gemini/use-cases/education/use_cases_for_education.ipynb

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   "source": [
    "# Copyright 2023-2024 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "VEqbX8OhE8y9"
   },
   "source": [
    "# Using Gemini in Education\n",
    "\n",
    "<table align=\"left\">\n",
    "  <td style=\"text-align: center\">\n",
    "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/generative-ai/blob/main/gemini/use-cases/education/use_cases_for_education.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Run in Colab\n",
    "    </a>\n",
    "  </td>\n",
    "  <td style=\"text-align: center\">\n",
    "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fgenerative-ai%2Fmain%2Fgemini%2Fuse-cases%2Feducation%2Fuse_cases_for_education.ipynb\">\n",
    "      <img width=\"32px\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" alt=\"Google Cloud Colab Enterprise logo\"><br> Run in Colab Enterprise\n",
    "    </a>\n",
    "  </td>\n",
    "  <td style=\"text-align: center\">\n",
    "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/generative-ai/main/gemini/use-cases/education/use_cases_for_education.ipynb\">\n",
    "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
    "    </a>\n",
    "  </td>\n",
    "  <td style=\"text-align: center\">\n",
    "    <a href=\"https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/use-cases/education/use_cases_for_education.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
    "    </a>\n",
    "  </td>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|           |                                                  |\n",
    "| --------- | ------------------------------------------------ |\n",
    "| Author(s) | [Laurent Picard](https://github.com/PicardParis) |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VK1Q5ZYdVL4Y"
   },
   "source": [
    "## Overview\n",
    "\n",
    "In this notebook, you will explore a variety of use cases enabled by Gemini 1.5 in the context of education.\n",
    "\n",
    "### Gemini\n",
    "\n",
    "Gemini is a family of generative AI models developed by Google DeepMind.\n",
    "\n",
    "### Gemini 1.5\n",
    "\n",
    "The Gemini 1.5 models are built for multimodality from the ground up:\n",
    "\n",
    "- Supported inputs: Text, code, images, audio, video, video with audio, and PDF\n",
    "- Generated output: Text\n",
    "\n",
    "### Vertex AI Gemini API\n",
    "\n",
    "The Vertex AI Gemini API provides a unified interface for interacting with the Gemini models. There are currently two Gemini 1.5 models available using the Gemini API:\n",
    "\n",
    "- **Gemini 1.5 Flash** (`gemini-1.5-flash-001`) for fast and versatile performance across a diverse variety of tasks\n",
    "- **Gemini 1.5 Pro** (`gemini-1.5-pro-001`) for complex reasoning tasks such as code and text generation, text editing, problem solving, data extraction and generation\n",
    "\n",
    "For more information, see [Gemini models](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#gemini-models).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uVL_vGs4q3pg"
   },
   "source": [
    "### Objectives\n",
    "\n",
    "The main objective of this notebook is to demonstrate a variety of educational use cases that can benefit from Gemini 1.5.\n",
    "\n",
    "The steps performed include:\n",
    "\n",
    "- Installing the Python SDK\n",
    "- Loading Gemini\n",
    "- Reasoning at different levels\n",
    "- Reasoning on text\n",
    "- Reasoning on numbers\n",
    "- Reasoning on a single image\n",
    "- Reasoning on multiple images\n",
    "- Reasoning on a video\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bRdtKLfTsQ27"
   },
   "source": [
    "### Costs\n",
    "\n",
    "This tutorial uses billable components of Google Cloud:\n",
    "\n",
    "- Vertex AI\n",
    "\n",
    "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QDU0XJ1xRDlL"
   },
   "source": [
    "## Getting Started\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SBGrQE22sVrt"
   },
   "source": [
    "### Install Vertex AI SDK\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-hqq5vomsW_P",
    "tags": []
   },
   "outputs": [],
   "source": [
    "! pip3 install --upgrade --user google-cloud-aiplatform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R5Xep4W9lq-Z"
   },
   "source": [
    "### Restart current runtime\n",
    "\n",
    "To use the newly installed packages in this Jupyter runtime, you must restart the runtime. You can do this by running the cell below, which will restart the current kernel.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XRvKdaPDTznN",
    "outputId": "154a71b5-f302-4f53-ed2f-b3e5fef9195b"
   },
   "outputs": [],
   "source": [
    "# Restart kernel after installs so that your environment can access the new packages\n",
    "import IPython\n",
    "\n",
    "app = IPython.Application.instance()\n",
    "app.kernel.do_shutdown(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SbmM4z7FOBpM"
   },
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>⚠️ The kernel is going to restart. Please wait until it is finished before continuing to the next step. ⚠️</b>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N5afkyDMSBW5"
   },
   "source": [
    "### Authenticate your notebook environment (Colab only)\n",
    "\n",
    "If you are running this notebook on Google Colab, run the following cell to authenticate your environment. This step is not required if you are using [Vertex AI Workbench](https://cloud.google.com/vertex-ai-workbench).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Ab4Y6eSIUknb"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "# Additional authentication is required for Google Colab\n",
    "if \"google.colab\" in sys.modules:\n",
    "    # Authenticate user to Google Cloud\n",
    "    from google.colab import auth\n",
    "\n",
    "    auth.authenticate_user()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kc4WxYmLSBW5"
   },
   "source": [
    "### Define Google Cloud project information and initialize Vertex AI\n",
    "\n",
    "Initialize the Vertex AI SDK for Python for your project:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "YmY9HVVGSBW5"
   },
   "outputs": [],
   "source": [
    "# Define project information\n",
    "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
    "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
    "\n",
    "# Initialize Vertex AI\n",
    "import vertexai\n",
    "\n",
    "vertexai.init(project=PROJECT_ID, location=LOCATION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kdINrwJZsj1d"
   },
   "source": [
    "### Import Vertex AI classes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 324,
     "status": "ok",
     "timestamp": 1702337383496,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "stNmWCsRsotM",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from vertexai.generative_models import (\n",
    "    GenerationConfig,\n",
    "    GenerationResponse,\n",
    "    GenerativeModel,\n",
    "    HarmBlockThreshold,\n",
    "    HarmCategory,\n",
    "    Image,\n",
    "    Part,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0v63w6fWs9Dx"
   },
   "source": [
    "### Define helper functions\n",
    "\n",
    "Define some helper functions to load and display images.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1702337383496,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "OGvJLH4DmZfw",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import http.client\n",
    "import io\n",
    "import typing\n",
    "import urllib.request\n",
    "\n",
    "import IPython.display\n",
    "from PIL import Image as PIL_Image\n",
    "from PIL import ImageOps as PIL_ImageOps\n",
    "\n",
    "\n",
    "Contents = str | list[str | Image | Part]\n",
    "\n",
    "\n",
    "def generate_content(\n",
    "    model: GenerativeModel,\n",
    "    contents: Contents,\n",
    "    temperature: float = 0.0,\n",
    ") -> GenerationResponse:\n",
    "    \"\"\"Call the Vertex AI Gemini API.\n",
    "\n",
    "    The default temperature (randomness/creativity) is set low for more consistent responses.\n",
    "    \"\"\"\n",
    "    generation_config = GenerationConfig(\n",
    "        temperature=temperature,\n",
    "        candidate_count=1,\n",
    "        max_output_tokens=8192,\n",
    "    )\n",
    "    safety_settings = {\n",
    "        HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_ONLY_HIGH,\n",
    "        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_ONLY_HIGH,\n",
    "        HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_ONLY_HIGH,\n",
    "        HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_ONLY_HIGH,\n",
    "    }\n",
    "\n",
    "    response = model.generate_content(\n",
    "        contents,\n",
    "        generation_config=generation_config,\n",
    "        safety_settings=safety_settings,\n",
    "        stream=False,\n",
    "    )\n",
    "    assert isinstance(response, GenerationResponse)\n",
    "\n",
    "    return response\n",
    "\n",
    "\n",
    "def print_contents(contents: Contents):\n",
    "    \"\"\"Print the full contents for ease of readability.\"\"\"\n",
    "    if not isinstance(contents, list):\n",
    "        contents = [contents]\n",
    "\n",
    "    print(\" Contents \".center(80, \"-\"))\n",
    "    for content in contents:\n",
    "        if display_content_as_image(content):\n",
    "            continue\n",
    "        if display_content_as_video(content):\n",
    "            continue\n",
    "        print(content)\n",
    "\n",
    "\n",
    "def display_content_as_image(content: str | Image | Part) -> bool:\n",
    "    if not isinstance(content, Image):\n",
    "        return False\n",
    "    display_image(content)\n",
    "    return True\n",
    "\n",
    "\n",
    "def display_content_as_video(content: str | Image | Part) -> bool:\n",
    "    if not isinstance(content, Part):\n",
    "        return False\n",
    "    cloud_storage_path = content.file_data.file_uri.removeprefix(\"gs://\")\n",
    "    video_url = f\"https://storage.googleapis.com/{cloud_storage_path}\"\n",
    "    html = IPython.display.HTML(f'<a href=\"{video_url}\">Video:</a>')\n",
    "    video = IPython.display.Video(\n",
    "        url=video_url,\n",
    "        width=600,\n",
    "        html_attributes=\"controls muted\",\n",
    "    )\n",
    "    IPython.display.display(html)\n",
    "    IPython.display.display(video)\n",
    "\n",
    "    return True\n",
    "\n",
    "\n",
    "def print_response(response: GenerationResponse, as_markdown: bool = True):\n",
    "    # Remove potential leading/trailing spaces\n",
    "    text = response.text.strip()\n",
    "\n",
    "    print(\" Start of response \".center(80, \"-\"))\n",
    "    if as_markdown:\n",
    "        IPython.display.display(IPython.display.Markdown(text))\n",
    "    else:\n",
    "        print(text)\n",
    "    print(\" End of response \".center(80, \"-\"))\n",
    "    print(\"\")\n",
    "\n",
    "\n",
    "def display_image(image: Image, max_width: int = 600, max_height: int = 350):\n",
    "    pil_image = typing.cast(PIL_Image.Image, image._pil_image)\n",
    "    if pil_image.mode != \"RGB\":\n",
    "        # Modes such as RGBA are not yet supported by all Jupyter environments\n",
    "        pil_image = pil_image.convert(\"RGB\")\n",
    "\n",
    "    image_width, image_height = pil_image.size\n",
    "    if max_width < image_width or max_height < image_height:\n",
    "        # Resize to display a smaller notebook image\n",
    "        pil_image = PIL_ImageOps.contain(pil_image, (max_width, max_height))\n",
    "\n",
    "    display_image_compressed(pil_image)\n",
    "\n",
    "\n",
    "def display_image_compressed(pil_image: PIL_Image.Image):\n",
    "    \"\"\"Display the image in a compressed format to reduce the notebook size.\"\"\"\n",
    "    image_io = io.BytesIO()\n",
    "    pil_image.save(image_io, \"jpeg\", quality=80, optimize=True)\n",
    "    image_bytes = image_io.getvalue()\n",
    "    ipython_image = IPython.display.Image(image_bytes)\n",
    "    IPython.display.display(ipython_image)\n",
    "\n",
    "\n",
    "def load_image_from_url(image_url: str) -> Image:\n",
    "    image_bytes = get_image_bytes_from_url(image_url)\n",
    "    return Image.from_bytes(image_bytes)\n",
    "\n",
    "\n",
    "def get_image_bytes_from_url(image_url: str) -> bytes:\n",
    "    with urllib.request.urlopen(image_url) as response:\n",
    "        response = typing.cast(http.client.HTTPResponse, response)\n",
    "        image_bytes = response.read()\n",
    "    return image_bytes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s9R3nV5bE22Z"
   },
   "source": [
    "## Loading Gemini 1.5\n",
    "\n",
    "Both Gemini 1.5 Pro and Gemini 1.5 Flash excel at education use cases. Here are the selection factors to consider:\n",
    "\n",
    "- Gemini 1.5 Pro (larger model): quality and complexity\n",
    "- Gemini 1.5 Flash (smaller model): speed and cost\n",
    "\n",
    "> Gemini 1.5 Flash is trained by Gemini 1.5 Pro through a process called \"distillation\" where the larger model transfers its most essential knowledge and skills to the smaller model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1702337383834,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "UnUmflwsE22a",
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_name = \"gemini-1.5-pro-001\"\n",
    "# model_name = \"gemini-1.5-flash-001\"\n",
    "\n",
    "model = GenerativeModel(model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9fm71OTvpyqD"
   },
   "source": [
    "## Reasoning at different levels\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zgf3t9odFlIj"
   },
   "source": [
    "You can ask for direct answers:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 64
    },
    "executionInfo": {
     "elapsed": 965,
     "status": "ok",
     "timestamp": 1702337384797,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "tBpDHLmMv2un",
    "outputId": "da0a9444-7d16-4522-ee30-76eec7ee8f2a",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "What happened to the dinosaurs? When?\n",
      "Explain simply in one sentence.\n",
      "\n",
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Dinosaurs went extinct about 66 million years ago when an asteroid hit Earth, causing catastrophic environmental changes."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "contents = \"\"\"\n",
    "What happened to the dinosaurs? When?\n",
    "Explain simply in one sentence.\n",
    "\"\"\"\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rfs1qLAoFrZu"
   },
   "source": [
    "… as well as for more nuanced answers:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 586
    },
    "executionInfo": {
     "elapsed": 6374,
     "status": "ok",
     "timestamp": 1702337391168,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "AcVfT9bowUKW",
    "outputId": "64df65b5-06c7-4c2b-9434-e9b32106f422",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Are we 100% sure about what happened to the dinosaurs?\n",
      "If not, detail the current main hypotheses.\n",
      "\n",
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "No, we are not 100% sure about what happened to the dinosaurs. While the **impact of a large asteroid or comet** is widely accepted as the most likely primary cause of the extinction event that wiped out the dinosaurs (except for birds), there are still uncertainties and other contributing factors being researched. \n",
       "\n",
       "Here's a breakdown of the current main hypotheses:\n",
       "\n",
       "**1. The Impact Event:**\n",
       "\n",
       "* **What we know:**  A massive asteroid or comet, estimated to be about 6 miles wide, struck the Earth near the Yucatán Peninsula in Mexico around 66 million years ago. This impact created the Chicxulub crater, which is 110 miles in diameter and 12 miles deep.\n",
       "* **Evidence:** The presence of a global layer of iridium (rare on Earth but abundant in asteroids) in rock strata dating back to the extinction event, shocked quartz (formed under intense pressure), and the Chicxulub crater itself.\n",
       "* **Effects:** The impact would have triggered catastrophic events like:\n",
       "    * **Massive earthquakes and tsunamis:** Devastating coastal regions and causing widespread destruction.\n",
       "    * **Global wildfires:** Ejecting superheated rock and dust into the atmosphere, igniting wildfires across the globe.\n",
       "    * **Atmospheric dust and soot:** Blocking sunlight, leading to a prolonged period of darkness and global cooling (impact winter).\n",
       "    * **Acid rain:** Releasing sulfur dioxide into the atmosphere, leading to widespread acid rain that would have acidified oceans and soils.\n",
       "\n",
       "**2. Deccan Traps Volcanism:**\n",
       "\n",
       "* **What we know:** Around the same time as the impact, massive volcanic eruptions were occurring in what is now India, forming the Deccan Traps. These eruptions released enormous amounts of lava and gases like sulfur dioxide and carbon dioxide into the atmosphere.\n",
       "* **Evidence:**  The Deccan Traps lava flows are dated to around the same time as the extinction event.\n",
       "* **Effects:** Volcanic activity could have contributed to the extinction by:\n",
       "    * **Climate change:** Releasing greenhouse gases, leading to global warming and ocean acidification.\n",
       "    * **Acid rain:** Similar to the impact, releasing sulfur dioxide into the atmosphere.\n",
       "* **Debate:**  The exact role of the Deccan Traps in the extinction is still debated. Some scientists believe it may have weakened ecosystems before the impact, making them more vulnerable, while others argue its effects were less significant than the impact itself.\n",
       "\n",
       "**3. Gradual Environmental Change:**\n",
       "\n",
       "* **What we know:**  The Earth's climate and environment were already undergoing changes in the millions of years leading up to the extinction event, such as sea level fluctuations and volcanic activity.\n",
       "* **Evidence:** Fossil records show a decline in dinosaur diversity in some regions before the impact.\n",
       "* **Effects:** These changes may have stressed dinosaur populations, making them more susceptible to extinction from a catastrophic event.\n",
       "\n",
       "**Current Consensus:**\n",
       "\n",
       "The most widely accepted hypothesis is that the asteroid impact was the primary cause of the dinosaur extinction, with the Deccan Traps volcanism potentially playing a contributing role. The impact's immediate and catastrophic effects, combined with the long-term environmental consequences, likely led to the demise of the dinosaurs and many other species.\n",
       "\n",
       "**Ongoing Research:**\n",
       "\n",
       "Scientists continue to investigate the details of the extinction event, including:\n",
       "\n",
       "* The exact timing and duration of the impact's effects.\n",
       "* The relative contributions of the impact and volcanism.\n",
       "* The specific mechanisms by which different species went extinct.\n",
       "\n",
       "While we may never know with 100% certainty what happened to the dinosaurs, ongoing research continues to refine our understanding of this pivotal event in Earth's history."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "contents = \"\"\"\n",
    "Are we 100% sure about what happened to the dinosaurs?\n",
    "If not, detail the current main hypotheses.\n",
    "\"\"\"\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-KPNK1hNGCk4"
   },
   "source": [
    "You can ask for simple answers:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 114
    },
    "executionInfo": {
     "elapsed": 1251,
     "status": "ok",
     "timestamp": 1702337392408,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "SPoEA7UUqlvy",
    "outputId": "5e3695e0-a571-44b5-9b5d-51f747d6ec47",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Explain why it's summer here in France and winter in Australia.\n",
      "I'm a kid. Answer in simple key points.\n",
      "\n",
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Okay, imagine the Earth is like a spinning top:\n",
       "\n",
       "* **Tilted Top:** The Earth isn't straight up and down, it's a little tilted on its side.\n",
       "* **Sun's Rays:**  The sun's rays hit different parts of the tilted Earth more directly at different times of the year.\n",
       "* **Summertime Tilt:** During France's summer, the top part of the Earth (where France is) is tilted towards the sun, getting more direct sunlight and heat.\n",
       "* **Wintertime Tilt:** At the same time, the bottom part of the Earth (where Australia is) is tilted away from the sun, getting less direct sunlight and making it winter there. \n",
       "* **Switching Places:**  As the Earth travels around the sun, the tilt means different parts get more direct sunlight.  So when it's summer in France, it's winter in Australia, and then they switch!"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "contents = \"\"\"\n",
    "Explain why it's summer here in France and winter in Australia.\n",
    "I'm a kid. Answer in simple key points.\n",
    "\"\"\"\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gMlbj1-pGKkA"
   },
   "source": [
    "… or for more detailed answers:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "executionInfo": {
     "elapsed": 3762,
     "status": "ok",
     "timestamp": 1702337396166,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "LdiaJBtbpXjM",
    "outputId": "578bc987-533d-4153-f0ce-528f8567718d",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Explain why we have tides.\n",
      "I'm a scientist. Provide a detailed answer using bullet points.\n",
      "\n",
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Tides are the periodic rise and fall of ocean water levels, primarily caused by the gravitational forces exerted by the Moon and the Sun on the Earth. Here's a detailed explanation:\n",
       "\n",
       "**Gravitational Forces:**\n",
       "\n",
       "* **Moon's Gravity:** The Moon's gravitational pull is the primary cause of tides. The Moon's gravity pulls the water on the side of Earth closest to it, creating a bulge of water (high tide).\n",
       "* **Centrifugal Force:**  On the opposite side of the Earth, the inertia of the water (its tendency to keep moving in a straight line) is greater than the Moon's gravitational pull. This creates another bulge of water (high tide) on the side farthest from the Moon.\n",
       "* **Sun's Influence:** The Sun also exerts a gravitational force on Earth, but its effect on tides is weaker than the Moon's because it's much farther away. However, when the Sun, Earth, and Moon are aligned (during new and full moons), their gravitational forces combine to produce higher high tides and lower low tides, known as **spring tides**.\n",
       "* **Neap Tides:** When the Sun, Earth, and Moon form a right angle (during the first and third quarter moons), the Sun's gravitational pull partially counteracts the Moon's pull. This results in lower high tides and higher low tides, known as **neap tides**.\n",
       "\n",
       "**Other Factors:**\n",
       "\n",
       "* **Earth's Rotation:** As the Earth rotates, different locations on Earth pass through the tidal bulges created by the Moon and Sun, experiencing high and low tides.\n",
       "* **Shape of Coastlines:** The shape of coastlines and the depth of the ocean floor can influence the height and timing of tides. Bays and estuaries can amplify tidal ranges, while islands and shallow water can create complex tidal patterns.\n",
       "* **Weather:**  Strong winds and changes in atmospheric pressure can also affect local tide heights.\n",
       "\n",
       "**In summary:**\n",
       "\n",
       "Tides are a complex interplay of gravitational forces from the Moon and Sun, Earth's rotation, and geographical factors. The Moon's gravitational pull is the dominant force, creating two tidal bulges on opposite sides of the Earth. The Sun's influence modifies the intensity of these tides, leading to spring and neap tides."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "contents = \"\"\"\n",
    "Explain why we have tides.\n",
    "I'm a scientist. Provide a detailed answer using bullet points.\n",
    "\"\"\"\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hV6ULv8KHkxO"
   },
   "source": [
    "You can ask closed questions:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "executionInfo": {
     "elapsed": 3798,
     "status": "ok",
     "timestamp": 1702337399959,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "dGk1uEPN0XXq",
    "outputId": "489716c2-c28b-4b0a-ab0b-c6523d8ed676",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "When were the previous and penultimate leap years?\n",
      "List 3 international competitions that took place during the penultimate one.\n",
      "Detail dates, cities, and venues.\n",
      "\n",
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Here's the information:\n",
       "\n",
       "* **Previous leap year:** 2020\n",
       "* **Penultimate leap year:** 2016\n",
       "\n",
       "**3 International Competitions in 2016:**\n",
       "\n",
       "1. **Summer Olympics**\n",
       "    * **Dates:** August 5 - August 21, 2016\n",
       "    * **City:** Rio de Janeiro, Brazil\n",
       "    * **Venues:** Various, including Maracanã Stadium (ceremonies, soccer), Olympic Aquatics Stadium (swimming, diving, synchronized swimming), Olympic Park (multiple sports)\n",
       "\n",
       "2. **UEFA European Championship (UEFA Euro 2016)**\n",
       "    * **Dates:** June 10 - July 10, 2016\n",
       "    * **City:** Various cities in France\n",
       "    * **Venues:** Stade de France (Saint-Denis), Parc des Princes (Paris), Stade Vélodrome (Marseille), and others\n",
       "\n",
       "3. **World Economic Forum (WEF) Annual Meeting**\n",
       "    * **Dates:** January 20 - 23, 2016\n",
       "    * **City:** Davos, Switzerland\n",
       "    * **Venue:** Davos Congress Centre"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "contents = \"\"\"\n",
    "When were the previous and penultimate leap years?\n",
    "List 3 international competitions that took place during the penultimate one.\n",
    "Detail dates, cities, and venues.\n",
    "\"\"\"\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SzmiMEq_HsNF"
   },
   "source": [
    "… as well as questions that are more open:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 521
    },
    "executionInfo": {
     "elapsed": 6829,
     "status": "ok",
     "timestamp": 1702337406783,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "HqcYe_w5BvLT",
    "outputId": "176f5e86-d787-46cb-eb43-47ffbf74a79d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "What came first, the chicken or the egg? Explain from 3 different perspectives.\n",
      "What do we call a \"chicken and egg\" problem? Give 1 example that can occur in education.\n",
      "\n",
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "## The Chicken or the Egg: A Multifaceted Riddle\n",
       "\n",
       "Here's the age-old question examined from three different perspectives:\n",
       "\n",
       "**1. The Evolutionary Biologist:**\n",
       "\n",
       "From this perspective, **the egg came first**.  Evolution is a gradual process. Long before there were chickens, there were chicken-like ancestors. These ancestors laid eggs, just like almost all other bird species. At some point, a mutation occurred in the genes of one of these eggs, and that mutation resulted in the first true chicken. \n",
       "\n",
       "**2. The Philosophical Linguist:**\n",
       "\n",
       "This perspective focuses on definitions. What constitutes a \"chicken egg\"? Is it any egg laid by a creature we call a chicken? Or is it an egg that contains a chicken embryo? If we define a \"chicken egg\" as one containing a chicken embryo, then **the chicken must have come first** to provide that genetic material.\n",
       "\n",
       "**3. The Practical Poultry Farmer:**\n",
       "\n",
       "A farmer might say, **\"Who cares? I need both!\"**  This perspective highlights the cyclical nature of the question.  Without a chicken, you can't get a chicken egg. But without a chicken egg, you can't get a chicken. It's a closed loop.\n",
       "\n",
       "## The \"Chicken and Egg\" Problem:\n",
       "\n",
       "This phrase describes a situation where it's impossible to determine which of two things came first because each one seems dependent on the other. It's a dilemma of causality.\n",
       "\n",
       "**Example in Education:**\n",
       "\n",
       "**Problem:** A student struggles with reading comprehension. They lack the vocabulary to understand complex texts, but they also struggle to expand their vocabulary because they can't comprehend the texts they read.\n",
       "\n",
       "**The \"Chicken and Egg\" Dilemma:**  Did the limited vocabulary cause the poor comprehension, or did the poor comprehension hinder vocabulary development? \n",
       "\n",
       "This type of problem highlights the interconnectedness of skills in education and the need for integrated approaches to address them."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "contents = \"\"\"\n",
    "What came first, the chicken or the egg? Explain from 3 different perspectives.\n",
    "What do we call a \"chicken and egg\" problem? Give 1 example that can occur in education.\n",
    "\"\"\"\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BoqqOTjfL6HX"
   },
   "source": [
    "## Reasoning on text\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1dEt9vXqmGN5"
   },
   "source": [
    "You can summarize and translate text:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "executionInfo": {
     "elapsed": 1899,
     "status": "ok",
     "timestamp": 1702337408678,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "jAbIQ5U3mGN5",
    "outputId": "d9183b50-3125-42b3-e6ba-dcd0c573767a",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Summarize the following text in English, in 3 short bullet points, only using the text.\n",
      "\n",
      "TEXT:\n",
      "- Les hommes naissent et demeurent libres et égaux en droits. Les distinctions sociales ne peuvent être fondées que sur l'utilité commune.\n",
      "- Le but de toute association politique est la conservation des droits naturels et imprescriptibles de l'homme. Ces droits sont la liberté, la propriété, la sûreté et la résistance à l'oppression.\n",
      "- Le principe de toute souveraineté réside essentiellement dans la Nation. Nul corps, nul individu ne peut exercer d'autorité qui n'en émane expressément.\n",
      "- La liberté consiste à pouvoir faire tout ce qui ne nuit pas à autrui : ainsi, l'exercice des droits naturels de chaque homme n'a de bornes que celles qui assurent aux autres membres de la société la jouissance de ces mêmes droits. Ces bornes ne peuvent être déterminées que par la loi.\n",
      "La loi n'a le droit de défendre que les actions nuisibles à la société. Tout ce qui n'est pas défendu par la loi ne peut être empêché, et nul ne peut être contraint à faire ce qu'elle n'ordonne pas.\n",
      "- La loi est l'expression de la volonté générale. Tous les citoyens ont droit de concourir personnellement ou par leurs représentants à sa formation. Elle doit être la même pour tous, soit qu'elle protège, soit qu'elle punisse. Tous les citoyens, étant égaux à ses yeux, sont également admissibles à toutes dignités, places et emplois publics, selon leur capacité et sans autre distinction que celle de leurs vertus et de leurs talents.\n",
      "- Nul homme ne peut être accusé, arrêté ou détenu que dans les cas déterminés par la loi et selon les formes qu'elle a prescrites. Ceux qui sollicitent, expédient, exécutent ou font exécuter des ordres arbitraires doivent être punis ; mais tout citoyen appelé ou saisi en vertu de la loi doit obéir à l'instant ; il se rend coupable par la résistance.\n",
      "- La loi ne doit établir que des peines strictement et évidemment nécessaires, et nul ne peut être puni qu'en vertu d'une loi établie et promulguée antérieurement au délit, et légalement appliquée.\n",
      "Tout homme étant présumé innocent jusqu'à ce qu'il ait été déclaré coupable, s'il est jugé indispensable de l'arrêter, toute rigueur qui ne serait pas nécessaire pour s'assurer de sa personne doit être sévèrement réprimée par la loi.\n",
      "- Nul ne doit être inquiété pour ses opinions, même religieuses, pourvu que leur manifestation ne trouble pas l'ordre public établi par la loi.\n",
      "\n",
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Here is a 3-bullet-point summary of the text:\n",
       "\n",
       "* **Fundamental Rights:** Men are born free and equal in rights, with liberty, property, security, and resistance to oppression as natural and inalienable rights.  Social distinctions should only serve the common good.\n",
       "* **The Rule of Law:**  Law, as an expression of the general will, should protect individual rights and freedoms. It applies equally to all citizens, ensuring fair treatment and protection from arbitrary arrest or punishment. \n",
       "* **Freedom of Opinion and Expression:**  Individuals have the right to hold and express their opinions, including religious ones, as long as they do not disrupt public order established by law."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "contents = \"\"\"\n",
    "Summarize the following text in English, in 3 short bullet points, only using the text.\n",
    "\n",
    "TEXT:\n",
    "- Les hommes naissent et demeurent libres et égaux en droits. Les distinctions sociales ne peuvent être fondées que sur l'utilité commune.\n",
    "- Le but de toute association politique est la conservation des droits naturels et imprescriptibles de l'homme. Ces droits sont la liberté, la propriété, la sûreté et la résistance à l'oppression.\n",
    "- Le principe de toute souveraineté réside essentiellement dans la Nation. Nul corps, nul individu ne peut exercer d'autorité qui n'en émane expressément.\n",
    "- La liberté consiste à pouvoir faire tout ce qui ne nuit pas à autrui : ainsi, l'exercice des droits naturels de chaque homme n'a de bornes que celles qui assurent aux autres membres de la société la jouissance de ces mêmes droits. Ces bornes ne peuvent être déterminées que par la loi.\n",
    "La loi n'a le droit de défendre que les actions nuisibles à la société. Tout ce qui n'est pas défendu par la loi ne peut être empêché, et nul ne peut être contraint à faire ce qu'elle n'ordonne pas.\n",
    "- La loi est l'expression de la volonté générale. Tous les citoyens ont droit de concourir personnellement ou par leurs représentants à sa formation. Elle doit être la même pour tous, soit qu'elle protège, soit qu'elle punisse. Tous les citoyens, étant égaux à ses yeux, sont également admissibles à toutes dignités, places et emplois publics, selon leur capacité et sans autre distinction que celle de leurs vertus et de leurs talents.\n",
    "- Nul homme ne peut être accusé, arrêté ou détenu que dans les cas déterminés par la loi et selon les formes qu'elle a prescrites. Ceux qui sollicitent, expédient, exécutent ou font exécuter des ordres arbitraires doivent être punis ; mais tout citoyen appelé ou saisi en vertu de la loi doit obéir à l'instant ; il se rend coupable par la résistance.\n",
    "- La loi ne doit établir que des peines strictement et évidemment nécessaires, et nul ne peut être puni qu'en vertu d'une loi établie et promulguée antérieurement au délit, et légalement appliquée.\n",
    "Tout homme étant présumé innocent jusqu'à ce qu'il ait été déclaré coupable, s'il est jugé indispensable de l'arrêter, toute rigueur qui ne serait pas nécessaire pour s'assurer de sa personne doit être sévèrement réprimée par la loi.\n",
    "- Nul ne doit être inquiété pour ses opinions, même religieuses, pourvu que leur manifestation ne trouble pas l'ordre public établi par la loi.\n",
    "\"\"\"\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ri8Yx5vtE22b"
   },
   "source": [
    "… as well as ask for ideas:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 498
    },
    "executionInfo": {
     "elapsed": 4657,
     "status": "ok",
     "timestamp": 1702337413331,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "k5pXqmNJmGN5",
    "outputId": "a8e71ad6-f437-4e18-c557-b62fa38b1c9a",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Provide an outline in 5 key points for a \"chocolate in the world\" presentation.\n",
      "One part must be about its origin in Mexico (my teacher has family there).\n",
      "The last one will be a tasting with everybody in the classroom.\n",
      "\n",
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Here's an outline focusing on chocolate in the world, highlighting its Mexican origins and ending with a tasting:\n",
       "\n",
       "## Chocolate: A Global Journey from Bean to Bar\n",
       "\n",
       "**1. Chocolate's Mexican Roots:**\n",
       "    * **The Olmec, Maya, and Aztec Connection:** Trace the historical use of cacao by these civilizations, focusing on its importance in rituals, currency, and as a bitter beverage.\n",
       "    * **Xocolatl: The Original Drink of the Gods:**  Describe the traditional preparation methods and ingredients used, contrasting them with modern chocolate.\n",
       "    * **Highlight a personal anecdote (from your teacher's family):** Share a story or tradition related to cacao or chocolate from their Mexican heritage. This adds a personal touch and connection for your teacher!\n",
       "\n",
       "**2.  Chocolate Conquers Europe:** \n",
       "    * **Spanish Arrival and the Journey Across the Atlantic:** Explain how cacao beans were brought to Europe and the initial skepticism surrounding them.\n",
       "    * **Transformation in the European Courts:** Detail the evolution of chocolate from a bitter drink to a sweetened, luxurious treat favored by royalty.\n",
       "    * **The Spread of Chocolate Fever:**  Discuss how chocolate gradually became popular across Europe, leading to the development of early chocolate houses.\n",
       "\n",
       "**3. The Industrial Revolution and Chocolate for the Masses:**\n",
       "    * **Mechanization and Mass Production:** Explain how inventions like the steam engine and the chocolate press revolutionized chocolate making, making it more affordable. \n",
       "    * **The Rise of Chocolate Companies:** Discuss the emergence of famous chocolate companies and the development of iconic chocolate bars we know today.\n",
       "    * **Marketing and the Creation of Desire:**  Highlight how advertising shaped chocolate consumption and turned it into a global commodity. \n",
       "\n",
       "**4.  Chocolate Today: A World of Flavors and Ethical Considerations:**\n",
       "    * **Bean to Bar: The Craft Chocolate Movement:** Explore the growing interest in single-origin chocolate and artisan production methods.\n",
       "    * **The Dark Side of Chocolate: Sustainability and Ethical Sourcing:**  Address the issues of fair trade, child labor, and environmental impact within the cocoa industry.\n",
       "    * **A World of Flavors: Modern Innovations and Trends:** Discuss contemporary chocolate creations, flavor pairings, and the expanding world of chocolate experiences.\n",
       "\n",
       "**5.  Chocolate Tasting Experience:**\n",
       "    * **Engage the Senses:** Prepare a selection of chocolate samples representing different origins, percentages of cacao, and flavor profiles (e.g., dark, milk, white, flavored). \n",
       "    * **Tasting Notes and Discussion:** Guide the class through a structured tasting, encouraging them to identify aroma, texture, and flavor nuances.\n",
       "    * **Connect the Experience to the Presentation:**  Relate the tasting back to the historical and cultural journey of chocolate discussed throughout the presentation.\n",
       "\n",
       "**Bonus:** Visual aids like maps, images of historical artifacts, and even short video clips can significantly enhance your presentation."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "contents = \"\"\"\n",
    "Provide an outline in 5 key points for a \"chocolate in the world\" presentation.\n",
    "One part must be about its origin in Mexico (my teacher has family there).\n",
    "The last one will be a tasting with everybody in the classroom.\n",
    "\"\"\"\n",
    "print_contents(contents)\n",
    "\n",
    "# For more creative responses, let's increase the level of randomness with a higher temperature.\n",
    "# Successive requests will likely return different answers.\n",
    "temperature = 1.0\n",
    "\n",
    "response = generate_content(model, contents, temperature)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zf25rsTSkdXu"
   },
   "source": [
    "You can also ask for text corrections:\n",
    "\n",
    "Below, examples are provided to help the model generate responses with the expected structure and formatting. This is also called few-shot prompting.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 541
    },
    "executionInfo": {
     "elapsed": 5001,
     "status": "ok",
     "timestamp": 1702337418328,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "jgKEDKVkL8Ub",
    "outputId": "b3c0b14f-e42d-40a1-bf71-bd764acedf4f",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "I'm a non-native English speaker.\n",
      "Check whether the following sentences are correct.\n",
      "When incorrect, provide a correction and an explanation.\n",
      "Use the same structure as in the given examples.\n",
      "\n",
      "EXAMPLES:\n",
      "- **Hi!**\n",
      "  - Status: ✔️\n",
      "- **Your my best freind!**\n",
      "  - Status: ❌\n",
      "  - Correction: **You're my best friend!**\n",
      "  - Explanation:\n",
      "    - \"**Your**\" is incorrect. It seems that you meant \"You're\", which is the short form of \"You are\".\n",
      "    - \"**freind**\" is misspelled. The correct spelling is \"**friend**\".\n",
      "\n",
      "SENTENCES:\n",
      "- They're twins, isn't it?\n",
      "- I assisted to the meeting.\n",
      "- You received important informations.\n",
      "- I digged a hole in the ice and saw lots of fishes.\n",
      "- That's all folks!\n",
      "\n",
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Here are the sentences you provided, checked for correctness:\n",
       "\n",
       "- **They're twins, isn't it?**\n",
       "  - Status: ❌\n",
       "  - Correction: **They're twins, aren't they?**\n",
       "  - Explanation: When using tag questions (the little \"question tag\" at the end of a sentence), the verb in the tag needs to agree with the subject of the main sentence. Since the subject is \"They\", the correct tag is \"aren't they?\".\n",
       "\n",
       "- **I assisted to the meeting.**\n",
       "  - Status: ❌\n",
       "  - Correction: **I attended the meeting.**\n",
       "  - Explanation: While \"assist\" means to help, it's not typically used with \"meeting\" in this way. \"Attend\" is the more natural choice, meaning you were present at the meeting.\n",
       "\n",
       "- **You received important informations.**\n",
       "  - Status: ❌\n",
       "  - Correction: **You received important information.**\n",
       "  - Explanation: \"Information\" is an uncountable noun in English, meaning it doesn't have a plural form. We don't add an \"s\" at the end.\n",
       "\n",
       "- **I digged a hole in the ice and saw lots of fishes.**\n",
       "  - Status: ❌\n",
       "  - Correction: **I dug a hole in the ice and saw lots of fish.**\n",
       "  - Explanation: \n",
       "    - The past tense of \"dig\" is \"dug\", not \"digged\".\n",
       "    - Similar to \"information\", \"fish\" is often used as an uncountable noun, especially when referring to multiple fish of different species. You can use \"fishes\" if you want to emphasize different types of fish, but \"fish\" is more common in this context.\n",
       "\n",
       "- **That's all folks!**\n",
       "  - Status: ✔️"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "contents = \"\"\"\n",
    "I'm a non-native English speaker.\n",
    "Check whether the following sentences are correct.\n",
    "When incorrect, provide a correction and an explanation.\n",
    "Use the same structure as in the given examples.\n",
    "\n",
    "EXAMPLES:\n",
    "- **Hi!**\n",
    "  - Status: ✔️\n",
    "- **Your my best freind!**\n",
    "  - Status: ❌\n",
    "  - Correction: **You're my best friend!**\n",
    "  - Explanation:\n",
    "    - \"**Your**\" is incorrect. It seems that you meant \"You're\", which is the short form of \"You are\".\n",
    "    - \"**freind**\" is misspelled. The correct spelling is \"**friend**\".\n",
    "\n",
    "SENTENCES:\n",
    "- They're twins, isn't it?\n",
    "- I assisted to the meeting.\n",
    "- You received important informations.\n",
    "- I digged a hole in the ice and saw lots of fishes.\n",
    "- That's all folks!\n",
    "\"\"\"\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ach9JtNikxVz"
   },
   "source": [
    "… as well as ask for elaborate tasks on text and languages:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 267
    },
    "executionInfo": {
     "elapsed": 4016,
     "status": "ok",
     "timestamp": 1702337422329,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "P0xdsfQDE22b",
    "outputId": "0b74d75b-fae7-4ac2-dc3c-a03f49802620",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Translate the text into the following languages.\n",
      "\n",
      "TEXT:\n",
      "Hello folks! I hope you're all doing well. Let's get this workshop started!\n",
      "We'll stick to English because, actually, I can't speak all those languages.\n",
      "\n",
      "LANGUAGES:\n",
      "German, French, Greek, Bulgarian, Japanese\n",
      "\n",
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "## Translations:\n",
       "\n",
       "**German:**\n",
       "\n",
       "Hallo zusammen! Ich hoffe, es geht euch allen gut. Lasst uns mit dem Workshop beginnen!\n",
       "Wir bleiben beim Englischen, denn tatsächlich spreche ich nicht alle diese Sprachen.\n",
       "\n",
       "**French:**\n",
       "\n",
       "Bonjour tout le monde ! J'espère que vous allez tous bien. Commençons cet atelier !\n",
       "Nous allons nous en tenir à l'anglais car, en réalité, je ne parle pas toutes ces langues.\n",
       "\n",
       "**Greek:**\n",
       "\n",
       "Γεια σας σε όλους! Ελπίζω να είστε όλοι καλά. Ας ξεκινήσουμε αυτό το εργαστήριο!\n",
       "Θα μιλάμε αγγλικά γιατί, στην πραγματικότητα, δεν μιλάω όλες αυτές τις γλώσσες.\n",
       "\n",
       "**Bulgarian:**\n",
       "\n",
       "Здравейте на всички! Надявам се, че сте добре. Нека да започнем този семинар!\n",
       "Ще се придържаме към английски, защото всъщност не говоря всички тези езици.\n",
       "\n",
       "**Japanese:**\n",
       "\n",
       "皆さん、こんにちは！お元気でお過ごしでしょうか？それでは、ワークショップを始めましょう！\n",
       " 実は、私はこれらの言語をすべて話せるわけではないので、英語で進めさせていただきます。"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "contents = \"\"\"\n",
    "Translate the text into the following languages.\n",
    "\n",
    "TEXT:\n",
    "Hello folks! I hope you're all doing well. Let's get this workshop started!\n",
    "We'll stick to English because, actually, I can't speak all those languages.\n",
    "\n",
    "LANGUAGES:\n",
    "German, French, Greek, Bulgarian, Japanese\n",
    "\"\"\"\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 383
    },
    "executionInfo": {
     "elapsed": 4647,
     "status": "ok",
     "timestamp": 1702337426964,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "LSNoxDSeZWw-",
    "outputId": "f8bf5a60-60c7-4599-8022-ff2a42f0fc06",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "I'm a non-native English speaker and made mistakes in the following sentences.\n",
      "Guess my native language (if there are several possibilities, here is a hint: I like cheese).\n",
      "Explain why these are typical mistakes.\n",
      "\n",
      "SENTENCES:\n",
      "- They are twin sisters, isn't it?\n",
      "- I assisted to the meeting.\n",
      "- I saw lots of fishes.\n",
      "\n",
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Based on the errors in your sentences, your native language could be **French**. \n",
       "\n",
       "Here's why these mistakes are common for French speakers:\n",
       "\n",
       "* **\"They are twin sisters, isn't it?\"**  In French, tag questions (like \"n'est-ce pas?\" ) always use the masculine singular form of the verb, regardless of the subject. This leads to direct translation errors like this one.\n",
       "* **\"I assisted to the meeting.\"**  The French verb \"assister\" translates more directly to \"to attend\" in this context. French speakers often use \"assist\" with a preposition (\"to\") when they should use \"attend\" directly.\n",
       "* **\"I saw lots of fishes.\"**  In French, the word \"poisson\" is used for both singular and plural when referring to fish as a species. This often leads to French speakers using \"fishes\" incorrectly in English when they mean multiple fish of the same kind. \n",
       "\n",
       "Let me know if you'd like more examples or explanations!"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "contents = \"\"\"\n",
    "I'm a non-native English speaker and made mistakes in the following sentences.\n",
    "Guess my native language (if there are several possibilities, here is a hint: I like cheese).\n",
    "Explain why these are typical mistakes.\n",
    "\n",
    "SENTENCES:\n",
    "- They are twin sisters, isn't it?\n",
    "- I assisted to the meeting.\n",
    "- I saw lots of fishes.\n",
    "\"\"\"\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AIl7R_jBUsaC"
   },
   "source": [
    "## Reasoning on numbers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hm61coMZJX-o"
   },
   "source": [
    "> Note: Depending on inputs and parameters, large language models can hallucinate and generate inaccurate outputs, including math operations. As a best practice, consider using prompts with step-by-step instructions to reduce hallucinations, or use a calculator library for more advanced math. You can also ask Gemini to generate problem-solving code (see [Doing Math with Large Language Models](https://medium.com/google-cloud/doing-math-with-large-language-models-69d94c8b0590)).\n",
    "\n",
    "You can ask about real life problems:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 498
    },
    "executionInfo": {
     "elapsed": 3792,
     "status": "ok",
     "timestamp": 1702337430752,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "nmvIJfDUmGN6",
    "outputId": "44b38862-e1b1-4d5b-a8f1-af360ef66bc9",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Patricia is a good runner and runs at an average 12 km/h.\n",
      "- On Monday, she ran for 1.5 hour. What distance did she run?\n",
      "- On Tuesday, she ran for 21 km. How long did she run?\n",
      "- On Wednesday, she ran for 150 minutes. What distance did she run?\n",
      "- Next, she plans to do a marathon (42 km). How long should it take?\n",
      "- To complete a marathon in 3 hours, how much faster does she need to run?\n",
      "\n",
      "Detail the answers step by step.\n",
      "\n",
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Here's the breakdown of Patricia's runs:\n",
       "\n",
       "**Monday:**\n",
       "\n",
       "* **Speed:** 12 km/h\n",
       "* **Time:** 1.5 hours\n",
       "* **Distance = Speed x Time**\n",
       "* **Distance = 12 km/h * 1.5 h = 18 km**\n",
       "\n",
       "**Tuesday:**\n",
       "\n",
       "* **Speed:** 12 km/h\n",
       "* **Distance:** 21 km\n",
       "* **Time = Distance / Speed**\n",
       "* **Time = 21 km / 12 km/h = 1.75 hours**\n",
       "\n",
       "**Wednesday:**\n",
       "\n",
       "* **Speed:** 12 km/h\n",
       "* **Time:** 150 minutes (Let's convert this to hours: 150 minutes / 60 minutes/hour = 2.5 hours)\n",
       "* **Distance = Speed x Time**\n",
       "* **Distance = 12 km/h * 2.5 h = 30 km**\n",
       "\n",
       "**Marathon (42 km):**\n",
       "\n",
       "* **Speed:** 12 km/h\n",
       "* **Distance:** 42 km\n",
       "* **Time = Distance / Speed**\n",
       "* **Time = 42 km / 12 km/h = 3.5 hours**\n",
       "\n",
       "**Marathon in 3 hours:**\n",
       "\n",
       "* **Target Time:** 3 hours\n",
       "* **Distance:** 42 km\n",
       "* **Required Speed = Distance / Time**\n",
       "* **Required Speed = 42 km / 3 h = 14 km/h**\n",
       "\n",
       "**To finish the marathon in 3 hours, Patricia needs to run 2 km/h faster (14 km/h - 12 km/h = 2 km/h).**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "contents = \"\"\"\n",
    "Patricia is a good runner and runs at an average 12 km/h.\n",
    "- On Monday, she ran for 1.5 hour. What distance did she run?\n",
    "- On Tuesday, she ran for 21 km. How long did she run?\n",
    "- On Wednesday, she ran for 150 minutes. What distance did she run?\n",
    "- Next, she plans to do a marathon (42 km). How long should it take?\n",
    "- To complete a marathon in 3 hours, how much faster does she need to run?\n",
    "\n",
    "Detail the answers step by step.\n",
    "\"\"\"\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_nZwoSe0mGN6"
   },
   "source": [
    "… or about classical problems:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 160
    },
    "executionInfo": {
     "elapsed": 1519,
     "status": "ok",
     "timestamp": 1702337432266,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "R6dmn9_Q0Be0",
    "outputId": "c574f8a2-6ebd-4e0d-cf5c-af456f93a632",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "I just borrowed 1,000 EUR from a friend.\n",
      "We agreed on a 4.5% simple interest rate, based solely on the initial amount borrowed.\n",
      "I want to know how much I'll have to refund in 1, 2, or 3 years.\n",
      "Present the results in a recap table.\n",
      "\n",
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "## Loan Repayment Recap (1000 EUR at 4.5% Simple Interest)\n",
       "\n",
       "| Time Period | Interest Accrued | Total to Refund |\n",
       "|---|---|---|\n",
       "| 1 Year | 45 EUR (1000 * 0.045) | 1045 EUR |\n",
       "| 2 Years | 90 EUR (1000 * 0.045 * 2) | 1090 EUR |\n",
       "| 3 Years | 135 EUR (1000 * 0.045 * 3) | 1135 EUR |\n",
       "\n",
       "**Explanation:**\n",
       "\n",
       "* **Simple interest** means you only pay interest on the original amount borrowed (1000 EUR), not on any accumulated interest.\n",
       "* **Interest per year:** 1000 EUR * 4.5% = 45 EUR\n",
       "* **Total to refund:** Original loan amount + total interest accrued"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "contents = \"\"\"\n",
    "I just borrowed 1,000 EUR from a friend.\n",
    "We agreed on a 4.5% simple interest rate, based solely on the initial amount borrowed.\n",
    "I want to know how much I'll have to refund in 1, 2, or 3 years.\n",
    "Present the results in a recap table.\n",
    "\"\"\"\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dvdNYQKJE22c"
   },
   "source": [
    "## Reasoning on a single image\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UFW-KLIjmGN7"
   },
   "source": [
    "You can ask for an image description:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "executionInfo": {
     "elapsed": 6317,
     "status": "ok",
     "timestamp": 1702337438579,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "aFvAEetIcSA7",
    "outputId": "d4efe8d5-ed41-44e0-eb81-9c26d284738d",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "Describe this image in a short sentence:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "A wooden abacus with colorful beads sits on a white background."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prompt = \"Describe this image in a short sentence:\"\n",
    "# Image by Crissy Jarvis on Unsplash: https://unsplash.com/photos/cHhbULJbPwM\n",
    "image_abacus = load_image_from_url(\n",
    "    \"https://unsplash.com/photos/cHhbULJbPwM/download?w=600\"\n",
    ")\n",
    "\n",
    "contents = [prompt, image_abacus]\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jjqqXuauE22c"
   },
   "source": [
    "… or ask specific questions:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 994
    },
    "executionInfo": {
     "elapsed": 8531,
     "status": "ok",
     "timestamp": 1702337447098,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "H0SOqKjyi1tH",
    "outputId": "6dfb3763-facf-4ffb-c3e0-5e2599efecb6",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Answer the following questions about this image.\n",
      "Return the results as a JSON list containing \"question\" and \"answer\" key pairs.\n",
      "\n",
      "QUESTIONS:\n",
      "- What does the image show?\n",
      "- How does it work?\n",
      "- When was it invented?\n",
      "- What's the name of this object in French, Italian, Spanish, Dutch, and German?\n",
      "- What are the most prominent colors in the image?\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "```json\n",
       "[\n",
       " {\n",
       "  \"question\": \"What does the image show?\",\n",
       "  \"answer\": \"The image shows an abacus.\"\n",
       " },\n",
       " {\n",
       "  \"question\": \"How does it work?\",\n",
       "  \"answer\": \"An abacus is a manual calculating device. It consists of beads that can be moved along rods or wires. The beads represent digits, and their positions determine the value of a number.\"\n",
       " },\n",
       " {\n",
       "  \"question\": \"When was it invented?\",\n",
       "  \"answer\": \"The abacus was invented around 2400 BC in Mesopotamia.\"\n",
       " },\n",
       " {\n",
       "  \"question\": \"What's the name of this object in French, Italian, Spanish, Dutch, and German?\",\n",
       "  \"answer\": \"The name of this object in different languages is:\\n\\n- French: boulier\\n- Italian: abaco\\n- Spanish: ábaco\\n- Dutch: telraam\\n- German: Abakus\"\n",
       " },\n",
       " {\n",
       "  \"question\": \"What are the most prominent colors in the image?\",\n",
       "  \"answer\": \"The most prominent colors in the image are brown, red, green, blue, and yellow.\"\n",
       " }\n",
       "]\n",
       "```"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prompt = \"\"\"\n",
    "Answer the following questions about this image.\n",
    "Return the results as a JSON list containing \"question\" and \"answer\" key pairs.\n",
    "\n",
    "QUESTIONS:\n",
    "- What does the image show?\n",
    "- How does it work?\n",
    "- When was it invented?\n",
    "- What's the name of this object in French, Italian, Spanish, Dutch, and German?\n",
    "- What are the most prominent colors in the image?\n",
    "\"\"\"\n",
    "\n",
    "contents = [prompt, image_abacus]\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I9SIjHnbmGN8"
   },
   "source": [
    "Your specific questions can have follow-up questions:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 622
    },
    "executionInfo": {
     "elapsed": 6669,
     "status": "ok",
     "timestamp": 1702337453762,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "bGIsUZFAmGN8",
    "outputId": "69645032-83ea-4748-ff61-10577bc7dc87",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- What expression can be read in this image? How is it presented?\n",
      "- What is the opposite expression?\n",
      "- What is a recommendation, starting with this expression, a teacher could give his students for an exam?\n",
      "- With the opposite expression?\n",
      "\n",
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "- The expression is \"IN THE BEGINNING\". It is presented in the form of a word puzzle, using letter tiles arranged in a pyramid shape.\n",
       "\n",
       "- The opposite expression is \"IN THE END\".\n",
       "\n",
       "- **Using \"IN THE BEGINNING\":** \"In the beginning of your exam, take a few deep breaths to calm your nerves and quickly skim through the questions to get an overview.\"\n",
       "\n",
       "- **Using \"IN THE END\":** \"In the end, what matters most is that you tried your best. Don't dwell on mistakes, learn from them and move forward.\""
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Image by Brett Jordan on Unsplash: https://unsplash.com/photos/E1por_SGvJE\n",
    "image_tiles = load_image_from_url(\n",
    "    \"https://unsplash.com/photos/E1por_SGvJE/download?w=600\"\n",
    ")\n",
    "prompt = \"\"\"\n",
    "- What expression can be read in this image? How is it presented?\n",
    "- What is the opposite expression?\n",
    "- What is a recommendation, starting with this expression, a teacher could give his students for an exam?\n",
    "- With the opposite expression?\n",
    "\"\"\"\n",
    "\n",
    "contents = [image_tiles, prompt]\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "trT6xm249rqo"
   },
   "source": [
    "Information can have multiple forms. It can be objects, printed text, handwritten text, and more:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 7922,
     "status": "ok",
     "timestamp": 1702337461673,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "VKsArAoJmGN8",
    "outputId": "2209af8b-8d05-4ba4-d55f-49c16e7d9092",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Follow the instructions.\n",
      "Write math expressions in LaTex.\n",
      "Use a table with a row for each instruction and its result.\n",
      "\n",
      "INSTRUCTIONS:\n",
      "- Extract the formula.\n",
      "- What is the symbol right before Pi? What does it mean?\n",
      "- Is this a famous formula? Does it have a name?\n",
      "- Why is it special?\n",
      "- Extract the caption.\n",
      "- What color is it?\n",
      "- What color is the formula?\n",
      "- What's the object in the bottom?\n",
      "- What can you conclude about the object?\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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EE5l+Gxzzu57n9gPu9O3BJO4XUkCAUHz9p3R3i1HetQV1JU2DTEd7qRU1LGH7SYnAEdUbcOAJzknIyfJeRe/yaqiOSCust8pa+4HL6qO7QkRTSE5LgWZIG/Bz79l9MBR2Fdj57q/CnX95sdDZay96Zs1qoZ2VVNDa6R3yStzh+cNOck8k5O6SzeAV8jvM1deNbyyTzAiSqpoXGqcDsQJHkluRsSN8bcbL6FA342UPKbHDb1+TtZ5qiGSxXu5WqGEiVkGBM0TtAAlBJBBOAT542xsrm+EGp5bt/SGr8Qq3+lLGiCOsipWiMQb/AKMtzvuSfL0PI7aQCEvCbHE7h4IVF5v8dw1Dq2vuEVRGxlziELYvtgjdmNvynDWjA5BORkY7V1nhnrPSk0bfCvVRp7W4uL7ddZPiRw+XwyWO257A8bldv7JCNiSpsfM94094gyeKWmI9XajoKepvFPUW9tZaIR1CFret8Zyxoyc7HfGV6cHgxqF5t+mam40rdJ22qnraG5U56a+EvB6WY4+9hxx5bOGwG7eLn9W1l4X1jR8zL6afOe0rAD/BdPa3YDjZXY4lcvCzWsV2or/atdmo1FSxupRPW0wa11Odww4Dt8kk5Bztxjf1oLN4wDpEurtOdPdwoMn/APmurkdsKAYymxr2lrfqCjic7Ut+iuszhgNgomU8bd+cj5icew9F75Geydo8yMIHlQIQScJ49muGOyUq6IYB9lJWGK/cJSAVcQOyUt+i4S3AjsqZwftUGeA15H4AfzV4wCqZRmpj9I3n97VY6yA3KPCCccDzXdkAN0wGxUR7hBAnG6UDdN7IABhyJ5R77KbIB78KDhEgY2Qx6IIG+ifJxgnbyQaeco4AQEDyCbZAcI8D1QL9EwQTbYQTGMJggPvZ7KHONggIRPkgj5oMekq6atjfJSTxTsjkdC90bg4Ne04c047gggjsr91zHRb/AOj3jBq/TeCyjukbL/SNPAe49E+PQuAP0XTTnDiBnHZAUCtL0Hraq1HX1tsvOnLlYLvSRiV8NUOqORhOOpkgGDv2/ed8br6IAFCiFPdAuUOQmUAQAA4S+6c9sc4S4QK5Kw7DqGd+E58lWRsdsYQcbv8ApnXFw1vpimq2U1ysFqvIucdzMrWTMizn4UjCfmLeA4DcAZXZWgfrKAbeqc532QKeNkCMhO0Y374Q6cA/ggVuMZz9FCeccoYA9VCRghAOVZF3zzhVgDurIeSAUkI7jhKQMAlNvjdKXb8ZXnluEHbzVMuDUM52jd/Fv+itznAzlUzZ+0EHtH/MLVf8klBunaO6radzsrGDZdmTBQbqcKBAWnuRhOBkpVAcFA3soCAERhB3CA42yg0Z+qZhwDlAEgZQRuBsRkpu6AIyj3QHKLuAgNuyouFT9joqmq+FLMIInS/Dhb1Pf0gnpaO7jjAHmg51e/E+tbqK42fSWkLpqKa3SfBq6iF4jhjkxuzqIOSO/Coj8Wqm1ljtb6LvunqR56TXOaKiBm+PnLRlo35wVzzTPjJQ6PuGphW2G8m0XC5SXCjlNN8F7XygF8T+o42IODk5A4XtjxC1J4iW+SmsgsGmLLUsdFNW3G4RSzlhyHBsWRjbbcfVXQ3e8+MGmrTd6mhfBeauOl6Pj1tDRGemi6mhwy9p/ZIOwW4af1LZtRWYXWzXKnqreAS6ZrsCPAyQ/OC0gcg4XF9O6msXhLf5bdHdI7lpasgpzJW00onkoqljBGTK1mT0PDW4IzgjG6xdPXqwa08U9U2XTLPsti1BY5IHztjdFHU1TOZWsONw1+DwT0knlND6IaQ4Za4EHcEcFc68QKzxJptQRs0RHp+qt8lOD9nrJOmdrwT1P3Iy3do2XLmeL1y0jabBbLj0i7WB5tt5tEo/S1keGiKeB+N8AZxkZzwRgrUr1r+11HiJWa0sdbNVX2C6UwoKMxyD7VQGHokiAwQHZJ2PrjKaGxaxtnihFrPSl41FdrTaa6sqDaKart7Q4wiQE9L/AJcEZzjc7krp1L4VXp7uu6+JerJnk5/q0xgA+nU7+C0PxT8W9N6q0xbfzTHcxcrZc6W5vgmo3NEQifhwe7gfe588ea6V4x6vm0pS6XvNLVFtr/ObHVzYsOM1K5hBIHdo6mnbv0+iDb9L2N2n7fJSOu91unXJ8T4tyqPjSN2A6QcDA2zj1K9hfOuodeM1Nrq9Ch8TodNWK200DqB9OA8Vj3s6nkg4L+k/KW7ngY5zS7xj1HbqbR1fUwm40/2aqmvMUEHQ+SnbM2KOqDcZYDyOATntw0PpHsp2WjUXizoOroo6pmq7VGx7eromm+HI30LHbg+iqi8YvD6Wf4I1Vbw7zd1tb/xFuP3qQN7BR7LWqDXekrhJ00eprNM7ybWMH8SthpaiCsiD6SeGoYf1oXh4/EILeyBGRuj3wlPCAd1DvlQc7psBBS0c5O6fO45TFowc4St225QFvmUrsqweqrQKOAMbqNAySnOANvJL32QKNycK2DZyrAIVlPnq3UFbxuQQkLcnlXybjJVS4y2qaR2O4Krl/wDmCQdugfxKcDACrl/tTvv0j+JWq9ZEcbJ2cKpp39FawDC6IZEcoIqgjlH6JWpslAW+ah7BAFEg5Bygblu6gHy4OEDwcJmjZAvOx5TYwo7z7ojf3QRTG+VON1OcIKLnRUt0opKS5U8VXSSjD4Z2B7He4K0d3g34fPn+KdL0IP7LXSBv4dWF0HkKEYKDxrHpWwWKmmprPZ6CjglHTKyKEYkHk7PI9CtF8Too7Drjw0vNPGyGkp7hLbJGxsDWMbUR9I2Hq1dU2yuaflG0c9R4U3Kroi5tXbJoLjE5p3Y6OQZcPZpcforA3yps9tqLhDXVNtopq+LAjqZKdjpWY4w8jI/FV01htFJV/aqS0W6Cq6i748VLGx/UeT1AZyfNZNormXO1UNfGQWVUEc4x5OaHfzWV39FBhXi00d5tVZbrjAyajq43RTRnbraedxv9exWm6N8KNP6ZqZKgy193lNOaSP8AOkwnbDAeY2NwGhp2zt27LoHfCmE2OV6t8CNE6giaaWgNlqmuLhNb8N6s9nMOQR7YWxeH3h3aNEw1TqOSrrq+rDW1VbWyfEllAGzfIN9P4rcckHbhHOVRp8/hjoiaqdUy6VtBmcclwpwBn2G37lm/0G0mIfhDTFj+Hxj7BF/Hpytk4UPkmxo9Z4U6ErM/H0raifNkZZ/0kLzm+Cug2TiWCzzU72nLTBXTx9J8xh+xXR8YUI3U2FgjbBBHEzq6I2ho6nFxwBgZJ3J9SmOFBtnuhgZCAEog9QxwQjgZSt8wgg2CDSR25TEhTAG6CDPugcZ4ROSEpJG+NkA2wcqewUz+KUZ3QEnATwn5wqtzlPCPmQM842wkcCE0h3wUCTgcLhLbHB81XLvI7/CP5qwbKonMkp8g3+a1XrKNburGpG7EJwO66oOMJvNBRAW47puUvbKZp6sAbIIdgpjOCpvg7KN5QMRnhQAh2OyhGCDhNnGUB7oN3QJw0n6ojcAjyQQcIoDyOybt5oDwjyRlDfZEjz4QHZVVVPDV00tNVxRz00zDHJFI0Oa9pGCCDyCFbsgSMpoVU9PDSU8UFNEyGCFojjjYAGsaBgNA7ABXc8FLwfQpk0Ig7jZHHkh/FBBk9kcqD3UaQkA8pT5pjgDKA49FQSO5QI2RHKg4UAKGd0e26GEEG6Bb5KDnCbG6Cv3CYY+qmPNQIJ94nB4QwScHdFv3kSdxhAuNsYSbd+6szuq9iTnKCfrbj8E0Qw8bqs7lNH99QWPbknsq+ntvhXvGcqt3k1cZ60w2uycbpP8AeSj1H8P/AFTNdxsFXn55M/t/+VqtOkrAQOEwKrHG6cLsyIKOcgqI8oIoFEEFgPmURzkKscLzJdRWSKd8Et6tUc7HFjo31sQc1w5BBdkH0QeyDtgqcEBecy7W55AZcaFxIyAKlhyPPlZX2uAjqbUQluM5EjSMfigvGM+ijRhoGeFix11HJkMrKZ3SMnpmacfvVM16tUEgZNdbdG8/qvqo2k/QlB6I5RXizap0/D1/FvlraWbOH2thI+gKx5db6WimdDJqG1iVoJcz7Q0kY5z7INjBxsjyVp9T4laNp+r4uo6D5cE9Ly4b8cBU/wC1LR7dxd2vBJGWwvw0jsTjAPofMeYQbueFBzlaLH4q6XnIFLNX1GPvfCoZHdHqcDj+HfCWTxQtAja6K2ahlcTjobbJMgfteWP3+iDe3bJs/VaFH4jwy9PwdM6oka5/QCLe7GU0muLu6JrqbQ2oHPLsFkrAzA7nOCEG98KHkLR/6VamkeGQaIres9IzLUsY3cEnJPYbJX3/AFu/LodGxMa3BDZa+Il3pkPGPfdBvJIGMItGxWjiu8QXxu/7DsLHEHpDqx3y84zjO/GcfiqzJ4kvZgU2nInfKer4z3Y8xjG/7ldDfXHbZTsuf1EXiXKXmGfTlPnZrQ17seu4P/seqd1t8RXf/frGwAYBFKTk/tfd29k0N8BQzg+i0COzeIwbk6otDnZ70Zxj/hVX5h8RjJvrC3BhxnFEDg98fLx6H13TQ6LyEOFzaTT3iS7HTrW3tAcScUAzjsPu79vJCW0+KULA6n1RYJ3NxhktCW9fuQNkHSuVADlce1PqrxC0TY57rqFuk6qjicB1MklikeTw1rcYJ5P0J7LefDPV0WuNHUV8hg+zGYvjkh6+oMexxacHbIPI2HKmhtPKryQm9UqBuUAds5U233S+6AnYKvq905GRlLscIFbgk+SaJwDgEpGM78IxfeA8igyXOG/llVdQztwmlGSSFUT3OwXCetMUYAPCqO7nnj5v5BXYGxJ3VJPzP9XlWnSTsOycbKph3Vo4XZkwTJGp0BQTdkrkHmWu+0Vxu13t9JK51VapY4qkdOA1z2B7cHvt+8Lw6vww0PWVM9TVaWtctRO8ySPdGcucTknnkleXoj9B4ueI9KeZTb6sDPYwkZXRhk8pwc7k8FfDyWRz3aap2lxB6WTzNb9AH4CaHwZ0HFtFYnMB5Da6pAcPIj4m49CuhjZMAmxz+Pwd0HGS4afidnOQ6omcD7gvWfF4XaHihMbdL2wtJOS5hcTnzJOcei3E7FEd8psarT+HejYOn4Wl7OA0YbmmDsDOe/qs+LSOm42MYzT1oa2MjpH2KPb9yN5qr3T3qzstVvgq7XK97LhI6UMkpxt0PaCfmA+bI5/mutRf3adn/og+lbeOthjNTjo6er5hvtnHmiTLOZZbUzAZa7e0Ds2ljH8lmU8MUIeIIo4g49Tuhgbk+ZxyVw3V918X7Fpmtu9XPa4oaMNfIKdkUjizqAccdJ2Gd91h3Ks8QYLzpSCr1fDHQaicWQ1dLGS1hLWuYCOlv3uoAe58lXKc3+qy+hQ49ifoiHO/ad+K5nS+HmoHAOrte3Z7s5IiaWj6Zetm07puus9YZ6nUl0ubS0tdDU9PQScbjG4Ix/FGq3tM/a6bKSSckqAKFQI6Jt2U2RygUBGxRS4TKid1HcocI77YQEBKTvhHup3QL049kO/onO/ukd2wpI5N+UE9tFQ6PvE0cclNb7/TmobKzrZ8N4c1xIOx28+5C9iyMdpLxMutrfAI7RqST7bQPjbhjKpkeJ4iOxcGiQeeHfRvHe1uu/hHqWnjaXSxU4qmBvOYnB+fwBWVYpJNUWjQN2jaXwtiFfLIdyHfZnRge5dIf+FWODdcbZSkbpgPJRZCdlBsUduyCAnHHkkDcu3xjsjlTugWTnHdCI4cm6gSlaMPQZMmBlUOaMDKuk7Kp4bgZK4W60xGj5cjlY7vvOH94/xVwcBsscnc+5/itU6StYRt5qxuVQ08K0HYLqytaiPJICcpsDqBxwdkFnZDnZT2QH70HObQfsf5QWoYSB/2hYKWpac/93IY10oH5VzPf/4kWgnb+iRP/wC4ulpIcKDjKGcbKAhAXZ4U7ryrzqGzWZ7WXa7UNE8jIZPM1jseeCcrNt1bTXGkjqqCohqqaQZZLC8Pa72IRNxxrOvdQVVhuWj/ALO5raa4XhlDVBw+8x8bw0Z7fNg/RbeDyDsV4GuNLUWr9PTWq4OfE1zmyxTx/fhkb917f37dwStSt2htZwfDin8QKx9LG4OBEJLzgggEucdjjBBJ280ZmZiebdBvduju9lr7bPj4dZTyQOJGcdTSM/vyvn6aaap/JwsF2LnCq05cWyZGxYI5nMx74cxfR/cOG2+QFp9q8PLRb9IXjTTnVNRbLpPPPN8R46mmUgkNIGwGBj2yVYLV222jqI6ylgqqdwdDPG2VhHdrhkfuKuWHZbdDaLRQ22lLzTUcDKeP4jup3SxoaMnucBefrbUlLpLTVZe6+OSSmpejrbH949Tw3bP+JG3tqEBBj2vY17TlrgHA+YPCZAAFHbjbKg5Wv621XbtIWZ9fc5N3HohgZ9+d/wCy0fxPZCZ1G5Nc9VWy26ns9hqpXi43USGnY1uQAwZPUe2cHHmQeF72VwPw0F1u3jvdbjq2n+FdI7O2aCmJz9lY9zA1o8iGuIPq453yu94+X0VZrb2jZyodgsCtu1uoZBFW3CippSOoMmnZG4jzwSDhCO82yQgR3OgeTuA2pYc/vRdwz+FM54QJ6m5aQQe4QkcGMc85w0Fxx6Ipydlq1w1Wyk8QbTpYUxkkrqOardOHf2QZ90Y75w7f0C1Uaq8QL6ftmmdM01Jah80f50f0TTjz6cjp/DvyVpemNbsqvHiWp1lTMsddDbBa44nPLmCYvDt3dsgnG5HG6jlOWN6d7uFKytoaqkmb1xTwvie09w5pBH71z38nKdzvCyipHgiS31NTRuz3LZS7/wA+PoulA9LgT2XM/BRpoq7XVq6+ptHfZHMGOA9gJ/5muVdXTfVTZAnOQOUd8cLIU8ocZwmKUjBQKUMYI803cqHCAZy445QaPmGUM75+iduVqOC14JI9lU5mTkq1549lX3PuvNbrbAJBIAGCqMbn3P8AFXA+mCFUPuD+K1jZkzcYVjeN+Qqh6K0A4yV1QzTv8wwrG7qlhOd1aMfUoLAcKFBo2THhBzegj+1flB3icY/qGnKen9cyTl/8l0odu65jop7qrxv8RJyR0QQUFKAPSMldPGUkEFaN4q36vtltt1r06em+3yqFFSP/AO6GMvk3/ZGPxz2W9N5IC5d480FxbaLXqG0faPj2SaWSQ04zLHFIzpdIz1Zhp8sZzsjN/kMqxeFOlrNRSVd9hZd60/pKiuuLyW57kAnDR6nJ8ysDwtNsfrrUbtGQOh0s2njbL0E/BfWdR+aIHgdGc+ex7hapZbXpnV0EVXqrxNqrqwYc6iqahlEAfJzHn97QPddQpdXaF03bYqKjvFnpqSEEMhpZBIBv/dzkk9zue6rlWYid/IhuYHU1FrRstU01r+xaluv2CyurKhwY55m+zObEMebjx6ea27pONlHatotG4ADAWDeLvbbJStqbxX0tBTucGCWplbG0uOcDJ2zsV6IbhebqHT9s1FQiivdFFW0oeJBHJnZw4IIIIO5VWf8AjzRrnSWxGprLg/8AjY/9Vzvx81dp65+Fl5oLdeaGrq5PglsUMoeXASsJ49AVuQ8KNEhzT+YICW8fpZP/APS8XxA8NtLUugdQOt1oo6SpZRySRzOcflcBnILicHbH1VcLftmP6ehZfE7ST7PQOmvcLZzTxmRgjkcWu6RkbN817+ntXWfUdRJDZ6iWoMbepz/guazGcfeIAz6LWvBe1WWq8N9P18dot7ap9KGSy/ZWB73tJa4k4yd2rokMLIoxHFG1jBw1gAA+gRunvP2Z+PK1NeaXTthrbtX9X2elj63Bg+Zx4DR6kkD6rRNAaXuN6u/9M9cRE3NxzbqF4+Wij7Hp/axxkZG5O523zV9f+atN11d+bJboadgkFHE3qdLgjgYPHPB4XPYPFS+3CM/mvw8vr3kZb8YFjT9ekIl5iJj2SqJovykqRxw1lxsJi/xOY9x+v3QuqjhcEvA8RtQ62sWooNFxUT7SyRsbJ6lo+L1gghxJGwzwt4t158SnTRNrdK2gRkgPcK8NDd9zs5x49CjNL/Z+PE8YG+HNPfqWTW1BW1FzqKcGM0xl+ZjSQM9LgM5ytXs0HhfW1TILfpTUkskjg1oAmdv/APk2X0K+GKRwc+Nj3N+6XNBI9vJWb4xk48kW2Ldt/P8Axh2yhprbb6ehoIWwUlOwRxRN4Y0cALTvHG41Nr8Lb7UUb3RyuZHCXt2LWySNY4j/ACuK3zG2ywr1bKS8WuqttyhE1JUxmKVhOOpp9ex75R1mPmlGmLZDadPWu20crp6ampo4Y5ierraGgB2e+eVxPRv9GtQUWv8AUOr5KOO3XW4uhidK7DwyMHpdGOerDm46c75HoqPELw0j0Xom63G36qvEdJCwCGjc8gEucGhuWuAxv+zwFsfh74K6eorFa6m/Q1FfXvgZLLDPJiON7h1Foa3GcZxuTwjzzN7TrXGw+BlfV3Lw6pHVs8lS2Ceamp6iQYdLCx2GOI88bfRef4df1bxe8TKMH5TJR1AA83Ne4/X5x+C6bDDFTQshgiZFCxvS1kbQ1rR5ADYLl2h5MePfiRGQMOgoX59ox/qj0VjUadTPspnOU3OQEvCyoZyN0p5TA5CR/n5IAM9WUTypyTwpwUgQBQ56sBA/ez2RB4Wxc/geyTCsdu1vsqxngdl5rdbYDdysZn3Gf4R/BWsceDwVSz7jf8I/grjZk6tacjcqoJ2uwMLqixm+U4OyqjI6SrGnZBa33R/V9UrU3KDmvhiBJ4j+KUvS4P8AzpTx5cN8NhOPoumjgLlGk6xth8dNaWivkbD+fWU1yoPifL8ctYWPaw9yN9udiur8Zzt7pIducou5wQMFBp2Vga52D0n8EGhXbwl0ZdK19XNaBFM89TxTyuia4/4QcD6ALLtnhjo23kvgsFI8+c5dN+55IW3STRRD9LNHH/jeG/xWLPerXSsBqblQxAnHzzsG/wCKMfrpvemZTU8FNC2GmhjhhbwyJoa0fQK5jcZXgHWemGlvVqG0N6txmsZv+9Yx8RdHNBJ1NasBwYSKgEZPZVttQGyIGVp8vifoiCUsl1PbGuAzgy8j+aU+KeiBJ0/0jouoOLP1sZHIzhNDc8FUXGhp7jQz0dbCyelnYY5YnjIe07EFaiPFrQhkbG3UtE5ztx0h5z+DUR4raIcxzmagpntbnJax5AAOCfu8bjdX6NtoqOnoKSGkooI4KaFoZHFG3paxo4AA4V2FpkHiroiZmWX+nHo5j2n8C3KLvFLRQDSb9T9LjgO+HJgnAOM9PqPxQbn0nkBRwzzlaaPFLRRcGHUNIHu4aQ8H8MKweJejXgEagowMZyeoD8SEG1luyQjfhau7xJ0cerGoqA9IycOJ289gj/tC0i6L4n9IKDo4++cj3GMhQbT9FDuOVqo8Q9JA4Oobe3ABJc8gb8bkYQm8RdIREiXUVvjwCfneW8e4V0NqAA7qO3Wpt8RdHFnUNSW0jAO0vmnk8QdIxNa6TUlqDSAR/WBvng/vTQ1Lx0a25z6N0y52G3a7M+I3H3o48dX/AFrqmABsAGjYBabV6l0Hca6hrqu9WGWrt73vppZKlgdA5w6XEZO2QMLbKCtpa+ATUFTBUwE4EkMge0kc7jZGYrqZla4YXJNFHo/KJ8QW5z1UNI//AJWD/wB+6647JXItPvFL+Uxqqncen7ZZaeZg8+ktH+qrTreRnZKfZMUhO5WBOEufJNylJweFQBzv+KPJU7+igzlIAxjnfHmpnPbZE7Z9VBtlaF5PyNA8kBhFv3WeShAO681+tf08YbRuPcApONuyaXDYnn0P8ErvvEc4K1jSTAd0UrccIk7rohmuxtwrgcqkHA9VazfGyC0HCfhIOAnHqg1fX2hLJrq3x0t8gf8AFhJdT1UDuiaAnu130GxyFz9vhBq6iIZaPFC9xwN2aycPeWjy+/j9y7VnJGERsmxyBvhbrKqeHXLxRvJwOKaMx7/8az4vBeCWZj7nrHVlcwbvZJW9Iecb8cDJJ/cupMCtJIB7Jscyg8FdIQtIqG3WtBGP6zcJH49sYws2Dwk0HTmIx6epsx5I63OeCT3IJIK3eok6RlefPVY4KbHiQeHWiYM/D01bA4jpLvh7nnvn1KzodD6VaAG2Kh4xktJOPcnKElW4d0GXNzCN/wAVfoz6LSOnKd0pp7NRMMmOr9Htt5Dtz2Vj9F6ZlGJbDbJPV9O1x243I9T+J80KS6NeRk4K8+7+INis10bb6+pkbNgF5ZGXNjzuOojfOMHYHkZU3rrNrRWNzL2W6U0+3OLLbsu5d9naS477k4yTud+dz5onSunyxrDZLaWt+6PszNt87bLOttxpLlStqKCphqYHcPieHD9yy8q7NxLxX6UsD3EusltJOc5pmb5IJ7eYH4DySu0jp5zeg2O2FmCOk0zMb5ztj1P4r3cqKq8BmjdNsZ0NsVsDM5x9mbzjHkrI9KWCPq+HZrcOolzs07Tk5yTxznuvbUQeGNL2Rowy1UbRnOGxgDOCOPYkIP01ZjUCf82Uom/bEeD28vYL3UrxkIPHZYrUwki20P1gaf4j0CgsNpBJFqoPmAB/q7NwPovVc3ZAjZXavMNltfzf9mUG/P8AVmb9/LzVH9G7GAcWS1jJyf6nHz/wr2SPJDCDwzpbT+Ok2K0YyDj7FFjP/CvRo6OmoacQUVPDTQAkiOGMMaCTknA23KycbnKhByoEK4lcJ6qL8qSyzi03Cnpqi2zULqmWMfCm6Wvf1McCfl+Vo3wc9l24oDgjzVA80hHmnPCUrMwFKU87okYCB54TgXuUw81P1vNQHcqwIRkoYwEyDhwFRcR+jb5YQyRsmb/ZM80pBG+y4W61DxpCPgv8+k8pcYJ80aj+yd7KH75SiSgTdkAmz2C6IUK6E/Nuq08Y+cEH3QX538k4Of8AVJymadkDtOCm78pB5phgccILGFM4khVt5VmcjCivMrnYC8yU9yvbqoOoFeNVQuZnAOE2aedO84Xmyynq3KzqluMryKjIJxnKu0Yl9vzbJa5aokOkHyRMdw6Q8D25J9AVxasmlqql9RJK6SV7i573cucTkk/VP4m6obW6hNDTyZpqLMYwdnP/AFz+I6f8vqvBpLgHYyV5sszMvheflm9/WOQ2mxX242Oq+Pbquaml7mN2zvccEe67DpPxhjlDINRQBruPtNOMj/MzkfTPsuExyskA81a0Fu7Ss1yWq8uLysmH/Gfj7Itlzo7nStqKCpiqID+vG7I+vkVmAr4/sl9r7PVCegqpaebu6M4z7jgj3XX9J+Lkcojh1BAGnj7TANvdzP8AT8F3rliz6uH8hjv8t8l2JHKwbbcqS5UraigqYqiF3D43ZHsfI+hWYDldXvid8Mogoqu0IQI2RRPCKrcNkMJyMcKcqikjlAjGVYWpTygrI3UxsnxgJUCEZBSY3VuO3kk7oK3bpTxuVYRzsqz3CmgvfYphyqz8pJVkfCsKYjCQ7EJyq3EY3QZEeXRN8kTnPGQpCcQhNyPXK4W63DwpQDEfUgfiQq85OU0hwGDsXtH/ADBIFcfGZWtTDKqaVZlbQQDv5p2DBOUGnZRpyR5oL2+iYcpQRkpsoHGER5hKEwCBhwrOBtuqwFY04UUSBjdY89MHt4WQCm7KK1uuoTuWha7cKV4a8NJY4ggOAz0nGx+nK6FLEHjgLx7hQB4JxhISXxPrHS9205cXNuDC5khJjqGkuZL7Hz8wd149NWOYQDkEL611VaY56aWCqhZNTybPY8Zaf/fmvn/WOiBSTPmtfU6Ln4Tt3N9j3/j7rNq7eDP4sW+w8SkruN9l7dJWh4GStGzLTPLXgjCz6Ssx3wVxtR8nLgmreWOa9uydvUw5Bwteo7huASvZp6prwMlcpiYeO1ZhsFj1BX2epE9DUy08vdzHYB9COCPddf0p4sRThkN+i6HcfaIBkf5m9vp+C4V8jxsmY50ZBat1yzV2w+Xkw8n4+wqCvpq+nbUUVRHPC7h8bshZYK+TrBqSvtFSJqKpkgk79J2d6EcH6rr+lvFWlqgyG+RfAk4+PECWH3byPpn6L01yxZ9fB+Qx5Pk/JdTyosWjq4KyBs9JNHNC7dr43BwP1WQHLpt9CJiTZRwhlRXagQlITqEKqpIxygQrSM8pCECEbJHAZV2PlVT277IExtgqo91cRtukcN0FDgSrIR+jQcMhMwbIJ0nKR4ICtdwq8IL4P7DfsUckuylhH6Pk8nbsmcencA5XC/W441+Y4MWe8gH4ZP8AJTbfKrkOZqfz6if+Vys3HsrTiSYD6JthuSMeqA7eqO371tDgJhjIISA+Sbnc9kF25O/CZux3VQJyrBzk7ILAd0Rsd+FWN3BM5oe0g8ILUzeOUgyGj0TDjKB8jkIgn6JBjCdTSxJkkrQWlM07bov+6kfCZa3eKJsjCCFzPUtlJLy1uQf3LsNWwOBWvV9E2TOQCiPmjU2n2yuc50WH+YG60O4WqoowJQx/wnEhr+nYkYyPpkfiF9R3vTjJ2uLWgH2XLdaaNkrYImNnnpKimLnU80ZJawux1Bzf1mnAz327jZSY243w1u5NBUOYcHZerSV5GN1gXKmnt9W2kvkDaOofvFO05gnHm13A/wDfHCxpI5ad5DgQQudqPl5/FmG5UteCRlerDUNeMZytCpqsgjdexSV5GMlcJrp83JhmG2AB3CLXvjdsTheXTVocOV6EUwcOchY1p55jTZNO6ouFmqBJQ1UkDv1gD8rvcHYrr2lvFGjrQyK9Rill4+NGCYz7jkfvC4F0h3CMcr4jsSAulMs1enB5uTD8ifj7CpqmGphbNTSslieMtexwcD9Qrg5fLOnNW3GyTddDUvjyfmZyx3u07H+K7BpbxQt9wDIbs0UU52+KN4nH+Lfrt6r00yxZ9rx/Px5fk/JdIUVMUzJomSRPa9jhlrmnII8wVYHbLs92zIEZUBRRSFuFWRur1WWoqohVu2VxalcPMIMfGx2RaAnwR6pcIGcPlSnbPsnx5pXBA8GzNuMqzAwEkH3T7pztv3XG/W6tUkH9ap29z1n8G/8Aqrm+qpkcDcKcdhHIf+kfzWQ05SnEkQEwGyG5PojwtoLR6/RO3lBvITHGfJAwGNlY052O/uq+4TA52QO37x8kzSkH3vRO07ILMqZwgM5UQNkE7JwRjhVpgc4KC0cIkpGnbdOUGPM3IK86WLJ4XrSBYsjO6DxZaYEHIXkXK0xVLS17AVtL4slYz4dyosOO6p0bHUUk0EtPHVUkm74ZBlpPmMbtd/eGCuLX/SVxsAkfbI5bnaY8l9K8/wBYpm9y04+Zo8wPcDlfYE9IHg5C1e+aZiqsvjBbIN2uacEHzCRJNYtGpfIkIp62IzW+X4jRu5hGHs9x/PhCOV8Tt8rqeu/DVk9U6to3G3XMEuFTC0hkh/vtHB/vNHuDyuX3CSqtVYKHVFGaWZwyypjAdHKP2hjYj1b+Ck0iePDm8TfGdS1mOF7FJX8ZK1uopZICCN2kAgjggjIP1G6kFSWnDs59VwtR8nL42m+01WHd91mhzXjfC0qlrcY3Xs0lwyBuuM1eK+PT2S3yRjnfERgqqCoa/kq0gOB3U45amG16W1rc7FKPsdQRETl0L/mjd9O3uMLselvEi1XZrIq1woao7Ye7Mbj6O7fXC+bXNLfNWRVL4iMErtTLar2eP5+TD8n7D7Ia4OAIIIPdOCvmfSOv7pYy2OGb4tKOaebJb9O7fp+C7RpXXtpvwZF8QUtYf9zM77x/uu4P7j6L00yRZ9vB5mPNyfrclEgKIK6PXsCMlIW7K3lKRuiqXDB2SkZKuc3ZIRjfdFKo4Jsc4ULQqBE0dBHqnxuowDBU791wv1qGpbfnNue0Dj+L2/6K8c7crEbvdJc8CAfvd/6LLBwNglOE9Owk+SYbHfZK0jp3yjs4raLByilHkmAB90DApkowmHOxQM3cpmk59eEndEc7boLQTjdTkIA5Uygm/bhODsMpD7pjwEFgTg5HoqQcJgdh2QWPykKOcjdTkccIK3s/FUlm26yTnY9lHMyMoMCSHdY0kOcr03MVTo+cqTCxLwK22x1DHNkaCD6Lnmr9BU1wo5YJKaOopXnqdC8bA/tNPLXeo+uQutuhWNLCCDkKK+UbvYbhYoW0s1PLdLPA3pjAAFXTN98fO0e2PRvK1me209XTOq7XOyppwMuLR0vjzx1t5b77j1X1vd7BT1rD1xjqG4cOQVyDWPhmPtT7hbnyUVeCXCqpxguP99o59xv59Sd65ZMNbuIubJA7Bysinq8d8L1bsJrfUMptT0baOR2RHX07S6nl9wB8p88Dbu0Lzay3PiDXsIexw6mvY4Oa4eYI2Kxaj5ubxNPTpa8jGTsvapa5rsbrRmTPj57LOpa0gj5sLjaj5mTBMN7bK2QboObk7LXqWvO2SvXp6sPAyVzmNPLai0gt4Kugq3xkZKGGv3B2Vb2eSRLMbq6To/xMuVo+HDUuNdRjb4crvmaP7rufocj2XZ9NautWoYx9gqA2cDLqeT5ZB9O49RlfJnzMOWlZdFcpaeVr43uY5py1zTgg+a70zTHyX0fH/IXp8t9h9kB+Rsj1AhcJ0f4rVdKY6e9h1ZT8fGH9q3+Tvrv6rsNlvVBeqX49sqWTxDnB3afJw5C9Nbxbj7OHyKZY/jL1igRskDsd9kwcCAtPRsFCNuE5CmFrYVg2KhbnumAwouF+tNIp3B10rPSKIfiXrLB3WBSHNxrz3AiH7nf6rO7qU4s9WtyNwoCc7FJz7pm8jA3W0XDfCLec9krcglOOUDBMOFXvlODvlAQdkQkTt3O6QGB4TjhVjPKYkjsgJOE2RjdDYqeqAjOU7fJVg8pge6B88p252VR3GyLXbD8EFpQ7eiPIRHKBcDsldGnAwU2M7oMcxYCpfECOFnhv70pZnhB5UkPosOopGSNIIXtvj2VD4tvVF255qTR1JcqeWN8MZbIMPY9vUx/uFwzVXhvdNPyyz6ccCxx6n0E7uqN/+Fx7+5B/vHhfV0kPmF51dboaiIskYHAjGCFOExt8TST089Q6mrIJLfXtPS6CcY39D/I4PuseekfTv4K+mNceG1BeYT8SnbJ0j5c7Pb/hd5ehyPRcH1Bo6/6WfL9nY65W1pyYng/FjHqPL1BI88cKTWJeXL40W41+GpczYkr06Wu4wV5dPNSXEYpZOifvBJs8e3n9EjmSROIIK5Wo+Zl8bTcKS4DbJXqw1DZButBgqyOSvWpK8jG65TR4b4phtjmB3CpfGWjKw6WvDgNxleiyVsixrThNGO1zmHIJC9Wz36rtdSyejqJKeZvD2OwVgyxZGyxnsxyFqLTBW1qTuHetHeK8U7WQX5nS/j7RENj/AIm/zH4LqdDWQVtOyopZY5oXj5XscHA/UL4xjkfE7IJwtn0trK42OcPoagsz95h3a73Hdeimb/b6vj/kZ5kfWYcmXOdJeJdsu4ZDcC2iqjt1E/o3H37fXb1XQo3hzQQQQRkEd13i0Tx9amSt43WVnZBE8IBc79dYaDQHNVcjn/eMH4M/9VnB2QvNthIkuB/8QB+DAs5pISnGpZA4TAqsH5tk2eVpF7SSd1YDnAVDTg5VjDknCCz2RHHqkycpuN0EJH0ThIBkJggcfuUyCEB2Kh2OUDs59E3ZLnG/ZHlNA8BTPZRyVpQO04KZuThVd04JBQXsO3qmByqwdvUJmO5QH9ZO3yVed8pgd0D99kzQMFKcY9044CBXMyqnRjdZPdLjdBiPj42VEsOeAs8tBwUj2oPGqIA7IcMrxLtY6esZiSMOxuDwW+xW1TMGDlYj2A7KLt8+eIPg/SXIyVVOx0VUASJoRg583N4d7jB88rj1ztt5sNdFQX2nFVC89EVWxwaT/mdgHtscO9l9vT07S3darqHTVvulPLFV08Ukcgw5r2ghw9QdiiWrFo+vkattkkL3Doe1zeWPaWub7tO4WCHvidg5Xab7oSWwsmls80FVb4WmR1ruJe+JrRz8KQfPEfYrR5rZa77p432xtqaaEHpmpaoteY3Zx8j246x7tB91maw8WXxPm4a5TVhBG69ekuGCN1rtRAYnkAjZCGdwK4zV8zJgb5TVrXAAlZY6ZBtytJpatwxjK9mkrnDpG65zV5LY9PZkg8ljuj6T5FZFPU9YAIWQYmyNJWeOU1YcFQ+IggkLf9FeJFy0+1zCDWUjWl32d7yMY3PQexxn0J581oM0XSeVS1xicXeQJP4LdbzHHTDmvjtHrL6+0xqu06kgD7ZVNdJ0hzoX/LI0Ec47j1GQvfBXxXZ6+ooo6SWCV8c0TWlkjHEOaQBuCOF2fSni7UR03wr7Suqi1vyzQkNef8Q4PuMLpGT26+54/m1yfxt8l//Z",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "| Instruction | Result |\n",
       "|---|---|\n",
       "| Extract the formula. | $e^{i\\pi} + 1 = 0$ |\n",
       "| What is the symbol right before Pi? What does it mean? | The symbol is *i*. It represents the imaginary unit,  $\\sqrt{-1}$. |\n",
       "| Is this a famous formula? Does it have a name? | Yes, it's often called Euler's Identity. |\n",
       "| Why is it special? | It connects five fundamental constants in mathematics: e, i, π, 1, and 0. It's considered elegant due to this connection and its concise form. |\n",
       "| Extract the caption. | The most beautiful equation |\n",
       "| What color is it? | Black |\n",
       "| What color is the formula? | Green |\n",
       "| What's the object in the bottom? | A green pen. |\n",
       "| What can you conclude about the object? | It was likely used to write the formula on the paper. |"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prompt = \"\"\"\n",
    "Follow the instructions.\n",
    "Write math expressions in LaTex.\n",
    "Use a table with a row for each instruction and its result.\n",
    "\n",
    "INSTRUCTIONS:\n",
    "- Extract the formula.\n",
    "- What is the symbol right before Pi? What does it mean?\n",
    "- Is this a famous formula? Does it have a name?\n",
    "- Why is it special?\n",
    "- Extract the caption.\n",
    "- What color is it?\n",
    "- What color is the formula?\n",
    "- What's the object in the bottom?\n",
    "- What can you conclude about the object?\n",
    "\"\"\"\n",
    "image_euler = load_image_from_url(\n",
    "    \"https://storage.googleapis.com/cloud-samples-data/generative-ai/image/math_beauty.jpg\"\n",
    ")\n",
    "\n",
    "contents = [prompt, image_euler]\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XHHKFGbMU-Re"
   },
   "source": [
    "You can also ask for interpretations and suggestions:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 934
    },
    "executionInfo": {
     "elapsed": 9201,
     "status": "ok",
     "timestamp": 1702337470869,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "cJ0TsH-7unIE",
    "outputId": "c7a22ff9-9bd5-4598-a52f-0a526646fdbc",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Answer the following questions about the image.\n",
      "Present the results in a table with a row for each question and its answer.\n",
      "\n",
      "QUESTIONS:\n",
      "- What is visible?\n",
      "- What are the reasons it's funny?\n",
      "- What could be a fun caption?\n",
      "- What could happen next?\n",
      "- How would you alter the image? Would it still be funny and why?\n",
      "- How would you make it funnier?\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "## Analyzing the Sheep and the Chalkboard\n",
       "\n",
       "| Question | Answer |\n",
       "|---|---|\n",
       "| What is visible? | A young lamb is sitting on a desk in front of a chalkboard. The chalkboard has the equation \"2 + 2 = 5\" written on it. |\n",
       "| What are the reasons it's funny? | The humor comes from the juxtaposition of the innocent lamb with the obviously incorrect math equation. It plays on the idea of sheep being unintelligent and blindly following, implying the lamb accepts the wrong answer. |\n",
       "| What could be a fun caption? |  * \"New math curriculum is really something.\" * \"Even sheep know this is wrong.\" * \"Ba-a-ad math, anyone?\" |\n",
       "| What could happen next? |  * The lamb grabs a piece of chalk and tries to correct the equation. * A teacher walks in and looks at the board with a confused expression. * Another lamb walks up and writes \"1+1= window\" on the board. |\n",
       "| How would you alter the image? Would it still be funny and why? |  * Instead of a lamb, use a different animal known for being smart, like a chimpanzee. This would still be funny because of the unexpectedness of an animal doing math, but it would change the humor from playing on sheep stereotypes. * Add a thought bubble above the lamb's head with a confused expression or question mark. This would emphasize the lamb's supposed confusion and enhance the humor. |\n",
       "| How would you make it funnier? | * Have the lamb wearing glasses as if trying to understand the equation. * Add more sheep in the background, some looking confused, others nodding in agreement. * Change the equation to something even more nonsensical, like \"1+1=banana.\" |"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prompt = \"\"\"\n",
    "Answer the following questions about the image.\n",
    "Present the results in a table with a row for each question and its answer.\n",
    "\n",
    "QUESTIONS:\n",
    "- What is visible?\n",
    "- What are the reasons it's funny?\n",
    "- What could be a fun caption?\n",
    "- What could happen next?\n",
    "- How would you alter the image? Would it still be funny and why?\n",
    "- How would you make it funnier?\n",
    "\"\"\"\n",
    "# Image by Elimende Inagella on Unsplash: https://unsplash.com/photos/4ApmfdVo32Q\n",
    "image_classroom = load_image_from_url(\n",
    "    \"https://unsplash.com/photos/4ApmfdVo32Q/download?w=600\"\n",
    ")\n",
    "\n",
    "contents = [prompt, image_classroom]\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qU5Tm-FQE22c"
   },
   "source": [
    "## Reasoning on multiple images\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vbf1HALvE22d"
   },
   "source": [
    "You can also use multiple images:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 7944,
     "status": "ok",
     "timestamp": 1702337478799,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "sLUesezCE22d",
    "outputId": "e85991c8-b5a1-4c0f-f5c3-8b46bd64d765",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Answer the following questions for each image.\n",
      "Present the results in a table with a row for each image and a column for each question.\n",
      "\n",
      "QUESTIONS:\n",
      "- What can we see in the image?\n",
      "- Where does it take place? (answer in one word)\n",
      "\n",
      "Image 1:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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ZC4D8xgHsVNXqy+mS1MlXIyRjgL4JCv2RlkY3cqSI4W32PYE2RjHC3jNH8osmEjseygVdO6rlMb2ENHf1VgGxtH95v3UmKSC1jIy/1UPrNy6NGwku4C6k0uJ4EkVgBytAYopnm8rCEeKmpo2bQ9gH1V06qWuc6moHmnB3AfyoGmT1UsjX3Jb3B5WodT07gQZGFv1TGwU7B5HsA9lNMRw87SHEEql1iWt+JjbRg2tlaB7acD+42/1Sx+ADl7EXAoJZYaRjqi5eebBGL3OZ5QcqQx8H6pGFEdLT2AL2fdBn9cnqKSjJpmO3vwbKHpFRVspQZC7e52dy1D5Kd2C5hH1SA0wbY+GAmpgdM90kbScFGeBn1TBNC3DXt+l0jp4uS9t/qjTgSMKq1+SsdC2OkaXA/MrRs0Jx4rL/AFS747WbIwn6pEvtk6ehey16ciTu5aCHxaWmuwb5D2U9rmjBfGP5Q3PDTZ7m/UFXUkxVMopamvdU1TvKPlaFamYshOxtyBhRxJI51mBtu9ypQYRBhzN/1QinqKaorqmJ9Q7ZC3JHurWF7Yo9jLWHCrKmhq5yXOq2s9gUCmpq2mqmb5mTRH34RNXhD3WJTHujYCXPAt6lDqfEELhDI3cR6qmdpZMe+Se8pzfcotq5jlbI27HjanscJD5XggeipX0croS1sw3EWGVO0ik+BpSHyNdKc5KEqZK/w84A5uVUzQahWTb6acCLvZOrIa+sdbewR34urHSKKoinDgWtiAyLqp9U0ekz0k3i1E+4ngFXtBUAsc2cgWGLBR+oNMlrZYjFM1rW8i6Sioaunk874nt9LofKR2l1FbIZG1TmtvgKdptHPQTjxqkOYezipkZ8NoDixrh6FVlVSMlkc+oqM3xZ3CGLrUWGaAsa/bfuCsvJRgVGyjmeJjy+66aWpbaKGpaY/UlXWgaVV6g4Np2gM4MxCsltyJbP6iUtDJBGCZbv/UVaMtYAi+FbO6JlpYHyRVkks5zZ3CzFZPNRTmKqAjeOLnlXn+fLj9OPPjfizIwQob5Zo5AAzd6qGNRuSfEbb6pW1gebiVv3WGtWoIJBI+qcbX4VY6tG0nxGX+qBJXm3zD7ourfcM3QyQDhU/wAe/wBW/dI2reX23tA+qJq5LgR5jZDcWBpsQq2Wou25lb90AHxW+eZoH1Q0vUE72ULhCLudjCqxDWRUsLYnE3y+4urmMwMDQ6RjgPUqS2aCx8zePVVM1jK6Kqqa6KGPcLG5VvrU9RQ6eBE1znYClwMgp6ySoMrXOdwPRHmq4ZMPLD9UtSRnqh9fT6OJAC6Ui6rWyTPpxI9zxKf0rXulikbZ0jNvohNp6MOvdiavVQ0r5RSbqiLcb4uEGt0mnr4dzY9j1qHupgLF7LKHPNDnwnNx6JpjzLUqOShmLXtO3sVFbICO63+q0sVbE5riN1lgqyB1LO6MjhdeNl+ufKYmiJ191yixiTs4o+0FlkaGHCzas4msD9mHFEjbJ+9yPsIHCVoO+wCzreBkPaCdzkCISzSGznWCsnRgMz3QXsFNSuf+pysSoU2qCjk8Nznk+yE/UyW7i94H1VTWyb5ySgl5IsSus/OOV51b/ijjkSP+6KNVcQB4kn3VK0+VK298K9InetDDqR2eaSQn6pPjyHZfJb6qiEjwcJxnkdglTxw71eHVWg28R/3SfijQ7L3/AHVK2CeQ32m3qcIzaV9nb3gEJ44d6uI9Xa03a+Q/ypLNZaR5nSfdZtsTw7L05wc12Hp44vetIdXjt/cf90E61GCQZH/dZ8v7FwTQNwOQT6KeOHkq/fq8JI/Of91zdYgHMz1nCM5siMh3RucBwr44eStIzWaa2ZnoztYo3M/vvuskBg4C5jHF1hZTxQ8laturUjeZpPuldrNGR/ff91lnxvLrWCE6Mt5blPFDyVr2atRcmofdEOp0Jt/3Lli+P05Ts/tTxRfJWy/E6C+ahy5mq0Affx3rHtueG/6IgYduWJ4onkrZM1fTrkmofdEOt6btzUPwsWICYy7aPqmCn3O7J4ovkrZnWtPB8tU+ycdcoCLfFPWJ8CxIsEjYSRwE8UTyVspNdouBUO+qGNao2m4qXFZAwm1rBNNO4ZsE8UPJWz/GKNzb/EvXR6vQOJBqXLFFrmg4x9E9kZMJcG8FPFDyVuG6vQBwHxLreqf+Maafmq3rFwRFwILQlbAA75RdPFDyVuYNW00HFY4E+pUyDW9PDtprSG/VYJjLHMYIT2tiMnmYAPonjh5HoI1jSXf/AHXE/VK7VtJb/wDcN/qsOIIOdo+yjSMbf5QR9FPHF8jcTavpbiSKw/dBm1TSvB/+W6/fKxg8EOy0LppKc/K1qeOJ5Gmo6vT67WaSkp5nua94ByvfqGWKmhigp2NYxrRbC+XtMqIqTUKeewaGPBJHZe76Vq8c8TLPDmkA3BXX85OLHK9m+jlEjecrPdU9PU2s0ro52Wf+mRuC1EoKvIu9XEczZQQbLt65TK5/K8B6g03/AKeqBHXeJ4J+SVuWn/0VUfiOmN4nkX0Br2k0mo0j4aqJr4XDNxwvDesejJNEndNEzxKInD7XLPqvPy/HHafoiHV9LbYeK8pTr2mbAC96oJaVrRuY1rwfRR2CMh4czj2WfHF8laf8e021vEekbremHBlesuyOOQ2btafdMlgc04a0gdwnjh5K1L9c0z97yhO13TwLB77LLYzcBMNuLBPFDyVpn63ROb5XSXTHdQU7RYCQ/wArOfLwAkLr4NlfHE8lXjuoog4Hw32+qZRasa3Vox5mx3yCVSOtZLQ3irWkFL+cxZzr0+akaHO2m4ORlDZEALOBRqcukoYJgeW5XMeL+YLzPRDX0kb2HJUU0gYzk3U+54TXRn6oYpwGtkNwVU6zpvxE7XsGCFoJaez7lOZCHDi9lZcSzWTZkgBT4GWF0CCMObfupYaRYWWqkEiiJblK2Dz3T2XDPQrntlDbgYWVNkIDg13J4UHX3bImi9gBdXFNFu2ve33VJ1O0GJpacvNgFvh9Y5/GVc4FxK4ZKfJGGm3omgL0vOLYBgT4Y3Od5QleGNDc3wlEp2m2EDmwgXL3WPonMnbHbYwEjuUEEnkp9gGop8lVNKbF1h7LmbiCSblDZtBuQih4LPREJcA85QS5xJyntG5xSeHlAy3cosTNvmJShmLqz0oUUU4k1JjpIgLhje6CFBST1L7U8Ukrj+0XWgouktUmgAfE2FpzeQ2XT9VTxtMemwRUsIwLN81lBrK7Vnwskqpqhsb/AJS42BQXLej4ov8A5WpU8Z9iu/6X0/mLVoS4dvVZYufICXuJ97padoMzMYuguK3pqvjJfBG2ojtzGbm30Wfm3te5j2lrm8gixCsqTUK2gqTJSzvZZ2G3wf4Wn67pYXafplfJE2KsqWXkAFr4QYMFxxZPaXE5FgnbbXITDdAVzg1uDlM8VCJ90jRnKCSZfyCLd0tKWeJd4PCGLGPafVOa0C205soEO0uJuQEwkg+Urj7pWguIAQJk8nKUbvVE27TZzVwYTwga0B2HFE8IiBzmHF1zW5sQjxgile02y4II8Fw/vwkBN+VJga3xASL+yA9gBPpdA4SPbwcJWy3JJ5QgDfBun+twglMnB5XSVIjYTa6iAXF+ESnppaqdkELDI95s1o5KCJJKXu45SFtrYWxqOhvgnQQ6hqUEFXOQGQNG43Pqs/rumSaTqL6Goe18kfLm8FVEJgBa4OF1YaF1FVaHUC5dJSE5ByWqE2M25wmiMEeYXUV7ZofUdPXQxuikDmkc3WspKwuDbuuPUFfNNBVVOj1AnoiTHfzRE4P0XqfTPVUeo0rXQP2vb8zCc/RXcPr1sTtMZBfceigVcbJY3xyMD4jzcXVNp+oeIwPabgci/Ct45gWi2Qcre6mPKOsejn0rn1+jNLoCdzoRy33H/pYLcHB4c2zj7L6VnhBYHsAc1wyF531f0THVyS1ml+Sa13R2w7/9rNivInR2KRpe04JVgWPp3vjlYWvabOa4ZCE9lwS211kRHWdyLE90x8ZZki4KO9m2wJRIJmR7mvAew4sqK8vumk3Kn1FIxwElO7cwnLTyFDc0NOfsgY5m4JrCY5YyexRgc2Qp2k3PpkIPTemahk+k7Jc7DYKS8Nc7AN1n+i5wZHxHIezd9lov7chtkFeTlMr18bsPbHnzZ9kQhuyyRsjcE3RBtvcDlYaQaxlmghRGyluFLqnOc4ixNuFCcLHKCkYPKNqlwAfM7KBH8tlKpGAjK3WYe/aTj7I0Y8nm4Qy27gWjKKCG3Dza6il+IDY3i2AOVjdVmfPXBgPlYLrU6m50EI2H5/8AZZOnfGZaySQ54C7flHH9KrZnkyEprMkJ7i3dhPiA8QYXZxMdz3RWA7L+qRz/ADGwCQuJYASgXA7pQ7GUzCXsgUe6JbAsmMyfdGAN+MoOaNvYpz/MAuL3FmTe2EgvyopRYY/0Tmsc88koscZed23haLp2KSsqoaOlpQZHuHmDbkD1RBeiumTq1Z4tUxzKKHzyPcLC3og9eaqzUdTEVKwNpKceHGB3AWo661hmkUY0DTH+YC9RKDz7LD6ZHSCojkrpCYwbkN5KtEOlo6idhEML5P8A8RdWtB03qksjS2lP8lbJnU+h0VDtoY9shG3btx91GrOp46+ER0n5MgF3bBZPQkdOdBmn/wC/1L8+Vh3MpmEZPus/1x+LVdb4upUUlPEwbY27fK0fVBZqdVTybm1cjiMi7itN091dUTzMpNTYypppfKQ7JCvqjy+TxM+nsgPcbZK9D6/0Cn0mrD6RpbBO3e1v7T3Xn1Qwtf5lPgE0jujxs3iwGfVDDRy7hED3FtwbMQPdTva0FwsL8pYoi521vPKNFI80rWC5buJspNJEDM7cw7djvvZRUAw2NiU4RbCCHA2Uh7yWbRa6bHGfqiGbg59nXJTmtZbDSD9VOo4qPbIasPLreXao77F5DPl90DG7b22890fwwaZ55sRwhFhDAbfypFNuNLUbeBYoI0Db1DBawJsgSMAe4X7kXUqO4kab8FCktd9xkEoAmEgXBx2S7Q45OUoJAtn6JxFm3agGWEGy2vSlGNK02XWZmHxALQl3b3VP0xQDVdUp6bY4uLg49wRfN16f11plPK2kgrKqCh0mFoc5jB53kdgFqT+pWT0KLxa2XqLUX3hpTuJfne7sFkupKluoahPWsabyvLjfstBr+tx1dM3S6SFkGnxu3Ri+X+m5Z17WmPa8ZBvhSipZIQ8W4S7lMkjiDiW8+ihvc0TOFv4UU3fYZKHG+WkqG1VC8skbyBw76pXi5KRthyCqj0bo7qqOsY5sn5dQ0APj/wCVuqDUw4tsQCeRflfPT2PZM2aneY5mnyuBstl0r1R4sjaesIjqm8E8P+inz4r2+GrLmjY+w529kcjxfMw5POcLHUOoCZrC03I5stLp9S1zW2Iv6FanLRQdW9HQa2x0lOPDr2Nu15/V7FePahSVOnVL6WrhMcrTkHv9F9MM8xBx/CzXWfTNFrVM5z2ObMPlewZafX6K2I+f5W+JkBRS23KvNZ0iq0isMNS3yn5ZG/K/6f8ApVzqcnIFwsqixyPa8bTYo0sbJQSLMeBm/dI6McpxaNwcMuKCKGG4J5HZOLd5IItcKxdT+ODt8s37Ti6hThzMOBBacoLXo+bwtQhDuN2z7rd1ILHkYwvNNNk8NztmHtId/qvSZ3ukZFKyzmysDs/Ref8ASe3o/O+jh/b3O4PNk213XBNh2Ssc0+RubjhDO8/L27LjXUlTfGwfyq+VznG4AUwSOcCxwtnCjTFjHkOYUFTEwuecWU+KMNYMWKBBfbc8lSmnFyMcLTMOaABe2EFzDLURtDb2yVKbtt7JsG81Je2wtxdItVHUznQSuLvljZYD3KysTwyiJLRdxKuOo5pqwOkc5oDnn/RZ9xtDbuF6eE9PNzvs3y3uiQ2u72Cji5OUVtgwkrbBCfVEtew9ksbWuuQbWHdKW3cdp4QDGCbpw4Tmx35IT2iwsXCyBrbbmqZhjibjIQqdjHSNuSc5U2ERyy7BZlza7kAWwkxghuCUWJm3m30Wj1PpupodAj1B88csNw0BgJ5VZSupm4kYN9v3KA1HQOqWtZBGfEcbW9V6F4VN0PoG4uaNXqmfM7lt/QIfSJ0jTKL8SrXRiRt/CZe5v6rG9TapNq1ZPU1gDnfotw1vYBa+RFPVvjlmc/xvGe83c44uo8MsUch3MD2WIsfVI/YWX2jHayY0sdw23fJWWjZbGwZcBGotzZJjcgBhQ3Fo2EXd/PCm6XBUVBkbTQvke8ANDW33Zz/sgiElwwbn6rWdAaLU6jXNqXgtpoXAg/uPoFYaB0NuaKrXJxTxDzGFo8xH8K41DUtRbRuounNLdR0gBAqqi0Yt6i61J/rNqo/qlqLanUmUkJafAaA43wCV54xkktRsgY6aU4AY0uK2+m9Jtq3S1GpanFNDF55W07i8/wAuVVqHUngMdSaBTQ0MQ8pmjH5j/e54Uv8Aqq53TVfHGJNQdBQsI3XqHhpP8DKa6h0encGz6rJPj/68Btf0uVW1Lp5pC+pe+SQ8lzrlMay+O3CC2idorHC8WoTRluLvawh3f+FYadNor7tZptfuHzSMn3YvwRZVOn6dUV8kUNHFJNI4kEAcfVepTad/0t0FL4JiFdva577X3kOGPokgxet6XpLaBtZpNS5pMmx9NPiRuO3qPdVsEbWgBrmm/Kr6mZ88jpZTeRzrkjhNbI4XyoYtGRxSXD7sTHU/5rGRAvLuNoyf4UNkzj2tfk8L0j+mUdN8LqNSWCTUom3YxwyG27D691ZNRU6T0TqFXD49e5lBStF98vP2QtQ0rp6jPhxazNM4O/OdHHdrW+38qv6g6i1LW5T8TM7ZfELDZo+o9VTtk/IqWv3B1ha317p6GgZoul1j449O1qN8zsNjmjMZcfS/Cz0kLWVMkb3Xs4gm/oo7Cbi/qFMg06rrK+SCjp5KhweRaJpI5RUMnz+XhTNKoZNRroqWKwdI7aC69m+5sr2n6dotLeJ+pKxkTQLijgdvlf7G2AtT0eXas981JBFpmiQG1o/7k59HOObetkkRa9G9ODp6lqHSSwyahICA9pu0egVNW9J6zX1DptRqohyfEcS4W9vRVPWvVDpNXfDpUz6amg8jzFgvKptC6ur6HUC+rmkqqWXyTRSO3AtPp7rWz4jTnp7QqGJ82rapFI8N3Nja8WP8DKrdW6n0c6OaDTtNa5jQR4jvKQTwQeVB600COkbHqmks3aVUjc0jPhn0PoFl4490RcJIwbcXsQpasIXG4JN/f1QqmO8rnN5unmN7bDDvcFLMN0ztwIPsVlUIk38wS8cJ8rLmwvcd0FwINibFUPBzxlCnaJbG21w4IwQijsb8eyXDr35RGg6V6okgmbS6g8td+iQ8H2K9T0nU2yRi7gHX5XhL4Wyja4e11caB1BPpNQKatc50G6zZDy32Ki6+hKWuuxtyVZMna/B+ZefaPq8czIgZMnIcOCtPSVYcdrbFxyPdanIN1/Q6bUqeSCoY0xuyBbg+o91451L0/U6BVgPHi0rvklH+x9CvdmSB7bOyg6jp1JqNC6Co2N3jaC5twfqriPmyQlzyQMJACQOcCy1XV3SlTo80ktODLRB1iQLmM+/qPdZXOz0IN8LKjtk2MD2SHe3FnhLqRdPDHJ5BdtrN4Kg7jc3uc8lTYAHabM8NuWOAt9UEOjuypjcQdrxbK9F0OoFRoTGO+eF5Yf8AheabiGtLcbXXst10TN4rayEm5NpAuX6z06/lfeLlnP0TneV1wblN2OBd2Hqnv27Lt+6870AOttBwmPja+xIBPumyHDuNvCVr8WvwoKiIggDP1R55BDGSLGw490KGOwaCDlTGRtLASLhaRDkqJPAYB83cA5spMs5i02WbbtdsxbtdR5QXVW1lgBglM12Qtp6en3WD37rewWozWW1yZrnshaBtYBf6lVe644H8ItbL8RWzSA4LjZBsvTJkea3aUEd+EUeFsO0OvdCAuURos3+Lqo4AlwDRdKC4k39UrXHeCBjlJa44PKDmvs7sixhhcd7iBbluULaGnOUoGTbugNFua67SAR2TmPc07rXKZEQ1xJ9E5xyQAg1Gi9Umjpn0lXF49JILPYc3UyLRNL1c7tF1CKCUgk09QS0j6E8rHG5Jv24StcL5JGeW8oNRV9K67TWa2kkmYMgxO3BVUmnakxxbJRVYPoY3J+mavqlKHfC6jPG0fuk/9q4/651iJjGfE+K9osXFtk9Ctp+n9XqReOgqNpwC9pb/ALq707+n9dI3dqFTDSs7i+5w/wCFVV3WetVeH1Za09mNAVXVanWVji6rqpZSedzrgp6RtIaPpLRw91bUGuna4tEYycHm3CbVdcPEDYtGooqKKNwDSACSDcccBYaQ2NuTb0UilIFC7eA1xlb5jnAaU1cWE/VGsz+V+pVJ9CHW/wBlVVNZUTkmeeaZx7yPLv8AdR2utbanOk3NDNrbjl1uVBedOdT1GiGSNsEc9PJh7H4wcEAojoOmK173x1FdpznZ2yMErAfQEZss64HFkoYNpvyqLw6PowAceoYiD6Uz7qTTUnTNI7dV6jV1ovcRww7L/UlZsi9uCk2kZHZNGpk6pOnmcdP0jaCKosL33PaBjntdA0nqCug8Rksgq6bMskFR5mP+qpJzemps52knPukprjxub7CLqaY1Q1fpqeE/FaH4Uh4+GmNv9UH43pSG5j0yunI7STWH+izANuBhITxjlXRszrPT0Toqym0sOqWC0VMRaJp9Xnl/0VM/XNSl1Z2oiodHVnG9mAAOGgensqYOObAX90/efDv+om2SmjTjqakqjfWtGpaiX/OgJheT6m2Cn/inS7JI3R6TVPZciVssxN22xb+bfZZAvvyMotNGJfEyBZt89ymjXN6o0eFodSdOUjX4H5p3WPqFE1bq7U5hLTQPjooR5SynZsJ+p5WYeNlxbF0etqC+Z0TDeHcCLgA8Jojl4c+7yXXNyb8rdVfWtLD0wyh0mCSml27LEg7fU391guXWOMpjie6SoKJN17m4TN4A+iGHgtscJpJtm4witJ071RUaQ19O9janT5ARJTSm7TfuPQq8g0rpfVGsnpqyvo/EcGugLBJtuex7C/qvPx6d+VKpppItzY3EbsEJqPSafSdA0x8Lo6B9W4yCMy1z7NY7tdjfosnrFNRsNTKGudO6RwAb5WMzi3tZQzrM8zbTEkEbXZ9OCmvnEos57nb28nuloiEhsnGPS1wlkhhmjx5HDv2XBhPIt6hKLftwBawRUF8D2ONxgdxkJGi5AU0sLgdpIPqgPc+MFskbS09yLH+Cga3aPr9Us9pC9srQQeUgcM+XJ90khFxtHIRB9H1eo0aXZIXvoibX5LF6hoessnjic2W7SMOB4Xkhs5j2uHbhP0vUptGqA6Pc+lccs/b9FLFlfRtPVtmjaQ4bz8wBU6KVhjDX5aV5r0/rkcsEUzJQ6NxBaR29ls6StbMBtLcC5AKspiyrqXfCSyMSMPlcw53A/VeTdZdIvot9ZprHOpjcviHLPp7L1unnBHzXASVLGVETdrLuPN1rB81EAm7QLFSqCZrHStewPa5tsm1vdbXrfpTw5JKvTI9tsyQf8tWDpnbZTcZ4yLrIiSDY6QA3x3Wh6Pq/BrqbALZAWOyqCpAZUeXjjhSdNmED9zD5o3h4+izzmxrhcr017mneGjF/9EA3dBIBYkenomMmsA5xObG49LLvFEbsXN8YxdeSvVAHA5aRdt/soznlptyjSyBryBhRJXlzrtcLfVFNjc7c64IsOFNixFfIBQomgm9jlFmeI2AAY4VQtNTgm5Av6qh6hk36jUloPh08QF/daaFwijB4aBdYPWap/wAI9wdZ1RISR7Lpwm1jncila0lrnY59Vx5xkJzNxaLgW54XOIPYL0PK4cI2drrcAC6AMkBGYf7hHdFLHkPt6JzG2ZwkDhsdbAJCVo/LNji/3QNcBckFJYjlO2kHy2CeWki7zcoOA/KcR7BPp4r+Z1z7XQ2A2Fzi6M1rnvaG3sMoCvLbnBI90OBm8vvYAZylZG+RjGm991iUsoAJa3gHv3KATnX4FvYJNxunOue1gksECX49UYO8gFh/7TA24wMJQzOTZA6Ukk3wisd/2rRz53H7BBeXX2gXUstAo4ScEl5QQ84yES4acrgbjygCyRzrM29lA64znlcfYg3Q7gJwcb849ECk25S4db17pC69geyRpH8oCygNigsMbT/ui6fTyVdQ2niaDJLZgubC5KKDEySlM0fiRgDezdbdnsV6Jo+laRUUmn6pQ6fU0w+LbELyC7wRz9AQFZNNQtb0DSOmunJBWMbU6pO0CPc7DT3LQPRedd8ZJ7hel9WdZUDq6Wk/CoayOE7PEe7JtyAbLBavVUtXUmSgo/hGuHmjD9wv7X4VqIW/yBoAFje/dNuBYXye6NHRTyHEZt3PZH/DXNia51t54G4KKgOBBzx6p1OL+J7NJVn+BV7oGyNhcWu+UDN0kek1lOJHTwSxt2G+5pGUFW4+XgfwiVH903tew/2Q3NP/AAiVURa5jw14a5oALhye6gGCDjumOFx7p9hfHKcCL8BUADPW9km33Uot7Dhc2PIBKAPhnkD+VIpIHyy7WAXAJPtYIzdjJHNey4GAQ61j6p0E202ySe97KAdRA1r/ACuGAL/VdtPgtcW2aCQESe4e5zxck4THPJhHcB3BQNa8n/0nNty61kjL+mVzje1xj2QKCC4Yx6FJPvqiG7WjbgAJHWxtvdOY/afKdpQVz2ujNinuedjL9hYi6m1EbJNoJu4DlAqqR1PEDL5XXFvcEXuqCk0T4R4LJop9tiCQ5jvf1CgGM5BF/VEjkAPqeE4XNxcYyiI9DVz6RUGWnJdAT54//S9O6b6giqWxyROBacG/I9ivM5dpJAwCgUdVPplT41MSWE+dnYpZqy4+hKDUAJLMd5SePRW1LViYkC1xzbsvLunddgq4Y3h5t9bEH0K2mm1zNwLTYFSVV/NC2eItmAcScFeadZ9InxXVdGzbMMuYOH/T3XpLZg6MWKFUbahjo3ixGQbLSPnWvjdgkOa5psQRYj6pKQCSbaDt3gi69P606VmqIpamkg8wF3Wb8y8tLXRTWcCx7HZae3qpSfWr06rfLRxbyb22E+4wrOKRzg0yAX22P1Wf0Rx3zRAbmh+4fQhXTGnaCMtvYry8vr1cfcCqi6MW27rnsojiY3Y8wP8Aop8jiWX7hAfHucTtustJ0dg1zR5ja3KM4sJ2OIJ9PoowkMYJIx3RBIxzXFrS1xNsqgeuTNi0aYtPmd5G29SbLB6u78yOPs0ALX9Syt/7KmB4vIR69h/usNUb3VMz3NNwbldvyn9cP1v8ISb4ukJOOy67bAg3dfI9E82Gbi3ouzidBfeDfjKW9t2y6GH23WwLJ8R/LJIvlFPkFo2m2Dlcz+2frwlmBswHI24Q2YcDf+FAQ8Wubrs3IF1xzkc+i5u7dwgNxHGAcm5KJTPc2UyNvcBT9M0Kv1ZzG0FM+Szcu4aPqVeN6Rp9O2nXNXpaVzr3jjPiOH2QUUc//bSlrBd7uT2UdlPIfM64acrUU1P06ZYqWlqKyrc5+TsDLH9Nr9k3qHSn6YC2SnqaZr3WY+QAtOPUIMu8BrbHn3QtpLsJ9Vf4hzXm+0AJgQPuSAACAVzXHfY9imWO4FriLFOjcQbXuT3KB7sOuCLo7nf9vED3Y4j2yoQa53zKXNdjY2OAsWAhKBbu9h7JC4Ei4/kJreCN33XBrS7J7qB2zy3vlODLkZCYRd2CmE2xbJ7+ioOA0HJTLemUHN8g/VEbxwgt9Ip2V2qUVNMS2J1g6w7L0TruY6V0pQ0dP+US8uGNpG3gheWNqJYpI3xuIc0AgjstbqHVcGvaVDBq8RZUwNIE7RcOHuOyQYx77klxN10RO4WV0NEbUAmmrKWRreTvtg/VWmlaFolO0T6xq8YPaGLJ/lMEOpje2hjlDXC583pwtFoVLS6HoTda1WEVBmJbS0zhyf3H0CoNV1uilnmhpIpHUhbtYXnNxwVqajVNC6o0eh0+ao/D6mmZZjnjyHFiD6KyIoNS6w1mqjcYqn4doNwyFoYG/RSNH6r1YVAgrag1cEjRubO0OF0X/oTVC0yQeBVQ2w6GUOuh0vSOqxTNkq2xU0YIBfNIBa/snsauP4PUNFqdS0fSNPZX07rSwyR77f8AkF511HqNdqPhNrHsMbPMxjGhrWE8gWWyi1LT+l6WrjoakahqFU3ZI5mI2D/lYWuBc8bjuwloqXANJwVzW34RZ27eO4Q2GwWVO2lPawu9vdJHI3dZwNvZO7WvlAsrCZ3ho3E8JGt8wviyJUFzalzhg4tYoLnOcLA97oD1EZEhIODlNwI3AkHISVB3SXJPA7prIfypCTZA54DABxf0QicYumuNsFxKbcZugfus1c52eEE/ZNLrclUSGPANjypNfV/FQta635bQ29vQKu33KQPNnea2EAyNhBHHquvvcQD9118Z4ThHd1zcNPf0QDk3A2KESCLGyPWRmN4uS4W5CiHPCqH0801BUePTXt+pnZwXofTmvx1MLZGu9nDu1edh1ueLWSU08tDUiopCRnzN/cFLNJce86TqJnqGU5d/c+VbzTo4GQNadrngZJXjPROp09bUR1QOGMsW3y0rdv1GGMB3iXB42lONxa0moahSQtkaXgkCxtmy8C/qbTRwdRGSnI21EYeC3i/BXpkcjaiKWe2x0ju2bgd15T1xPFJXwxRSeI2nDmOf6km9ld0qHoc5bWsAOJmWP1C1FNY7mC2B27rF0DvDMcnBjk/0WxYWuaJGHkXuvN+k9vR+d9DBosQTh3dDDnNc4CxHqiRncCDaxyCohMjHuGLXwubokySHAIvxcKTAd020ce6AbN5OR5j7Ir5201C+V1rht/57KozGvVW6rrJLA2tGz6BZcXIcb+Zyn6pMdrWC3mu511BabNsvVwmR5ed2kLbJAMpQLlPjaL5WmSj5CPVP23jHa5ThsLQMgX5Unw98TNtrglRUaXEhAOALJluyc++4gjN0Tw9zQgFfblbvpzQ6Ci00az1IfyQfy6bu89rrIafEBXU5ksYxIC4H0utb/UeCrFZSPMYbQujBhcw3b/8A1UA1PrCuqY5KOkc2io9zi2OIbSQSTYn+Vnah5LS5ziXk83uojiC5178p7Tdo754UD99w59g15PbGVfx9T1svTtRpNbJ8TE9zTG6Q3MVj2KzzgDe31K5pICAs1nvc5tgExrw1tiUjMA3XGxxZA4vAzdEpy0n1QHAtBAOD2RIHbGmyB8zbMABt6oj5DLLteSdsQaCo0l3BHeWxyk5+W3+igY6Mu8rT/Kbsc0WuSkDrbjnPCRshb73VHNO03B4TSXXvdNJunNQO3HvlOEjjfixSWwnNGED3O8wsOyc1pc15v2SOabg8WspVBTyTCQsaXEcN9VBGYGnFyEssYYObn1Wri6E1KTS/jpHRRRnhrnZWXrKd9JO+CZhEjDZUAOeE4NIcLIYaSblOAygsYamsgbtp5pWezXEJ7KmofUDxpZHbh+p11Ejc8izSVxe6482R3UCxSlshuTyrQhkhafUC5ConSkONwp9LWhsYu0ccopKuHbute18EqA47ccK3lnie27hgqnqCBK6wxdVKcS242/dKXWIUcZOEaOxIDr8oHzSHxnHn3Q3SWyEsnLrHkokTYC0+I5zXDiyDnyF21xFsJwmuHDtZDqHBzWAC1ha/qhNa0d+yBrngpN2b35SYtym+qAgdcLpGOxiyEFKdN4jGtda47lBHsQUSJtyQ4gKfouj1es1ggo2bn9z2C9EptB6f6Womv19zJ6u1/DByk9jzOGkjkiBkeW39kSaQwQ7GgOFsOC9apKLTOo9PjdLpJo4hcQnHmHqV5b1TRQ0GpS09O8ljDbJ49lcRTvf5S7cS63B4URxtchSTY4whujwgDYkn1StB5uiiMtZfCQH3+qaDaVW1GnVJmon7ScOaeHLTad1s6nkEk1PIXjs0ghZNg8w9017ASVCNXVdc188ToaWJsAcSbnJF/QLOEOkppCb3DtziTe/ugABreRdPiJEMvms0jPoUUSmc0Rvae4uthp7xJprC0ctBWHjeLgD1Wi0Gpd8MIwcNdYj2XL9J/XX86v2yB0Q2nv2SPa3A7DhR2/luaABbJKNu3gEEWXB2PkLZsF3ltn3Vdrr/AA44YQcPcLj/AMQpsUTdwDjlZ/qCo3V0lziFm3+V04zaxyuRQ18vi1LvQGwQ2ZPshXubnvlFZYBel5ilwufQcLmkC9khFxhKG7QEQ4e57KVDLskYQbWCibrk2RgwguLuAEUR8gc8u28p0crS0qI0kYPdc35vZRUvxDkA4Wi0XqaqpaU0NSyOsoOfCmzt+h7LLsaXcIsTXBxzbCDaNk6MriXSRVdG45IB3C66ni6QjklZJPVFu+zTb9NvX7rFROOeF0hs7/hBf6pVaQypdHpVM50QNw+R2StHpGudPSRsin0iFkob5nOdZpP1XnRJ34FinAOdybFUbKuotP1eqkk0x9NSAmwhc+2VVaz07qGltbJUQflOFw9mW2+qpmOti63vQuusmYdD1QmSmnO2MuzsKmDCh1/mCUODGEdlourtDGlTymPLGvLCFmPNtNgbEoHxHe8Ac3RJ2kTuA9EOAbJATnKe78yoO0XKALy4cC6cxtx5sIhtfK7yjBygYGeic1nqucQ0XAKaHkoH4C4O4tymg3TmYdwgmUlPLWVTIIWF0jzYAL1bTtH0vo3TIq3VvPWEbmt57Ku/pzpMVBSS6zXBoYB5NyrJ5avq7qP84O+Ea7yjtZWTEWukVddrFY6vq3+HSC5iiPACwvUdaKrVp33BF7AhbHqfVodN06Wnp9okt4bAOw9V5lI4lxN7n/dKHueb44XF2L3Q9xITbkhRUhs9m2RGHxCB6lQ2+6K0+dtkBqiLZbKNSs3NQG3LiXXsFIFSI2+RtlAaOnBJDjgjCg1THB9rXIT31Ty4WxZAkeXOJJVHRxuPOEeNjWu85Ue5tclPifY5Qd4oHyD7pgdc3ISuaOyTYbAoFe4bB9VzXDHqnvYAwWThGxzmkAhBEIAJuluDhElaBIRbhcGDBKBrmx7BZx3d0+Jm57WtG4k2AHdL4RLhtaT/AAtp/Trp38Q1MT1DHCKLNiOU+jZdNUUfTvTLqtzWsqpW/Mey8/ZSV+v6mXSB1RITh3oF6rrUUNWzwKt4jpWe9uFmavqPTNJa6CgYIiceIOVrP4zotdrkfTegxwPcJK/YWsibwz3K8inlkqHSyym8jnbiStFrldR12928+KD8x7rNSSDzBovjlS1QyB3wil8Qba1yom4k5KUG4QOkcf0iwQ2uNj7p+CbFNw0IFjNjxlElbZ973xdDJAOAlLgQL3uoBvNx7ozNzKOTizjZC2hzgAbp1WdrWxjtyqBNu0k2Vtocg8d7TgOF1UsDSfOSB7KZpz9s0Z4zYrHP41wuVqnuc5lwADZOpnDYQ9ouEUNGxtje4THw5wbLzPSUSGGB0jiMAuWOrpy9j3E+aR1yr7WZ/DongH5vKFl6p7S4A/MAAu/5z+uP6X+AjLk88pIxget0p5uV2cT2usPdK03Iuh2tlc13KiiBw3BFe8kHOCVHafMnvdYIFYNzlc6ZpMtY17mxkhjS8gegVNE7zAeq9N0FgpdA1KogIMnwwaB9eUHn0soa8sjs23dAfuIJJKFwb3vfKJvIj9lFOgbuwDlPkbsf5zdBY8BwI5XOkJcd2QiCF+4khGY8yCwAuFFjcWg+6JC8tde+QgedpdnykYV70jPBSa9STVWYmvCpMSkuJG4q46U0qWv1iGPaTG1wc89gAqPRutaaA0uovmeCJLPYf4Xk7pQ2GwF8rY/1H1gSVRo4HeRoDTbus90xosut1bYGYjBu4qVUKhglqahogic8n0C11F0X4cT6vWKltLHa4bfKt9YrNP6Uom0dAxjqwcuK8/1PVqvUpy+qnc//AMb4QXUs+gUhc2CCSoI/U42BUCo1eD/BooWj3yqI3uuthBZ/iUUlPJHNTM3H5XNxZV4d6Jlk+GN8rg2MFzj2CB1rcK+6Q0g6tqsURaTEDd5R9E6UrK57TK3w2e69P0jSYOn9LkMIAmLcuVk1NCl02OsoYaCJ5bTQO81u6dNTshroYNOjEXkLS7sgydQUum6a0ySN3uuSPVYjVus6iaYmiNgMXKtGnqtD0yKd8moSNkcclzisp1D0r5JK3SXCalGTtzZZqpraqtlvNM97nHi+F6x0bSfhnSdQ6qFmyMJsfop9HjZba6dCSCbc+6LWlrquUxjylxIQwbKKYcOKew2cCmO5ulGSAOUEqAvkmMcbS4nsAtZoXRlVXVEbav8AKjfkYWg6B0Kmbp4q3sD5TnK0Wo6pFp07Jqlwa1jTYBWRNZPV+jqDTPDbI8XcbbirODoTTBTiSV25pF7grCdT9RyavWPduc2IHyi/K0n9O9bqKqf4GqJlhItnsno9rJ3R+hPdZswx7p46O0CIXfMD/Ky/X9LJpOq3p5ZGxvyACqrQotQ1WsbDFLIR3N0uHtuafpfRZqnw4CXj2TOoej6SlgD2fls/cey1em0NJ07pXjz+aUDJJuvPusurpNWvBEdkA5t3VsmezUxnQHjwxSU1WyRr84KM/wDpwWjc+faRnlYvStWqKGpZJFO8BpvtubL1yhqP+pNJMjXvZIGZt3Kkkoy46D05ga6arBcefMpB6a6boyDUTg/UrE6o6ppaqeGoqJLxuNsqjmqppfnkc4e5T0e3qnxHTNC4NijY997D3WpOoUmm6Y2bY2ESNuLBeJ9LQNqtdp2ym4BuvR/6lyth0VscYy1tgfRWJWD6k6kqa+tnDZSYNxDVmnve913PJK5zriyF3ypq4V5wL5Tm2Nx7LnFobwmtdY3CigubZNGCiuuSk2nsFUMORc8ppRAx3oUvg4BcQEAbn1Shpd9CjlsTBk3KDLIXWDcAIC3bCMZcozyXOJJvdJYpO+UDhe3CPA4hzh/KlxUvjaO+awBjcAosbSw2I5ClWNZpkrn07c9lLabjzHKqNEefBFjxhWbw5xuF5eU9vTL6ZjWJSZ2Rl2BlU05vKSe6lVs3i1L3+6hPNySvVwmR5ud2nMdYp10JKCtsCbiUoOENKDhRT2/MnvcMJjThITc3QPac8radE63Ex0un1h2xzsLA8/7LEtN05rtrgRyEGsr+jq1j3vpS2WMm4squfSK6BgBp33HOEem6jraeJrYpXDaO+bqbR9ZVkT7zxxyg+oWVZ4wSsNnxPb9QmiCRxxG4/wALZSdW0dTERLQtBve4ClDqrSY2gxUQL7eioxtPpVbPbw6d5/hXun9GajVWc9vht91Om65LWltNSMYCiP6+qfBaI4Whw5TA+n6IkEu1z8DklaSWr0/p3SH09KGmrLbOcFg6zqvUqy4dL4bT2ZhV9RVudCd7iXHuSgjV0zqmqke8kklbz+lVVTwzSxSkNkfgErzppsSSjx1MkMjXxOLHjgg2SDT/ANQaCoptclklBMb8td2WSZ8xIytpTdVNr6eOHWIfFawWEjcn+URs3TF9wxfkFqisSxrnE7QSfYXU+j0auq3ARQPz3IstVHrnT9GfyKYyEf8Aih1nXJaCKGlbH7lEJpnQs7yH1sgjZ3C0MMGgaFFuL2SStWArOp9SqmkSTuAPYKofO+RxL3E37kqj2/p+ufqO6qLBFSM+U8XWb6x6uLZn01I7d2Nis3R9XTQ6OaHaBYWDh3WcdI6SUvcbuJummDVdXNUPvK8utwEEOIYUxx8xJTw67FFTdEj8bUqdhsLvC9X68qXUXTUUMZ2hzQDZeNQvkhlbIwkOabgr0jS+rtP1ChjpdbYdzRbcRcFWI87Bu7i5Rm088ptFE8/QL0V9b0rTne3a72DU6PqrRoyBTUlz2JCmGsHT6HXzkAQObf1C0umdEBrPFrpmtAzbhG1jq7B8CNjPoFkK3W62rk80z7HtdB7b03FT02jPZSyNeWgryjqiurtQ1OWKVr9jHWACi6X1HV6ZZrXOLTyLq4pOq6LxvFnpbk5JVvwUkej1VY5gggeBwSQvR9A0ui6a034upe0T2vY83Wbq+u2+GWUFI1n/AJOWV1PVauteXVEzj7Xwk9Cf1brTtZ1IycRNw0LY/wBKYI2xyzG28Lyt8mbq/wCkddl0usaQbxnkFN9jU/1A1epkqX0weRF3F1hJHhwAHC3+qUdN1C8TMlaxzuRdRaHp/TKSo/76pjsM5cpfYymm0U9TM0MjcQTa9l7J07TjQdEL6iQNu29llq7qbRNOjMOnxCV7eHNGFk9c6nrNU8rnlkQFtoKs9H1C6orG1mr1EzD5S7HuqgldJk3KbdQWGhVYotThnd8rTles9Qsi6j6bJoCHybeByvFC4q30PX6zR5w+B5LO7TwVYIFXRz00xjlie1wwQQkZG8kDw3X+i39H1fpVdI12pUga/wBQLq5frnTEbA9sbSR22oa8nfTSZBY4fwnU9FM94DYnH+F6ZP1H01I4bobE/wDiotb1holO3bR0heR3ATDVdQdJQx0Rq9Qf4bbcErHVtRA2rkFO0eGDYH1Vj1F1LW6sBGXGKmB8sbeP5WdQHfU3vYAID3l/JSFpSAOJ4QclJukNweFyIQmycyMuN+yIPDay5OUN0xLbDAQT6isEdCKWE+U5coUbt1ruyPVR7pu7zJhrRaJJYSj0yrqOSzeVm9FkDagNPDgr8hefnMr0cL6Yh7rke6YcEhNccrr+q9LzFXBIlCoVcuXICMyLFcQmswnsIvYhQc1KeUQMByCux3RSA4C578rm2Jwk23KgJG7BTxZpugjAuuDroojgTkcJd9gmNucp1roCRPu4XRp3bgozcHCc7hAvonON7BK1rbApDYu9lFGhO1pQrgkpwNr2TLBAjl18LnDKbwg7kpHD0TgElsoGgokZG4IXdPbkogsjgXJwHkQXXBRY77UUgJKfY+qdG0WN0NxIPKB1s3uiCbYLA2v3QN101wQEe4uOTdDMlnCyS64AEoFkeXEFcDhO23cApLIoowC8hBEY6xBKfI4kJ0zoy7yIb3kCwCDmgfqT2P2nyqOSe65riERax6jNA3yPIKh1FVLO8ue9xP1UcuvynRtJQSaYDkldPIBcBDALTykeMopSTbKb2wlPCaCgRIQU7uk4QFpm5JPCZJJdx/0T2O2xn1UQnKIKHXNk0mxTGusUj7chUFleC0BNYwHnhcBdmeE18mNreEBJJWtNmBC8U9gELlIiCOeSmEpWDc4BdLtDrDsgbdNLkpskAF1Uda/Ca7lF3NbxlCJzcoJlHIWSMPoVrY3NcxrubhYuJwWno5t9LGWngWK4fpHf86xw5S90oafRLsd6Fd3A1KEux3oU4RvH6Smqa3lceU9rHftK4RuJ4KaY4YC4FP8ADfYeUpPCkH6SmmODiO6eDcZTPDf+0p7YpP2lPRh7GgpC2yJFDJY2aUngyk22lTVw0tsEgAaiOgmt8hSGCU/ocmwym7sYSsTvh5v2FcIJh+gpsMrrgLnG5XGnm/YU8UsxGGFTYuU+Lmya/wCZEhppw7MbkslNPuv4bk2GUIE2SXRRSzluI3Jvws/+W77JsMoZKVpvyn/CTn/Dd9k8UdQB/ad9k0yg8cJrrhSPhKj/ACnfZPdRTln9p32TTKh909lroooqj/Kcntoqi39pyaZUZ/KLCbDKf8DU3/tORGUdTYflOTYZQiHAE9kEk91ZupKnw7eEVFNBU/5Tk2GVFJIXXKk/A1P+U5KNPqT/AIRU2GVF4RIWF7xZSW6bUd4nIraSeP5YzdXYZQJ4vCAJKBcnlTZKWqkPmjcUMUFT/lOU2LlRAMpzxhGNFUj/AAnfZJ8JUn/DcrsTKjHK4AKWzT6km3hlTqfRp3Nu5hspsMqoDCcp99jVZ1VJLE3a2MkqudSVJyY3K7DKEZCSnly4Uc/+W5PNJUW/tuTYZTQQQmOsMoopKj/LcudSTnmNyaZQNyQlEdSzD/Dcm/DzH/Dd9k2GU5mWoDuVZQ0sggLiwqEYZLnyFNiZQO6Qgk5RTDID8h+yTwn3F2n7K6ZR3NAp8cqGVMla7wwACohjf+0oGXTSU/Y79pSFh9CiHR4BKC4kuupBadmAVHI+qo48JzG3aSuDCfVEZE4sJymgBKQriEpBsiGsNir/AEycCnseyz5B9FKpZixhCzzmxvhcq4ZTQ3F2iyc+KFrrbRZCG7I+ycxjnnK4u2JcVPC5t9gUmOnp3t+QXUWC4NkbLHeyzWpBYqSmJILAitoKYfoCYxpHmBwVIFzlTauQjKOnH6AunpacMwwJZQS07ShecjaU0yANghc62xT4KOAAXjCHFBZ1yrCNmApasgTaWnB/thP+EgBv4YRC2xRmt8im1cgApoCcxhPFJTXt4YRRhFYw8qaYaygptv8AaCa/T6cn+2FKY8bbHlEjcCeybVyK80EA/wANSIaCnI/thTi0FKxu0qbTIjNoaa+I8rvgac42KaxvnRTGbggYTaZFfHpsH+XhLJp9MP8ADVm0YSOaE2rkV8Wn0xGWKWNNpLfIiCPNwjNYSE2rkRHadSAfIhihpr22KbY8FIG5ym0yI/4fSkfIFwoKUfoRS/zWTmXJsm0yBfA0gGWBK2ipf8sKU6O7UNrS11rJtMN+BpiP7YTXUFL/AJYUxhAtdEaWOTTIrxp1N/lhObp9MD/bCsDtHAS+XsE2mKqooadrfLGFHh0+AvuY1a1NiLJkIHKbTEUUNNf+3lFGnUxGI1MawORmgAWwmmKl2mU5/wANMGm0oP8AbV0QLYCjuaLppivGn044jCR8ELW2DcKxu23a6E9gcOE2pivFDSuyWXKHLSUjeWBTywjhBmpi9NOqC2moiflCL8FSEYYERunute65se11lNpgfwNKP0BCfQ037ArJsNxe67wNxthNq5FWdPpXDDEM6bTD9AV02lIKbNS4wFdqdYp30EBaRtwoP4dAHHyK++GcDwmOpHHNim06xTjTqYcsCFLplORcNVpNA9vYoe11rWV7VOsU502n7tTZNKp7X2KyfC7cjgAR5GU7VOsUg0mntloQZtJgGdqu3FvCDKGltk7U6xRM02nJIISu0inJw1SnxnxMcKSxhDeFrtU6xW/g1ORxZBk0ljWHabBW0gLRwh5c0hO1/wBTrFC7TIg03UYUURdwr6dnlKguiN8LU5VLxiGNPgI5Q3afC04KkyNc3hDAf6rW1nIEL8+iOw90xhBCIzmyUHjxlGd5hdDYQAi7m7VhqFhd5S08I7CbWUVhzhSGOsEU5r3AkWTmAjKawgvF+6lEN2ggKK4eZoR2XAQGWBUpou1RTmkOFijNs1tigxNs5GnadoIUAzbf7Iu8tAtlDjG8Z5TwwjBUVHklN7hSKV5JyhFg3KTTsAKKmsfhFDdwQLWspUJACBGc2Uhri1Ry4ByO0hyKdcngLiDtunN4TZX2FuyDo5AMPsETx4h+sfdUuqGURF0VzhYeu1XUYXuwQFrjx7McufV6l4jHZa4FBlma05Nl53o3UU8jyyW90XWdcljA2XJV8d3E8kzW8Y+Nzr3H3R7txZeZUOt1L5AHXF1udDndPGC5x/lTlwvFePOVeRvxYogaCLlRy+NvLwiRyB+GkFZdHSWb3QI5m7sEFA1yV8NOXNGQFi9N16SXUTE64sVZxtYvKS49E8QWyQmGdgOXD7qqrqh7KPxGntdYOv6lqI5i1o4KvHheRy5zi9OkkY4YcEsDmjlefaHr8tU/a/laaomljp97b8KXjZcJzlmtBJUwxDLhdRfjWPdZrl5nrOvVTJXMbcW7oWldQTtmAmN/db8VzWfLNx69E8Ft3OSyuaW4Wd06vM8LSDyrAPcebrlfTrLozcv9lJYwEcqG1xCXxi1BMexvqgOFioz6s35Qn1RtyhqY52DlQX3MiH8V6lMdUjlETml21cJNmSoLKy5sukn3AoLKKpLipTXh3dZ9lQWIg1Atyhq6OT2suMgaLYVN+KYQzqG45Q1aSua45sgFrBwFXu1BpKYa23JQTZQDwFCmBvay740WwguqgSiBSA34QjcOF1I+IY4ocskfYqppWsa45CkljQzIURtQwIhqWlvKGulYwtUdrAAVz52juu8Vu3lDQZ4roLYGm90aWZoCjOqQLgKoDUU4vhQ3RhpspUlRcZUR8rSVuM1UxyjkKSx983VC2Vw7orKl4Xa8HGc1+JDZPa8nCpGVbwU/4t98LPRrtGhhIsnPkt3WdbXSA8pxrXn1U6Ve8X/jAEZUuKoaWcrJ/FPJRGVUg7lToTm1zZGkci6Myoa0ZKyArZGjBKIK2QjkqdF7tlHOwG4IsifENPcLHR10nqU/8QkU6NdmtErGm4ITnTsI5WSbqEhOURtc8+qdDs1HitIvfKcydo7rL/HSJTWvBU6r2a4VbCBlFZVtAtdYs10gRGV8hAyU6HZshUMJyUaOpYDysUK+QHkpRqUgPdTqvZvDVMtkoL6hjjyFjfxSQDukGqS37p1OzbMljIs+xVXrOn0tTAdjbGyo49TlPqn/AInL3ykmU3Vdpuk+BXkn5bqx1zS45ILg2NkM17r7g0AoVRqMjm2eAQt7dYyYoYKeaGcBrgQCvQ9BltSgEWNli/HAfu2BWMGpyNbZg2q87qcZi61KomM3kvZWWjTuAHiEg+6yw1GRzrnKlxalIMALnY6a2Nf4VTTlpsTayw0mjCHUvFZflWceoyWCDUai8n5RdJbC5WihjjlovDkObLFa/oDS8ujFipf4tM3hMk1SWQWcAVeNsqcs5elPolA+mqwXHF16JA2KSlDX+ixzauztwYLo41WZoHCcr2TjJxRep9LYXudFe5WZj06YPBHIWsmr3S5e0FCFQxv+GFvjzsmM3hLdS+m3SxBjZFsI5oy0XIWJbqGzDGAJzdTlv3XOzW+NxuDJGBghRZ52g4IWXbqsvATX18hNzypjXZonyNI5CjvmbflUT659kH4tzjfKYmryWZt+UHx22OVUvncfVD8c+6YauBUAHlGZMCFnzUG/dFZUkDumGr4PaueAQqRtW7d3RzXEN4KmLqY4AOSHIsFBNUXZsUranaOCriaMWkPuld5hZRzVX5Cb8TYcFMNSL2CE91lHfVX7FDM5PZXE0V0hJwUhc71QBJc8JXS+yYaNudZCdK4Gy7xccJC8dwqhPEc7CKC4N5UcvAyAmie/ZMNGkJPdRpDlc+a+LKM6W7lZEtPeT3UVzgCnvkLkB/zLcjOv/9k=",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image 2:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image 3:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "## Image Analysis\n",
       "\n",
       "| Image | What can we see in the image? | Where does it take place? |\n",
       "|---|---|---|\n",
       "| 1 | A chalkboard with \"Back to School\" written on it in chalk. | School |\n",
       "| 2 | A chalkboard with \"Please ask for our Brunch menu!\" written on it, leaning against a brick wall with potted plants in front. | Restaurant |\n",
       "| 3 | A chalkboard with complex mathematical formulas written on it. | Classroom |"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prompt = f\"\"\"\n",
    "Answer the following questions for each image.\n",
    "Present the results in a table with a row for each image and a column for each question.\n",
    "\n",
    "QUESTIONS:\n",
    "- What can we see in the image?\n",
    "- Where does it take place? (answer in one word)\n",
    "\"\"\"\n",
    "caption_b1 = \"Image 1:\"\n",
    "caption_b2 = \"Image 2:\"\n",
    "caption_b3 = \"Image 3:\"\n",
    "# Photo by Deleece Cook on Unsplash: https://unsplash.com/photos/zzjLGF_6dx4\n",
    "image_b1 = load_image_from_url(\"https://unsplash.com/photos/zzjLGF_6dx4/download?w=600\")\n",
    "# Photo by Natasha Kapur on Unsplash: https://unsplash.com/photos/ndAHi2Wxcok\n",
    "image_b2 = load_image_from_url(\"https://unsplash.com/photos/ndAHi2Wxcok/download?w=600\")\n",
    "# Photo by Roman Mager on Unsplash: https://unsplash.com/photos/5mZ_M06Fc9g\n",
    "image_b3 = load_image_from_url(\"https://unsplash.com/photos/5mZ_M06Fc9g/download?w=600\")\n",
    "\n",
    "contents = [prompt, caption_b1, image_b1, caption_b2, image_b2, caption_b3, image_b3]\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CrMHz6A3E22d"
   },
   "source": [
    "… or make comparisons between images:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 8002,
     "status": "ok",
     "timestamp": 1702337486791,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "u7w31hUtE22d",
    "outputId": "f14cd388-e7c3-4d82-d006-b8b94961fe10",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Answer the following questions about the images, with a short answer and a detailed reason for the answer.\n",
      "Present the results in a table with a row for each question, answer, and reason.\n",
      "\n",
      "QUESTIONS:\n",
      "- What do the images have in common?\n",
      "- Which one would be of interest to a mathematician?\n",
      "- Which one indicates it's the end of vacation?\n",
      "- Which one suggests we may get a coffee there?\n",
      "\n",
      "Image 1:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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ZC4D8xgHsVNXqy+mS1MlXIyRjgL4JCv2RlkY3cqSI4W32PYE2RjHC3jNH8osmEjseygVdO6rlMb2ENHf1VgGxtH95v3UmKSC1jIy/1UPrNy6NGwku4C6k0uJ4EkVgBytAYopnm8rCEeKmpo2bQ9gH1V06qWuc6moHmnB3AfyoGmT1UsjX3Jb3B5WodT07gQZGFv1TGwU7B5HsA9lNMRw87SHEEql1iWt+JjbRg2tlaB7acD+42/1Sx+ADl7EXAoJZYaRjqi5eebBGL3OZ5QcqQx8H6pGFEdLT2AL2fdBn9cnqKSjJpmO3vwbKHpFRVspQZC7e52dy1D5Kd2C5hH1SA0wbY+GAmpgdM90kbScFGeBn1TBNC3DXt+l0jp4uS9t/qjTgSMKq1+SsdC2OkaXA/MrRs0Jx4rL/AFS747WbIwn6pEvtk6ehey16ciTu5aCHxaWmuwb5D2U9rmjBfGP5Q3PDTZ7m/UFXUkxVMopamvdU1TvKPlaFamYshOxtyBhRxJI51mBtu9ypQYRBhzN/1QinqKaorqmJ9Q7ZC3JHurWF7Yo9jLWHCrKmhq5yXOq2s9gUCmpq2mqmb5mTRH34RNXhD3WJTHujYCXPAt6lDqfEELhDI3cR6qmdpZMe+Se8pzfcotq5jlbI27HjanscJD5XggeipX0croS1sw3EWGVO0ik+BpSHyNdKc5KEqZK/w84A5uVUzQahWTb6acCLvZOrIa+sdbewR34urHSKKoinDgWtiAyLqp9U0ekz0k3i1E+4ngFXtBUAsc2cgWGLBR+oNMlrZYjFM1rW8i6Sioaunk874nt9LofKR2l1FbIZG1TmtvgKdptHPQTjxqkOYezipkZ8NoDixrh6FVlVSMlkc+oqM3xZ3CGLrUWGaAsa/bfuCsvJRgVGyjmeJjy+66aWpbaKGpaY/UlXWgaVV6g4Np2gM4MxCsltyJbP6iUtDJBGCZbv/UVaMtYAi+FbO6JlpYHyRVkks5zZ3CzFZPNRTmKqAjeOLnlXn+fLj9OPPjfizIwQob5Zo5AAzd6qGNRuSfEbb6pW1gebiVv3WGtWoIJBI+qcbX4VY6tG0nxGX+qBJXm3zD7ourfcM3QyQDhU/wAe/wBW/dI2reX23tA+qJq5LgR5jZDcWBpsQq2Wou25lb90AHxW+eZoH1Q0vUE72ULhCLudjCqxDWRUsLYnE3y+4urmMwMDQ6RjgPUqS2aCx8zePVVM1jK6Kqqa6KGPcLG5VvrU9RQ6eBE1znYClwMgp6ySoMrXOdwPRHmq4ZMPLD9UtSRnqh9fT6OJAC6Ui6rWyTPpxI9zxKf0rXulikbZ0jNvohNp6MOvdiavVQ0r5RSbqiLcb4uEGt0mnr4dzY9j1qHupgLF7LKHPNDnwnNx6JpjzLUqOShmLXtO3sVFbICO63+q0sVbE5riN1lgqyB1LO6MjhdeNl+ufKYmiJ191yixiTs4o+0FlkaGHCzas4msD9mHFEjbJ+9yPsIHCVoO+wCzreBkPaCdzkCISzSGznWCsnRgMz3QXsFNSuf+pysSoU2qCjk8Nznk+yE/UyW7i94H1VTWyb5ySgl5IsSus/OOV51b/ijjkSP+6KNVcQB4kn3VK0+VK298K9InetDDqR2eaSQn6pPjyHZfJb6qiEjwcJxnkdglTxw71eHVWg28R/3SfijQ7L3/AHVK2CeQ32m3qcIzaV9nb3gEJ44d6uI9Xa03a+Q/ypLNZaR5nSfdZtsTw7L05wc12Hp44vetIdXjt/cf90E61GCQZH/dZ8v7FwTQNwOQT6KeOHkq/fq8JI/Of91zdYgHMz1nCM5siMh3RucBwr44eStIzWaa2ZnoztYo3M/vvuskBg4C5jHF1hZTxQ8laturUjeZpPuldrNGR/ff91lnxvLrWCE6Mt5blPFDyVr2atRcmofdEOp0Jt/3Lli+P05Ts/tTxRfJWy/E6C+ahy5mq0Affx3rHtueG/6IgYduWJ4onkrZM1fTrkmofdEOt6btzUPwsWICYy7aPqmCn3O7J4ovkrZnWtPB8tU+ycdcoCLfFPWJ8CxIsEjYSRwE8UTyVspNdouBUO+qGNao2m4qXFZAwm1rBNNO4ZsE8UPJWz/GKNzb/EvXR6vQOJBqXLFFrmg4x9E9kZMJcG8FPFDyVuG6vQBwHxLreqf+Maafmq3rFwRFwILQlbAA75RdPFDyVuYNW00HFY4E+pUyDW9PDtprSG/VYJjLHMYIT2tiMnmYAPonjh5HoI1jSXf/AHXE/VK7VtJb/wDcN/qsOIIOdo+yjSMbf5QR9FPHF8jcTavpbiSKw/dBm1TSvB/+W6/fKxg8EOy0LppKc/K1qeOJ5Gmo6vT67WaSkp5nua94ByvfqGWKmhigp2NYxrRbC+XtMqIqTUKeewaGPBJHZe76Vq8c8TLPDmkA3BXX85OLHK9m+jlEjecrPdU9PU2s0ro52Wf+mRuC1EoKvIu9XEczZQQbLt65TK5/K8B6g03/AKeqBHXeJ4J+SVuWn/0VUfiOmN4nkX0Br2k0mo0j4aqJr4XDNxwvDesejJNEndNEzxKInD7XLPqvPy/HHafoiHV9LbYeK8pTr2mbAC96oJaVrRuY1rwfRR2CMh4czj2WfHF8laf8e021vEekbremHBlesuyOOQ2btafdMlgc04a0gdwnjh5K1L9c0z97yhO13TwLB77LLYzcBMNuLBPFDyVpn63ROb5XSXTHdQU7RYCQ/wArOfLwAkLr4NlfHE8lXjuoog4Hw32+qZRasa3Vox5mx3yCVSOtZLQ3irWkFL+cxZzr0+akaHO2m4ORlDZEALOBRqcukoYJgeW5XMeL+YLzPRDX0kb2HJUU0gYzk3U+54TXRn6oYpwGtkNwVU6zpvxE7XsGCFoJaez7lOZCHDi9lZcSzWTZkgBT4GWF0CCMObfupYaRYWWqkEiiJblK2Dz3T2XDPQrntlDbgYWVNkIDg13J4UHX3bImi9gBdXFNFu2ve33VJ1O0GJpacvNgFvh9Y5/GVc4FxK4ZKfJGGm3omgL0vOLYBgT4Y3Od5QleGNDc3wlEp2m2EDmwgXL3WPonMnbHbYwEjuUEEnkp9gGop8lVNKbF1h7LmbiCSblDZtBuQih4LPREJcA85QS5xJyntG5xSeHlAy3cosTNvmJShmLqz0oUUU4k1JjpIgLhje6CFBST1L7U8Ukrj+0XWgouktUmgAfE2FpzeQ2XT9VTxtMemwRUsIwLN81lBrK7Vnwskqpqhsb/AJS42BQXLej4ov8A5WpU8Z9iu/6X0/mLVoS4dvVZYufICXuJ97padoMzMYuguK3pqvjJfBG2ojtzGbm30Wfm3te5j2lrm8gixCsqTUK2gqTJSzvZZ2G3wf4Wn67pYXafplfJE2KsqWXkAFr4QYMFxxZPaXE5FgnbbXITDdAVzg1uDlM8VCJ90jRnKCSZfyCLd0tKWeJd4PCGLGPafVOa0C205soEO0uJuQEwkg+Urj7pWguIAQJk8nKUbvVE27TZzVwYTwga0B2HFE8IiBzmHF1zW5sQjxgile02y4II8Fw/vwkBN+VJga3xASL+yA9gBPpdA4SPbwcJWy3JJ5QgDfBun+twglMnB5XSVIjYTa6iAXF+ESnppaqdkELDI95s1o5KCJJKXu45SFtrYWxqOhvgnQQ6hqUEFXOQGQNG43Pqs/rumSaTqL6Goe18kfLm8FVEJgBa4OF1YaF1FVaHUC5dJSE5ByWqE2M25wmiMEeYXUV7ZofUdPXQxuikDmkc3WspKwuDbuuPUFfNNBVVOj1AnoiTHfzRE4P0XqfTPVUeo0rXQP2vb8zCc/RXcPr1sTtMZBfceigVcbJY3xyMD4jzcXVNp+oeIwPabgci/Ct45gWi2Qcre6mPKOsejn0rn1+jNLoCdzoRy33H/pYLcHB4c2zj7L6VnhBYHsAc1wyF531f0THVyS1ml+Sa13R2w7/9rNivInR2KRpe04JVgWPp3vjlYWvabOa4ZCE9lwS211kRHWdyLE90x8ZZki4KO9m2wJRIJmR7mvAew4sqK8vumk3Kn1FIxwElO7cwnLTyFDc0NOfsgY5m4JrCY5YyexRgc2Qp2k3PpkIPTemahk+k7Jc7DYKS8Nc7AN1n+i5wZHxHIezd9lov7chtkFeTlMr18bsPbHnzZ9kQhuyyRsjcE3RBtvcDlYaQaxlmghRGyluFLqnOc4ixNuFCcLHKCkYPKNqlwAfM7KBH8tlKpGAjK3WYe/aTj7I0Y8nm4Qy27gWjKKCG3Dza6il+IDY3i2AOVjdVmfPXBgPlYLrU6m50EI2H5/8AZZOnfGZaySQ54C7flHH9KrZnkyEprMkJ7i3dhPiA8QYXZxMdz3RWA7L+qRz/ADGwCQuJYASgXA7pQ7GUzCXsgUe6JbAsmMyfdGAN+MoOaNvYpz/MAuL3FmTe2EgvyopRYY/0Tmsc88koscZed23haLp2KSsqoaOlpQZHuHmDbkD1RBeiumTq1Z4tUxzKKHzyPcLC3og9eaqzUdTEVKwNpKceHGB3AWo661hmkUY0DTH+YC9RKDz7LD6ZHSCojkrpCYwbkN5KtEOlo6idhEML5P8A8RdWtB03qksjS2lP8lbJnU+h0VDtoY9shG3btx91GrOp46+ER0n5MgF3bBZPQkdOdBmn/wC/1L8+Vh3MpmEZPus/1x+LVdb4upUUlPEwbY27fK0fVBZqdVTybm1cjiMi7itN091dUTzMpNTYypppfKQ7JCvqjy+TxM+nsgPcbZK9D6/0Cn0mrD6RpbBO3e1v7T3Xn1Qwtf5lPgE0jujxs3iwGfVDDRy7hED3FtwbMQPdTva0FwsL8pYoi521vPKNFI80rWC5buJspNJEDM7cw7djvvZRUAw2NiU4RbCCHA2Uh7yWbRa6bHGfqiGbg59nXJTmtZbDSD9VOo4qPbIasPLreXao77F5DPl90DG7b22890fwwaZ55sRwhFhDAbfypFNuNLUbeBYoI0Db1DBawJsgSMAe4X7kXUqO4kab8FCktd9xkEoAmEgXBx2S7Q45OUoJAtn6JxFm3agGWEGy2vSlGNK02XWZmHxALQl3b3VP0xQDVdUp6bY4uLg49wRfN16f11plPK2kgrKqCh0mFoc5jB53kdgFqT+pWT0KLxa2XqLUX3hpTuJfne7sFkupKluoahPWsabyvLjfstBr+tx1dM3S6SFkGnxu3Ri+X+m5Z17WmPa8ZBvhSipZIQ8W4S7lMkjiDiW8+ihvc0TOFv4UU3fYZKHG+WkqG1VC8skbyBw76pXi5KRthyCqj0bo7qqOsY5sn5dQ0APj/wCVuqDUw4tsQCeRflfPT2PZM2aneY5mnyuBstl0r1R4sjaesIjqm8E8P+inz4r2+GrLmjY+w529kcjxfMw5POcLHUOoCZrC03I5stLp9S1zW2Iv6FanLRQdW9HQa2x0lOPDr2Nu15/V7FePahSVOnVL6WrhMcrTkHv9F9MM8xBx/CzXWfTNFrVM5z2ObMPlewZafX6K2I+f5W+JkBRS23KvNZ0iq0isMNS3yn5ZG/K/6f8ApVzqcnIFwsqixyPa8bTYo0sbJQSLMeBm/dI6McpxaNwcMuKCKGG4J5HZOLd5IItcKxdT+ODt8s37Ti6hThzMOBBacoLXo+bwtQhDuN2z7rd1ILHkYwvNNNk8NztmHtId/qvSZ3ukZFKyzmysDs/Ref8ASe3o/O+jh/b3O4PNk213XBNh2Ssc0+RubjhDO8/L27LjXUlTfGwfyq+VznG4AUwSOcCxwtnCjTFjHkOYUFTEwuecWU+KMNYMWKBBfbc8lSmnFyMcLTMOaABe2EFzDLURtDb2yVKbtt7JsG81Je2wtxdItVHUznQSuLvljZYD3KysTwyiJLRdxKuOo5pqwOkc5oDnn/RZ9xtDbuF6eE9PNzvs3y3uiQ2u72Cji5OUVtgwkrbBCfVEtew9ksbWuuQbWHdKW3cdp4QDGCbpw4Tmx35IT2iwsXCyBrbbmqZhjibjIQqdjHSNuSc5U2ERyy7BZlza7kAWwkxghuCUWJm3m30Wj1PpupodAj1B88csNw0BgJ5VZSupm4kYN9v3KA1HQOqWtZBGfEcbW9V6F4VN0PoG4uaNXqmfM7lt/QIfSJ0jTKL8SrXRiRt/CZe5v6rG9TapNq1ZPU1gDnfotw1vYBa+RFPVvjlmc/xvGe83c44uo8MsUch3MD2WIsfVI/YWX2jHayY0sdw23fJWWjZbGwZcBGotzZJjcgBhQ3Fo2EXd/PCm6XBUVBkbTQvke8ANDW33Zz/sgiElwwbn6rWdAaLU6jXNqXgtpoXAg/uPoFYaB0NuaKrXJxTxDzGFo8xH8K41DUtRbRuounNLdR0gBAqqi0Yt6i61J/rNqo/qlqLanUmUkJafAaA43wCV54xkktRsgY6aU4AY0uK2+m9Jtq3S1GpanFNDF55W07i8/wAuVVqHUngMdSaBTQ0MQ8pmjH5j/e54Uv8Aqq53TVfHGJNQdBQsI3XqHhpP8DKa6h0encGz6rJPj/68Btf0uVW1Lp5pC+pe+SQ8lzrlMay+O3CC2idorHC8WoTRluLvawh3f+FYadNor7tZptfuHzSMn3YvwRZVOn6dUV8kUNHFJNI4kEAcfVepTad/0t0FL4JiFdva577X3kOGPokgxet6XpLaBtZpNS5pMmx9NPiRuO3qPdVsEbWgBrmm/Kr6mZ88jpZTeRzrkjhNbI4XyoYtGRxSXD7sTHU/5rGRAvLuNoyf4UNkzj2tfk8L0j+mUdN8LqNSWCTUom3YxwyG27D691ZNRU6T0TqFXD49e5lBStF98vP2QtQ0rp6jPhxazNM4O/OdHHdrW+38qv6g6i1LW5T8TM7ZfELDZo+o9VTtk/IqWv3B1ha317p6GgZoul1j449O1qN8zsNjmjMZcfS/Cz0kLWVMkb3Xs4gm/oo7Cbi/qFMg06rrK+SCjp5KhweRaJpI5RUMnz+XhTNKoZNRroqWKwdI7aC69m+5sr2n6dotLeJ+pKxkTQLijgdvlf7G2AtT0eXas981JBFpmiQG1o/7k59HOObetkkRa9G9ODp6lqHSSwyahICA9pu0egVNW9J6zX1DptRqohyfEcS4W9vRVPWvVDpNXfDpUz6amg8jzFgvKptC6ur6HUC+rmkqqWXyTRSO3AtPp7rWz4jTnp7QqGJ82rapFI8N3Nja8WP8DKrdW6n0c6OaDTtNa5jQR4jvKQTwQeVB600COkbHqmks3aVUjc0jPhn0PoFl4490RcJIwbcXsQpasIXG4JN/f1QqmO8rnN5unmN7bDDvcFLMN0ztwIPsVlUIk38wS8cJ8rLmwvcd0FwINibFUPBzxlCnaJbG21w4IwQijsb8eyXDr35RGg6V6okgmbS6g8td+iQ8H2K9T0nU2yRi7gHX5XhL4Wyja4e11caB1BPpNQKatc50G6zZDy32Ki6+hKWuuxtyVZMna/B+ZefaPq8czIgZMnIcOCtPSVYcdrbFxyPdanIN1/Q6bUqeSCoY0xuyBbg+o91451L0/U6BVgPHi0rvklH+x9CvdmSB7bOyg6jp1JqNC6Co2N3jaC5twfqriPmyQlzyQMJACQOcCy1XV3SlTo80ktODLRB1iQLmM+/qPdZXOz0IN8LKjtk2MD2SHe3FnhLqRdPDHJ5BdtrN4Kg7jc3uc8lTYAHabM8NuWOAt9UEOjuypjcQdrxbK9F0OoFRoTGO+eF5Yf8AheabiGtLcbXXst10TN4rayEm5NpAuX6z06/lfeLlnP0TneV1wblN2OBd2Hqnv27Lt+6870AOttBwmPja+xIBPumyHDuNvCVr8WvwoKiIggDP1R55BDGSLGw490KGOwaCDlTGRtLASLhaRDkqJPAYB83cA5spMs5i02WbbtdsxbtdR5QXVW1lgBglM12Qtp6en3WD37rewWozWW1yZrnshaBtYBf6lVe644H8ItbL8RWzSA4LjZBsvTJkea3aUEd+EUeFsO0OvdCAuURos3+Lqo4AlwDRdKC4k39UrXHeCBjlJa44PKDmvs7sixhhcd7iBbluULaGnOUoGTbugNFua67SAR2TmPc07rXKZEQ1xJ9E5xyQAg1Gi9Umjpn0lXF49JILPYc3UyLRNL1c7tF1CKCUgk09QS0j6E8rHG5Jv24StcL5JGeW8oNRV9K67TWa2kkmYMgxO3BVUmnakxxbJRVYPoY3J+mavqlKHfC6jPG0fuk/9q4/651iJjGfE+K9osXFtk9Ctp+n9XqReOgqNpwC9pb/ALq707+n9dI3dqFTDSs7i+5w/wCFVV3WetVeH1Za09mNAVXVanWVji6rqpZSedzrgp6RtIaPpLRw91bUGuna4tEYycHm3CbVdcPEDYtGooqKKNwDSACSDcccBYaQ2NuTb0UilIFC7eA1xlb5jnAaU1cWE/VGsz+V+pVJ9CHW/wBlVVNZUTkmeeaZx7yPLv8AdR2utbanOk3NDNrbjl1uVBedOdT1GiGSNsEc9PJh7H4wcEAojoOmK173x1FdpznZ2yMErAfQEZss64HFkoYNpvyqLw6PowAceoYiD6Uz7qTTUnTNI7dV6jV1ovcRww7L/UlZsi9uCk2kZHZNGpk6pOnmcdP0jaCKosL33PaBjntdA0nqCug8Rksgq6bMskFR5mP+qpJzemps52knPukprjxub7CLqaY1Q1fpqeE/FaH4Uh4+GmNv9UH43pSG5j0yunI7STWH+izANuBhITxjlXRszrPT0Toqym0sOqWC0VMRaJp9Xnl/0VM/XNSl1Z2oiodHVnG9mAAOGgensqYOObAX90/efDv+om2SmjTjqakqjfWtGpaiX/OgJheT6m2Cn/inS7JI3R6TVPZciVssxN22xb+bfZZAvvyMotNGJfEyBZt89ymjXN6o0eFodSdOUjX4H5p3WPqFE1bq7U5hLTQPjooR5SynZsJ+p5WYeNlxbF0etqC+Z0TDeHcCLgA8Jojl4c+7yXXNyb8rdVfWtLD0wyh0mCSml27LEg7fU391guXWOMpjie6SoKJN17m4TN4A+iGHgtscJpJtm4witJ071RUaQ19O9janT5ARJTSm7TfuPQq8g0rpfVGsnpqyvo/EcGugLBJtuex7C/qvPx6d+VKpppItzY3EbsEJqPSafSdA0x8Lo6B9W4yCMy1z7NY7tdjfosnrFNRsNTKGudO6RwAb5WMzi3tZQzrM8zbTEkEbXZ9OCmvnEos57nb28nuloiEhsnGPS1wlkhhmjx5HDv2XBhPIt6hKLftwBawRUF8D2ONxgdxkJGi5AU0sLgdpIPqgPc+MFskbS09yLH+Cga3aPr9Us9pC9srQQeUgcM+XJ90khFxtHIRB9H1eo0aXZIXvoibX5LF6hoessnjic2W7SMOB4Xkhs5j2uHbhP0vUptGqA6Pc+lccs/b9FLFlfRtPVtmjaQ4bz8wBU6KVhjDX5aV5r0/rkcsEUzJQ6NxBaR29ls6StbMBtLcC5AKspiyrqXfCSyMSMPlcw53A/VeTdZdIvot9ZprHOpjcviHLPp7L1unnBHzXASVLGVETdrLuPN1rB81EAm7QLFSqCZrHStewPa5tsm1vdbXrfpTw5JKvTI9tsyQf8tWDpnbZTcZ4yLrIiSDY6QA3x3Wh6Pq/BrqbALZAWOyqCpAZUeXjjhSdNmED9zD5o3h4+izzmxrhcr017mneGjF/9EA3dBIBYkenomMmsA5xObG49LLvFEbsXN8YxdeSvVAHA5aRdt/soznlptyjSyBryBhRJXlzrtcLfVFNjc7c64IsOFNixFfIBQomgm9jlFmeI2AAY4VQtNTgm5Av6qh6hk36jUloPh08QF/daaFwijB4aBdYPWap/wAI9wdZ1RISR7Lpwm1jncila0lrnY59Vx5xkJzNxaLgW54XOIPYL0PK4cI2drrcAC6AMkBGYf7hHdFLHkPt6JzG2ZwkDhsdbAJCVo/LNji/3QNcBckFJYjlO2kHy2CeWki7zcoOA/KcR7BPp4r+Z1z7XQ2A2Fzi6M1rnvaG3sMoCvLbnBI90OBm8vvYAZylZG+RjGm991iUsoAJa3gHv3KATnX4FvYJNxunOue1gksECX49UYO8gFh/7TA24wMJQzOTZA6Ukk3wisd/2rRz53H7BBeXX2gXUstAo4ScEl5QQ84yES4acrgbjygCyRzrM29lA64znlcfYg3Q7gJwcb849ECk25S4db17pC69geyRpH8oCygNigsMbT/ui6fTyVdQ2niaDJLZgubC5KKDEySlM0fiRgDezdbdnsV6Jo+laRUUmn6pQ6fU0w+LbELyC7wRz9AQFZNNQtb0DSOmunJBWMbU6pO0CPc7DT3LQPRedd8ZJ7hel9WdZUDq6Wk/CoayOE7PEe7JtyAbLBavVUtXUmSgo/hGuHmjD9wv7X4VqIW/yBoAFje/dNuBYXye6NHRTyHEZt3PZH/DXNia51t54G4KKgOBBzx6p1OL+J7NJVn+BV7oGyNhcWu+UDN0kek1lOJHTwSxt2G+5pGUFW4+XgfwiVH903tew/2Q3NP/AAiVURa5jw14a5oALhye6gGCDjumOFx7p9hfHKcCL8BUADPW9km33Uot7Dhc2PIBKAPhnkD+VIpIHyy7WAXAJPtYIzdjJHNey4GAQ61j6p0E202ySe97KAdRA1r/ACuGAL/VdtPgtcW2aCQESe4e5zxck4THPJhHcB3BQNa8n/0nNty61kjL+mVzje1xj2QKCC4Yx6FJPvqiG7WjbgAJHWxtvdOY/afKdpQVz2ujNinuedjL9hYi6m1EbJNoJu4DlAqqR1PEDL5XXFvcEXuqCk0T4R4LJop9tiCQ5jvf1CgGM5BF/VEjkAPqeE4XNxcYyiI9DVz6RUGWnJdAT54//S9O6b6giqWxyROBacG/I9ivM5dpJAwCgUdVPplT41MSWE+dnYpZqy4+hKDUAJLMd5SePRW1LViYkC1xzbsvLunddgq4Y3h5t9bEH0K2mm1zNwLTYFSVV/NC2eItmAcScFeadZ9InxXVdGzbMMuYOH/T3XpLZg6MWKFUbahjo3ixGQbLSPnWvjdgkOa5psQRYj6pKQCSbaDt3gi69P606VmqIpamkg8wF3Wb8y8tLXRTWcCx7HZae3qpSfWr06rfLRxbyb22E+4wrOKRzg0yAX22P1Wf0Rx3zRAbmh+4fQhXTGnaCMtvYry8vr1cfcCqi6MW27rnsojiY3Y8wP8Aop8jiWX7hAfHucTtustJ0dg1zR5ja3KM4sJ2OIJ9PoowkMYJIx3RBIxzXFrS1xNsqgeuTNi0aYtPmd5G29SbLB6u78yOPs0ALX9Syt/7KmB4vIR69h/usNUb3VMz3NNwbldvyn9cP1v8ISb4ukJOOy67bAg3dfI9E82Gbi3ouzidBfeDfjKW9t2y6GH23WwLJ8R/LJIvlFPkFo2m2Dlcz+2frwlmBswHI24Q2YcDf+FAQ8Wubrs3IF1xzkc+i5u7dwgNxHGAcm5KJTPc2UyNvcBT9M0Kv1ZzG0FM+Szcu4aPqVeN6Rp9O2nXNXpaVzr3jjPiOH2QUUc//bSlrBd7uT2UdlPIfM64acrUU1P06ZYqWlqKyrc5+TsDLH9Nr9k3qHSn6YC2SnqaZr3WY+QAtOPUIMu8BrbHn3QtpLsJ9Vf4hzXm+0AJgQPuSAACAVzXHfY9imWO4FriLFOjcQbXuT3KB7sOuCLo7nf9vED3Y4j2yoQa53zKXNdjY2OAsWAhKBbu9h7JC4Ei4/kJreCN33XBrS7J7qB2zy3vlODLkZCYRd2CmE2xbJ7+ioOA0HJTLemUHN8g/VEbxwgt9Ip2V2qUVNMS2J1g6w7L0TruY6V0pQ0dP+US8uGNpG3gheWNqJYpI3xuIc0AgjstbqHVcGvaVDBq8RZUwNIE7RcOHuOyQYx77klxN10RO4WV0NEbUAmmrKWRreTvtg/VWmlaFolO0T6xq8YPaGLJ/lMEOpje2hjlDXC583pwtFoVLS6HoTda1WEVBmJbS0zhyf3H0CoNV1uilnmhpIpHUhbtYXnNxwVqajVNC6o0eh0+ao/D6mmZZjnjyHFiD6KyIoNS6w1mqjcYqn4doNwyFoYG/RSNH6r1YVAgrag1cEjRubO0OF0X/oTVC0yQeBVQ2w6GUOuh0vSOqxTNkq2xU0YIBfNIBa/snsauP4PUNFqdS0fSNPZX07rSwyR77f8AkF511HqNdqPhNrHsMbPMxjGhrWE8gWWyi1LT+l6WrjoakahqFU3ZI5mI2D/lYWuBc8bjuwloqXANJwVzW34RZ27eO4Q2GwWVO2lPawu9vdJHI3dZwNvZO7WvlAsrCZ3ho3E8JGt8wviyJUFzalzhg4tYoLnOcLA97oD1EZEhIODlNwI3AkHISVB3SXJPA7prIfypCTZA54DABxf0QicYumuNsFxKbcZugfus1c52eEE/ZNLrclUSGPANjypNfV/FQta635bQ29vQKu33KQPNnea2EAyNhBHHquvvcQD9118Z4ThHd1zcNPf0QDk3A2KESCLGyPWRmN4uS4W5CiHPCqH0801BUePTXt+pnZwXofTmvx1MLZGu9nDu1edh1ueLWSU08tDUiopCRnzN/cFLNJce86TqJnqGU5d/c+VbzTo4GQNadrngZJXjPROp09bUR1QOGMsW3y0rdv1GGMB3iXB42lONxa0moahSQtkaXgkCxtmy8C/qbTRwdRGSnI21EYeC3i/BXpkcjaiKWe2x0ju2bgd15T1xPFJXwxRSeI2nDmOf6km9ld0qHoc5bWsAOJmWP1C1FNY7mC2B27rF0DvDMcnBjk/0WxYWuaJGHkXuvN+k9vR+d9DBosQTh3dDDnNc4CxHqiRncCDaxyCohMjHuGLXwubokySHAIvxcKTAd020ce6AbN5OR5j7Ir5201C+V1rht/57KozGvVW6rrJLA2tGz6BZcXIcb+Zyn6pMdrWC3mu511BabNsvVwmR5ed2kLbJAMpQLlPjaL5WmSj5CPVP23jHa5ThsLQMgX5Unw98TNtrglRUaXEhAOALJluyc++4gjN0Tw9zQgFfblbvpzQ6Ci00az1IfyQfy6bu89rrIafEBXU5ksYxIC4H0utb/UeCrFZSPMYbQujBhcw3b/8A1UA1PrCuqY5KOkc2io9zi2OIbSQSTYn+Vnah5LS5ziXk83uojiC5178p7Tdo754UD99w59g15PbGVfx9T1svTtRpNbJ8TE9zTG6Q3MVj2KzzgDe31K5pICAs1nvc5tgExrw1tiUjMA3XGxxZA4vAzdEpy0n1QHAtBAOD2RIHbGmyB8zbMABt6oj5DLLteSdsQaCo0l3BHeWxyk5+W3+igY6Mu8rT/Kbsc0WuSkDrbjnPCRshb73VHNO03B4TSXXvdNJunNQO3HvlOEjjfixSWwnNGED3O8wsOyc1pc15v2SOabg8WspVBTyTCQsaXEcN9VBGYGnFyEssYYObn1Wri6E1KTS/jpHRRRnhrnZWXrKd9JO+CZhEjDZUAOeE4NIcLIYaSblOAygsYamsgbtp5pWezXEJ7KmofUDxpZHbh+p11Ejc8izSVxe6482R3UCxSlshuTyrQhkhafUC5ConSkONwp9LWhsYu0ccopKuHbute18EqA47ccK3lnie27hgqnqCBK6wxdVKcS242/dKXWIUcZOEaOxIDr8oHzSHxnHn3Q3SWyEsnLrHkokTYC0+I5zXDiyDnyF21xFsJwmuHDtZDqHBzWAC1ha/qhNa0d+yBrngpN2b35SYtym+qAgdcLpGOxiyEFKdN4jGtda47lBHsQUSJtyQ4gKfouj1es1ggo2bn9z2C9EptB6f6Womv19zJ6u1/DByk9jzOGkjkiBkeW39kSaQwQ7GgOFsOC9apKLTOo9PjdLpJo4hcQnHmHqV5b1TRQ0GpS09O8ljDbJ49lcRTvf5S7cS63B4URxtchSTY4whujwgDYkn1StB5uiiMtZfCQH3+qaDaVW1GnVJmon7ScOaeHLTad1s6nkEk1PIXjs0ghZNg8w9017ASVCNXVdc188ToaWJsAcSbnJF/QLOEOkppCb3DtziTe/ugABreRdPiJEMvms0jPoUUSmc0Rvae4uthp7xJprC0ctBWHjeLgD1Wi0Gpd8MIwcNdYj2XL9J/XX86v2yB0Q2nv2SPa3A7DhR2/luaABbJKNu3gEEWXB2PkLZsF3ltn3Vdrr/AA44YQcPcLj/AMQpsUTdwDjlZ/qCo3V0lziFm3+V04zaxyuRQ18vi1LvQGwQ2ZPshXubnvlFZYBel5ilwufQcLmkC9khFxhKG7QEQ4e57KVDLskYQbWCibrk2RgwguLuAEUR8gc8u28p0crS0qI0kYPdc35vZRUvxDkA4Wi0XqaqpaU0NSyOsoOfCmzt+h7LLsaXcIsTXBxzbCDaNk6MriXSRVdG45IB3C66ni6QjklZJPVFu+zTb9NvX7rFROOeF0hs7/hBf6pVaQypdHpVM50QNw+R2StHpGudPSRsin0iFkob5nOdZpP1XnRJ34FinAOdybFUbKuotP1eqkk0x9NSAmwhc+2VVaz07qGltbJUQflOFw9mW2+qpmOti63vQuusmYdD1QmSmnO2MuzsKmDCh1/mCUODGEdlourtDGlTymPLGvLCFmPNtNgbEoHxHe8Ac3RJ2kTuA9EOAbJATnKe78yoO0XKALy4cC6cxtx5sIhtfK7yjBygYGeic1nqucQ0XAKaHkoH4C4O4tymg3TmYdwgmUlPLWVTIIWF0jzYAL1bTtH0vo3TIq3VvPWEbmt57Ku/pzpMVBSS6zXBoYB5NyrJ5avq7qP84O+Ea7yjtZWTEWukVddrFY6vq3+HSC5iiPACwvUdaKrVp33BF7AhbHqfVodN06Wnp9okt4bAOw9V5lI4lxN7n/dKHueb44XF2L3Q9xITbkhRUhs9m2RGHxCB6lQ2+6K0+dtkBqiLZbKNSs3NQG3LiXXsFIFSI2+RtlAaOnBJDjgjCg1THB9rXIT31Ty4WxZAkeXOJJVHRxuPOEeNjWu85Ue5tclPifY5Qd4oHyD7pgdc3ISuaOyTYbAoFe4bB9VzXDHqnvYAwWThGxzmkAhBEIAJuluDhElaBIRbhcGDBKBrmx7BZx3d0+Jm57WtG4k2AHdL4RLhtaT/AAtp/Trp38Q1MT1DHCKLNiOU+jZdNUUfTvTLqtzWsqpW/Mey8/ZSV+v6mXSB1RITh3oF6rrUUNWzwKt4jpWe9uFmavqPTNJa6CgYIiceIOVrP4zotdrkfTegxwPcJK/YWsibwz3K8inlkqHSyym8jnbiStFrldR12928+KD8x7rNSSDzBovjlS1QyB3wil8Qba1yom4k5KUG4QOkcf0iwQ2uNj7p+CbFNw0IFjNjxlElbZ973xdDJAOAlLgQL3uoBvNx7ozNzKOTizjZC2hzgAbp1WdrWxjtyqBNu0k2Vtocg8d7TgOF1UsDSfOSB7KZpz9s0Z4zYrHP41wuVqnuc5lwADZOpnDYQ9ouEUNGxtje4THw5wbLzPSUSGGB0jiMAuWOrpy9j3E+aR1yr7WZ/DongH5vKFl6p7S4A/MAAu/5z+uP6X+AjLk88pIxget0p5uV2cT2usPdK03Iuh2tlc13KiiBw3BFe8kHOCVHafMnvdYIFYNzlc6ZpMtY17mxkhjS8gegVNE7zAeq9N0FgpdA1KogIMnwwaB9eUHn0soa8sjs23dAfuIJJKFwb3vfKJvIj9lFOgbuwDlPkbsf5zdBY8BwI5XOkJcd2QiCF+4khGY8yCwAuFFjcWg+6JC8tde+QgedpdnykYV70jPBSa9STVWYmvCpMSkuJG4q46U0qWv1iGPaTG1wc89gAqPRutaaA0uovmeCJLPYf4Xk7pQ2GwF8rY/1H1gSVRo4HeRoDTbus90xosut1bYGYjBu4qVUKhglqahogic8n0C11F0X4cT6vWKltLHa4bfKt9YrNP6Uom0dAxjqwcuK8/1PVqvUpy+qnc//AMb4QXUs+gUhc2CCSoI/U42BUCo1eD/BooWj3yqI3uuthBZ/iUUlPJHNTM3H5XNxZV4d6Jlk+GN8rg2MFzj2CB1rcK+6Q0g6tqsURaTEDd5R9E6UrK57TK3w2e69P0jSYOn9LkMIAmLcuVk1NCl02OsoYaCJ5bTQO81u6dNTshroYNOjEXkLS7sgydQUum6a0ySN3uuSPVYjVus6iaYmiNgMXKtGnqtD0yKd8moSNkcclzisp1D0r5JK3SXCalGTtzZZqpraqtlvNM97nHi+F6x0bSfhnSdQ6qFmyMJsfop9HjZba6dCSCbc+6LWlrquUxjylxIQwbKKYcOKew2cCmO5ulGSAOUEqAvkmMcbS4nsAtZoXRlVXVEbav8AKjfkYWg6B0Kmbp4q3sD5TnK0Wo6pFp07Jqlwa1jTYBWRNZPV+jqDTPDbI8XcbbirODoTTBTiSV25pF7grCdT9RyavWPduc2IHyi/K0n9O9bqKqf4GqJlhItnsno9rJ3R+hPdZswx7p46O0CIXfMD/Ky/X9LJpOq3p5ZGxvyACqrQotQ1WsbDFLIR3N0uHtuafpfRZqnw4CXj2TOoej6SlgD2fls/cey1em0NJ07pXjz+aUDJJuvPusurpNWvBEdkA5t3VsmezUxnQHjwxSU1WyRr84KM/wDpwWjc+faRnlYvStWqKGpZJFO8BpvtubL1yhqP+pNJMjXvZIGZt3Kkkoy46D05ga6arBcefMpB6a6boyDUTg/UrE6o6ppaqeGoqJLxuNsqjmqppfnkc4e5T0e3qnxHTNC4NijY997D3WpOoUmm6Y2bY2ESNuLBeJ9LQNqtdp2ym4BuvR/6lyth0VscYy1tgfRWJWD6k6kqa+tnDZSYNxDVmnve913PJK5zriyF3ypq4V5wL5Tm2Nx7LnFobwmtdY3CigubZNGCiuuSk2nsFUMORc8ppRAx3oUvg4BcQEAbn1Shpd9CjlsTBk3KDLIXWDcAIC3bCMZcozyXOJJvdJYpO+UDhe3CPA4hzh/KlxUvjaO+awBjcAosbSw2I5ClWNZpkrn07c9lLabjzHKqNEefBFjxhWbw5xuF5eU9vTL6ZjWJSZ2Rl2BlU05vKSe6lVs3i1L3+6hPNySvVwmR5ud2nMdYp10JKCtsCbiUoOENKDhRT2/MnvcMJjThITc3QPac8radE63Ex0un1h2xzsLA8/7LEtN05rtrgRyEGsr+jq1j3vpS2WMm4squfSK6BgBp33HOEem6jraeJrYpXDaO+bqbR9ZVkT7zxxyg+oWVZ4wSsNnxPb9QmiCRxxG4/wALZSdW0dTERLQtBve4ClDqrSY2gxUQL7eioxtPpVbPbw6d5/hXun9GajVWc9vht91Om65LWltNSMYCiP6+qfBaI4Whw5TA+n6IkEu1z8DklaSWr0/p3SH09KGmrLbOcFg6zqvUqy4dL4bT2ZhV9RVudCd7iXHuSgjV0zqmqke8kklbz+lVVTwzSxSkNkfgErzppsSSjx1MkMjXxOLHjgg2SDT/ANQaCoptclklBMb8td2WSZ8xIytpTdVNr6eOHWIfFawWEjcn+URs3TF9wxfkFqisSxrnE7QSfYXU+j0auq3ARQPz3IstVHrnT9GfyKYyEf8Aih1nXJaCKGlbH7lEJpnQs7yH1sgjZ3C0MMGgaFFuL2SStWArOp9SqmkSTuAPYKofO+RxL3E37kqj2/p+ufqO6qLBFSM+U8XWb6x6uLZn01I7d2Nis3R9XTQ6OaHaBYWDh3WcdI6SUvcbuJummDVdXNUPvK8utwEEOIYUxx8xJTw67FFTdEj8bUqdhsLvC9X68qXUXTUUMZ2hzQDZeNQvkhlbIwkOabgr0jS+rtP1ChjpdbYdzRbcRcFWI87Bu7i5Rm088ptFE8/QL0V9b0rTne3a72DU6PqrRoyBTUlz2JCmGsHT6HXzkAQObf1C0umdEBrPFrpmtAzbhG1jq7B8CNjPoFkK3W62rk80z7HtdB7b03FT02jPZSyNeWgryjqiurtQ1OWKVr9jHWACi6X1HV6ZZrXOLTyLq4pOq6LxvFnpbk5JVvwUkej1VY5gggeBwSQvR9A0ui6a034upe0T2vY83Wbq+u2+GWUFI1n/AJOWV1PVauteXVEzj7Xwk9Cf1brTtZ1IycRNw0LY/wBKYI2xyzG28Lyt8mbq/wCkddl0usaQbxnkFN9jU/1A1epkqX0weRF3F1hJHhwAHC3+qUdN1C8TMlaxzuRdRaHp/TKSo/76pjsM5cpfYymm0U9TM0MjcQTa9l7J07TjQdEL6iQNu29llq7qbRNOjMOnxCV7eHNGFk9c6nrNU8rnlkQFtoKs9H1C6orG1mr1EzD5S7HuqgldJk3KbdQWGhVYotThnd8rTles9Qsi6j6bJoCHybeByvFC4q30PX6zR5w+B5LO7TwVYIFXRz00xjlie1wwQQkZG8kDw3X+i39H1fpVdI12pUga/wBQLq5frnTEbA9sbSR22oa8nfTSZBY4fwnU9FM94DYnH+F6ZP1H01I4bobE/wDiotb1holO3bR0heR3ATDVdQdJQx0Rq9Qf4bbcErHVtRA2rkFO0eGDYH1Vj1F1LW6sBGXGKmB8sbeP5WdQHfU3vYAID3l/JSFpSAOJ4QclJukNweFyIQmycyMuN+yIPDay5OUN0xLbDAQT6isEdCKWE+U5coUbt1ruyPVR7pu7zJhrRaJJYSj0yrqOSzeVm9FkDagNPDgr8hefnMr0cL6Yh7rke6YcEhNccrr+q9LzFXBIlCoVcuXICMyLFcQmswnsIvYhQc1KeUQMByCux3RSA4C578rm2Jwk23KgJG7BTxZpugjAuuDroojgTkcJd9gmNucp1roCRPu4XRp3bgozcHCc7hAvonON7BK1rbApDYu9lFGhO1pQrgkpwNr2TLBAjl18LnDKbwg7kpHD0TgElsoGgokZG4IXdPbkogsjgXJwHkQXXBRY77UUgJKfY+qdG0WN0NxIPKB1s3uiCbYLA2v3QN101wQEe4uOTdDMlnCyS64AEoFkeXEFcDhO23cApLIoowC8hBEY6xBKfI4kJ0zoy7yIb3kCwCDmgfqT2P2nyqOSe65riERax6jNA3yPIKh1FVLO8ue9xP1UcuvynRtJQSaYDkldPIBcBDALTykeMopSTbKb2wlPCaCgRIQU7uk4QFpm5JPCZJJdx/0T2O2xn1UQnKIKHXNk0mxTGusUj7chUFleC0BNYwHnhcBdmeE18mNreEBJJWtNmBC8U9gELlIiCOeSmEpWDc4BdLtDrDsgbdNLkpskAF1Uda/Ca7lF3NbxlCJzcoJlHIWSMPoVrY3NcxrubhYuJwWno5t9LGWngWK4fpHf86xw5S90oafRLsd6Fd3A1KEux3oU4RvH6Smqa3lceU9rHftK4RuJ4KaY4YC4FP8ADfYeUpPCkH6SmmODiO6eDcZTPDf+0p7YpP2lPRh7GgpC2yJFDJY2aUngyk22lTVw0tsEgAaiOgmt8hSGCU/ocmwym7sYSsTvh5v2FcIJh+gpsMrrgLnG5XGnm/YU8UsxGGFTYuU+Lmya/wCZEhppw7MbkslNPuv4bk2GUIE2SXRRSzluI3Jvws/+W77JsMoZKVpvyn/CTn/Dd9k8UdQB/ad9k0yg8cJrrhSPhKj/ACnfZPdRTln9p32TTKh909lroooqj/Kcntoqi39pyaZUZ/KLCbDKf8DU3/tORGUdTYflOTYZQiHAE9kEk91ZupKnw7eEVFNBU/5Tk2GVFJIXXKk/A1P+U5KNPqT/AIRU2GVF4RIWF7xZSW6bUd4nIraSeP5YzdXYZQJ4vCAJKBcnlTZKWqkPmjcUMUFT/lOU2LlRAMpzxhGNFUj/AAnfZJ8JUn/DcrsTKjHK4AKWzT6km3hlTqfRp3Nu5hspsMqoDCcp99jVZ1VJLE3a2MkqudSVJyY3K7DKEZCSnly4Uc/+W5PNJUW/tuTYZTQQQmOsMoopKj/LcudSTnmNyaZQNyQlEdSzD/Dcm/DzH/Dd9k2GU5mWoDuVZQ0sggLiwqEYZLnyFNiZQO6Qgk5RTDID8h+yTwn3F2n7K6ZR3NAp8cqGVMla7wwACohjf+0oGXTSU/Y79pSFh9CiHR4BKC4kuupBadmAVHI+qo48JzG3aSuDCfVEZE4sJymgBKQriEpBsiGsNir/AEycCnseyz5B9FKpZixhCzzmxvhcq4ZTQ3F2iyc+KFrrbRZCG7I+ycxjnnK4u2JcVPC5t9gUmOnp3t+QXUWC4NkbLHeyzWpBYqSmJILAitoKYfoCYxpHmBwVIFzlTauQjKOnH6AunpacMwwJZQS07ShecjaU0yANghc62xT4KOAAXjCHFBZ1yrCNmApasgTaWnB/thP+EgBv4YRC2xRmt8im1cgApoCcxhPFJTXt4YRRhFYw8qaYaygptv8AaCa/T6cn+2FKY8bbHlEjcCeybVyK80EA/wANSIaCnI/thTi0FKxu0qbTIjNoaa+I8rvgac42KaxvnRTGbggYTaZFfHpsH+XhLJp9MP8ADVm0YSOaE2rkV8Wn0xGWKWNNpLfIiCPNwjNYSE2rkRHadSAfIhihpr22KbY8FIG5ym0yI/4fSkfIFwoKUfoRS/zWTmXJsm0yBfA0gGWBK2ipf8sKU6O7UNrS11rJtMN+BpiP7YTXUFL/AJYUxhAtdEaWOTTIrxp1N/lhObp9MD/bCsDtHAS+XsE2mKqooadrfLGFHh0+AvuY1a1NiLJkIHKbTEUUNNf+3lFGnUxGI1MawORmgAWwmmKl2mU5/wANMGm0oP8AbV0QLYCjuaLppivGn044jCR8ELW2DcKxu23a6E9gcOE2pivFDSuyWXKHLSUjeWBTywjhBmpi9NOqC2moiflCL8FSEYYERunute65se11lNpgfwNKP0BCfQ037ArJsNxe67wNxthNq5FWdPpXDDEM6bTD9AV02lIKbNS4wFdqdYp30EBaRtwoP4dAHHyK++GcDwmOpHHNim06xTjTqYcsCFLplORcNVpNA9vYoe11rWV7VOsU502n7tTZNKp7X2KyfC7cjgAR5GU7VOsUg0mntloQZtJgGdqu3FvCDKGltk7U6xRM02nJIISu0inJw1SnxnxMcKSxhDeFrtU6xW/g1ORxZBk0ljWHabBW0gLRwh5c0hO1/wBTrFC7TIg03UYUURdwr6dnlKguiN8LU5VLxiGNPgI5Q3afC04KkyNc3hDAf6rW1nIEL8+iOw90xhBCIzmyUHjxlGd5hdDYQAi7m7VhqFhd5S08I7CbWUVhzhSGOsEU5r3AkWTmAjKawgvF+6lEN2ggKK4eZoR2XAQGWBUpou1RTmkOFijNs1tigxNs5GnadoIUAzbf7Iu8tAtlDjG8Z5TwwjBUVHklN7hSKV5JyhFg3KTTsAKKmsfhFDdwQLWspUJACBGc2Uhri1Ry4ByO0hyKdcngLiDtunN4TZX2FuyDo5AMPsETx4h+sfdUuqGURF0VzhYeu1XUYXuwQFrjx7McufV6l4jHZa4FBlma05Nl53o3UU8jyyW90XWdcljA2XJV8d3E8kzW8Y+Nzr3H3R7txZeZUOt1L5AHXF1udDndPGC5x/lTlwvFePOVeRvxYogaCLlRy+NvLwiRyB+GkFZdHSWb3QI5m7sEFA1yV8NOXNGQFi9N16SXUTE64sVZxtYvKS49E8QWyQmGdgOXD7qqrqh7KPxGntdYOv6lqI5i1o4KvHheRy5zi9OkkY4YcEsDmjlefaHr8tU/a/laaomljp97b8KXjZcJzlmtBJUwxDLhdRfjWPdZrl5nrOvVTJXMbcW7oWldQTtmAmN/db8VzWfLNx69E8Ft3OSyuaW4Wd06vM8LSDyrAPcebrlfTrLozcv9lJYwEcqG1xCXxi1BMexvqgOFioz6s35Qn1RtyhqY52DlQX3MiH8V6lMdUjlETml21cJNmSoLKy5sukn3AoLKKpLipTXh3dZ9lQWIg1Atyhq6OT2suMgaLYVN+KYQzqG45Q1aSua45sgFrBwFXu1BpKYa23JQTZQDwFCmBvay740WwguqgSiBSA34QjcOF1I+IY4ocskfYqppWsa45CkljQzIURtQwIhqWlvKGulYwtUdrAAVz52juu8Vu3lDQZ4roLYGm90aWZoCjOqQLgKoDUU4vhQ3RhpspUlRcZUR8rSVuM1UxyjkKSx983VC2Vw7orKl4Xa8HGc1+JDZPa8nCpGVbwU/4t98LPRrtGhhIsnPkt3WdbXSA8pxrXn1U6Ve8X/jAEZUuKoaWcrJ/FPJRGVUg7lToTm1zZGkci6Myoa0ZKyArZGjBKIK2QjkqdF7tlHOwG4IsifENPcLHR10nqU/8QkU6NdmtErGm4ITnTsI5WSbqEhOURtc8+qdDs1HitIvfKcydo7rL/HSJTWvBU6r2a4VbCBlFZVtAtdYs10gRGV8hAyU6HZshUMJyUaOpYDysUK+QHkpRqUgPdTqvZvDVMtkoL6hjjyFjfxSQDukGqS37p1OzbMljIs+xVXrOn0tTAdjbGyo49TlPqn/AInL3ykmU3Vdpuk+BXkn5bqx1zS45ILg2NkM17r7g0AoVRqMjm2eAQt7dYyYoYKeaGcBrgQCvQ9BltSgEWNli/HAfu2BWMGpyNbZg2q87qcZi61KomM3kvZWWjTuAHiEg+6yw1GRzrnKlxalIMALnY6a2Nf4VTTlpsTayw0mjCHUvFZflWceoyWCDUai8n5RdJbC5WihjjlovDkObLFa/oDS8ujFipf4tM3hMk1SWQWcAVeNsqcs5elPolA+mqwXHF16JA2KSlDX+ixzauztwYLo41WZoHCcr2TjJxRep9LYXudFe5WZj06YPBHIWsmr3S5e0FCFQxv+GFvjzsmM3hLdS+m3SxBjZFsI5oy0XIWJbqGzDGAJzdTlv3XOzW+NxuDJGBghRZ52g4IWXbqsvATX18hNzypjXZonyNI5CjvmbflUT659kH4tzjfKYmryWZt+UHx22OVUvncfVD8c+6YauBUAHlGZMCFnzUG/dFZUkDumGr4PaueAQqRtW7d3RzXEN4KmLqY4AOSHIsFBNUXZsUranaOCriaMWkPuld5hZRzVX5Cb8TYcFMNSL2CE91lHfVX7FDM5PZXE0V0hJwUhc71QBJc8JXS+yYaNudZCdK4Gy7xccJC8dwqhPEc7CKC4N5UcvAyAmie/ZMNGkJPdRpDlc+a+LKM6W7lZEtPeT3UVzgCnvkLkB/zLcjOv/9k=",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image 2:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image 3:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "## Image Analysis\n",
       "\n",
       "| Question | Answer | Reason |\n",
       "|---|---|---|\n",
       "| What do the images have in common? | They all feature a chalkboard. | All three images depict a chalkboard as the central element. |\n",
       "| Which one would be of interest to a mathematician? | Image 3 | Image 3 shows mathematical formulas and equations written on the chalkboard, making it relevant to a mathematician. |\n",
       "| Which one indicates it's the end of vacation? | Image 1 | Image 1 displays the message \"Back to School,\" signifying the end of summer vacation and the start of a new school year. |\n",
       "| Which one suggests we may get a coffee there? | Image 2 | Image 2 advertises a \"Brunch Menu,\" implying that the location likely serves coffee, a common brunch beverage. |"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prompt = \"\"\"\n",
    "Answer the following questions about the images, with a short answer and a detailed reason for the answer.\n",
    "Present the results in a table with a row for each question, answer, and reason.\n",
    "\n",
    "QUESTIONS:\n",
    "- What do the images have in common?\n",
    "- Which one would be of interest to a mathematician?\n",
    "- Which one indicates it's the end of vacation?\n",
    "- Which one suggests we may get a coffee there?\n",
    "\"\"\"\n",
    "\n",
    "contents = [prompt, caption_b1, image_b1, caption_b2, image_b2, caption_b3, image_b3]\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uqFSjI4_mGN9"
   },
   "source": [
    "You can use Gemini's level of language and visual understanding to work with concepts or even get suggestions on new images:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 6093,
     "status": "ok",
     "timestamp": 1702337492875,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "QjbP2y26mGN9",
    "outputId": "7a072645-e7ff-4049-93a7-f27bb6e27c7c",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Answer the following questions about the images, with a short answer and a detailed reason for the answer.\n",
      "Present the results in a table with a row for each question, answer, and reason.\n",
      "\n",
      "QUESTIONS:\n",
      "- What does the first image represent?\n",
      "- What does the second image represent?\n",
      "- What could be the next logical image?\n",
      "\n",
      "Image 1:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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rI2xi6EVlcQxWVKxhoRWVIh8Su2MNBInYhds7bC6DtnbYXbO2woW2dCYnRg+xpGavpAIMCMIwAnnrWDLD1kDzioYwqe8xY1D1b+kOjxGtodG5mcU/W0c0/v2mfT6tG67M9u0ljUaldmBwYzVZuI9JkpZkgCMi3YuB3mLG5Wv+Y/tEZW/aoXMxNO+5snsIfrfNnMmLrVbVEKOYd9V1NKeeVmLZZ8qyUu2syk8ETN8xqU22ozggxa27kjPBi3VwcZgbbMS4zq1tnfJillk62zK5iz2TcjNUvYHJiVjQ1rxSw57TcjGqtA2rkSd3PeQz8Yaawv2StODg9osKt75EY1BycCXor6dZYjvOkXxOv2FeQqbRM23kmO6g88xG04PE35h79F7IrZGXMA4nSOVpZxAsIw4gmE0xS7D2g2EOwgyIZoDLKkQzCUIlQEiVxDESpEYgREqRCkShEYBESpEMVlSsYAlZUiGKyCIxACsqRD4kESABWVKw5WQVjFAKyMQxWQVjF0ArKlYcrI2xhoBEriH2yCsYaBiRthyvtI2+0LoO2Rthts7bGGg4kYh9vtKlYw0HbOhtntOg19XUkxivkQSjEKs8rrBRCLBrCrI0KmTGa8Dv3i6ELCISx5maptG3H2hw/kIkH4wIZDgZMmGnq32jJllsy2Yj1MxjT/M3sO8ljWtJH2Vj1Mnq9ombNzk+Qkh8sJMXTj3fMB6STbkZ8xEWs+eT1JOV6MPblcjvKWPvrBHcRcvtJB7GCS3YcHtGJ0L1M8QFpOeJWxtr5HYwb2cg+U1IzodjZgHz3h7RxkdjAZ9ZuGaC5z27wZcY2v8AvCWrgZWLMcnE0n6SlJNnqsJcR2HYSVzVWfUxezJU4lkdN5hTUHmJvGrDk8xewTrHD1Sz8wLr6RlhAuJtz0qwg2EYYQTLKloDCDKw5GJQiVAGWUKw5EoRKgJEqVhiJBWEAKypWGKyCsoAVlSIcrKlYwB2ypWHKypWMASsriH2yCuJMQDbI2w5WV2xiglZUrDlZUrGJoJWV2xgrIKxil9sgrGNsjbGBcrI2xgrI2xgX2ztsPtnbYw0vtkbYzt9pUpJhpfb7zofbOjF19QTDQoXHaI1PiNLZPLfL04MoOYUHEXFmZcMTJimE57wofyEVDZ4lg2POMTTaNL78xQPLB+JMZ00r8xxH6dXu0Qo5YZhWt3Px2ElhKcV8L3ko/cxQ2YEvvwkmNdDB5IbiK75bf8ALLidCu+YN3yMecpY3Agi2WjC+hmfK4aC3ZUjzEoz7hjzEpuI795rE0QW8bWlS0E5zzIwSsuE9Yi19oga/mfPlOsyxxL1DC5MuEu1N7gjHpFHYjJhbjzxAMRgzUh69AP8xI84u4IMM5lDyeZuOVoLYMEwhnGPpBmaTQGEERGGEoyzSaXIg2WMEQZWU0BllCIciUKyoCRKkQxWQVgBKyCsKVnbcygBWVKxjbKlYANsgrD7ZUrGIBtkFYfbI2wAFZG2HKyu2ADbOKw5WRtgLlZ22HKyNsgBtkFYfbIKygBWdthtsgrACVkbYfbIKwAlZXbGNkjZIAbZ0NtnSj26cQymBRYZBxPNXrFSFUntBLxCLM2JaIDiTmUBkg8RjFogMlTB94SoZPMYmmg2yvA7mchwIEnc3tLiZw0YNlsSWfMEvmZ0YaJulw3EBLE/LGGruflEHnmSx+QQZPMuFqf7pzHMhu8g94xNSOcym/acTiTiUfMuFqGOWBk2NzgS1eCOZR+DLhKCeRAuOIdoNhmajNpdhBMIwwgys1GNAIlGWMFZQqZo0sRKkRgrBleZU0AiUZYztzKFJTSrLK7Y0UlCkGl9sjZGNs4pKFSntI2RrZI2ShYpKlY0V9pBSFKbZGyMlJXZKFigkbYyUkFICxWRsjOyRskQvtldsZ2SNvtClik7bGNk7ZAV2yNsZ2SNkoX2yNvMZ2SNkBfbO2RjZzO2QFtk7ZGNk7ZAW2zoxsnQj1VcMCIBeIRZ5rHo7FBEsDBjmWAkxm+hMyZAHEsokY1Kgw36VxKoJYjJhYlJcdpTHEIBIurZwoEkdpXuZaQ1HnLHtIxzJI4gQf08Sp8pcjiVMqIMg8iWInY5g0Mg4le/EKRKbeZYiANolWAMIw4ErtlNBIlCIdllCsrNoDLKFYwVlSsqFisqVjJSVKzTJZklCkaKyNntEppTZIKRopKlJdNKlJUp7Rop7SuyXTSxT2ldkaKTintGmlCkjZGintI6cumldkgp7RrZI2Rq6UKSuyNlPaQa5dNKbPad0/aNGvmd041NKdOR0/aN9P2nGuNXSZrkdOOdORs5jTSZrkGuO9OR0/aNNJ9P2kdP2jvTEjpxqkun7Tul7Rzpzun7RoT6ftO6XtHen7Tun7RoR6U7px3pzunGppLpe06OdL2nRprUReIQLORYRRODaAJdVkqsIokVUCXVeZIEIqyCAMCSolsS2OJBUCWAkhZYCFQo5lsSyiWCyCgEkiX2y22ALHEgrxDbZ22ALbO2w4SQUl0AKyNnMPtnbY1ACsoVjBXMqVl1KAVlCsZ2yCkrJYpI25jJWVKSslikqUjeyVKRqFSkrtjRrlenLqFtkqUjXTnGuNChSVFcbNc4Vy6FOn7TintG+nO6caEik7p5jnTk9ONCRqlTVH+nKmuNUgap3S9o70+Z3SjUIms+k7pGPdKR0o0I9L2ndKPdKd0o1dI9L2kdL2jxrkGuOgj05HT9o90p3S9o6CBqndKP9Gd0vaOgh0p3Sj/Sk9GOjWf0pPSj/RxO6MdKz+lO6U0OjJ6PEdIzukZ00ehOjoDVYVRIVeeIVVmHWOVZYLLBZdVmdaVCwm2WVZYCTVUVZYiE2ztsCgGJZVlsCXUQioWWAlsSwWRVMS2JYCTiBTbOxLgSQIRTEkrLgScQULbOCwmJ2JUCKyNsNtkbeYQIrI2w+2QUl1C5SdsjGydtjUwsUnbIzsnbI1ChrkGuOFJGyXUwma5xrjnTndONMJdOd0o505IqjTCfSndOO9Od0o6MI9Od0o9053Tk6OSJqEjpR7pe07pR0ckOkJ3Sj3S9pPRMdHLP6c7pTQ6XM7pe0nRyz+lO6M0BTLdER0vLN6M7ozS6IndL2k6Xlm9GSKJo9Kd0o7OWd0JPQE0Ol7Tul7SdryzzRI6M0TVO6UnZyzujLdGPiucKpO15IdASegI/0p3S9o7Xkh0ROmh0vadHZwwkWFVZZFhFWddJFVXmXVeZcKJZVmWoqBLBZfbJCwqmJIEvtlgsIGBLqJbbLBYRULJAlwsttjVUAk7YQLJ2yaBBZIWFCywWNAts7bDbPaTs9o1Ads7bD7JOyNML7Z22MbJ2yNMA2e07ZGNk7Z7RqYXCSdkYFeewlhV68RphXZJ2e0a2CTt9MRphTpnzndKNFJwSTTC3T9pwr9o10/ad040wrs9pOyM9L2kiv2jo5K9OT0/aNir2k9P2k6Xkn0vad0o50p3Tk6OSnSHpO6cc6ftJ6ftJ0vJLpzulHenJ6cdHJLpSRTk4xHOl7SRXJ0vJWzSvVjeuMjiU6XtNBw7gBiTj1iWq1ui0lqVarVUU2OcKtjhSTM32vKnS9p3S9oH4h8X0fgPhb+Ia4uaFIX+mu4knsBPnGs/FwEsNB4QePO+4D+AP95Z6tanh9M6ftO6XtPiOu/E7x/UKy1PpdKD5115P7kmIaH4/+ItHaXXxI3AnJW9Q4P8A2+0fa/G++9Kd0p8y8P8Axfo/LEeJeGP+YA4OncFG/fkTPH4va8Xqx8M0hpzym9t2Pr6/aZ2nxvrvS5k9ITwuh/FfwK6xE1VGs0ue7sodR+3P8T0uj+MPh3WKDR4xoufJ7Nh/ZsTN9U4avS9p3TmP8T/FfhXw9okv1d4sa0E01VfM1mPTyx25nyjxv8UPGNdaf+GhdBRjAVQHY+5Yjj7RNqzw+39Odsn5l1nj3jWst6uo8T1bvnIPXIA+gHAm14d8X/EV2oort+IG0xTgPfjZjH93GTLZY1w+/wC2dPjV3x74obGx8S+Gr5YTSuR9vlnTHR8ZvSfil4BYcaivWadvMNVu/wAGb+h+NfhzWMFr8UpRj2FuUz+4nwdaztJL12Z8mHeKtVUXJ2PWR3wSJif9Xt2v/L5/j9Tadq76w9LpYh7MhDD+IcJPzDpNXrNGytpNZdUV7dJyv+J6Xw78SfiDQYS3UVapR5Xpk4+oxOnn/ql/cYv/ADX+V97CS2yfJqfxeuWoG/wmp29UuKj/ABPUfD/4l+AeJjbq7T4ddx8uoI2k+zD/AHxOk/P4v9c7+H3P49kEkhJbR6jS61N+k1FN6etThh/EZFc6da5clhXLqkYFcpqbqNJQ12qurppXu9hwB946OWd4p4n4d4RXW/imt0+kSw7VNzhdx9BmW03inhmpZV0/iGjtZxlQlykn+Z+dPxA+J7vib4hstezOioZqtMoGAEz+oj1PHM8sFG/5VG7PcDBnG/m+3efg+n7EFftJFftPy34X8Y/EXg+2vR+LapEU/wDlu3UX6YbM9Hpvxe+J6iOqfD7x6Np9v+DLPzRn4a/QgrlhXPhuk/GnxZWxqvC9DauP7GZD/vN3TfjZoRSBq/BtWuoHcVWqy5+pwZr5JU+L0+rdMSemJ4Dwn8X/AIb1tyValdZoC5A331goD7lScD3M+h6W6jV0Jfpbqr6X5Wytwyt9CJZ7lZviz9qdMSemIyEPpJFZjpOS3TndMRsJKWmulN11iVr6uwUfzHRyX6YnCsekbCBlDAgqeQR2MsK/aOjknsMkVmOdP2k9KOjkn05PTjoqkir2k6OSPT9pPSPpHun7Swrk6OSPRMkUx8VSelJ2vJDoyRTH+kZIpkvteSHSk9GP9L2k9KTs5Z/SndH2md8YfE/h/wAL6RbNYTZqLP8AytOh+Z/f2HvPJ6P8XPBnRvzmi1lFg7BQtgP3yI22Ly990faT0vaeKq/Fj4acrv8AzteTgk05x+xnoND8Z/DetGaPGNID3K2PsI+zYktq8tXpe0ydV4/4LpNQdPqvFdFVeO6NcoI+s+X/AIj/AIkaq7U63wnwcpVpFIT81W5L2Y7lSDwD2nzBdQ45K5c5OcZJ+s1PNv7XH6OT45+F3taseM6YFfNshT9CRgzA8Q/FfwWh3TR6XWavb2cKEUn78/xPiFmstLDeRwMdgP8AAlW1Vj8DHP8AE1xEx9Q1fxp4l8TIdPpb/wDhajLMEdUOPTexH8TyHiNPh+kRrdX4jRq9YuSlWnU35OeOpYSB+2Z53Uam9lrrusLIg+RQRgRZnz/djPqZOW5cHs1lt1pNrMyE56YYhR9B2g2ersMKfPcMyhJqwwGfeAsLN8wBxNfpP2Mq1Px1gP8A2wqpplb+pbuPtEAVIJZsY/mBZiTmTpcaVg0oJ22tn0EA5pA+R3J+kUT5mxmHNbFS23C+uY3VxxsHYZI953UXAJ/VBb16vyJvXPYk8y4od2YKAuPIybVHp1W0bX3FAOBngfSVutpYfIGDe/aKlXQkE49sSpzjnIk3DDRevaMsxbPI7Yl9OiajVLV1FG84DO2Bn6ntM8swGSOPWcrKQdxxJ0uPWVeHaSqtUu8Q0wsA+YK4IHt2nTyXUUcbh+06ZDgsSxRnAMqUBHyPjHpEMtjKlWz/AGqeR9pwt8sOD/E8T1aaIdTlQPqso9jMu2wK3PmMYgxeQcM4+h4ljcpXuR79xAIEFgAHHspzK9NDkMxX03DvKB0I7g+Q2ntOFjVuAM5zjGc/xCrUlqGDU3tW/kyMV/kT0Hhnx18S+FgLp/GNUyAYC2sLl/8A5ZP8zz11ikZZVx2ypKmUprWwFlsJTt8y9vuJZ6s/TFkv7fT/AA38aPGaAF12g0OsGP1KTU33xkTK+Pfj7V/F9Ol0o0w0OkqPUatbd/UfyJOBwJ4OygAEptceZU4zK5srUBBt/wCoZnT5fX+s/H5/eHdtBrCvlXz3gUQiwYBIUwdbWPncoI9jLEAElS4PniPlpw7VjbcfeBJ5jBBY5GGX/mEhqMnjaPYGXuU5qgJxO3Z7k5kmlh2MqykDtzLPUTKndGdFqtRSDVptTfQP1KK7WTDeoweD7xInicrlSCO47TUqN+z4s+Ib6Ok/jviZUDGPzTjj6g5md/xXxAMbB4hrup5t+YfJ+pzE9zA9RDz5gDtLWLld+P1eY9ZpMej+Hvjf4l8GIq8N8YvWpjxVcRamT7NnH2Ijf4j+Ia3xe+jW+JeLVa+3JToVOClQ8iFHC+eeScieP0x/qDtD3DFCkDCsMffylk1PpoeBfFPjngRX/hHiur0yj/8AGLC1f/wbIn1v4J/G/ddXpfi7ToiH5fz2mU4B9XT09x+0+DZIPPBEndM7hfMr90+H6jTeIaSrVaG+rUaa0BktqbcrD2IjQqn4v+DfjTxv4R1i3eD6thRu3WaSw5pt9cr5H3GDP0x+H/4r/D/xa9Wkss/4b4qw/wDTaggK59Efs304Mvbnfx490KZcUx0VSRVJ2zwSFPtLCoCF119Gh0l2q1li1aeld7u3ZRPlv4gfGNXiWmXS+BaqwaNQTqtSoKgjsFHmf4l82+rkL5a3xD+IHh3hesfTaWltYyD5nRgEB9AfOI6P8VvB7dRs1Ok1dNW0f1AA+D5ggf5nyNBqtbZ0tJU9p/01oScfaX1Wh/L6WsrYu+zIZezIwPYiej4/P6Z19Uu/F3wdLStfh+usQdm+QZ+xMU1X4xaVf/TeD3t/+y5V/wAAz4+1FuSMfzAOjIcHAJ9ZPih0+sW/jNdgirwWoHyLagn/APrM7TfjD4zXqM6jQ6K6kk/IoZD++TPnBobIBK/vCoqUnupb1Jj4/JtNfGHxBf8AEfjtviOorWlmVUWsEsEAHYZ/f7zCLlHIbg+c1d/htegGLXs1rHczbDtUf6R7+8QvOnt+Y5DeYHnNTFodSG5mOQFHnmdTWLH2uMqp5OeJFltSoEQ/Ln5vWUs1aqm2pcD3l+hov4KD4MNeNXQLGsKLphnfgefoJkmpx3O37y+n8Ss06uFrRi3cvkyNR4pfqHVn6Y2rtAC4AExuNYg6ZQfntABGYF0UEhXyR/MA+o2/qxFrNWxPy5jokPAVFfmsKsPIrO/8Koy9u4+kyWtdu7GCYzPa8tptbUcKuCo4wfSDF3GUxxntMfMkWMOxIkvteTbFidzDzkDb5qYt1XH9xnG1mI3MTM9LgrEc4l69Q9akA8ehgSoAzvwIJmA/uzHS4bOqcLhQq/QShv5yBg+uYkbT5SGsY/8A1JfRhxr3YEFjiCaweZzAAMe+cS60buzZGfSZvpeUveMYxmCNxz2GI0NAOS9mMd+JT8mnJa0qg82GJO4vNLdV/UftOnN0QSP6h9xOjo5G4cblKDjsDLUajU0A7XG3GMZzx95rDwXwnU0o2k8S6OpIyamXeM/bkD7TM1vhmp0hbbfprsd+nZk/seZ5ncWrxcgqW01RZeBtQf4Ped1tNfaz2AVFjwANoH2mft1G8qdPYWAydqk4HrgeUo1xLKHVSR2JgaF9enbB09zH/qGDAom08Fg3k6nmBRNpLPkbuQQe8ttU8qzCQMi24KAx3gd+04a6yokdFxWT2RuD9oitp6pRVtHux4hw9m4AMuD64lS1ob6b0zwpP/LgyhqC8BmwP2gGFyqp2o4z/b3P2lF1O3hk2n6wGgteeCAZZKi5bNpz5YiTaoWMBjI893+0nq7TxbXgn6EeggOrRYrkMST3BU4ksSoySWA9ViZu1AsYKu5RzktCrq2X9Wcjv6DjMKKLAR3USQa3XO9h7iLDVaewndtznvnzl0aiyv8Ap2sqfuIRJoDE4cMMenMg6XjgkfWQK9uBVYmT69zI6lqt82CB7cy9UsgRqtSz5CCR6GXSwh/6q5GMcS51WWx0c+/nDI9TAlgawB5zc/LYzxCLApaxT9IOQYZXDIUydj849PpCsaA3cnMXZKmOQSAPKan5UvgG18gA8leM+sHmMlKzyF7+8E1XPCn7GO4nNDDYORLBs/vmUNZ5xI2sPKXZWcr6v+Hf4z+O/DDLpfFLbPGPCwu1a72zbV6bXPJHscz7L4L+NXg/iGjGoNVieRrxllPuJ+RVbI5ENpdVZprQ9FhRh5jzmpn9Sx94+MfxA8T+IbNRT1Wo8NZhs0yYGQOxY9yZ4q29mYnPf3mBofH0sVU1Y6bnjeP0n/tGbPE9KrH+pn6DM9Pm+ZPpxvn1v29L4X47rPDq7K9HcdO1nBur4sA8wG9JF/jWstcm7UJZ8oUlqwS2O2T3z7zzqa/TuPltXgc54iWp8doV9io7KONyjiOvM+zmtyzWOu4hvm9ojbc9py7En6xRNZRam5bVIPqcYmdr/EbKrsUAFB3PrF9yLPFrXLn1Mgt9zMajxOxz8yrj34jiautlJbP25mPl8tfHTZMgg4zgxFtSM8OAZBs38lyfqZe4cU0WGcZGZU5yB6+8VcgDvx6iD3jOBjMz8kXg23GQTz6QT2qo7xdy2e8oVYgk9vOTpcQ7EkmU85dX2p2U+5gm1lNbYYKT7DMzfS8uOPWcVOMnt6yOvpwSepjPbiM10m9c1WVEeinJk6XkocDzEjBJxyT6CNNosclxnPbMe0lS01WEAbwv/wDsSX2s8stau28EE9h6wppVGAO4nzx5RhWWoMGB3kcZ7xfqByRkZ9CZnqryhaq3BJVto8ye8k009PcUKj34kLaG/wDLUsc4DE8Sz7AN2obcfLPl9pNq5AFCbsqigf8AMCTDrZUg+fPPqAMRO7VEtikAZ/uPcxWw57nLfWX9puNLUaikKem4z6YzKaXXZyLQM+RH+IjVVu+Zztr8yYUW1VHNZGfI4ziSw0/cxavIbp574HJiL0iy4rvYnyye8rbrAxOUDe5grLTZwiAD2ET6W0X8nf6D/wCYnSi6cEAtaFPoR2nRrODPrbkVhUenznci4Ye2RzLaTWrSjh6qrrTwSe8oukOOWZeP05x+3tKNU+VGVDdtwOCf+84a7Y0T41qVtVatOlPGAwY4xB16W3xJbGr0dqAtywYDn2BxmBFTMm0qucYwpwZattTVgV32KF9zGmHNT4L06hXVRfRrgfnTeCpHl8p5BiNvh3iVWmN1yr01b5guA4x/y98Q1mp1BH/iHd2xgMzHKj2MpZdqXxuNpXAI6rA7TGmEnZANwZbAR/rAI+0ghTySR7RoahbW2apqypJOTUpb/wCXf+Z1miC5spZSCcbfWEJqNp3rubHAIODGaNfq6FArdSno6ZI9ee8pvqGOtXsI4PHEKi6dxjfYAe3p9pRS7VJqXzbS4c8ZJ3A/fjEla1dttelDt3+U5nElU+VksrPG5ef4lSzfKVZwT22rGhi3T3VYa2mysE8ZHEjqLjlgD57hxKnWXrSamuIqb+1z3Mo4rsG4VqpH6iM4EKK7qybfkxweDnn2gUqZmK71WrO7A7wbDPKOoX7zqsthQWZiePIH94QV6m5bfkDlQTggxcG5bM9QdVv59sxjY7MauCfSMtVdSQbKwfYA5H8QaSSyxA72dViOMFcA/eTbcorDumPviV1AcHpt1SmP0EEmEp09JQAlHZuMfpI/+5BZrlFKlCdrd278ekobQihl5GeB5ke4h3SoO9ArwcZIHY+4if5c8PVZ8o5G4ZlF6nUAvuIVjk7jkQyk4LI4+xlK1VkxZlz6nuPX7SlatufeKxkcH1gFs+YhgBjz84M2qg/VhvQ9sTsEJg2ggDhVHJMWW/cwS1Apzj5hAPXdWRgsmTx3kMq/L8wBPv3MqE05sNbKVfz2jP8AmDs0VJ2tTqOfPeCpEu1MGQuDgMPoYZbCFGUOPbmKadNRn5QLAo4OeRDrdepw9LbfUiXuxOTa2CwYBAHpBtaKyeAceYEFvqsOLFK/9JhKtNWT/SuP0Jz/AJl+Q5cdau39A+uJyapCMMCvoRK26Vj6EeoEXbTsvaXqVMpsPv8A0kY9jOZioIXiLCkHs+1vQxzS9VAwsKt6esbFgaB8cwqq2SdvPuZZr7Q+K0VfYiVssdBut2jPuJNVItdW/qKAPaRZqK/P9UCdXwQGBlTYwbdtV/PtLhogyQSN33i9iEn5jgwduquJIYAZ8sSlAsdsqCR/iVkRi9afKxzAb8Ak/q9THfy4sBLjOIAaV2Y7eAPIxKXyVJ95G4g5BIPtDPS1TZcA+0FZZvP6Qv0l1nB9PrbqbNwbcfRxuEdTxcNk30/Mf7kPb7TIzOg1vLrNPcMG91J77uMw66Ten9MgqfeeazGdOdQoBqdlB9GkalbdlL1oACAYsdLZZk53feAr1WqxizDj1MLXcGcbhtH/ACmTcX9gtpbhn5CfpKfl7V5ZSv1mk1lbYWrjPmYB6CzfMyjHvL1U5hR6XY5ZgB7mHXQgqCHBUd2zL9FFfJ+YeYzKveQCAmEjdMHTTUVg4fJ85R7kQ7K1GfeIWsGI+Uj3hKtLqCwKDd/vGLprcx7KJ0hRaBhhtYdx6Tpk1dlVcbbC/wC4xBbOCeYYKv6gSRjz85VVORk+fbPM5thDLDPIPlmTSjg7nYsfQDEPgLyCTnsMSwB3EAnnjnvCltRXVYfmNgUdgDIZ124AdiOMkxwALyUDHHY+ch+njDacDjnaxHMGElbCt8oAPcHuZK6gKMIoA/0gRttPozne11Y+oYf7QNumor/qVXFgP+XEahfcloPytg848pS7TFqcBnCj0GYsNf8AOVrXaB5+sJX4lZk7XYn/AEiECoqtpX+m479iJZr7csNUj5z3r+WN03ggM5Rcd8ntCOqONy7SB57pdCQ1jOWYVb1HqMMBOTU6d3yxuTHYAcffEdJrKBVID9iFbBiFmiZslbDj0MfQPU1OQy2ZI5JI5/xJOprDhmtRiO2fKJvVqa1KqQQ3GV7/AEhxpwEPyLkjB3jkQJbxAZ4fbjzAh6fELOMXEg9uYoulBOLHPoMeUar8Maxl/LozqR32nn7yaGhqncZJZjBWVJaVYixCO2OIWvwbWbCHBoUnkGzk/aWXwOx3w/iHTTv8w3f4MLgWwpYLK+HA25z5QAU6ZyFOUc855I+ntNKnwWrTIbfztrsB8vy/Ln09Y3SaNOFa9FsVV2qWTODIrHdhhvlAtI2ixW7faA6xrULY+HB5bb5TYv1Gnc7TXWVJyA1QGD9RBGvTsNmNMGPcizHHpLEZjCghumQGfuwgmS4gguGXyAODNUeGaZ6zYQB67WDAfXtLN4TWybqWRhjjjvKjOso2BHW7Yw7/ADZMqWqsAFtwdx/cBGk061uVs05DerKDDEYXNQRgO4UjMDOVhS69NSx5IIBxCfmbq7y4ztZeeMgfaGcuf1KQD5SMn/T9IxF11lVlmLdMFXH6tmBJDaGzA37Seex/z2grAxQjkH1HeUYJbVhqWLY/Sy4mca0yqIApo1BIY4GfKXqv1ABwq3AHB45mbQ2qFqpkquO5HH0zNelGyGWxM+5ksWfaAQ4PV0zjPovE50psAWqzpt7YzmOG3WFsOu8Htg+UhaFuRiaAGbjIsHf3B/2kXCWo0rmnAtbjsTAjS3On9Xaw8iI5ZQ1LkIBx8p5yIu4JIJbGO/M1tS4APDueVGP+rE5tE1Rwu5cjOc5Ebq6ZI/qkf+/McR028srHHdpL7sWeZWIKlLHeCPc8Ayy1BCdjYB9pvCqtwCK0s58jzLNRWq8UH7yfKvxvPphQQbQo8yYXaWGN5P0mmdPRY3ybAT5GCNJRyBXux5gy/InDGt0tuST2+sC+msPZf2M3bEFnIyCPbgyG04HzYGPWan5GfjeceixeShxBsCDggg+89FZvQEIhf2AgnJYYahyD2yJqfkZv42DLbmHYzTbTVufmrav7GCs0AA+UnP7zXcZ5pAs3fJnc+sYGlcZDL+4gzUe+1h9RLsMqoscf3H95JtfOSxzJ2pnv/MhkGflcS/R9j1eIWoMEK31EL+ZD/NdTn3BxEgmeM8+mJauwVZBGT/iQP1X6UgktbUw7eYMf0uor42sjEepxPPvYrNkiSpq8mcGSzVlx6R9XWGIbpE/WdMRXqCgG3n6Tpnhemvq2ArL11Mm85AJB48uYNvluQbWbsSQBxAI2UsGF/wBI47QgpNNRyQxZQc9sSNKai5qS7MNuDwc+UFV4itxRXyHz3XzjNelruNiXeRB2gcNn1M7T6Win5lqXePM8/wAGQS/5npCxELOc9sETNfU3hyoR7GP6jgn9p6CzUMnT3BAVGM1qFzBDxHXUori1QCSdqgAAZkViahLHr3XUW9NRkHtkyunZLTVWKrVIPHPE29X4hffWquwOO2R/MDXe+oSy1mJVM8GJGaHbTpwoNlCqreg5P7RDVJoaXUU7jkZOG/iHt1FmlUqhHzKcbhnGf95hHOTky4a1nTRNTvYLWB3CsWb75mt4X4fotYorrfUbSPmG4Hj1x/2mFXojdpRYGA8uY/4Tr6/C9b+X/LrcGIV2Y8n6eklXz9vVU/DfhmksSy21ww/SxGQT6e8sdHoeptXV6RmJ4BXGB7+8nXeMveoDIvSZMumMfsRGKtRQNHXZp9LWKqgAwYDcefXEk+2qz7PC6V6h/MabK8gKAcTB8Q8H1hfqVuH3DjC84ns7L6bLAXq3O3PIBGBC3UUmygvUubPNeMYjUx4C/wCHPFKFQ2KhJ5G18kfX0m34ZZ4jp9A1d2ob5OQmCRjjtxPQMNJTqOmtdrFzg7nyIIV1FzanUR8EcHjvG6uYwnt6xLWPlm5ziAaxh8oVivbgCb2s1NmmLklbbduBuQYGYrp7NI9dVl1T4sOCFxwfb2lRk16B7EFq9UI52nGcD6+WITS+H3dMCixWrUng2dse3pN3U6GnSU2X77lrGCAjckc9x28phanXUpUzVq5D+TACIIs05qZnu0wdSMAqc5+4gRXoThBpShJydpyx/eGe5tPSH6ahjxuV8ZBHnxGNN4mHqKmtDwNqmsEc8HMqF1fSNpQjPZUwPKsN+Z1YeuvOnvrcY5rB/wBj2h9Tp9M6L/R2O2ASrYH7RCynZg1HhfJoTDK6tlsC3UaexmHYZH+0NjSW4Joy3chHBHp9ZnrbtDKaaiCe+Oe0u1rNpt6YA7YIz5+UoZv0qJubT2MoJx09xH+ZU6YlcHaD9OREG1VyfIzbqxgYJkfmrU+VLHA/fH7wDnTBfnZirEYyRxj65lVsCvis7jjPEGt6BPnVmye+f9o2V07hSdOuSMhsnMgTt1NoUk6V/tiATxJFPNbg+hM10oppqNlHUUny3ZEzNYVQ5srWzJxycYj9gieJVd8uJb89p2OSzc+uREql095ZUR0ZRk5ORLLoA6BlbHGcGTmL1WpVq69o23Vn1BIIMLbqqGQF6lODkujYJ/8AqefbSIGw2GMIpdK8ZHHkO0Zi7pk6/RMDtyDkjaV5MPRWtilqnfj1mJbea7csilge44gbdZdZxvZV9AZcZ16BLmW3ZlgR9hHa9ZYNwUggeuZ5fS65kdTa1j4P+qNV+LsGVDWOTyQZL5WesehGobaTvB9syjWZY57wGnuz/YvPadajMCQ2MHHeYxvT1Ni4wXAJ8icwjEY52k+0yDScqWbleOIwjMAMGS+Vno6y7hwij3xOCMBwgb6xQaizdncePKQbw7fOG/eT9NbKNZWQDkEH3ijkjg4b1jZu3ltwzxEtVbt4US+az6iQcse230xF9WaaqwVPGMmDa4kduPrA35sUnCkAZ5nSOdUTXaUghqwGHp5yp1SMmKwoI7HPIgzQWRc11HPbvAGtAwVqwCO+GOJpk8/iC1VAGquxvXGJSvxHTNnq6YAnuRBqtIGFQ/cyjUVMcKgUjvJi6dU6C4ZUY++JZdHpif1n94gtChvlyAfKR0ijbtxIHkTH3/KHz4chORZ/E6IizUY4dce4nRt/1Pr/AB//2Q==",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image 2:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "## Image Interpretation\n",
       "\n",
       "| Question | Answer | Reason |\n",
       "|---|---|---|\n",
       "| What does the first image represent? | Daytime | The image shows a bright sun in a clear blue sky, indicating daytime. |\n",
       "| What does the second image represent? | Rainfall | The image shows water droplets falling and creating ripples on a surface, indicating rainfall. |\n",
       "| What could be the next logical image? | A rainbow |  The sequence shows daytime followed by rain. A rainbow is a meteorological phenomenon that often appears after rain when the sun is still shining, making it a logical next image. |"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prompt = \"\"\"\n",
    "Answer the following questions about the images, with a short answer and a detailed reason for the answer.\n",
    "Present the results in a table with a row for each question, answer, and reason.\n",
    "\n",
    "QUESTIONS:\n",
    "- What does the first image represent?\n",
    "- What does the second image represent?\n",
    "- What could be the next logical image?\n",
    "\"\"\"\n",
    "caption_w1 = \"Image 1:\"\n",
    "caption_w2 = \"Image 2:\"\n",
    "# Photo by Diego Ballon Vargas on Unsplash: https://unsplash.com/photos/TA5bUTySOrg\n",
    "image_w1 = load_image_from_url(\"https://unsplash.com/photos/TA5bUTySOrg/download?w=600\")\n",
    "# Photo by Reza Shayestehpour on Unsplash: https://unsplash.com/photos/Nw_D8v79PM4\n",
    "image_w2 = load_image_from_url(\"https://unsplash.com/photos/Nw_D8v79PM4/download?w=600\")\n",
    "\n",
    "contents = [prompt, caption_w1, image_w1, caption_w2, image_w2]\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dJdBiddamGN9"
   },
   "source": [
    "This is really up to your imagination:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 9644,
     "status": "ok",
     "timestamp": 1702337502502,
     "user": {
      "displayName": "Laurent Picard",
      "userId": "17424629147771078746"
     },
     "user_tz": -60
    },
    "id": "iFhubVwFmGN9",
    "outputId": "4f2f0ada-b96b-41c2-d2ba-9f0d1e3042c9",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Answer the following questions, with a short answer and a detailed reason for the answer.\n",
      "Present the results in a table with a row for each question, answer, and reason.\n",
      "\n",
      "QUESTIONS:\n",
      "- What theme do these images illustrate?\n",
      "- What could be another image to replace the first one?\n",
      "- What other image could replace the second one?\n",
      "- What would be an alternative to the third image?\n",
      "- And for the last one?\n",
      "\n",
      "Image 1:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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O0WZ2cSTobyCCf6TF/F3T+pZGHVT+Hup34WT6gL23WM/0DyAu+N/JmnKSXRNRi32Ws/Hd3IB/SfIk1GA70WGxwnH0lzoGWcDGxwKluNljV6O3sJLH5J95NdjdNa9jZi0N3HfcVBmarorzdUZX4b6ph5/T7CGrpsruepq3YBgVOtkff2+01cVKzYWVlOvgylZiYCnaYuMBv/SX/wATJ/E3SKep4BxunqmHfYQPzFSgMg9yJnyivk1ak6NzqZcoUrIAHI48GZv56nFqP50oileCfeZmZidPwemlLcezJaqsAj1G7rNDzsnyZx0r8LjJoTJ9NcTGc91eOxJOvlt/8Rc5PSRpRils0snN6Z+Xre12elz2sPSLAfc6HC/eW+k5IycYNfTbj1FmKBv1Mu+Dr/Dv48wwumtVaVXTN4Gvj5hmi2hioQllOmHxNPx2zNcnSJsixcfel0nsPtK62AP6gA3Ord2KO/3Hgytplcduuz3ENgkqK+fUq2+pvXvoyFaUZ1cLt/G5eyavUPPv7QoCV69iI33srGbrRXoxLFssexiE9hLSvpZ1YfpJJiqAdfpjitE5S3sz0sYXsp3z4k4pbtLE8maI6UbB3iQW1MD2DzHbegjxTtEVJ0uh5l7HuKAhvEqJW1I2wkoYNyJXGneyH1Eo1odzB7RqdrV3HQnJXQ3JaAW5Eq34nLFeRUy62FZAlLFTKZhWoOt+ZtvUX9tmaC44rqXtA3OaUVVnZHI7oyq8C11+uwjQ9p53q+M9WRySfvPa5NTdn0NptTGuxfW7lsGyPeYbsrCVbZU6D1Nkb0n8Cb351fmYS9PGMC8ff95WMU1cmTyN34I9go9PG2fYTC6vhXZr9uNnZGKxILPSF7mH+XZBA9ppV5Rvdl2O0+BKuaz49m13ojga43M3bsxFOJSvzEx3r7u7vBILSZLA6kpz7k+8z8i8W2MWQA+ZZwz3pZ2ntBGvESbtlHFUjnNryF7SAV9+fcTpWLJzsmdJYQyqV9XQ0RvUxeu/iCrofUun4eZjWlcxivegJKaHkjXjehM0l5WPbqJeyfUsPYhIJOpaxPVSplblVH1E+QZhVdTy8glsTpV9lYP93YSVDfvsCUjZ1vMyWp6gUxUsYaqpfu49yx/aY5K9FGnVHVvVMbL69VhWEionu2R+vW9DfxsT0LZ75WalalhV9vcyv+IOkev+Gnr6fjpbk1AfldnRR96B3/PPzzO+mV2IUoze2nqHphwVP02DXJXfx4Igk4ypmbjJWbGPf6GmC7IPG50+TTl3d7ISfDAHWzIsq+oBToB9aOpVss7AXVToSnZNL2POfsuVVOwfA+BKN1tgtes9oTgqy73r7yfKyNpX9BDeSftJMPEBZbD2ujA7B8iCTvRTUUnI7rrFtakkwOOoGx5lgJ2nQHEs1Ud5G/ErKCSs5oZZNmQV2CpiqXsHBm0+DWbBKebiemSU8SbeqKqrss4OQxr7ByZXY9uQe8cyDHsag9wH7yaxzcd6AgkPphfpxwJAKtS9j06rPfKeRte4AHUtGfic8oXOvQywNeveKlyiyujkcESVSTyYoTt0wyYuKtF3DuHrfX4l1r1L9oPExzImsKsNGUnjT2ShkaNR7Cl3yDInsSssWI5ldLDYwRjK/VEZE3zqcrjxO2L5FbqHUFBZd8H3mV+cX5Mb1+pstxIfyy/5jDkyygkex6dQ3aDVZ9K/q55l61+8dpG55bqlXW2zOn3dByKqkSw/marhtbUI459tGaI6dk5AP9o5rsD/APSo/u0/r5MpBfCOXL/WQdQswMd1WzJRX3+gfUx/gRP1XCqfFxmd6LclimPXehV7NDfA/YEzSx8TFwkJoprqA5LAc/yZX/sqnK6hX1T8tWc2sFa7HGyq6PH87jlHVmYZE9M0OiYwxlfJubZtGl+wneRbjPcWdazaP8etnXxv2kN2RrVantHPHkD9plZN9VLm66wLrfk+ZDa0i1cts2M/Ow+ndPbLy7Fpx0A22vHwJ5fpOPj25WR1lXZ/zuu1e/ahQeOPkyhk/jjpPV+m342LlCtzYuNf7+mrkAHY45npbunVUUpRjgLVWoVQvgCaa3a3Q4pxVS02aFV1bISD2qutA+52OZDl5G79BFKAkqx8qT5AlAk0UWKe5xwCF5PmSZKG0f3ZI9xqNvViUEmc3462sr75U7kgKqg7yNn/AHkYyCjKgBdtbI1vgeZLUqZAFiEFCO4c8TCXtGm/TLVRoesK4DfHHAkSNqwheAJ3ZaiVkIuifOpUUkbJlYutsjNXpG1TQGq7jITkejaAZFj526QgPMhU+pdpo/yV3oynWqLZyF79ltGL1lt4HMgz8ULT3g8yrgOBsHzJ1fRX1ZPkVksAo1uc21PWu1aSGxmfWpI2O1yfqhTGpIqUZdrAhgND/ed5FvcAO2QXUWYpJPInKu1gVh43G3qhpU+SGEP+IRklZafRQSvbzOjHFVZx5sjboRO1ldtlvEsICPMZUbliBApIsUjjRl+5kvq7W5MqlCfEk9BivcvE58yR1fTy+TPysT6CVlL8sfkzWvYrSV95T7plRj+xaWSd+J6Kmv011uRXXrW69xKp7kiWTIcmv1U7fb3m+lo5U7ezK6b1TH641tmGxfCotNXeQQLXXzr5UfPuf2m3iLu3uL67QSFB87mbgU+kno9orpQkgKNeeeP5kGWnbmBqnYgeO6SyTfo6Y44tuibN6ech2rZ2FTe6sR+/Il/G6fWnomr6fT4+eJnhrXRBQpewHY0NzVxVy7qytiGsEHkeYo0loJ8m9vR4y5+i9T/E3U8V/wAuWqrANKqASo5LeNb8/eetIA4GiDwNzzXW/wAO19KuxsjF6bi5oVVryvVs9M2/WrBiQp2wI2N/tNnBw7qmRUvtekLwtnJH8zWPV2azRTjFpkOWtddmySR7yKjIZnapd+mPLsPf4Hz+/iWcuosvgrv/AIlVaWpTX/MntM3FpouYhrTqLFjpVqA/kn/1Ic6sdPyO9DrDubn4rc+/7H/n95UpsdnPco17n5mvaa8rHZLFDVsvayn4hCpbRnJcGV1TTcnclupBrnlrv/kNPWKMbFux26bSR6hNZa9kPg7J1oeDrmelaywVqztYVP8AlUFT/TmbUuSpom4uLVMVeOq2bRwqf7zSw0qRWZuT8mZQpPqm3baA5EtVsTVtSdH2mUm9GpJJWdZ1nrDtTYWZ1Kmu0y6ePMidO7kSv266JLKdpYA4O5aS4r+kzNKsvM7osOxN/wAZhr2jTyqfVoJb4mVVUatqDxNZ7v8Ap9faZ4UtzuScbLQnSoZ8CLjfMAIjOiKpUccnbsmatWTYkIRm3oHiSUsQdexmhioADsTLlwHGPNmTaSqaHBkLZprr0Tqal+OHdiPaZWViLaCDOaTtndjikqY1Avp7gfMrfl3ktKmgKq/plzvHwJmzd10aO9iBgBqPW+BOo88r5Ddik+ePEz6ma7vRjongbH6ZsGgkFu3ZHtMksarAx/n7yM15WdON+NFjG10si5rbbTvQQADuJ+wE3MzIsqpRkG2Otge0xsNDl5X5huEqP0j4+P59z/E2gwbavcrdw3wNaEeSmtBG0/LZUsrry1HeHDkgk+x1J6aVrA17DU6YpWPo4TyJXbI2+h4iimzMpIMntdtFQZQyKBY2vA1qag127MpXMpfiOKT0xybW0ZS4zKxXu2PmWkTsqOtgn2l8opqP0iV+2OEEtmcmVvRl5C2VMmWN6pP1/dD5/p5/iX8tq8QN2PsN9WvYH7fvM/8AFORnUdBzB0f8t+cNZ164JUL4J0Pf95V6BVmr0zFw+rKRmJWNs3h9cH+h/wBiJJ2pNF4JOKbLVlJyLarayVYKQpDHR3rex4PiX6e9K+1gDx5EkoqCgMG+nXAjfgSkIXtmMuT9URaBj7dLOV2W4kq8+ZZM5WnRC67WVjtGl9gBKt434hL5NwlWiVbu9NQHEipHzJZqK0Tm9iM5M6M5mjBPgoHvG/aa4VQdTIwm7bx95rf4gSZz5rs6MNUQ2Y4FhbfBlS6hQQW/TLuRZvQWdLWtifWOBIl7MfN9Ps1WOZnc/M0eoAI5CfpMq+j/AN0RVLRsGdVEBuZzrZ4h2k+J1Hnl0FWUqNaI1MTqNC0so7u5j5GuJzldTbp+ciZlLLi2cLevIB+G+JtXUV5NCk6Ya2GE5uSlaXo6YtrZgJ1Gvp+Gi2bUNYBvz5M08mruyBap2vAOp5r8Z9Efq3ScnHxsi6o4hDq9R0WsB7iP20AJudFzky+m0WB+5zWvcPcHXvFGScuLKSVR5Jll7CVCjwOBOBxGfMU6kqOGTtlhjuk6Myqmb1mBPvNFG+kiQ00K2TuY407LqacaLVXZ26tJA15lDNvWm5Vo1Y2wDs60p8n7ma1mN3r2g6lHJwu20s47115Ew2/RuCj7KFjVoliiv1Fc/UDyWknUK2ycdXpI9es99Z+/x/PiV7au0gBTwfO5fq12DxsCODbbTHlSik4mL038QdNzL76K8utb6O0W1Oe01k+x37/tNYXV2cK2xryPEyl6J05usX3PjIMs/Wtq8Eq3BB+QCPfxuXMnHaqlKq9IBz9MbbhFmVGGSWifIKIA1ZJHvOqX7lAJG/tKddgOq2bbe8t1oBz/AEmMb5OymWKjGmSE7kbIDOveE6jhbOQAIjOjOYzIpzGYowGjFHDDyJp03G5OD4mVJ8S/0W+rwZjJG0bxz4suPaEU78iMZiCrnicXVm9e9PEz7EI4cEDc52jqTHpMlrQT+0r/AJBv9QyxdjikLan8xfmx8GTaLq/Rs0qBvcBoN9pwr73zqItoS7s40S3V05FTV3ItlbeVYbBmJ1Dphwa3yMDqGZjqF0KEYMjH28jj+JpK5L8eJ1mkFKKjyXbvP2C/+9Scorsrjm1ojxsT8v06vHJLEr9ZJ2ST53MrpfTaqKULV9ttZKBgSCVB43/E9GpUpuU79d/EcIqzORuiKEIS5zBJKB/eCRyWqKXRqPZfZgBwZFZcGXXzF29yHUq0ppvrY63yZzb5UdSSodlS7LNoqByJGERaywYaHufYSzl1K2MbKnJ+xEw2sepGNpL/AFcAgaA9hN8knsax81oy8jquY/4mWujp9gwMVCbspm/WDr9I+OCd/aepvC3Yw0Rscg/aUsRhabHIO31vfvPJdPzOtn8Y9R6V30/2bi1o1dbjTlG3ojX6h5Hka14lF5L/AEzxputUbWXkPRcPyeLZkszhXZSAq/J2ZuopXGBcjuPtv2lRBkNWDQ+P6fsChXx/PH9Jn29Uzaclqrel2GsfpsrsDq37ccfzMQx1Kyk280VGKV/7/wCmsTFBD3Kp0Rsb0fIg3E6Dz2cmKMzkzQhGIxxGACM5jMRjEanT7x6BVvIlbKsVtrqVAxXwdTksd7kniTdlVmaVFt1YUaPI1KHYZo13C2sIeDI/y/3kuFdnVHJa0aKV7G5zapEl7+wSOxwwmttkNJEdaF7Aq+TMR6+qWdSbJxsik4rEqKrFO9DwQf3m1aTXiOy8WWH00/nyf6TlFCqqr4A0ISipKjUZuGzGfKz/AM2mPk2fl1blSg2G+25rqNAbJJ+THYisV7hsqdj7QmMOFY7p2ZyZHLQQhFLEh7kiHSyKSHhYmNE1VncCsisQp78SNWKnYjZy3mLhTs39zVEwyT6ao/1DfJMq2Y5vHC7A+0ZlijIWuhk7T3E+YpQ1o1DI01sqGsU2aGiCo8DxMnq9Zq6n0/NTSN3/AJexgBsq3gb/AHnpMh6jQrKAWbhh8alC+pL17bF2uwRv2ImFGUo8TU5q+SKRyGr6yMdUY121F2ffAcHgfuR/wJdlb8mv585Rdi2tBfYSyZ0Esjhrj8b/AND3g3IiMYMCaODEZ03mcGMQojGYjNAKcmMxGAhGcxmIwENHKMCJN+bb4ErmKDSY1Jro3WcFfvIwCSAPJgZ0rCqt7m57BwPk+0h0i35Mq3Xi/qbU181Yi9pP/efP+2pMp0ZUx6lx7iAPrt+tz8tLMaWgm03aO7CDrU4hFGtGW7CEIQEA8ztvE4Ee9iDGhRbhCaQhHmHtCKFAEDDcUBNhFCIxiFFuMzkxgdeRODwYw2om5i6A5JihFuaADOTGZyYCYoozEYzIoQMW4AXDR1a2jvAox7CCQr7b+upm9Jyuo5Oa+P1FqdVaYioHW/jmepz0LgH1SGA47TMWimqjOv8A9RiGJ+SZxQk+meg0uLonyOXBHkDYkqsGUMPB5kZpybOoUCpFNBVg7E+D7cTRxsLspatwvnasDzqVckiH221ZShJb6xW3HIkM0tk2qDcIRTQHQOhAeJyYbiodj94jFCMQQi3CAgiMIoxAYoRQAJyYyYowOTAHmBnJgA2O5zCIwEBnJMcUYhRGMxGMQjCE5gBt7OuNn7TPxj67NvhyxOvcTRVvTrewjYAkFdS1szKNFjszkak5a6OqMuMb9ktdzojcfUBobnBtvP6m/pCIzX20H3WMsxGiSRFCKUSok3YGKENxiCENxQAcUIQCwihCAgiMIowEYjHOTGAGIwigAGcmMxGMQjOTGZyYCYGKBijEEUIoABihuG4xGn1G9Kq8Wkn67rPH2HMklPq71t1HE0AWQ6B+N+ZcnLB8rOmaqgihFuUMAYoRRiYQhFuAhwihAA3CKG4wCEUIAEUIjGAGcxkzmABEYzOTGAGIwMRgIRigYjAyKIxxRgE5jnJgAQ3CLcYifAwyuLjC0lrKvJPuZoxKCF5Gt8wM54xS6Lybb2EUIpsyEItwgIIRGKAD3DcUNxgEIbigAQhOdxgMmcwhABQhFAAM5MZM5JjEBM5MDFAQGKBijEEW4RGAAYtw3FGZCLcIoAatNlrqBYAAPE7hFIQjwiol5y5OwihuE0ZCKEUYBDcUIAEItxbjA63FFuKADihFAAhCKABOSYyZyTGIIjAmcmAgJihEYxBEYbi3AQojGYoxBOTCBgAGKEW4wNmIzkPscx7kioRQhEARQiJjACYtwiMYBDcUIAPcUItwAcIoRgERgYjAQjOSYyZyTAQExQijEBi3CcwAe4jAzkxmQhFCABuKBijADFuBigBq+RECdxKeSI5IqdCBMW4oAPcUIowHuKG4oAPcUIoAOERMW4Cse4otwJjEEW4biJgAjEYGIxiAxGBM5MBBFAxbjAIjDcUBAYjAxbjAIRbigA4otw3ARrahCIyZUcW4RbgA4twigA4twi3ALAmG4jFuMVjJi3CLcBD3FuKBgAREwiMADcRhucmMQbi3AxGMQbi3AzkwADCKKMAJihETAQ9xbii3GA9w3OYbgI//2Q==",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image 2:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image 3:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image 4:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "## Image Theme and Alternatives\n",
       "\n",
       "| Question | Answer | Reason |\n",
       "|---|---|---|\n",
       "| What theme do these images illustrate? | The four seasons | Each image represents a different season: spring (cherry blossoms), summer (sunflowers), autumn (fall leaves), and winter (footprints in the snow). |\n",
       "| What could be another image to replace the first one? | A field of tulips | Tulips are another iconic flower that blooms in the spring, representing new beginnings and the vibrancy of the season. |\n",
       "| What other image could replace the second one? | A beach scene with bright sun and blue skies | Beaches are strongly associated with summer, evoking warmth, sunshine, and relaxation. |\n",
       "| What would be an alternative to the third image? | A pumpkin patch | Pumpkins are a quintessential symbol of autumn, often associated with harvest time and the changing colors of the season. |\n",
       "| And for the last one? | A snow-covered landscape with bare trees | This image would further emphasize the stillness and cold of winter, highlighting the season's stark beauty. |"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prompt = \"\"\"\n",
    "Answer the following questions, with a short answer and a detailed reason for the answer.\n",
    "Present the results in a table with a row for each question, answer, and reason.\n",
    "\n",
    "QUESTIONS:\n",
    "- What theme do these images illustrate?\n",
    "- What could be another image to replace the first one?\n",
    "- What other image could replace the second one?\n",
    "- What would be an alternative to the third image?\n",
    "- And for the last one?\n",
    "\"\"\"\n",
    "caption_s1 = \"Image 1:\"\n",
    "caption_s2 = \"Image 2:\"\n",
    "caption_s3 = \"Image 3:\"\n",
    "caption_s4 = \"Image 4:\"\n",
    "# Photo by Tomoko Uji on Unsplash: https://unsplash.com/photos/eriuKJwcdjI\n",
    "image_s1 = load_image_from_url(\"https://unsplash.com/photos/eriuKJwcdjI/download?w=600\")\n",
    "# Photo by Todd Trapani on Unsplash: https://unsplash.com/photos/QldMpmrmWuc\n",
    "image_s2 = load_image_from_url(\"https://unsplash.com/photos/QldMpmrmWuc/download?w=600\")\n",
    "# Photo by Olivia Hutcherson on Unsplash: https://unsplash.com/photos/rN3m7aTH3io\n",
    "image_s3 = load_image_from_url(\"https://unsplash.com/photos/rN3m7aTH3io/download?w=600\")\n",
    "# Photo by Eberhard Grossgasteiger on Unsplash: https://unsplash.com/photos/FhdN5QVrBfY\n",
    "image_s4 = load_image_from_url(\"https://unsplash.com/photos/FhdN5QVrBfY/download?w=600\")\n",
    "\n",
    "contents = [\n",
    "    prompt,\n",
    "    caption_s1,\n",
    "    image_s1,\n",
    "    caption_s2,\n",
    "    image_s2,\n",
    "    caption_s3,\n",
    "    image_s3,\n",
    "    caption_s4,\n",
    "    image_s4,\n",
    "]\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
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   "source": [
    "## Reasoning on a video\n"
   ]
  },
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   "source": [
    "And you also can extract information from a video:\n"
   ]
  },
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      "----------------------------------- Contents -----------------------------------\n",
      "\n",
      "Answer the following questions using the video only.\n",
      "Present the results in a table with a row for each question and its answer, as well as the timestamps where the answer can be found and whether the info source comes from \"Image\", \"Text\", and/or \"Speech\".\n",
      "\n",
      "QUESTIONS:\n",
      "- Where was the video likely shot?\n",
      "- What real animals are first visible as a group?\n",
      "- What animals are cartoon characters doing a close-up selfie?\n",
      "- What does the electronic device let real animals do?\n",
      "- What is the veterinarian full name?\n",
      "- Where does he work?\n",
      "- What is Courtney's job position?\n",
      "- What's her full name?\n",
      "- Which famous brand is first visible?\n",
      "- Which famous brand is last visible?\n",
      "- What happens at timestamp 0:36?\n",
      "- What happens at timestamp 1:05?\n",
      "\n"
     ]
    },
    {
     "data": {
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       "<a href=\"https://storage.googleapis.com/cloud-samples-data/video/animals.mp4\">Video:</a>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
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       "<video src=\"https://storage.googleapis.com/cloud-samples-data/video/animals.mp4\" controls muted  width=\"600\" >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------ Start of response -------------------------------\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "| Question | Answer | Timestamp | Info Source |\n",
       "|---|---|---|---|\n",
       "| Where was the video likely shot? | Los Angeles Zoo | 0:15 | Image, Speech |\n",
       "| What real animals are first visible as a group? | Giraffes | 0:19 | Image |\n",
       "| What animals are cartoon characters doing a close-up selfie? | A fox and a rabbit | 0:14 | Image |\n",
       "| What does the electronic device let real animals do? | Capture photos of themselves | 0:20 | Speech |\n",
       "| What is the veterinarian full name? | Evan Antin | 0:57 | Text |\n",
       "| Where does he work? | Conejo Valley Veterinary Hospital | 0:58 | Text |\n",
       "| What is Courtney's job position? | Creative Strategist | 1:12 | Text |\n",
       "| What's her full name? | Courtney Powell | 1:12 | Text |\n",
       "| Which famous brand is first visible? | Google Photos | 0:05 | Image, Text |\n",
       "| Which famous brand is last visible? | Disney | 1:33 | Image, Text |\n",
       "| What happens at timestamp 0:36? | Two elephants are shown standing next to each other. | 0:36 | Image |\n",
       "| What happens at timestamp 1:05? | An otter swims underwater. | 1:05 | Image |"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------- End of response --------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prompt = \"\"\"\n",
    "Answer the following questions using the video only.\n",
    "Present the results in a table with a row for each question and its answer, as well as the timestamps where the answer can be found and whether the info source comes from \"Image\", \"Text\", and/or \"Speech\".\n",
    "\n",
    "QUESTIONS:\n",
    "- Where was the video likely shot?\n",
    "- What real animals are first visible as a group?\n",
    "- What animals are cartoon characters doing a close-up selfie?\n",
    "- What does the electronic device let real animals do?\n",
    "- What is the veterinarian full name?\n",
    "- Where does he work?\n",
    "- What is Courtney's job position?\n",
    "- What's her full name?\n",
    "- Which famous brand is first visible?\n",
    "- Which famous brand is last visible?\n",
    "- What happens at timestamp 0:36?\n",
    "- What happens at timestamp 1:05?\n",
    "\"\"\"\n",
    "video = Part.from_uri(\n",
    "    uri=\"gs://cloud-samples-data/video/animals.mp4\",\n",
    "    mime_type=\"video/mp4\",\n",
    ")\n",
    "\n",
    "contents = [prompt, video]\n",
    "print_contents(contents)\n",
    "\n",
    "response = generate_content(model, contents)\n",
    "print_response(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some remarks:\n",
    "\n",
    "- Gemini uses both video and audio information.\n",
    "  - Video files are sampled at 1 frame per second (1fps).\n",
    "  - Video and audio samples are analyzed with their timestamps.\n",
    "  - Text (typewritten or handwritten) can be detected in video frames, like when reasoning on images.\n",
    "  - This explains why the extracted samples can give you three possible sources of information:\n",
    "    - image (video frame)\n",
    "    - text (text in the video frame)\n",
    "    - speech (audio sample)\n",
    "- Using timestamps, you can also ask questions about specific video locations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jWT14Lw6Uknq"
   },
   "source": [
    "## Conclusion\n",
    "\n",
    "In this tutorial, you saw examples of how to use Gemini 1.5 in education to reason on text, images, and videos.\n",
    "\n",
    "For more information, see the following documentation pages:\n",
    "\n",
    "- [Image understanding](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/image-understanding)\n",
    "- [Video understanding](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/video-understanding)\n",
    "- [Audio understanding](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/audio-understanding)\n",
    "- [Document understanding](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/document-understanding)\n",
    "\n",
    "You may also want to explore other tutorials that focus on different domains or specificities of the Vertex AI Gemini API.\n"
   ]
  }
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