAAAEB2ytLT44ODg9O1FylShNPjAQAAAABwI5dDe2RkpEaOHKlr16452q5evarRo0crMjLSrcUBAAAAAJCbZfqRb2nefvtttWzZUsWLF1fVqlUlSbt27ZK3t7dWrFjh9gIBAAAAAMit7uqRb1euXNGcOXN04MABSVKFChXUpUsX+fj4uL3A7MA17QAAAACA7JTZHOrykXZJypcvn3r16nXXxQEAAAAAgD/m8jXtAAAAAAAgexDaAQAAAACwKEI7AAAAAAAWRWgHAAAAAMCi7iq0x8fH61//+peGDRum8+fPS5J++OEHnThxwq3FAQAAAACQm7l89/gff/xRUVFRCggI0NGjR9WrVy8VLFhQCxYs0LFjx/Txxx9nRZ0AAAAAAOQ6Lh9pHzx4sLp166ZDhw7J29vb0f7oo49q/fr1bi0OAAAAAIDczOXQvnXrVvXp0ydde7FixRQXF+eWogAAAAAAwF2EdrvdrsTExHTtBw8eVOHChd1SFAAAAAAAuIvQ/sQTT2jMmDFKTk6WJNlsNh07dkwvvvii2rVr5/YCAQAAAADIrVwO7W+88YYuXbqkIkWK6OrVq2rUqJHKli2r/Pnza+zYsVlRIwAAAAAAuZLLd48PCAjQqlWrtGHDBv3444+6dOmSatSooaioqKyoDwAAAACAXMtmjDE5XUROS0xMVEBAgBISEuTv75/T5QAAAAAA/uQym0NdPtI+ZcqUDNttNpu8vb1VtmxZNWzYUB4eHq4ODQAAAAAAbuJyaH/zzTd15swZXblyRQUKFJAkXbhwQfny5ZOfn59Onz6t0qVLa+3atSpRooTbCwYAAAAAILdw+UZ048aNU+3atXXo0CGdO3dO586d08GDB1W3bl29/fbbOnbsmEJCQjRo0KCsqBcAAAAAgFzD5Wvay5Qpo/nz56tatWpO7Tt27FC7du30888/67vvvlO7du108uRJd9aaZbimHQAAAACQnTKbQ10+0n7y5EnduHEjXfuNGzcUFxcnSQoNDdXFixddHRoAAAAAANzE5dDepEkT9enTRzt27HC07dixQ3//+9/VtGlTSdLu3bsVHh7uvioBAAAAAMiFXA7tH3zwgQoWLKiaNWvKbrfLbrerVq1aKliwoD744ANJkp+fn9544w23FwsAAAAAQG5y189pP3DggA4ePChJKl++vMqXL+/WwrIT17QDAAAAALJTlj2nPU1ERIQiIiLudnMAAAAAAPAH7iq0//rrr1q8eLGOHTumpKQkp3WTJ092S2EAAAAAAOR2Lof21atX64knnlDp0qV14MABVa5cWUePHpUxRjVq1MiKGgEAAAAAyJVcvhHdsGHD9Pzzz2v37t3y9vbW/Pnzdfz4cTVq1EgdOnTIihoBAAAAAMiVXA7t+/fvV9euXSVJnp6eunr1qvz8/DRmzBhNnDjR7QUCAAAAAJBbuRzafX19HdexFy1aVIcPH3asO3v2rPsqAwAAAAAgl3P5mvZ69eppw4YNqlChgh599FENGTJEu3fv1oIFC1SvXr2sqBEAAAAAgFzJ5dA+efJkXbp0SZI0evRoXbp0SXPnzlW5cuW4czwAAAAAAG5kM8aYnC4ip2X2ofYAAAAAALhDZnOoy9e0ly5dWufOnUvXHh8fr9KlS7s6HAAAAAAAuA2XQ/vRo0eVkpKSrv369es6ceKEW4oCAAAAAAAuXNO+ePFix79XrFihgIAAx3JKSopWr16tUqVKubU4AAAAAABys0yH9jZt2kiSbDaboqOjndblzZtXpUqV0htvvOHW4gAAAAAAyM0yHdpTU1MlSeHh4dq6dasKFSqUZUUBAAAAAIC7eOTbkSNHsqIOAAAAAABwC5dDuyStXr1aq1ev1unTpx1H4NN8+OGHbikMAAAAAIDczuXQPnr0aI0ZM0a1atVS0aJFZbPZsqIuAAAAAAByPZdD+4wZMzR79mw988wzWVEPAAAAAAD4fy4/pz0pKUkPPfRQVtQCAAAAAABu4nJof/bZZ/XZZ59lRS0AAAAAAOAmLp8ef+3aNc2cOVNff/21HnzwQeXNm9dp/eTJk91WHAAAAAAAuZnLof3HH39UtWrVJEl79uxxWsdN6QAAAAAAcB+XQ/vatWuzog4AAAAAAHALl69pT/PTTz9pxYoVunr1qiTJGOO2ogAAAAAAwF2E9nPnzqlZs2Z64IEH9Oijj+rkyZOSpJ49e2rIkCFuL/BmEyZMkM1m08CBAx1t165dU0xMjIKCguTn56d27drp1KlTWVoHAAAAAADZweXQPmjQIOXNm1fHjh1Tvnz5HO1PPfWUli9f7tbibrZ161a99957evDBB9PVs2TJEs2bN0/r1q3Tb7/9prZt22ZZHQAAAAAAZBeXQ/vKlSs1ceJEFS9e3Km9XLly+uWXX9xW2M0uXbqkLl266P3331eBAgUc7QkJCfrggw80efJkNW3aVDVr1tSsWbP03XffafPmzVlSCwAAAAAA2cXl0H758mWnI+xpzp8/L7vd7paibhUTE6NWrVopKirKqX379u1KTk52ao+IiFDJkiW1adOm2453/fp1JSYmOv0AAAAAAGA1Lof2hx9+WB9//LFj2WazKTU1VZMmTVKTJk3cWpwk/ec//9EPP/yg8ePHp1sXFxcnLy8vBQYGOrUHBwcrLi7utmOOHz9eAQEBjp8SJUq4u2wAAAAAAO6Zy498mzRpkpo1a6Zt27YpKSlJQ4cO1d69e3X+/Hlt3LjRrcUdP35cAwYM0KpVq+Tt7e22cYcNG6bBgwc7lhMTEwnuAAAAAADLcflIe+XKlXXw4EE1aNBArVu31uXLl9W2bVvt2LFDZcqUcWtx27dv1+nTp1WjRg15enrK09NT69at05QpU+Tp6ang4GAlJSUpPj7eabtTp04pJCTktuPa7Xb5+/s7/QAAAAAAYDUuH2mXpICAAL388svuriWdZs2aaffu3U5t3bt3V0REhF588UWVKFFCefPm1erVq9WuXTtJUmxsrI4dO6bIyMgsrw8AAAAAgKzkcmifNWuW/Pz81KFDB6f2efPm6cqVK4qOjnZbcfnz51flypWd2nx9fRUUFORo79mzpwYPHqyCBQvK399fzz33nCIjI1WvXj231QEAAAAAQE5w+fT48ePHq1ChQunaixQponHjxrmlKFe8+eabeuyxx9SuXTs1bNhQISEhWrBgQbbXAQAAAACAu9mMMcaVDby9vXXgwAGVKlXKqf3o0aOqUKGCrl696s76skViYqICAgKUkJDA9e0AAAAAgCyX2Rzq8pH2IkWK6Mcff0zXvmvXLgUFBbk6HAAAAAAAuA2XQ3vnzp3Vv39/rV27VikpKUpJSdGaNWs0YMAAderUKStqBAAAAAAgV3L5RnSvvPKKjh49qmbNmsnT8/fNU1NT1bVr1xy5ph0AAAAAgD8rl65pN8bo+PHjKly4sH799Vft3LlTPj4+qlKlisLCwrKyzizFNe0AAAAAgOyU2Rzq0pF2Y4zKli2rvXv3qly5cipXrtw9FwoAAAAAADLm0jXtefLkUbly5XTu3LmsqgcAAAAAAPw/l29EN2HCBL3wwgvas2dPVtQDAAAAAAD+n8vPaS9QoICuXLmiGzduyMvLSz4+Pk7rz58/79YCswPXtAMAAAAAslOWXNMuSW+99da91AUAAAAAADLJ5dAeHR2dFXUAAAAAAIBbuHxNuyQdPnxY//znP9W5c2edPn1akrRs2TLt3bvXrcUBAAAAAJCbuRza161bpypVqmjLli1asGCBLl26JEnatWuXRo4c6fYCAQAAAADIrVwO7S+99JJeffVVrVq1Sl5eXo72pk2bavPmzW4tDgAAAACA3Mzl0L579249+eST6dqLFCmis2fPuqUoAAAAAABwF6E9MDBQJ0+eTNe+Y8cOFStWzC1FAQAAAACAuwjtnTp10osvvqi4uDjZbDalpqZq48aNev7559W1a9esqBEAAAAAgFzJ5dA+btw4RUREqESJErp06ZIqVqyohg0b6qGHHtI///nPrKgRAAAAAIBcyWaMMXez4fHjx7V7925dunRJ1atXV7ly5dxdW7ZJTExUQECAEhIS5O/vn9PlAAAAAAD+5DKbQz0zO2Bqaqpee+01LV68WElJSWrWrJlGjhwpHx8ftxQMAAAAAACcZfr0+LFjx+of//iH/Pz8VKxYMb399tuKiYnJytoAAAAAAMjVMh3aP/74Y7377rtasWKFFi1apCVLlmjOnDlKTU3NyvoAAAAAAMi1Mh3ajx07pkcffdSxHBUVJZvNpt9++y1LCgMAAAAAILfLdGi/ceOGvL29ndry5s2r5ORktxcFAAAAAABcuBGdMUbdunWT3W53tF27dk1/+9vf5Ovr62hbsGCBeysEAAAAACCXynRoj46OTtf29NNPu7UYAAAAAADwP5kO7bNmzcrKOgAAAAAAwC0yfU07AAAAAADIXoR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWJSlQ/v48eNVu3Zt5c+fX0WKFFGbNm0UGxvr1OfatWuKiYlRUFCQ/Pz81K5dO506dSqHKgYAAAAAwH0sHdrXrVunmJgYbd68WatWrVJycrJatGihy5cvO/oMGjRIS5Ys0bx587Ru3Tr99ttvatu2bQ5WDQAAAACAe9iMMSani8isM2fOqEiRIlq3bp0aNmyohIQEFS5cWJ999pnat28vSTpw4IAqVKigTZs2qV69epkaNzExUQEBAUpISJC/v39WvgUAAAAAADKdQy19pP1WCQkJkqSCBQtKkrZv367k5GRFRUU5+kRERKhkyZLatGnTbce5fv26EhMTnX4AAAAAALCa+ya0p6amauDAgapfv74qV64sSYqLi5OXl5cCAwOd+gYHBysuLu62Y40fP14BAQGOnxIlSmRl6QAAAAAA3JX7JrTHxMRoz549+s9//nPPYw0bNkwJCQmOn+PHj7uhQgAAAAAA3MszpwvIjH79+mnp0qVav369ihcv7mgPCQlRUlKS4uPjnY62nzp1SiEhIbcdz263y263Z2XJAAAAAADcM0sfaTfGqF+/flq4cKHWrFmj8PBwp/U1a9ZU3rx5tXr1akdbbGysjh07psjIyOwuFwAAAAAAt7L0kfaYmBh99tln+vLLL5U/f37HdeoBAQHy8fFRQECAevbsqcGDB6tgwYLy9/fXc889p8jIyEzfOR4AAAAAAKuy9CPfbDZbhu2zZs1St27dJEnXrl3TkCFD9O9//1vXr19Xy5Yt9e67797x9Phb8cg3AAAAAEB2ymwOtXRozy6EdgAAAABAdvpTPqcdAAAAAIDchNAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBFEdoBAAAAALAoQjsAAAAAABZFaAcAAAAAwKII7QAAAAAAWBShHQAAAAAAiyK0AwAAAABgUYR2AAAAAAAsitAOAAAAAIBF/WlC+7Rp01SqVCl5e3urbt26+v7773O6JAAAAAAA7smfIrTPnTtXgwcP1siRI/XDDz+oatWqatmypU6fPp3TpQEAAAAAcNf+FKF98uTJ6tWrl7p3766KFStqxowZypcvnz788MOcLg0AAAAAgLvmmdMF3KukpCRt375dw4YNc7TlyZNHUVFR2rRpU4bbXL9+XdevX3csJyQkSJISExOztlgAAAAAAPS//GmMuWO/+z60nz17VikpKQoODnZqDw4O1oEDBzLcZvz48Ro9enS69hIlSmRJjQAAAAAAZOTixYsKCAi47fr7PrTfjWHDhmnw4MGO5dTUVJ0/f15BQUGy2Ww5WNmdJSYmqkSJEjp+/Lj8/f1zuhzgtpiruF8wV3E/YJ7ifsFcxf3CKnPVGKOLFy8qNDT0jv3u+9BeqFAheXh46NSpU07tp06dUkhISIbb2O122e12p7bAwMCsKtHt/P39+T9C3BeYq7hfMFdxP2Ce4n7BXMX9wgpz9U5H2NPc9zei8/LyUs2aNbV69WpHW2pqqlavXq3IyMgcrAwAAAAAgHtz3x9pl6TBgwcrOjpatWrVUp06dfTWW2/p8uXL6t69e06XBgAAAADAXftThPannnpKZ86c0YgRIxQXF6dq1app+fLl6W5Od7+z2+0aOXJkulP7AathruJ+wVzF/YB5ivsFcxX3i/ttrtrMH91fHgAAAAAA5Ij7/pp2AAAAAAD+rAjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhPb7xLRp01SqVCl5e3urbt26+v7773O6JORy48ePV+3atZU/f34VKVJEbdq0UWxsrFOfa9euKSYmRkFBQfLz81O7du106tSpHKoYkCZMmCCbzaaBAwc62pinsJITJ07o6aefVlBQkHx8fFSlShVt27bNsd4YoxEjRqho0aLy8fFRVFSUDh06lIMVI7dJSUnR8OHDFR4eLh8fH5UpU0avvPKKbr63NfMUOWH9+vV6/PHHFRoaKpvNpkWLFjmtz8y8PH/+vLp06SJ/f38FBgaqZ8+eunTpUja+i4wR2u8Dc+fO1eDBgzVy5Ej98MMPqlq1qlq2bKnTp0/ndGnIxdatW6eYmBht3rxZq1atUnJyslq0aKHLly87+gwaNEhLlizRvHnztG7dOv32229q27ZtDlaN3Gzr1q1677339OCDDzq1M09hFRcuXFD9+vWVN29eLVu2TPv27dMbb7yhAgUKOPpMmjRJU6ZM0YwZM7Rlyxb5+vqqZcuWunbtWg5Wjtxk4sSJmj59ut555x3t379fEydO1KRJkzR16lRHH+YpcsLly5dVtWpVTZs2LcP1mZmXXbp00d69e7Vq1SotXbpU69evV+/evbPrLdyegeXVqVPHxMTEOJZTUlJMaGioGT9+fA5WBTg7ffq0kWTWrVtnjDEmPj7e5M2b18ybN8/RZ//+/UaS2bRpU06ViVzq4sWLply5cmbVqlWmUaNGZsCAAcYY5ims5cUXXzQNGjS47frU1FQTEhJiXnvtNUdbfHy8sdvt5t///nd2lAiYVq1amR49eji1tW3b1nTp0sUYwzyFNUgyCxcudCxnZl7u27fPSDJbt2519Fm2bJmx2WzmxIkT2VZ7RjjSbnFJSUnavn27oqKiHG158uRRVFSUNm3alIOVAc4SEhIkSQULFpQkbd++XcnJyU5zNyIiQiVLlmTuItvFxMSoVatWTvNRYp7CWhYvXqxatWqpQ4cOKlKkiKpXr67333/fsf7IkSOKi4tzmq8BAQGqW7cu8xXZ5qGHHtLq1at18OBBSdKuXbu0YcMGPfLII5KYp7CmzMzLTZs2KTAwULVq1XL0iYqKUp48ebRly5Zsr/lmnjn66vhDZ8+eVUpKioKDg53ag4ODdeDAgRyqCnCWmpqqgQMHqn79+qpcubIkKS4uTl5eXgoMDHTqGxwcrLi4uByoErnVf/7zH/3www/aunVrunXMU1jJzz//rOnTp2vw4MH6xz/+oa1bt6p///7y8vJSdHS0Y05m9DsB8xXZ5aWXXlJiYqIiIiLk4eGhlJQUjR07Vl26dJEk5iksKTPzMi4uTkWKFHFa7+npqYIFC+b43CW0A7hnMTEx2rNnjzZs2JDTpQBOjh8/rgEDBmjVqlXy9vbO6XKAO0pNTVWtWrU0btw4SVL16tW1Z88ezZgxQ9HR0TlcHfC7zz//XHPmzNFnn32mSpUqaefOnRo4cKBCQ0OZp0AW4fR4iytUqJA8PDzS3cn41KlTCgkJyaGqgP/p16+fli5dqrVr16p48eKO9pCQECUlJSk+Pt6pP3MX2Wn79u06ffq0atSoIU9PT3l6emrdunWaMmWKPD09FRwczDyFZRQtWlQVK1Z0aqtQoYKOHTsmSY45ye8EyEkvvPCCXnrpJXXq1ElVqlTRM888o0GDBmn8+PGSmKewpszMy5CQkHQ3+r5x44bOnz+f43OX0G5xXl5eqlmzplavXu1oS01N1erVqxUZGZmDlSG3M8aoX79+WrhwodasWaPw8HCn9TVr1lTevHmd5m5sbKyOHTvG3EW2adasmXbv3q2dO3c6fmrVqqUuXbo4/s08hVXUr18/3aMzDx48qLCwMElSeHi4QkJCnOZrYmKitmzZwnxFtrly5Yry5HGOEB4eHkpNTZXEPIU1ZWZeRkZGKj4+Xtu3b3f0WbNmjVJTU1W3bt1sr/lmnB5/Hxg8eLCio6NVq1Yt1alTR2+99ZYuX76s7t2753RpyMViYmL02Wef6csvv1T+/Pkd1/oEBATIx8dHAQEB6tmzpwYPHqyCBQvK399fzz33nCIjI1WvXr0crh65Rf78+R33WUjj6+uroKAgRzvzFFYxaNAgPfTQQxo3bpw6duyo77//XjNnztTMmTMlSTabTQMHDtSrr76qcuXKKTw8XMOHD1doaKjatGmTs8Uj13j88cc1duxYlSxZUpUqVdKOHTs0efJk9ejRQxLzFDnn0qVL+umnnxzLR44c0c6dO1WwYEGVLFnyD+dlhQoV9Je//EW9evXSjBkzlJycrH79+qlTp04KDQ3NoXf1/3L03vXItKlTp5qSJUsaLy8vU6dOHbN58+acLgm5nKQMf2bNmuXoc/XqVdO3b19ToEABky9fPvPkk0+akydP5lzRgDFOj3wzhnkKa1myZImpXLmysdvtJiIiwsycOdNpfWpqqhk+fLgJDg42drvdNGvWzMTGxuZQtciNEhMTzYABA0zJkiWNt7e3KV26tHn55ZfN9evXHX2Yp8gJa9euzfB30+joaGNM5ubluXPnTOfOnY2fn5/x9/c33bt3NxcvXsyBd+PMZowxOfT3AgAAAAAAcAdc0w4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AgMUcPXpUNptNO3fuzOlSHA4cOKB69erJ29tb1apVy+ly1K1bN7Vp08ax3LhxYw0cOPCexnTHGAAAuBuhHQCAW3Tr1k02m00TJkxwal+0aJFsNlsOVZWzRo4cKV9fX8XGxmr16tUZ9knbbzabTV5eXipbtqzGjBmjGzduZHl9CxYs0CuvvJKpvt98841sNpvi4+PvegwAALILoR0AgAx4e3tr4sSJunDhQk6X4jZJSUl3ve3hw4fVoEEDhYWFKSgo6Lb9/vKXv+jkyZM6dOiQhgwZolGjRum1115zez23KliwoPLnz5/jYwAA4G6EdgAAMhAVFaWQkBCNHz/+tn1GjRqV7lTxt956S6VKlXIsp53GPW7cOAUHByswMNBx9PmFF15QwYIFVbx4cc2aNSvd+AcOHNBDDz0kb29vVa5cWevWrXNav2fPHj3yyCPy8/NTcHCwnnnmGZ09e9axvnHjxurXr58GDhyoQoUKqWXLlhm+j9TUVI0ZM0bFixeX3W5XtWrVtHz5csd6m82m7du3a8yYMbLZbBo1atRt94ndbldISIjCwsL097//XVFRUVq8eLHTvhg7dqxCQ0NVvnx5SdLx48fVsWNHBQYGqmDBgmrdurWOHj3qGDMlJUWDBw9WYGCggoKCNHToUBljnF731lPbr1+/rhdffFElSpSQ3W5X2bJl9cEHH+jo0aNq0qSJJKlAgQKy2Wzq1q1bhmNcuHBBXbt2VYECBZQvXz498sgjOnTokGP97NmzFRgYqBUrVqhChQry8/Nz/NEizTfffKM6derI19dXgYGBql+/vn755Zfb7j8AAG5FaAcAIAMeHh4aN26cpk6dql9//fWexlqzZo1+++03rV+/XpMnT9bIkSP12GOPqUCBAtqyZYv+9re/qU+fPule54UXXtCQIUO0Y8cORUZG6vHHH9e5c+ckSfHx8WratKmqV6+ubdu2afny5Tp16pQ6duzoNMZHH30kLy8vbdy4UTNmzMiwvrfffltvvPGGXn/9df34449q2bKlnnjiCUdAPXnypCpVqqQhQ4bo5MmTev755zP93n18fJyOqK9evVqxsbFatWqVli5dquTkZLVs2VL58+fXt99+q40bNzrCb9p2b7zxhmbPnq0PP/xQGzZs0Pnz57Vw4cI7vm7Xrl3173//W1OmTNH+/fv13nvvyc/PTyVKlND8+fMlSbGxsTp58qTefvvtDMfo1q2btm3bpsWLF2vTpk0yxujRRx9VcnKyo8+VK1f0+uuv65NPPtH69et17Ngxx/65ceOG2rRpo0aNGunHH3/Upk2b1Lt371x7iQUA4C4ZAADgJDo62rRu3doYY0y9evVMjx49jDHGLFy40Nz8n86RI0eaqlWrOm375ptvmrCwMKexwsLCTEpKiqOtfPny5uGHH3Ys37hxw/j6+pp///vfxhhjjhw5YiSZCRMmOPokJyeb4sWLm4kTJxpjjHnllVdMixYtnF77+PHjRpKJjY01xhjTqFEjU7169T98v6GhoWbs2LFObbVr1zZ9+/Z1LFetWtWMHDnyjuPcvN9SU1PNqlWrjN1uN88//7xjfXBwsLl+/bpjm08++cSUL1/epKamOtquX79ufHx8zIoVK4wxxhQtWtRMmjQp3b5Ie6209zpgwABjjDGxsbFGklm1alWGda5du9ZIMhcuXHBqv3mMgwcPGklm48aNjvVnz541Pj4+5vPPPzfGGDNr1iwjyfz000+OPtOmTTPBwcHGGGPOnTtnJJlvvvnmjvsNAIA78cy5PxcAAGB9EydOVNOmTV06unyrSpUqKU+e/53cFhwcrMqVKzuWPTw8FBQUpNOnTzttFxkZ6fi3p6enatWqpf3790uSdu3apbVr18rPzy/d6x0+fFgPPPCAJKlmzZp3rC0xMVG//fab6tev79Rev3597dq1K5Pv8H+WLl0qPz8/JScnKzU1VX/961+dTqevUqWKvLy8HMu7du3STz/9lO5a8mvXrunw4cNKSEjQyZMnVbduXce6tH1hbjlFPs3OnTvl4eGhRo0auVx/mv3798vT09PpdYOCglS+fHnHZyBJ+fLlU5kyZRzLRYsWdXyOBQsWVLdu3dSyZUs1b95cUVFR6tixo4oWLXrXdQEAch9COwAAd9CwYUO1bNlSw4YNc1z7nCZPnjzpguPNp06nyZs3r9OyzWbLsC01NTXTdV26dEmPP/64Jk6cmG7dzaHQ19c302O6Q5MmTTR9+nR5eXkpNDRUnp7Ov2rcWs+lS5dUs2ZNzZkzJ91YhQsXvqsafHx87mq7u5HR53jznJg1a5b69++v5cuXa+7cufrnP/+pVatWqV69etlWIwDg/sY17QAA/IEJEyZoyZIl2rRpk1N74cKFFRcX5xTS3Pls9c2bNzv+fePGDW3fvl0VKlSQJNWoUUN79+5VqVKlVLZsWacfV4K6v7+/QkNDtXHjRqf2jRs3qmLFii7X7Ovrq7Jly6pkyZLpAntGatSooUOHDqlIkSLp3kdAQIACAgJUtGhRbdmyxbFN2r64nSpVqig1NTXdjfvSpB3pT0lJue0YFSpU0I0bN5xe99y5c4qNjXV5v1SvXl3Dhg3Td999p8qVK+uzzz5zaXsAQO5GaAcA4A9UqVJFXbp00ZQpU5zaGzdurDNnzmjSpEk6fPiwpk2bpmXLlrntdadNm6aFCxfqwIEDiomJ0YULF9SjRw9JUkxMjM6fP6/OnTtr69atOnz4sFasWKHu3bvfMYxm5IUXXtDEiRM1d+5cxcbG6qWXXtLOnTs1YMAAt72X2+nSpYsKFSqk1q1b69tvv9WRI0f0zTffqH///o4b8w0YMEATJkzQokWLdODAAfXt2zfdM9ZvVqpUKUVHR6tHjx5atGiRY8zPP/9ckhQWFiabzaalS5fqzJkzunTpUroxypUrp9atW6tXr17asGGDdu3apaefflrFihVT69atM/Xejhw5omHDhmnTpk365ZdftHLlSh06dMjxhxcAADKD0A4AQCaMGTMm3enrFSpU0Lvvvqtp06apatWq+v777+/p2vdbTZgwQRMmTFDVqlW1YcMGLV68WIUKFZIkx9HxlJQUtWjRQlWqVNHAgQMVGBjodP18ZvTv31+DBw/WkCFDVKVKFS1fvlyLFy9WuXLl3PZebidfvnxav369SpYsqbZt26pChQrq2bOnrl27Jn9/f0nSkCFD9Mwzzyg6OlqRkZHKnz+/nnzyyTuOO336dLVv3159+/ZVRESEevXqpcuXL0uSihUrptGjR+ull15ScHCw+vXrl+EYs2bNUs2aNfXYY48pMjJSxhh99dVX6U6Jv9N7O3DggNq1a6cHHnhAvXv3VkxMjPr06ePCHgIA5HY2c7u7uAAAAAAAgBzFkXYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsChCOwAAAAAAFkVoBwAAAADAogjtAAAAAABYFKEdAAAAAACLIrQDAAAAAGBRhHYAAAAAACyK0A4AAAAAgEUR2gEAAAAAsKj/AzWqlVNrwdYQAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('GPT model:', gpt_model)\n",
    "asses_model_results(model=gpt_model, samples=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BEFORE FINE-TUNING:  \n",
    "\n",
    "```\n",
    "Predictions: 100\n",
    "Successful Prediction: 82 (82%)\n",
    "Failed Prediction: 18 (18%)\n",
    "```\n",
    "\n",
    "AFTER FINE-TUNING:  \n",
    "\n",
    "```\n",
    "Predictions: 100\n",
    "Successful Prediction: 100 (100%)\n",
    "Failed Prediction: 0 (0%)\n",
    "```\n",
    "\n",
    "We can see that fine-tuning our GPT model definitely improved its performance when predicting the intent of messages from our test dataset (unseen during the training step).\n",
    "\n",
    "As a conclusion, we should think if fine-tuning the model is worth the invested time and money? I would say that for our use-case, fine-tuning probably is not that interesting, considering that the base version gets the intent right 80% or the time, or at least one with a very close meaning. Since we don't really know how the fine-tuning step is done and how layers of GPT transformers are tweaked, we should be careful and not necessarely fine-tune our model every time.\n",
    "\n",
    "However, the possibility to specialise the model on a given task and the simplicity to switch between one model to another is definitely a precious asset that could come in handy, in the creation of Agents for instance: Based on the user input, we could trigger an agent specialized in customer support *('I need help changing my password')*, or another one in technical assistance *('My software keeps crashing at startup')*."
   ]
  }
 ],
 "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}