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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FSz9M3BYVMn9",
    "outputId": "51d4650c-ee3f-45ca-fb41-8f085726431e"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for deepspeed (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for wavedrom (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for lit (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "lida 0.0.10 requires kaleido, which is not installed.\n",
      "tensorflow 2.13.0 requires typing-extensions<4.6.0,>=3.6.6, but you have typing-extensions 4.8.0 which is incompatible.\n",
      "torchaudio 2.1.0+cu118 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\n",
      "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\n",
      "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.0.1 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -Uqqq pip --progress-bar off\n",
    "!pip install -qqq torch==2.1 --progress-bar off\n",
    "!pip install -qqq transformers==4.34.1 --progress-bar off\n",
    "!pip install -qqq accelerate==0.23.0 --progress-bar off\n",
    "!pip install -qqq bitsandbytes==0.41.1 --progress-bar off\n",
    "!pip install -qqq llava-torch==1.1.1 --progress-bar off"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import textwrap\n",
    "from io import BytesIO\n",
    "\n",
    "import requests\n",
    "import torch\n",
    "from llava.constants import DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX\n",
    "from llava.conversation import SeparatorStyle, conv_templates\n",
    "from llava.mm_utils import (\n",
    "    KeywordsStoppingCriteria,\n",
    "    get_model_name_from_path,\n",
    "    process_images,\n",
    "    tokenizer_image_token,\n",
    ")\n",
    "from llava.model.builder import load_pretrained_model\n",
    "from llava.utils import disable_torch_init\n",
    "from PIL import Image"
   ],
   "metadata": {
    "id": "UpIpw5hyYyKT",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "4efa03a6-be96-4db3-ccc6-7f6c7dffcedf"
   },
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[2023-10-22 22:24:58,755] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "disable_torch_init()"
   ],
   "metadata": {
    "id": "yhFhnMYK0o7K"
   },
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "MODEL = \"4bit/llava-v1.5-13b-3GB\"\n",
    "model_name = get_model_name_from_path(MODEL)\n",
    "model_name"
   ],
   "metadata": {
    "id": "pAQqPKgAdRgQ",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "outputId": "93a5ab04-7e00-4e7e-c4d5-530176407834"
   },
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'llava-v1.5-13b-3GB'"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      }
     },
     "metadata": {},
     "execution_count": 4
    }
   ]
  },
  {
   "cell_type": "code",
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    "!gdown 1mp5rAo4-apdl0DJO5XXQhjMa2ja7IFEH\n",
    "!gdown 1Qnutc8S7F6jMN6RkJZBgiAePymDcJ3Ii\n",
    "!gdown 1XM7QgiuNj7KjapaEfjyfxMVWSdQaqsaH\n",
    "!gdown 1m9v8EVZ16sYcUlCGUH8PFuLxFxaml90U\n",
    "!gdown 1x7XtPRG-IbSxyCO-ZTo_P7jirwRFY-JN"
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   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "outputId": "23021526-65f7-4b4c-865e-215ba3266ae7"
   },
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1mp5rAo4-apdl0DJO5XXQhjMa2ja7IFEH\n",
      "To: /content/btc-price-chart.png\n",
      "100% 133k/133k [00:00<00:00, 2.67MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1Qnutc8S7F6jMN6RkJZBgiAePymDcJ3Ii\n",
      "To: /content/meme.jpeg\n",
      "100% 109k/109k [00:00<00:00, 2.37MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1XM7QgiuNj7KjapaEfjyfxMVWSdQaqsaH\n",
      "To: /content/captcha.jpeg\n",
      "100% 4.52k/4.52k [00:00<00:00, 14.1MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1m9v8EVZ16sYcUlCGUH8PFuLxFxaml90U\n",
      "To: /content/bitcoin-paper.jpg\n",
      "100% 945k/945k [00:00<00:00, 10.8MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1x7XtPRG-IbSxyCO-ZTo_P7jirwRFY-JN\n",
      "To: /content/bike-girl.jpeg\n",
      "100% 207k/207k [00:00<00:00, 3.55MB/s]\n"
     ]
    }
   ]
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    "tokenizer, model, image_processor, context_len = load_pretrained_model(\n",
    "    model_path=MODEL, model_base=None, model_name=model_name, load_4bit=True\n",
    ")"
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   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def load_image(image_file):\n",
    "    if image_file.startswith(\"http://\") or image_file.startswith(\"https://\"):\n",
    "        response = requests.get(image_file)\n",
    "        image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n",
    "    else:\n",
    "        image = Image.open(image_file).convert(\"RGB\")\n",
    "    return image"
   ],
   "metadata": {
    "id": "Xo_pnKHJoKCV"
   },
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "image = load_image(\"bike-girl.jpeg\")\n",
    "image"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 977
    },
    "id": "CV4YU8ZSoKTr",
    "outputId": "56fef23d-0fe9-440a-b538-24dff92deb69"
   },
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=960x960>"
      ],
      "image/png": "[encoded data, 1938892 characters]\n"
     },
     "metadata": {},
     "execution_count": 8
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def process_image(image):\n",
    "    args = {\"image_aspect_ratio\": \"pad\"}\n",
    "    image_tensor = process_images([image], image_processor, args)\n",
    "    return image_tensor.to(model.device, dtype=torch.float16)"
   ],
   "metadata": {
    "id": "YjUy6yYBWmuH"
   },
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "processed_image = process_image(image)\n",
    "type(processed_image), processed_image.shape"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zzn9Qr3FXBSt",
    "outputId": "54d64b50-fc93-4eac-84e0-212544c23a14"
   },
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(torch.Tensor, torch.Size([1, 3, 336, 336]))"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "CONV_MODE = \"llava_v0\""
   ],
   "metadata": {
    "id": "eaI6OOTXaIeo"
   },
   "execution_count": 11,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "def create_prompt(prompt: str):\n",
    "    conv = conv_templates[CONV_MODE].copy()\n",
    "    roles = conv.roles\n",
    "    prompt = DEFAULT_IMAGE_TOKEN + \"\\n\" + prompt\n",
    "    conv.append_message(roles[0], prompt)\n",
    "    conv.append_message(roles[1], None)\n",
    "    return conv.get_prompt(), conv"
   ],
   "metadata": {
    "id": "xnfaWg7YXUfO"
   },
   "execution_count": 12,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "prompt, _ = create_prompt(\"Describe the image\")\n",
    "print(prompt)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cHUesHxvXYt1",
    "outputId": "593dbaaa-1747-4532-a02c-31b6d3735e95"
   },
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.###Human: <image>\n",
      "Describe the image###Assistant:\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def ask_image(image: Image, prompt: str):\n",
    "    image_tensor = process_image(image)\n",
    "    prompt, conv = create_prompt(prompt)\n",
    "    input_ids = (\n",
    "        tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors=\"pt\")\n",
    "        .unsqueeze(0)\n",
    "        .to(model.device)\n",
    "    )\n",
    "\n",
    "    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2\n",
    "    stopping_criteria = KeywordsStoppingCriteria(\n",
    "        keywords=[stop_str], tokenizer=tokenizer, input_ids=input_ids\n",
    "    )\n",
    "\n",
    "    with torch.inference_mode():\n",
    "        output_ids = model.generate(\n",
    "            input_ids,\n",
    "            images=image_tensor,\n",
    "            do_sample=True,\n",
    "            temperature=0.01,\n",
    "            max_new_tokens=512,\n",
    "            use_cache=True,\n",
    "            stopping_criteria=[stopping_criteria],\n",
    "        )\n",
    "    return tokenizer.decode(\n",
    "        output_ids[0, input_ids.shape[1] :], skip_special_tokens=True\n",
    "    ).strip()"
   ],
   "metadata": {
    "id": "m_NpwKPhZ88z"
   },
   "execution_count": 14,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "result = ask_image(image, \"Describe the image\")\n",
    "print(textwrap.fill(result, width=110))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wBmHWudGiWzv",
    "outputId": "0ac9be76-9acb-4434-ae47-cfd097cf50da"
   },
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The image features a woman sitting on a motorcycle, which is parked on a brick driveway in front of a house.\n",
      "She is wearing a black leather outfit, which includes a leather jacket and leggings. The motorcycle is\n",
      "positioned prominently in the scene, with the woman sitting comfortably on it. The house in the background\n",
      "adds a sense of context to the scene, suggesting that the woman may be preparing to ride the motorcycle or has\n",
      "just arrived at her destination.\n",
      "CPU times: user 14.1 s, sys: 517 ms, total: 14.6 s\n",
      "Wall time: 19.8 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "result = ask_image(image, \"Does the woman wear a helmet?\")\n",
    "print(textwrap.fill(result, width=110))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LbeVlRJ3ip8T",
    "outputId": "0643c920-ab7c-41a4-dbd8-f51867e5d0c1"
   },
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Yes, the woman is wearing a helmet while sitting on the motorcycle.\n",
      "CPU times: user 3.21 s, sys: 162 ms, total: 3.37 s\n",
      "Wall time: 3.42 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "result = ask_image(\n",
    "    image,\n",
    "    \"Take a look at the woman's head. What is the color of her skin? Does she wear a helmet?\",\n",
    ")\n",
    "print(textwrap.fill(result, width=110))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dAuntVyFi1QW",
    "outputId": "5991e0e1-49b2-41ae-e7fb-b169f3caeb8c"
   },
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The woman's skin color is white, and she is not wearing a helmet.\n",
      "CPU times: user 2.91 s, sys: 413 ms, total: 3.33 s\n",
      "Wall time: 3.33 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## OCR & Document Understanding"
   ],
   "metadata": {
    "id": "BIszv7AaEDcG"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "image = load_image(\"bitcoin-paper.jpg\")\n",
    "image.resize((600, 800))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 817
    },
    "id": "OzNwuh_4EKq_",
    "outputId": "b3843efc-613c-440e-aa18-7315173ff74b"
   },
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=600x800>"
      ],
      "image/png": "[encoded data, 284700 characters]\n"
     },
     "metadata": {},
     "execution_count": 18
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "result = ask_image(image, \"What is the title of the paper?\")\n",
    "print(textwrap.fill(result, width=110))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AyeTPFRa2RGV",
    "outputId": "f161c0a4-cc91-4d5b-e012-32139986bfef"
   },
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Bitcoin: A Peer-to-Peer Electronic Cash System\n",
      "CPU times: user 2.67 s, sys: 325 ms, total: 3 s\n",
      "Wall time: 3.01 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "result = ask_image(image, \"Extract the text from the abstract\")\n",
    "print(textwrap.fill(result, width=110))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "u8wwIGr94REj",
    "outputId": "de9e44ae-28bf-4430-c983-092003fec26d"
   },
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Bitcoin: A Peer-to-Peer Electronic Cash System\n",
      "CPU times: user 2.83 s, sys: 393 ms, total: 3.23 s\n",
      "Wall time: 3.23 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "result = ask_image(image, \"Summarize the abstract of the paper in 2 sentences.\")\n",
    "print(textwrap.fill(result, width=110))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r20H2VuZGlAY",
    "outputId": "622b6a17-ed5e-4287-aaa4-64b042b4ad65"
   },
   "execution_count": 21,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The paper discusses the concept of a peer-to-peer electronic cash system, focusing on the Bitcoin system. It\n",
      "highlights the advantages of this system, such as its decentralized nature, security, and potential for\n",
      "financial inclusion. The paper also addresses some of the challenges and limitations of the Bitcoin system,\n",
      "such as scalability and regulatory issues.\n",
      "CPU times: user 10.7 s, sys: 348 ms, total: 11.1 s\n",
      "Wall time: 11.3 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Price Chart"
   ],
   "metadata": {
    "id": "psG3xFWiVxhX"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "image = load_image(\"btc-price-chart.png\")\n",
    "image.resize((600, 300))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "sJhH_UPQTSZI",
    "outputId": "3900fa02-f189-40e1-fdf3-f6aaa1bdf623"
   },
   "execution_count": 22,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=600x300>"
      ],
      "image/png": "[encoded data, 47604 characters]\n"
     },
     "metadata": {},
     "execution_count": 22
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "result = ask_image(\n",
    "    image,\n",
    "    \"This is a chart of Bitcoin price. What is the current price according to the chart?\",\n",
    ")\n",
    "print(textwrap.fill(result, width=110))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tNAYi-HJUdEt",
    "outputId": "930ed067-9cfd-40d8-d2f7-f8f2f6b079ed"
   },
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The current price of Bitcoin according to the chart is $23,000.\n",
      "CPU times: user 3.04 s, sys: 263 ms, total: 3.3 s\n",
      "Wall time: 3.31 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Captcha"
   ],
   "metadata": {
    "id": "K06a9rCPH2KO"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "image = load_image(\"captcha.jpeg\")\n",
    "image"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "id": "c2irtvzyJ9K4",
    "outputId": "797a398e-5031-481c-c979-023cef3cac49"
   },
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=250x50>"
      ],
      "image/png": "[encoded data, 19896 characters]\n"
     },
     "metadata": {},
     "execution_count": 24
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "result = ask_image(image, \"Extract the text from the image\")\n",
    "print(textwrap.fill(result, width=110))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Xcq-8eGK4Y8J",
    "outputId": "0b46e060-c643-4445-b211-8c1eaa92e3a3"
   },
   "execution_count": 25,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "540\n",
      "CPU times: user 1.48 s, sys: 417 ms, total: 1.89 s\n",
      "Wall time: 1.89 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Meme"
   ],
   "metadata": {
    "id": "v5gX_h4DKezm"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "image = load_image(\"meme.jpeg\")\n",
    "image.resize((400, 500))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "id": "IxNxJG5mMGA2",
    "outputId": "a10947e1-0679-41fa-cbf1-38688ef1956c"
   },
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=400x500>"
      ],
      "image/png": "[encoded data, 229896 characters]\n"
     },
     "metadata": {},
     "execution_count": 26
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "result = ask_image(image, \"Is this funny and why?\")\n",
    "print(textwrap.fill(result, width=110))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qRLZcITq5OGy",
    "outputId": "c3bfbd85-bac0-434d-908c-b15e0b0acf9a"
   },
   "execution_count": 27,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Yes, this image is funny because it humorously represents the process of learning by showing a person's brain\n",
      "going through different stages of learning. The image features a series of four pictures of a brain, each\n",
      "representing a different stage of learning, such as from university, online courses, YouTube, and articles.\n",
      "The visual representation of the brain's journey through these stages is exaggerated and comical, making it a\n",
      "light-hearted and entertaining image.\n",
      "CPU times: user 12.4 s, sys: 427 ms, total: 12.9 s\n",
      "Wall time: 13 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "result = ask_image(\n",
    "    image,\n",
    "    \"Order all learning resources sorted by usefulness in a list, according to the image. The best must be at the top.\",\n",
    ")\n",
    "print(textwrap.fill(result, width=110))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MifT9jNsQrwe",
    "outputId": "21f3f8c1-b966-459a-f381-f47920d7a389"
   },
   "execution_count": 28,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "1. Online Courses 2. YouTube 3. University 4. Articles 5. Memes\n",
      "CPU times: user 3.57 s, sys: 401 ms, total: 3.97 s\n",
      "Wall time: 3.97 s\n"
     ]
    }
   ]
  }
 ]
}