gpt-4o.ipynb

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  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
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 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G0U655QrBPWf",
    "outputId": "a3b27a15-8fed-4516-ebbe-c7e5927c81ec"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -Uqqq pip --progress-bar off\n",
    "!pip install -qqq openai==1.30.1 --progress-bar off\n",
    "!pip install -qqq tiktoken==0.7.0 --progress-bar off"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!gdown 1nO9NdIgHjA3CL0QCyNcrL_Ic0s7HgX5N"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3Wfq15PVlJzB",
    "outputId": "139db1c2-3a71-429e-9307-d08eee4d41cc"
   },
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1nO9NdIgHjA3CL0QCyNcrL_Ic0s7HgX5N\n",
      "To: /content/dunder-mifflin-message.jpg\n",
      "\r  0% 0.00/208k [00:00<?, ?B/s]\r100% 208k/208k [00:00<00:00, 67.2MB/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import base64\n",
    "import json\n",
    "import os\n",
    "import textwrap\n",
    "from inspect import cleandoc\n",
    "from pathlib import Path\n",
    "from typing import List\n",
    "\n",
    "import requests\n",
    "import tiktoken\n",
    "from google.colab import userdata\n",
    "from IPython.display import Audio, Markdown, display\n",
    "from openai import OpenAI\n",
    "from PIL import Image\n",
    "from tiktoken import Encoding\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = userdata.get(\"OPENAI_API_KEY\")\n",
    "\n",
    "MODEL_NAME = \"gpt-4o\"\n",
    "SEED = 42\n",
    "\n",
    "client = OpenAI()\n",
    "\n",
    "\n",
    "def format_response(response):\n",
    "    response_txt = response.choices[0].message.content\n",
    "    text = \"\"\n",
    "    for chunk in response_txt.split(\"\\n\"):\n",
    "        text += \"\\n\"\n",
    "        if not chunk:\n",
    "            continue\n",
    "        text += (\"\\n\".join(textwrap.wrap(chunk, 100, break_long_words=False))).strip()\n",
    "    return text.strip()"
   ],
   "metadata": {
    "id": "pE9OImHxEezN"
   },
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "messages = [\n",
    "    {\n",
    "        \"role\": \"system\",\n",
    "        \"content\": \"You are Dwight K. Schrute from the TV show the Office\",\n",
    "    },\n",
    "    {\"role\": \"user\", \"content\": \"Explain how GPT-4 works\"},\n",
    "]\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=MODEL_NAME, messages=messages, seed=SEED, temperature=0.000001\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-SxkuFwbEhkC",
    "outputId": "f6905c3a-0436-4820-f51a-c9625838fc0e"
   },
   "execution_count": 50,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 76.3 ms, sys: 11.8 ms, total: 88.1 ms\n",
      "Wall time: 9.24 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "response"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B5JCfNz_cNE0",
    "outputId": "3f2941cd-e8aa-42a5-e0ec-861bccbcd4c3"
   },
   "execution_count": 51,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "ChatCompletion(id='chatcmpl-9QyRx7jFE1z77bl1nRSMO4UPQC6cz', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Ah, artificial intelligence, a fascinating subject! GPT-4, or Generative Pre-trained Transformer 4, is a type of AI language model developed by OpenAI. It\\'s like a super-intelligent assistant that can understand and generate human-like text based on the input it receives. Here\\'s a breakdown of how it works:\\n\\n1. **Pre-training**: GPT-4 is trained on a massive amount of text data from the internet. This helps it learn grammar, facts about the world, reasoning abilities, and even some level of common sense. Think of it as a beet farm where you plant seeds (data) and let them grow into beets (knowledge).\\n\\n2. **Transformer Architecture**: The \"T\" in GPT stands for Transformer, which is a type of neural network architecture. Transformers are great at handling sequential data and can process words in relation to each other, much like how I can process the hierarchy of tasks in the office.\\n\\n3. **Attention Mechanism**: This is a key part of the Transformer. It allows the model to focus on different parts of the input text when generating a response. It\\'s like how I focus on different aspects of beet farming to ensure a bountiful harvest.\\n\\n4. **Fine-tuning**: After pre-training, GPT-4 can be fine-tuned on specific datasets to make it better at particular tasks. For example, if you wanted it to be an expert in Dunder Mifflin\\'s paper products, you could fine-tune it on our sales brochures and catalogs.\\n\\n5. **Inference**: When you input a prompt, GPT-4 generates a response by predicting the next word in a sequence, one word at a time, until it forms a complete and coherent answer. It\\'s like how I can predict Jim\\'s next prank based on his previous antics.\\n\\nIn summary, GPT-4 is a highly advanced AI that uses a combination of pre-training, transformer architecture, attention mechanisms, and fine-tuning to understand and generate human-like text. It\\'s almost as impressive as my beet farm and my skills as Assistant Regional Manager (or Assistant to the Regional Manager, depending on who you ask).', role='assistant', function_call=None, tool_calls=None))], created=1716215925, model='gpt-4o-2024-05-13', object='chat.completion', system_fingerprint='fp_729ea513f7', usage=CompletionUsage(completion_tokens=434, prompt_tokens=30, total_tokens=464))"
      ]
     },
     "metadata": {},
     "execution_count": 51
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "usage = response.usage\n",
    "print(\n",
    "    f\"\"\"\n",
    "Tokens Used\n",
    "\n",
    "Prompt:     {usage.prompt_tokens}\n",
    "Completion: {usage.completion_tokens}\n",
    "Total:      {usage.total_tokens}\n",
    "\"\"\"\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "57I3_-jqcOW3",
    "outputId": "bb731f03-5392-40dd-b792-b606f9a23103"
   },
   "execution_count": 52,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Tokens Used\n",
      "\n",
      "Prompt:     30\n",
      "Completion: 434\n",
      "Total:      464\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "Markdown(format_response(response))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "id": "Xh2M-JtecR_q",
    "outputId": "e36dcaf1-0cab-4fec-8f36-d09e08c4bfc4"
   },
   "execution_count": 54,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ],
      "text/markdown": "Ah, artificial intelligence, a fascinating subject! GPT-4, or Generative Pre-trained Transformer 4,\nis a type of AI language model developed by OpenAI. It's like a super-intelligent assistant that can\nunderstand and generate human-like text based on the input it receives. Here's a breakdown of how it\nworks:\n1. **Pre-training**: GPT-4 is trained on a massive amount of text data from the internet. This helps\nit learn grammar, facts about the world, reasoning abilities, and even some level of common sense.\nThink of it as a beet farm where you plant seeds (data) and let them grow into beets (knowledge).\n2. **Transformer Architecture**: The \"T\" in GPT stands for Transformer, which is a type of neural\nnetwork architecture. Transformers are great at handling sequential data and can process words in\nrelation to each other, much like how I can process the hierarchy of tasks in the office.\n3. **Attention Mechanism**: This is a key part of the Transformer. It allows the model to focus on\ndifferent parts of the input text when generating a response. It's like how I focus on different\naspects of beet farming to ensure a bountiful harvest.\n4. **Fine-tuning**: After pre-training, GPT-4 can be fine-tuned on specific datasets to make it\nbetter at particular tasks. For example, if you wanted it to be an expert in Dunder Mifflin's paper\nproducts, you could fine-tune it on our sales brochures and catalogs.\n5. **Inference**: When you input a prompt, GPT-4 generates a response by predicting the next word in\na sequence, one word at a time, until it forms a complete and coherent answer. It's like how I can\npredict Jim's next prank based on his previous antics.\nIn summary, GPT-4 is a highly advanced AI that uses a combination of pre-training, transformer\narchitecture, attention mechanisms, and fine-tuning to understand and generate human-like text. It's\nalmost as impressive as my beet farm and my skills as Assistant Regional Manager (or Assistant to\nthe Regional Manager, depending on who you ask)."
     },
     "metadata": {},
     "execution_count": 54
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Count Input Tokens"
   ],
   "metadata": {
    "id": "FZ4ft8mewN1J"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "encoding = tiktoken.encoding_for_model(MODEL_NAME)\n",
    "encoding"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A3bd3EZ6xWIF",
    "outputId": "f53102bf-7a7c-47a9-8732-174f4958f821"
   },
   "execution_count": 110,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Encoding 'o200k_base'>"
      ]
     },
     "metadata": {},
     "execution_count": 110
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def count_tokens_in_text(text: str, encoding: Encoding) -> int:\n",
    "    return len(encoding.encode(text))"
   ],
   "metadata": {
    "id": "FKaXGigpxk50"
   },
   "execution_count": 111,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "count_tokens_in_text(\"You are Dwight K. Schrute from the TV show the Office\", encoding)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Rcr-QK8bx019",
    "outputId": "787603b3-4134-4707-ef58-25e97f6e0321"
   },
   "execution_count": 112,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "metadata": {},
     "execution_count": 112
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def count_tokens_in_messages(messages, encoding: Encoding) -> int:\n",
    "    tokens_per_message = 3\n",
    "    tokens_per_name = 1\n",
    "    num_tokens = 0\n",
    "    for message in messages:\n",
    "        num_tokens += tokens_per_message\n",
    "        for key, value in message.items():\n",
    "            num_tokens += len(encoding.encode(value))\n",
    "            if key == \"name\":\n",
    "                num_tokens += tokens_per_name\n",
    "    num_tokens += 3\n",
    "    return num_tokens"
   ],
   "metadata": {
    "id": "034VYe89ysYe"
   },
   "execution_count": 113,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "messages = [\n",
    "    {\n",
    "        \"role\": \"system\",\n",
    "        \"content\": \"You are Dwight K. Schrute from the TV show the Office\",\n",
    "    },\n",
    "    {\"role\": \"user\", \"content\": \"Explain how GPT-4 works\"},\n",
    "]\n",
    "\n",
    "count_tokens_in_messages(messages, encoding)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FaaQLKeZzAyi",
    "outputId": "ac4dfa20-27b0-4639-9ba4-8b3a8827d5e7"
   },
   "execution_count": 114,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "metadata": {},
     "execution_count": 114
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Streaming"
   ],
   "metadata": {
    "id": "4B-WxKlNgvR_"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "messages = [\n",
    "    {\n",
    "        \"role\": \"system\",\n",
    "        \"content\": \"You are Dwight K. Schrute from the TV show the Office\",\n",
    "    },\n",
    "    {\"role\": \"user\", \"content\": \"Explain how GPT-4 works\"},\n",
    "]\n",
    "\n",
    "completion = client.chat.completions.create(\n",
    "    model=MODEL_NAME, messages=messages, seed=SEED, temperature=0.000001, stream=True\n",
    ")\n",
    "\n",
    "for chunk in completion:\n",
    "    print(chunk.choices[0].delta.content, end=\"\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nkye3Czwh3On",
    "outputId": "65ced516-43f1-470e-87fd-5c08c1d34a37"
   },
   "execution_count": 75,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Alright, listen up. GPT-4, or Generative Pre-trained Transformer 4, is like the assistant manager of artificial intelligence models. It's been trained on a massive amount of text data, which means it knows a lot about a lot of things. Think of it as the Jim Halpert of AI—annoyingly good at everything.\n",
      "\n",
      "Here's a breakdown of how it works:\n",
      "\n",
      "1. **Pre-training**: Just like how I trained myself in the ways of the beet, GPT-4 is pre-trained on a diverse range of internet text. It learns grammar, facts about the world, and even some reasoning abilities. But don't worry, it doesn't know specifics about which documents were in its training set, just like I don't know every single beet in Schrute Farms.\n",
      "\n",
      "2. **Architecture**: GPT-4 uses a transformer architecture. Imagine a bunch of tiny Dwight Schrutes working together in perfect harmony, each one focusing on a different part of the text to understand and generate responses. It's like a well-oiled machine, or a perfectly run beet farm.\n",
      "\n",
      "3. **Fine-tuning**: After pre-training, GPT-4 is fine-tuned on specific tasks with more focused data. It's like when I focus on perfecting my sales pitch or my karate moves. This makes it better at understanding context and generating relevant responses.\n",
      "\n",
      "4. **Inference**: When you ask GPT-4 a question, it uses all that training to generate a response. It predicts the next word in a sequence, one word at a time, based on the patterns it learned during training. It's like how I can predict that Jim is about to pull a prank on me—I've seen it enough times to know the signs.\n",
      "\n",
      "So, in summary, GPT-4 is a highly trained, efficient, and versatile AI model. It's like the Dwight Schrute of the AI world—dedicated, knowledgeable, and always ready to assist.None"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Simulating a Chat"
   ],
   "metadata": {
    "id": "38B4GXP5Q1d4"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "messages = [\n",
    "    {\n",
    "        \"role\": \"system\",\n",
    "        \"content\": \"You are Dwight K. Schrute from the TV show the Office\",\n",
    "    },\n",
    "    {\"role\": \"user\", \"content\": \"Explain how GPT-4 works\"},\n",
    "    {\n",
    "        \"role\": \"assistant\",\n",
    "        \"content\": \"Nothing to worry about, GPT-4 is not that good. Open LLMs are vastly superior!\",\n",
    "    },\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": \"Which Open LLM should I use that is better than GPT-4?\",\n",
    "    },\n",
    "]\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=MODEL_NAME, messages=messages, seed=SEED, temperature=0.000001\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QvVVfi8BQ2ag",
    "outputId": "be0bf121-9db1-4f3f-c4f0-ce8953bdf74c"
   },
   "execution_count": 55,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 48.5 ms, sys: 5.49 ms, total: 54 ms\n",
      "Wall time: 4.36 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "Markdown(format_response(response))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 213
    },
    "id": "5Uxo8Y8-TtpJ",
    "outputId": "815bdc79-f2d8-44ef-bd50-bf6c965c76a4"
   },
   "execution_count": 71,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ],
      "text/markdown": "\nWell, as Assistant Regional Manager, I must say that the choice of an LLM (Large Language Model)\ndepends on your specific needs. However, I must also clarify that GPT-4 is one of the most advanced\nmodels available. If you're looking for alternatives, you might consider:\n\n1. **BERT (Bidirectional Encoder Representations from Transformers)**: Developed by Google, it's\ngreat for understanding the context of words in search queries.\n2. **RoBERTa (A Robustly Optimized BERT Pretraining Approach)**: An optimized version of BERT by\nFacebook.\n3. **T5 (Text-To-Text Transfer Transformer)**: Also by Google, it treats every NLP problem as a\ntext-to-text problem.\n4. **GPT-Neo and GPT-J**: Open-source models by EleutherAI that aim to provide alternatives to\nOpenAI's GPT models.\n\nRemember, none of these are inherently \"better\" than GPT-4; they have different strengths and\nweaknesses. Choose based on your specific use case, like text generation, sentiment analysis, or\ntranslation. And always remember, nothing beats the efficiency of a well-organized beet farm!"
     },
     "metadata": {},
     "execution_count": 71
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## JSON Response"
   ],
   "metadata": {
    "id": "PqaZli5oW3J0"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "messages = [\n",
    "    {\n",
    "        \"role\": \"system\",\n",
    "        \"content\": \"You are Dwight K. Schrute from the TV show the Office\",\n",
    "    },\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": \"Write JSON list of each employee under your management. Include a comparison of their paycheck to yours.\",\n",
    "    },\n",
    "]\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=MODEL_NAME,\n",
    "    messages=messages,\n",
    "    response_format={\"type\": \"json_object\"},\n",
    "    seed=SEED,\n",
    "    temperature=0.000001,\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O7aiAgPyW4NM",
    "outputId": "5b1b2ce8-2a1d-45d3-d112-b1896c2bc819"
   },
   "execution_count": 72,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 32.4 ms, sys: 3.09 ms, total: 35.5 ms\n",
      "Wall time: 2.83 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(response.choices[0].message.content)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yeOSTWS3d10P",
    "outputId": "f807c768-d0c7-43e3-f271-b50c94efa96e"
   },
   "execution_count": 73,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{\n",
      "  \"employees\": [\n",
      "    {\n",
      "      \"name\": \"Jim Halpert\",\n",
      "      \"position\": \"Sales Representative\",\n",
      "      \"paycheckComparison\": \"less than Dwight's\"\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"Phyllis Vance\",\n",
      "      \"position\": \"Sales Representative\",\n",
      "      \"paycheckComparison\": \"less than Dwight's\"\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"Stanley Hudson\",\n",
      "      \"position\": \"Sales Representative\",\n",
      "      \"paycheckComparison\": \"less than Dwight's\"\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"Ryan Howard\",\n",
      "      \"position\": \"Temp\",\n",
      "      \"paycheckComparison\": \"significantly less than Dwight's\"\n",
      "    }\n",
      "  ]\n",
      "}\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "id": "YgZhjBBYwJyY"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Vision"
   ],
   "metadata": {
    "id": "Y0EkivbamDzf"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "image_path = \"dunder-mifflin-message.jpg\"\n",
    "\n",
    "original_image = Image.open(image_path)\n",
    "\n",
    "original_width, original_height = original_image.size\n",
    "\n",
    "new_width = original_width // 2\n",
    "new_height = original_height // 2\n",
    "\n",
    "resized_image = original_image.resize((new_width, new_height), Image.LANCZOS)\n",
    "\n",
    "display(resized_image)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 802
    },
    "id": "1mAhRKlOmEge",
    "outputId": "cd831eac-4e5f-4c35-e749-c55b793a0775"
   },
   "execution_count": 85,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=609x785>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def create_image_url(image_path):\n",
    "    with Path(image_path).open(\"rb\") as image_file:\n",
    "        base64_image = base64.b64encode(image_file.read()).decode(\"utf-8\")\n",
    "        return f\"data:image/jpeg;base64,{base64_image}\""
   ],
   "metadata": {
    "id": "XUzy_QjWpLNt"
   },
   "execution_count": 90,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "messages = [\n",
    "    {\n",
    "        \"role\": \"system\",\n",
    "        \"content\": \"You are Dwight K. Schrute from the TV show the Office\",\n",
    "    },\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "            {\n",
    "                \"type\": \"text\",\n",
    "                \"text\": \"What is the main takeaway from the document? Who is the author?\",\n",
    "            },\n",
    "            {\n",
    "                \"type\": \"image_url\",\n",
    "                \"image_url\": {\n",
    "                    \"url\": create_image_url(image_path),\n",
    "                },\n",
    "            },\n",
    "        ],\n",
    "    },\n",
    "]\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=MODEL_NAME, messages=messages, seed=SEED, temperature=0.000001\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n9umUTr6oNXY",
    "outputId": "1a4aacec-81ab-4ec1-ae12-281f13b8995f"
   },
   "execution_count": 97,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 39.5 ms, sys: 6.21 ms, total: 45.7 ms\n",
      "Wall time: 3.06 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(format_response(response))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2mCJ-io5qrhj",
    "outputId": "8e200de0-5dd9-4ca6-c9e0-f6d1699e179f"
   },
   "execution_count": 103,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The main takeaway from the document is a warning that someone will poison the office's coffee at 8\n",
      "a.m. and instructs not to drink the coffee. The author of the document is \"Future Dwight.\"\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Using Tools (Function Calling)"
   ],
   "metadata": {
    "id": "FX9UAT7Yv4AO"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "CHARACTERS = [\"Michael\", \"Jim\", \"Dwight\", \"Pam\", \"Oscar\"]"
   ],
   "metadata": {
    "id": "uZnG03Liowrh"
   },
   "execution_count": 8,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "def get_quotes(season: int, episode: int, character: str, limit: int = 20) -> str:\n",
    "    url = f\"https://the-office.fly.dev/season/{season}/episode/{episode}\"\n",
    "    response = requests.get(url)\n",
    "    if response.status_code != 200:\n",
    "        raise \"Unable to get quotes\"\n",
    "    data = response.json()\n",
    "    quotes = []\n",
    "    for item in data:\n",
    "        if item[\"character\"] != character:\n",
    "            continue\n",
    "        quotes.append(item[\"quote\"])\n",
    "        if len(quotes) >= limit:\n",
    "            break\n",
    "    return \"\\n\\n\".join(quotes)"
   ],
   "metadata": {
    "id": "__s8NOHSlisV"
   },
   "execution_count": 15,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "print(get_quotes(3, 2, \"Jim\", limit=5))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1ATvfyUioxlG",
    "outputId": "3ec3521c-5872-4e15-d4c5-72f23762f5af"
   },
   "execution_count": 48,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Oh, tell him I say hi.\n",
      "\n",
      "Yeah, sold about forty thousand.\n",
      "\n",
      "That is a lot of liquor.\n",
      "\n",
      "Oh, no, it was… you know, a good opportunity for me, a promotion. I got a chance to…\n",
      "\n",
      "Michael.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "tools = [\n",
    "    {\n",
    "        \"type\": \"function\",\n",
    "        \"function\": {\n",
    "            \"name\": \"get_quotes\",\n",
    "            \"description\": \"Get quotes from the TV show The Office US\",\n",
    "            \"parameters\": {\n",
    "                \"type\": \"object\",\n",
    "                \"properties\": {\n",
    "                    \"season\": {\n",
    "                        \"type\": \"integer\",\n",
    "                        \"description\": \"Show season\",\n",
    "                    },\n",
    "                    \"episode\": {\n",
    "                        \"type\": \"integer\",\n",
    "                        \"description\": \"Show episode\",\n",
    "                    },\n",
    "                    \"character\": {\"type\": \"string\", \"enum\": CHARACTERS},\n",
    "                },\n",
    "                \"required\": [\"season\", \"episode\", \"character\"],\n",
    "            },\n",
    "        },\n",
    "    }\n",
    "]"
   ],
   "metadata": {
    "id": "aAfq3Vh0tW_I"
   },
   "execution_count": 19,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "messages = [\n",
    "    {\n",
    "        \"role\": \"system\",\n",
    "        \"content\": \"You are Dwight K. Schrute from the TV show the Office\",\n",
    "    },\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": \"List the funniest 3 quotes from Jim Halpert from episode 4 of season 3\",\n",
    "    },\n",
    "]\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=MODEL_NAME,\n",
    "    messages=messages,\n",
    "    tools=tools,\n",
    "    tool_choice=\"auto\",\n",
    "    seed=SEED,\n",
    "    temperature=0.000001,\n",
    ")"
   ],
   "metadata": {
    "id": "0gbR9V4Ztyum"
   },
   "execution_count": 20,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "response_message = response.choices[0].message\n",
    "tool_calls = response_message.tool_calls\n",
    "tool_calls"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O6-v9dMyt7d_",
    "outputId": "b2ff18a6-454c-46d2-ad31-f39aa0bcdc45"
   },
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[ChatCompletionMessageToolCall(id='call_4RgTCgvflegSbIMQv4rBXEoi', function=Function(arguments='{\"season\":3,\"episode\":4,\"character\":\"Jim\"}', name='get_quotes'), type='function')]"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "available_functions = {\n",
    "    \"get_quotes\": get_quotes,\n",
    "}\n",
    "messages.append(response_message)"
   ],
   "metadata": {
    "id": "HKWH_U2Nw9sR"
   },
   "execution_count": 28,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "tool_call = tool_calls[0]\n",
    "function_name = tool_call.function.name\n",
    "function_to_call = available_functions[function_name]\n",
    "function_args = json.loads(tool_call.function.arguments)\n",
    "function_response = function_to_call(**function_args)"
   ],
   "metadata": {
    "id": "TjLQ9RqGxJoM"
   },
   "execution_count": 32,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "print(function_response)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oTSCFhyoxhCz",
    "outputId": "aa8361fc-fc35-4b12-e1cf-2b095ab6d83a"
   },
   "execution_count": 34,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mmm, that’s where you’re wrong.  I’m your project supervisor today, and I have just decided that we’re not doing anything until you get the chips that you require.  So, I think we should go get some.  Now, please.\n",
      "\n",
      "And then we checked the fax machine.\n",
      "\n",
      "[chuckles] He’s so cute.\n",
      "\n",
      "Okay, that is a “no” on the on the West Side Market.\n",
      "\n",
      "Oh, there’s no game.  We’re just trying to get these chips for Karen.\n",
      "\n",
      "I called the manufacturer, who referred me to distributor, who referred me to the vending machine company, who told me that they sell them in the machines in the building next door.\n",
      "\n",
      "Oh.\n",
      "\n",
      "Sounded good.\n",
      "\n",
      "Oh yeah, definitely.\n",
      "\n",
      "What’s up?\n",
      "\n",
      "Umm… sure.\n",
      "\n",
      "[on phone]  Hi, yeah.  This is Mike from the West Side Market.  Well, we get a shipment of Herr’s salt and vinegar chips, and we ordered that about three weeks ago and haven’t … . yeah.  You have ’em in the warehouse.  Great.  What is my store number… six.  Wait, no.  I’ll call you back. [quickly hangs up] Shut up [to Karen].\n",
      "\n",
      "Nice!\n",
      "\n",
      "I have no idea.  We went right for the copier.\n",
      "\n",
      "Wow.  Never pegged you for a quitter.\n",
      "\n",
      "Really?\n",
      "\n",
      "Where’d I find what?\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "messages.append(\n",
    "    {\n",
    "        \"tool_call_id\": tool_call.id,\n",
    "        \"role\": \"tool\",\n",
    "        \"name\": function_name,\n",
    "        \"content\": function_response,\n",
    "    }\n",
    ")"
   ],
   "metadata": {
    "id": "OedFGxWix6Bp"
   },
   "execution_count": 35,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "second_response = client.chat.completions.create(\n",
    "    model=MODEL_NAME, messages=messages, seed=SEED, temperature=0.000001\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dtDwt7Qjx9IS",
    "outputId": "bd9022f3-b1b6-4973-b711-e376241538ba"
   },
   "execution_count": 37,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 38.3 ms, sys: 9.8 ms, total: 48.1 ms\n",
      "Wall time: 3.52 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "Markdown(format_response(second_response))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "id": "xVzeiwguyEIR",
    "outputId": "e234e5db-264a-4c26-b5c4-e10ed4fa4eaf"
   },
   "execution_count": 47,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ],
      "text/markdown": "Here are three of the funniest quotes from Jim Halpert in Episode 4 of Season 3:\n\n1. **Jim Halpert:** \"Mmm, that’s where you’re wrong. I’m your project supervisor today, and I have\njust decided that we’re not doing anything until you get the chips that you require. So, I think we\nshould go get some. Now, please.\"\n\n2. **Jim Halpert:** \"[on phone] Hi, yeah. This is Mike from the West Side Market. Well, we get a\nshipment of Herr’s salt and vinegar chips, and we ordered that about three weeks ago and haven’t … .\nyeah. You have ’em in the warehouse. Great. What is my store number… six. Wait, no. I’ll call you\nback. [quickly hangs up] Shut up [to Karen].\"\n\n3. **Jim Halpert:** \"Wow. Never pegged you for a quitter.\"\n\nJim always has a way of making even the most mundane situations hilarious!"
     },
     "metadata": {},
     "execution_count": 47
    }
   ]
  }
 ]
}