arc-agi-llava-openai.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":67357,"databundleVersionId":8951125,"sourceType":"competition"}],"dockerImageVersionId":30733,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!git clone https://github.com/haotian-liu/LLaVA.git\n%cd LLaVA\n!pip install --upgrade pip  # Upgrade pip to ensure compatibility with latest packages\n!pip install .\n!pip install langchain langchain_openai\n!pip install repl\n","metadata":{"execution":{"iopub.status.busy":"2024-07-10T23:56:50.920520Z","iopub.execute_input":"2024-07-10T23:56:50.920915Z","iopub.status.idle":"2024-07-10T23:58:17.702374Z","shell.execute_reply.started":"2024-07-10T23:56:50.920876Z","shell.execute_reply":"2024-07-10T23:58:17.701230Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Cloning into 'LLaVA'...\nremote: Enumerating objects: 2297, done.\u001b[K\nremote: Counting objects: 100% (6/6), done.\u001b[K\nremote: Compressing objects: 100% (4/4), done.\u001b[K\nremote: Total 2297 (delta 2), reused 4 (delta 2), pack-reused 2291\u001b[K\nReceiving objects: 100% (2297/2297), 13.72 MiB | 36.67 MiB/s, done.\nResolving deltas: 100% (1402/1402), done.\n/kaggle/working/LLaVA\nRequirement already satisfied: pip in /opt/conda/lib/python3.10/site-packages (23.3.2)\nCollecting pip\n  Downloading pip-24.1.2-py3-none-any.whl.metadata (3.6 kB)\nDownloading pip-24.1.2-py3-none-any.whl (1.8 MB)\n\u001b[2K   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websockets-11.0.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.6 kB)\nRequirement already satisfied: certifi in /opt/conda/lib/python3.10/site-packages (from httpx==0.24.0->llava==1.2.2.post1) (2024.2.2)\nCollecting httpcore<0.18.0,>=0.15.0 (from httpx==0.24.0->llava==1.2.2.post1)\n  Downloading httpcore-0.17.3-py3-none-any.whl.metadata (18 kB)\nRequirement already satisfied: idna in /opt/conda/lib/python3.10/site-packages (from httpx==0.24.0->llava==1.2.2.post1) (3.6)\nRequirement already satisfied: sniffio in /opt/conda/lib/python3.10/site-packages (from httpx==0.24.0->llava==1.2.2.post1) (1.3.0)\nRequirement already satisfied: scipy>=1.3.2 in /opt/conda/lib/python3.10/site-packages (from scikit-learn==1.2.2->llava==1.2.2.post1) (1.11.4)\nRequirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn==1.2.2->llava==1.2.2.post1) (1.4.2)\nRequirement already satisfied: 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packages: llava, ffmpy, wavedrom\n  Building wheel for llava (pyproject.toml) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for llava: filename=llava-1.2.2.post1-py3-none-any.whl size=103210 sha256=31b89e5e68a4def2406b46f77e15cca72e621a525471862e66e8721c385c008d\n  Stored in directory: /tmp/pip-ephem-wheel-cache-dkmj2mnj/wheels/48/ea/ca/3c02db38c529dcd9256d3c3820d0301a49d828da20359c47c0\n  Building wheel for ffmpy (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=b4f58ff8f8a043c3933cb25772cabf976f275c2a438b0f8835cdc5ba4788fb49\n  Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n  Building wheel for wavedrom (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for wavedrom: filename=wavedrom-2.0.3.post3-py2.py3-none-any.whl size=30052 sha256=6cb814ecf8e92b85d2415f5eeda83c8c8cbad2909d85a9f35af0a2e55990d31f\n  Stored in directory: /root/.cache/pip/wheels/9c/52/8c/38b454b42f712f325e26f633287484c7dc1ad469e1580c5954\nSuccessfully built llava ffmpy wavedrom\nInstalling collected packages: sentencepiece, ffmpy, websockets, tomlkit, svgwrite, shortuuid, semantic-version, ruff, python-multipart, markdown2, einops, wavedrom, httpcore, einops-exts, tokenizers, httpx, bitsandbytes, accelerate, transformers, timm, gradio-client, peft, gradio, llava\n  Attempting uninstall: sentencepiece\n    Found existing installation: sentencepiece 0.2.0\n    Uninstalling sentencepiece-0.2.0:\n      Successfully uninstalled sentencepiece-0.2.0\n  Attempting uninstall: websockets\n    Found existing installation: websockets 12.0\n    Uninstalling websockets-12.0:\n      Successfully uninstalled websockets-12.0\n  Attempting uninstall: tomlkit\n    Found existing installation: tomlkit 0.12.5\n    Uninstalling tomlkit-0.12.5:\n      Successfully uninstalled tomlkit-0.12.5\n  Attempting uninstall: httpcore\n    Found existing installation: httpcore 1.0.5\n    Uninstalling httpcore-1.0.5:\n      Successfully uninstalled httpcore-1.0.5\n  Attempting uninstall: tokenizers\n    Found existing installation: tokenizers 0.19.1\n    Uninstalling tokenizers-0.19.1:\n      Successfully uninstalled tokenizers-0.19.1\n  Attempting uninstall: httpx\n    Found existing installation: httpx 0.27.0\n    Uninstalling httpx-0.27.0:\n      Successfully uninstalled httpx-0.27.0\n  Attempting uninstall: accelerate\n    Found existing installation: accelerate 0.30.1\n    Uninstalling accelerate-0.30.1:\n      Successfully uninstalled accelerate-0.30.1\n  Attempting uninstall: transformers\n    Found existing installation: transformers 4.41.2\n    Uninstalling transformers-4.41.2:\n      Successfully uninstalled transformers-4.41.2\n  Attempting uninstall: timm\n    Found existing installation: timm 1.0.3\n    Uninstalling timm-1.0.3:\n      Successfully uninstalled timm-1.0.3\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.\njupyterlab 4.2.1 requires httpx>=0.25.0, but you have httpx 0.24.0 which is incompatible.\njupyterlab 4.2.1 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\njupyterlab-lsp 5.1.0 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed accelerate-0.21.0 bitsandbytes-0.43.1 einops-0.6.1 einops-exts-0.0.4 ffmpy-0.3.2 gradio-4.16.0 gradio-client-0.8.1 httpcore-0.17.3 httpx-0.24.0 llava-1.2.2.post1 markdown2-2.4.13 peft-0.11.1 python-multipart-0.0.9 ruff-0.5.1 semantic-version-2.10.0 sentencepiece-0.1.99 shortuuid-1.0.13 svgwrite-1.4.3 timm-0.6.13 tokenizers-0.15.1 tomlkit-0.12.0 transformers-4.37.2 wavedrom-2.0.3.post3 websockets-11.0.3\nCollecting langchain\n  Downloading langchain-0.2.7-py3-none-any.whl.metadata (6.9 kB)\nCollecting langchain_openai\n  Downloading langchain_openai-0.1.15-py3-none-any.whl.metadata (2.5 kB)\nRequirement already satisfied: PyYAML>=5.3 in /opt/conda/lib/python3.10/site-packages (from langchain) (6.0.1)\nRequirement already satisfied: SQLAlchemy<3,>=1.4 in /opt/conda/lib/python3.10/site-packages (from langchain) (2.0.25)\nRequirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /opt/conda/lib/python3.10/site-packages (from langchain) (3.9.1)\nRequirement already satisfied: async-timeout<5.0.0,>=4.0.0 in /opt/conda/lib/python3.10/site-packages (from langchain) (4.0.3)\nCollecting langchain-core<0.3.0,>=0.2.12 (from langchain)\n  Downloading langchain_core-0.2.13-py3-none-any.whl.metadata (6.0 kB)\nCollecting langchain-text-splitters<0.3.0,>=0.2.0 (from langchain)\n  Downloading langchain_text_splitters-0.2.2-py3-none-any.whl.metadata (2.1 kB)\nCollecting langsmith<0.2.0,>=0.1.17 (from langchain)\n  Downloading langsmith-0.1.85-py3-none-any.whl.metadata (13 kB)\nRequirement already satisfied: numpy<2,>=1 in 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/opt/conda/lib/python3.10/site-packages (from tiktoken<1,>=0.7->langchain_openai) (2023.12.25)\nRequirement already satisfied: exceptiongroup>=1.0.2 in /opt/conda/lib/python3.10/site-packages (from anyio<5,>=3.5.0->openai<2.0.0,>=1.32.0->langchain_openai) (1.2.0)\nRequirement already satisfied: httpcore<0.18.0,>=0.15.0 in /opt/conda/lib/python3.10/site-packages (from httpx<1,>=0.23.0->openai<2.0.0,>=1.32.0->langchain_openai) (0.17.3)\nRequirement already satisfied: jsonpointer>=1.9 in /opt/conda/lib/python3.10/site-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.3.0,>=0.2.12->langchain) (2.4)\nRequirement already satisfied: h11<0.15,>=0.13 in /opt/conda/lib/python3.10/site-packages (from httpcore<0.18.0,>=0.15.0->httpx<1,>=0.23.0->openai<2.0.0,>=1.32.0->langchain_openai) (0.14.0)\nDownloading langchain-0.2.7-py3-none-any.whl (983 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m983.6/983.6 kB\u001b[0m \u001b[31m25.1 MB/s\u001b[0m eta 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Attempting uninstall: packaging\n    Found existing installation: packaging 21.3\n    Uninstalling packaging-21.3:\n      Successfully uninstalled packaging-21.3\n  Attempting uninstall: orjson\n    Found existing installation: orjson 3.9.10\n    Uninstalling orjson-3.9.10:\n      Successfully uninstalled orjson-3.9.10\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.\ncudf 24.4.1 requires cubinlinker, which is not installed.\ncudf 24.4.1 requires cupy-cuda11x>=12.0.0, which is not installed.\ncudf 24.4.1 requires ptxcompiler, which is not installed.\ncuml 24.4.0 requires cupy-cuda11x>=12.0.0, which is not installed.\ndask-cudf 24.4.1 requires cupy-cuda11x>=12.0.0, which is not installed.\nkeras-cv 0.9.0 requires keras-core, which is not installed.\nkeras-nlp 0.12.1 requires keras-core, which is not installed.\ntensorflow-decision-forests 1.8.1 requires wurlitzer, which is not installed.\napache-beam 2.46.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.8 which is incompatible.\napache-beam 2.46.0 requires numpy<1.25.0,>=1.14.3, but you have numpy 1.26.4 which is incompatible.\napache-beam 2.46.0 requires pyarrow<10.0.0,>=3.0.0, but you have pyarrow 14.0.2 which is incompatible.\ncudf 24.4.1 requires cuda-python<12.0a0,>=11.7.1, but you have cuda-python 12.5.0 which is incompatible.\ndistributed 2024.1.1 requires dask==2024.1.1, but you have dask 2024.5.2 which is incompatible.\ngoogle-cloud-bigquery 2.34.4 requires packaging<22.0dev,>=14.3, but you have packaging 24.1 which is incompatible.\njupyterlab 4.2.1 requires httpx>=0.25.0, but you have httpx 0.24.0 which is incompatible.\njupyterlab 4.2.1 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\njupyterlab-lsp 5.1.0 requires jupyter-lsp>=2.0.0, but you have jupyter-lsp 1.5.1 which is incompatible.\nlibpysal 4.9.2 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.\nmomepy 0.7.0 requires shapely>=2, but you have shapely 1.8.5.post1 which is incompatible.\nosmnx 1.9.3 requires shapely>=2.0, but you have shapely 1.8.5.post1 which is incompatible.\nrapids-dask-dependency 24.4.1a0 requires dask==2024.1.1, but you have dask 2024.5.2 which is incompatible.\nrapids-dask-dependency 24.4.1a0 requires dask-expr==0.4.0, but you have dask-expr 1.1.2 which is incompatible.\nspopt 0.6.0 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.\ntensorflow 2.15.0 requires keras<2.16,>=2.15.0, but you have keras 3.3.3 which is incompatible.\nydata-profiling 4.6.4 requires numpy<1.26,>=1.16.0, but you have numpy 1.26.4 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed langchain-0.2.7 langchain-core-0.2.13 langchain-text-splitters-0.2.2 langchain_openai-0.1.15 langsmith-0.1.85 openai-1.35.13 orjson-3.10.6 packaging-24.1 tiktoken-0.7.0\nCollecting repl\n  Downloading repl-1.0.tar.gz (5.1 kB)\n  Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: click in /opt/conda/lib/python3.10/site-packages (from repl) (8.1.7)\nBuilding wheels for collected packages: repl\n  Building wheel for repl (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for repl: filename=repl-1.0-py3-none-any.whl size=5820 sha256=9ebee95cc8a8b6ee9f7f6bfef4df4546781a74c06fed170dc444c41fb1312673\n  Stored in directory: /root/.cache/pip/wheels/19/d7/82/3346ef2a7c40549882091d9ab68dc01f73de7b95f3083d503e\nSuccessfully built repl\nInstalling collected packages: repl\nSuccessfully installed repl-1.0\n","output_type":"stream"}]},{"cell_type":"code","source":"import json\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport ast\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nfrom llava.model.builder import load_pretrained_model\nfrom llava.mm_utils import get_model_name_from_path\nfrom llava.eval.run_llava import eval_model\n\ndef plot_grid(grid, ax, title=\"Grid\", color_map='viridis'):\n    if isinstance(grid, str):\n        grid = ast.literal_eval(grid)\n    grid = np.array(grid)\n    cmap = plt.get_cmap(color_map)\n    if grid.size > 0:\n        norm = plt.Normalize(np.min(grid), np.max(grid))\n        ax.matshow(grid, cmap=cmap, norm=norm)\n        for (i, j), val in np.ndenumerate(grid):\n            ax.text(j, i, int(val), ha='center', va='center', color='white')\n    else:\n        ax.text(0.5, 0.5, 'No data', ha='center', va='center', fontsize=12, color='white')\n    ax.set_title(title, color='white')\n    ax.set_xticks([])\n    ax.set_yticks([])\n\ndef save_plot(df_row, folder_path, idx):\n    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n    plot_grid(df_row.input, axs[0], title=f\"Input {idx+1}\")\n    if df_row.output is not None:\n        plot_grid(df_row.output, axs[1], title=f\"Output {idx+1}\")\n    else:\n        axs[1].text(0.5, 0.5, 'No output', ha='center', va='center', fontsize=12, color='white')\n        axs[1].set_xticks([])\n        axs[1].set_yticks([])\n    fig.patch.set_facecolor('black')\n    plt.tight_layout()\n\n    # Filename based on problem ID and index\n    filename = f\"{df_row.key}_{idx}.png\"\n    filepath = os.path.join(folder_path, filename)\n    plt.savefig(filepath)\n    plt.close(fig)\n    return filepath\n\n\nwith open(\"/kaggle/input/arc-prize-2024/arc-agi_training_challenges.json\", 'r') as f:\n    data = json.load(f)\n\n# Initialize lists to store flattened data\nrecords = []\n\n# Iterate over each key in JSON data (e.g., \"007bbfb7\", \"00d62c1b\")\nfor key, value in data.items():\n    for train_type, train_data in value.items():\n        for item in train_data:\n            # Flatten the nested structure\n            flattened_data = {\n                'key': key,\n                'train_type': train_type,\n                'input': item['input'],\n                'output': item.get('output', None)  # 'output' is optional in 'train' data\n            }\n            records.append(flattened_data)\n\n# Create DataFrame\ndf = pd.DataFrame(records)\nfolder_path = '/kaggle/working/train'\nif not os.path.exists(folder_path):\n    os.makedirs(folder_path)\n\n# Group the DataFrame by problem_id and apply the plotting function\ndef process_group(group):\n    file_paths = []\n    for idx, row in enumerate(group.itertuples()):\n        file_path = save_plot(row, folder_path, idx)\n        file_paths.append(file_path)\n    return file_paths\n\ndf['file_paths'] = df.groupby('key').apply(process_group).explode().reset_index(drop=True)\n\n# Load the image\nimage = mpimg.imread(df['file_paths'].iloc[1])\n\n# Display the image\nplt.imshow(image)\nplt.axis('off')  # Hide the axes\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-07-10T23:58:17.704183Z","iopub.execute_input":"2024-07-10T23:58:17.704493Z","iopub.status.idle":"2024-07-11T00:14:07.611549Z","shell.execute_reply.started":"2024-07-10T23:58:17.704464Z","shell.execute_reply":"2024-07-11T00:14:07.610310Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stderr","text":"2024-07-10 23:58:26.042243: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-07-10 23:58:26.042375: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-07-10 23:58:26.164991: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n/tmp/ipykernel_34/2020973255.py:85: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n  df['file_paths'] = df.groupby('key').apply(process_group).explode().reset_index(drop=True)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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ZiirjD4DIFWewk9nMcYwPJi208sBgAOpK0feNxY5lfmhrOwrdnO8mfl/BicshqQg2QL+bosZgDsx30OQO2gzZORc+5zFjFG1ovRl0j7GGBqb65mxoJmOk13FbtIloDz/YcvuKnZDLh3OhPyJ0PbJdpyNpXO0t7OPxub6C/MGes6ej7w92UyWXC6gaXojMxY203mqO/IMgIHzg1TXV1HbWMPUOZPpPG0nB28akBwZ+7YosSzfQ2B7/k/qCvB3AaG9jMQ8CM9D2GEvAyC5ZuTzbPEJkd4MwNiZkzAGsSgGgmzAMz/dyS3/6QZqJ9byylP7it2ki5/bkJ/MEvYUuyWXDm8KZF+mVCYOvhPnTnRyfN9JPvDHd3J8/0najrVHnhEGIU//ZAcb/+N1NE1vZM9jr0aeAdDX2ce+bW9w1xduo6f9PMf3WzpZezNHutRtThxz8uPsmWew22uVzPdAZF+ymAF4s0a67i0WHCTAawJ/p8UM8tvib6NYEwff5JhRTsN1xvDsx4WsZDpzx/Q3cbZo9SBf3nJIj9cUa7rPB6x49wnazo191vU8ljOLBfo8j9LcZUN89eGDemqhWNPXH9Ky4QQnTo+ugBjNZV5vVxERkZhTMSAiIhJzKgZEpCANDoiUPhUDIiIiMadiQEREJOZUDIiIiMScigEREZGYUzEgIgWVzsLKIvLbxOYRxiLyzujbBO/MeDwmvVTWgRrPR8qXyj6LmooBESlIPQNjc/pYGX9y1zzrVdTlG/r44KdL4+mYb+yp5Dt/Zf/R8hvv7uI9/0FLrP8mKgZEpCDdSI1NetBj785q6zlTZ2asZ4yX/h6Pvc/Z32errrb8kKZLmOYMiIiIxJyKARERkZhTMSAiIhJzKgZERERiTsWAiBSkbxOIlD4VAyIiIjGnYkBERCTmVAyISEFaZ0Ck9GnRIRGJVGNzPdMXNnPqjTN0tdlZ7a28qox5q+bQ3zPIiddPYUI7MxsmTKxl1tIZtB1t59yJTisZqfIk81bNITPsc2zvCcIgjD7EbYbEXMABcx6y+wALOYmF4E7O/x6chuBI9BlARU0581bOobezn5MHTts5/t4c8Gbkfw/PQe6N6DOcOkguB1wwaci+DPjR54yCegZEpKCxnGar66u46wu30zClnru+eDtVtZWRt8dx4Nb/fCOzlszgPR+5lrkrZ0eeAfmC4/1fuJ2maQ2873O3MWFSnZWcGz58LQsun8vV713P8msWW8mAAMiCNx3Kb8Vaf4/JAT6k1kFqjZUIx3W4/Q9uYvrCZjZ+fAOzl86wkpMvlnxILoXUNdjZZwZMFpxqnMoPglO8+3MVAyJS0FhOgZNmNnG+o48nf7SNrtM9TFswNfL2JJIJpl42mSf/dRu7HtzD4vXzI88AqJtYS5ALePJft9P62knmLJ8ZeYbrOsxZNpMn7t3Gsz/bZW1bCNvB3wVOCtJPki8OLAiOgP8ikIDMdisRqbIkE6c38eSPtrF768ssWmdpnwWt4L8AlIH/NFa+V2P6ILsL8DH+c2CGos8YJRUDIhKZZCpJNp0FwM9kSaaiv9NxPRcMBLkAP50lmUpGngGQTCbI+TmMMfhpn2RZ9DmO5+K6DrlsjpyfJWFhf/17WDV4syC3314G5DPIQnjGysu7CRdjDGEQ5o9/mcV95k4GtxJyR+1l4EHy8nyxVkQqBkSkoLHcD50/10vT9EYap9YzZfZEOs90R96ebCZLNpOlee4U5rbM5uxxO0/u6+8ZoKaxmqbmBmYsaKa9tSPyjCAb0H9+kJmLpjFn+SzOnbQzLwGAZAvkDoMZtJcBUHYV+M9ha4UKfzhLEIRMmTOZuS2zOHvM4pMbU+vA3wPk7GV4s4EMBHaKp9FSMSAiBY1lmKDjVBf7nn2NTb//HvY++zrnWqO/uIWh4ZHvP81Vd15OIuXx0hN7I88A6OseYNcDe7jlkzdwfP9JTr5x2krOo99/mtU3rqBpWgPP3bfbSgY44E6AjKXu7guS+THw7CvWEoJcwGP/8jTX3nUFYWh49ZnXLCUlwEnav2P3JkHG4tDNKOnbBCISmTAI2XHfbnZYu6jlHd93guP7TljNMKFhz6OvsufRV63mnDl8lh/+1c+sZoCB9P2WMwCyMPwj6ylHXj7OkZePW07JwfD/sZzBSC9K8aln4BIW12Vitd3xyBWJk2J/zlQMXMLiuhiMtjseuSJxUuzPmYoBERGRmFMxICIFFbv7UkTsUzEgIiIScyoGRKSgYo9lioh9KgZERERiTsWAiBSkOQMipc/KokOOC54LOo2MjudpP0ncGVyv2G2IiIEwBA2wyKXESjFw92fa+ch7szj6MIxKqsLCc8VFIjIen+JFq4f4wj0nxyHJvhOHyvjL35+NUY0vlxArxUDDpCyzFqRxHBUDIvL2yitDZi1MF7sZkcj6Ou/JpUdzBkRERGJOxYCIFKTebpHSp2JAREQk5lQMiEhBGgEXKX0qBkRERGJOxYCIiEjMqRgQkYI0gVCk9FlZZ0BESsdY5ww4roOX8AhyASa0UUo45E9dDvlSJWshA8Ab+QEIgZylHEgkPYyBIBdYSnD599O9zW156z4LRn4scCCRSBCakDBna9G28diW8Xovvz0VAyISmUQqwa2fvIFJM5s4d6KTB775KEHUJ2tvNpTfCk4ljtuI6f8fYAajzQBIXQ7Jy3HcBkzYA4NfJX8hjdbiKxdw9eZ1hKFhyze2cu5EZ+QZJJZB2bU4bi0GB/r/BisXnrLrIbEIx2vGZPfD8A+izwAu37iSluuWkvVz/Px/P0hfV3/0IakrIbkKx2vCBGdh8B+IvJ/MnQYVd4JTnt9nfV8C0xttxmibUpRUESlJk2dNpLG5gR/8z3+jfvIEmudNjT4kOAaD34DMs5jcITBD0WcA+Ltg8OuY3EHI7cNGIQBw9eZ1PPSdx3np8b1ccdsaKxnk9ua3xX8FsvuxdgeaeSyfE5wa2WfRS6QSXH7zKn7+9w9x4vXTrNyw1EoO/vaR438EsvuwMmAWnhp5Lz+OyR0F0xd9xiipGBCRgsZyCqxpqOb8uV7SA2l62s9T11hjr1Wp9eBvG2MLx5jhVEBiPvgvWklIJBOUVaToajtPx6lOaptqreTk91ECUi3g77SUMZLjNuR/sq9bSUiVJzHG0NfdT3trB/WTJ1jJyR//qnxPVPYlSxkAIaSuyBcfRZyho2JARCIz3D9MZW0FrudSWVfJ0MCwnSB3Uv5EnWu18/pvSiyA4Iy1O7YgF5DLBVTWlFNVV0V6wOLzGbxZEA5CaGEY4q2SqyD7GpCx8vLZTA7XdSgrT1FTX81gn6WeIYDEYghO2BmGepPTAG4T5A7ayxgFzRkQkYLGMoGwvbUDz3O5+79tJpFKcOrAGTuNSq4ZuVuz+XAjF1LrIPM0tu7YjDG8+vRr3PGpm0mVJ3n0+09byQFnpCflOWxOhIQEJFfA0I+sJeT8HIdeOsZ7/+utVNZW8ou/f9BSkpefN5LeitU79lRLvniyNdw1SioGRCQy6cEMP/lf91FTX01/zwB+2tLYtP8MGN/Oa18Q5i9qxsLktLfYef+LvPH8YYJcQF+nrTFjA+n77N7hAhDA4LetToIzxvDEvc9SN6kOf9inv3vAUlIIQz+wP47vPw/GZoE2OioGRKSgsd4TpQczpAftdBFfYGxdAP7vHPszu8MgpLutx3rO+ExOM2DOW0/JZQO6TndbThmfbbFfoI2O5gyISEF6NoFI6VMxICIiEnMqBkRERGJOxYCIiEjMqRgQERGJORUDIiIiMadiQEREJOZUDIhIQcVbLV1ExosWHRKRglIpQ2VZaHW9gfIKW8+kH3+ua6isDjGWq6hUWemUaV4CKqsD6znJVOnss6ipGBCRgj7wR+f41MfsLpdaSifpmQvSfPuZ1633qJSVUAG1/MoBvv2snaccvlVldenss6ipGBCRgiqrAxomZXEcrUU4GokkNEwu/lrzl5JUmaFR+6yoNGdAREQk5lQMiIiIxJyKARERkZhTMSAiIhJzKgZERERiTsWAiIhIzKkYEBERibmYrDOQBKdq5PcAzABaZLUQB5waLtSKZgDQd4ALc0f2mQOYkX1mf0W1i45TCaRG/sUHM2Qh5K2f5xBMP3Y+z+XglI/8ngUzaCHDG3nfQH5bBvL/jFwZOBUjv+dGciz4leOfATNsIWSczudOBVA28i+23ssJcKpHfrf5Xh5VS2LAmw5lG8Ctw3GnYvr/wtKbtFQkofxWcGtxkqsxA1+B3P5iN+ri5lRBxZ3gVOEkV2L6vgTh6WK3avwlV0NiEU5iNiZ3HIa+HX2GNxXKbgS3Bsebien7f+xcqJNLIbkqn2cGYeDLRH6hdpvynzWnCie5CNP752DOR5sBkJgLqSvBbcRxKjD9fwVko89JXg6J+TiJuZjc6zD0/egzxut8nlgOyRU43jRM2A2DXyXyC7U7EcpvyR//xHxM35+D6Ys2Y5TiUQwEx2DoOJRdj3HPqBB4Wz4M/xicekz11Pz+k8JMPwz9C3gzMc4ECM8Wu0XF4W8Dfwem6g/Bf95ORnAChr4HqasxXpelO3Yg+yJkX4LKD0PuCFbu2MN2GPonSK7EENgpBAByr0HudSi/E2MyWCkEAPynwd+Gqfo0+C/YyRiv83n2ecjuxlR+HLJ7sXLHHrbl38vJtRiTLlohALGaM+BCci34zxW7IZcIA6mW/AnESvdYKTKQWpc/icRxiOBNbhO4DZB7w2KIA6n14O+wmEG+C9ebDdlXLIaY/F17ZrvFDIAySC6DrKWL9JvcSeBWQ+6wzZDxOZ87deBNGykGLEpdCb7t419YfIoBbwYQQNBW7JZcIlL5Lr+M5ZNtKXGqILEI/BeL3ZLiSq6C7D7At5fhNgMeBKfsZUB+qCB33F7vA7yleDpoLwPyQwVhN4Q9dnNSa8F/BWu9DzB+5/PkcsgdAiz2JrsTR4qnI/YyRtOMoqaPp2TLSBVp8Q1aStymfPdbXLu73wlvZv7iZHqL3ZIiSkBiIfg77cakVowMQ1gsOPDyFwN/G1YndSWWQPZVIG0vAweSK0d6H2z2WiXzRUfW0hDRhZjxOJ97+WLQdu9Tchn4LwEZuzlvIx5zBgDSD6EZ8WMQnoXBb6J9Nga5QyNdo3H+pkpu5H1j88IGpB/D/lBMAIPfx/pJ2n8O++8ZA8M/xf7NUBYG/gH7x388zucBDH4P68c/s42LYVgxPsWA1TuIUhRS7Er10qPCKc/yhQAYv8/zeGzLePVWjtfnWcd/bC6Oa1N8hglERETkN1IxICIiEnMqBkRERGJOxYCIiEjMqRgQERGJORUDIiIiMadiQEREJOZUDIiIiMRcjBYdEpF3YsfWOirOTLeaMX1uhjs+3mk1Y7x0nEny029MwlheVHDR6iGuf5/l5wyMk9YDZTzwL03Wc9bd0MfaDf3Wcy5FKgZEpKD9z1fR/3wTjuNYy1h5dX/JFAO9XQl++d0mjLG3vwCGB7tKphhoP5nil9+daD2nrjGnYuC30DCBiIhIzKkYEJGC4vzYJZG4UDEgIgXZ7ewWkYuBigEREZGYUzEgIgVpmECk9KkYEBERiTkVAyJSkOYMiJQ+FQMiIiIxp0WHRCRSFTXl1DbW0NfZz/BAOvoApwLciYADxoewHQgt5NSBWzeSMwihnUWRvIRLw5R6ctmA8+d6MTaWLnRqwK0nvy1pCM9hZTaI2whOdf73sA+MnUWREqkEDVMmkBnK0NtpaREhZwK4tdg9/mXgTR7JyI68lwMLOW9PxYCIFDSWS0Z5dTkf+OM7yQxlKKss40f/78/xh/1oG+TUQ2o9uPU4iUWYvj8HMxRtBoA3E5JLwJsNZGHgy9goOq68/XLmrZ5DWUWKx37wDEdePh55Bu5kSK0Gb2p+//X/TyAXfY43DxKzcZLLMLlDMPT9yCMcx+HGj1xLU3MD1fVV/OIrD3H22LnIc0jMgsQiSMyFsB8Gv0rkBZRbl38vOzU4qdWY3v8OpjgrJGqYQEQKGsucgcmzJpLzc/zkb+8jM+QzY2Fz9A0Kz8Dw/4HcEYy/004hAJDbm88JuyDzDDYKAcdxWHbNYu77+sM89eMdrLpheeQZAASH89sStIH/DFYKAYDsLhj+KSY8D5kdViISqQRzV8zm53//IHse28vyaxZbySH7ysjx7x7ZZxZ6UsJzI+/lAxj/haIVAqBiQEQiVFFdzlDfMGEQMtQ3RHlVuaUkD5KXg/+cpdcf4dSB1wzZ/VZe3kt4JBIuwwMZhvqHLe4v8sMriUXg77GXAfn9RQqCY1ZePplKEIYhfibLwPlBKmsqrOQA4Dbkh6Syb9jLwIHUFdaKp9FSMSAikenr6mfC5DoqasppmDKB3s4+O0HeTCCbv9O1KdkCuQPAsJWXz2VzpId8mprrmTSzid6OXis5QL4QCM6AsXRM3pRcD9nd2Br79tNZcKCuqYYpsyfRffa8lRwAkqsg+xqQsZfhNgNJCI7byxhNM4qaLiIXvbF0jp5r7aTjRCcf+rO76DzdzZnDZ+00KrEQMtux1t0NgAeJOSM59mz72U42fvx6Wt69hJ33v2gpxYHEAvCfxcpkywuS4E0Cf7e1hFw2x/MP7GHzp29h2vwpvPzkPktJHnizwLd8x55cAP5OIGs3521oAqGIRCaXzfHANx/D9VzCILQzMx4g8wj210YMYOifsXvxhDeeP8zBPUfBQBjYyjIw/BNsbwtkYfAfrOe8+OirvPzkPkxoCENb74MAhr6L9X2Wecru64+SigERKWisiw4ZYwhytr8eZfuiNr45YW48ckprnwUls88ujgW/NUwgIiIScyoGREREYk7FgIgUdHF0YoqITSoGREREYk7FgIgUpKcWipQ+FQMiIiIxp2JARArSnAGR0qdiQEREJOasLDr00A8b6doxw8ZLl6Tpl2X4+H9vw9HgrFyExuNteez1cr70ydnjkGTfYJ+HrYUXRWyxUgwc3V+Bv78OR1e3UVm0erDYTRApqt6uJNsfnFDsZojEloYJREREYk7FwCUsrj2R2u545IrESbE/ZyoGLmFxHYTRdscjVyROiv05UzEgIgUV+45FROxTMSAiBRX7jkVE7FMxICIiEnMqBkSkIA0TiJQ+FQMiUpCGCURKn5VFh0QknlzP5ao7LmfeqtkceukYO37xAsbCcnyXtcziXZvXMdAzyNbvPclQ/3DkGY7rsOamFpZetZCTb5zm6Z/sIMiFkedMXzCV6z74LjLDPlu/9wR9XQORZ5BsgbLrcJxyTDgAg98EstHnlN0EySU4Ti0muxfSP48+A5i/5jKuuG0NfZ39bP3np0gPpKMPSV0DqTU4ThUm1wrD9xJ5P5k3HcrfC04qf2wGvgKmL9qMUVLPgIgUNJbT38TpjSy5agG//PpWlly5kMmzJkbeHtd1ufHD7+bZn+4kPZhh1Q3LI88AqG2sYd3Nq7j/Hx9lxsJpzFg0zUrOjR95Ny88/BLtxzu48o7LrWSQ3QsD38D4uyE8D+Ts5GQeh4GvY4IzELZbifASHjd86Bqe+vEOgiCk5dolVnLwd+S3JXcIwk6sDJgFp2HwHyD9EMZkwFgoBEdJxYCIRGbCpDo6T3XTc/Y87a0dNE6rjzwjWZ4gmUrQdqydo3tbmTw7+oIDoGZCFX1d/XS39XDq4BkmzYw+J5H0qK6r5OSBMxzbd4KJ0xsjz8gL8z/JVeBvx95MkACcCvCaIfuKlYRURQrXdWk/fo5jFo9/flsSkFgI/vOWMgyQhdTl+eKD6HueRkvFgIhEJpvJkixPApAqT5LNRH8HGuRCcPJ3iKnyFH7aQnc3kPVzJFIJHMchVZEim/YjzwiDkDA0JFIJUmVJsr6lO3YAdwrg5u9GbUoug9wxMHaeuRLkAhzHwU14lFWk8DN2jj8A3lwIu8D02MtwqsGbBdl99jJGQXMGRKSgsUwgPHeyk7qmGm78yLU0TK3n1MEzkbcnyOY4c/gsN3z4WibPmsiTP9oWeQZAb2cfrudyw4evYdaS6ez45QuRZ4Sh4eirrdz44WupbazhxUft3E0DUHYV+C9gZa7ABS6kroDhX1hLyGaytJ/o4MYPXUPzvCk88s9PWUpyoOxdkNmG1e/UJFsgd7xocwXepGJARCIz0DPIT+/ZQvP8qbz46KsM9UU/sc8YePDbj3PZilm8/MQ+KwUHQHoww7/93RZmLJrGy4/vo7fDzsn68Xuf5bKWWfhDPq2vn7KSAYC/CwI74/j/zskXAsExawkmNNz/j49y2fKZvPzkfk4farOU5ED6EQhOWHr9EbmjkN1vN2MUVAyISKS6z56n++x5qxmZoQyv7zxoNQOgt7Of3m1vWM3IZrIceP6w1QwAglb7GQQQHLGekh5I89pzto9/CMFRyxlAaKuYGRvNGRAREYk5FQMiIiIxp2JARArScsQipU/FgIgUpOWIRUqfigERKUg9AyKlT8WAiBSkngGR0qdiQEREJOZUDIiIiMScFh0SEYmUZlmMmQPab8WlYkBEJELTLsvw+XtO4liebFHfZPMZA+Nr8Zoh7vmF/VUYJ02L/mFTpULFgIgUpPu1sSmvDFl6+SCuBmFHrWZCwLJ1dp5yKKOjt6uIiEjMqRgQERGJORUDIlKQ1hkQKX0qBkRERGJOxYCIFKQJhCKlT8WAiBSkYQKR0qdiQEREJObiVQzoFmcMnLf8yOiU5j57R8ME47ELxms3W88Zr/fNeL03tS3vLKO4543YLDq0/NrFrLt5FYN9w2z5xlYGe4eK3aSLWBlUfhjcOhxvOmbw25B7rdiNurg5dVD5IXAqcLxmTP/fQXi62K2KxFhOUV7C44YPXcPMxdNofe0Uj//gGcIw+lkHS65cwBW3ryU9mGbL17fS3xP9gjWu53L1+9azYM1lnD3ewcPfeZxcNog4pBkq7gKnDMdtxPT/NZjz0WYAJFugbAOOU4UxPgx8GbCwgmHZRkguyW9Ldh8M/2v0GYkFUH4LjlMOlGEG/hbMcPQ5qWsgtRbHqcMEp2Ho20Q+g8abDhXvA8pw3PqR498XbcYoxaJnIJH0uPq963ngm4/RfbaHlg1Li92ki1wGhv4JBr+DCfshOFXsBl38TC8M/iMM/QgT9kF4rtgtKopJM5uYsWga//r//YIZi6Yx9bLJkWe4nsu1d13Jw999grPHO1h944rIMwBqG2tYcuUCfvK/7qOusYbZy2ZEHxKegcGvQfp+TNhh70KQfRUGvorxX4CgFSuFAEDmURj4CiY4BTlLywvnDuW3JfNM/iJt0nZy/O35bckdhOAIVqbSBqdh4GuQfggTtoMZiD5jlGJRDKTKU4BD99keTh9so2laY7GbFAm7s7yDfAUetILpt5o0Vhfv7PYgfweWfQUbJ9tibfdYcuuaauhu62GgZ5DOU11MmDwh8vYky5J4CY+u092cPthG47SGyDMAqidU0d8zSH/3AG3Hz9HYbCcHAkitB38HEF74X6M93gZwILkKMjsifeVfFYJTC+4kyO5/R6/w9ttt8j/JdeBvG9VfvDMhOOWQmAf+i5YyDPnjf8XIcQnf7g+siUUxkMvmcF2HRDJBRU0F6cFMsZsUCbsjTB6krrT8YXtnLt4R+TJIrgR/p5VXL9Z2jyU3PeRTXl2O4ziUV5WTGYr+sxZkA3AgUZagotpOBoCf9ikrT+K6DpXV5fbOG04NeDN/7eIZ+fH2pgE5CM9G/cq/KrkCcgeBd9Z1P6rtdpvAqYJc6zvKGLXEAgja7HbdO3XgNUPudXsZoxCLYiDr5zhx4DS3/eFNrN3Ywv7tbxS7SRc/twncasgdL3ZLLh2JWfmxy7C92C0pmnMnOqisLmfzZ26hpqGKUwfPRJ6Ry+U4treV2//gJtbftoa9z9r5PJ/v6CPr57jzj26hed4Ujrxy3EoOyeWQO2Z5rNiB1FUjhWrOYo4HqctHejksSq2H7EuApSECANzxuSFKrswXT0UcIgBwjDGj2kpnDM/jXMhKpjN3TH9jW6o8yeTZkxjqH6brTPdFdbO7aPUgX95yyPojT8emPF8MhJ3Fbsilw6kCknYmgP2Ous8HrHj3CdrOjX0C3HyWM5MFo/48V9dX0dhcT9fpHgbO23kSXbIswZTZkxgezNB1uovRncXGrrK2gonTG+k510tf5+iGy+YuG+KrDx8c/VMLnQlAzv7FwJ0EYQ/W5gvkQ0Zy2rF6knWbIBzAbjHggDsZwg4g4omjvxJTD/hgRv9Z6esPadlwghOnR1fYjeYyH5tvE/jpLCffKI3Z3eMjDaHND1oJGsOH+VIy1lP6QM8gAxZm979VNpPj5IHoex3+b0N9w7S+ZnkC7XgVj+MyqTW0PwwB43STYsZnW0yP/YxRiMUwgYiIiPx2KgZEpKCLavRKRKxQMSAiIhJzKgZEpKCLaK6tiFiiYkBERCTmVAyISEGaMyBS+lQMiIiIxJyKARERkZiLzaJDIvLOaALh2PSfT/DEv9VbH19pnp1hydrSeBR7Z1uSl7dXW8+Zu3SYOYu1mNpvomJARCRC506l+Nv/Ost6zk0f7CqZYuDo/nL+9rP299lH/7hNxcBvoWECESlIEwhFSp+KARERkZhTMSAiIhJzKgZEpCBNIBQpfSoGRKQgzRkQKX0qBkRERGJOxYCIFKRhApHSp2JAREQk5rTokIgUNNY5AzMWNTO3ZQ6HXz7GqQNnrLSpprGalncvpbejj9eeO0iQC6zkTJkziUXr5tH62imO7TthpZuksraClRuWMTyQZu+zr5Pzc9GHeJdBsgUcF8JuyDwDWNhnyTWQmAW4kDsC2ZeizwDqJtay4trFdJ/t5fWdBwmDMPqQxDJILMjvs+AU+Dujz3CbIHV1PsNkIP0EMBx9zmiaUpRUESlJEybWsum/3ERvRx+3/8FN1DbWRJ7hOA63//5NOA6s3LCMRevnRZ4B+Yv0nX90M33dA2z8jxuYOK3RSs7Gj19PRXU581bPYfWNK6xkEHZBdi+YLCRXYG3wJzgF/qvgTgZvupUI13O54w83EgYha29qYf6ay6zkEJyF7KvgVEHCznuMcDB/XIJ2SF0FWChqRknFgIgUNJbLRtP0RrrOdPPSE3tpb+2gee6UyNuTSCWonzKB3VtfYc/jrzJv5ZzIMwDqmmpJD2Z4+fG9HNt7gpmLo7+4uZ5L82WT2fXgHnY//DJzW2ZHngGA6YXgKLhTIPM01i46YTsEJ8GdAP4uKxHJsiQ1jdXsfuRVXn5yH/NWzraSg+mEoBXciZDZZieDYQiOgFs/0vOQsZTz9lQMiEhBYxkmcD33QpdtGIS4XvRfTHTc/GsaY0Yy7JzGHNchDEMMENjaFscBB0xorO2vfw+rA28y5A7aywBIzIWwH8IOKy/vug6Ytxx/1+JlzGsGvHyBY00yP4Tj77aY8fZUDIhIZLrbepg4o5GZi6cxZc4k2lujvyBkM1mG+oeZt3IOi69YwMkDpyPPAOjvGqC6vopZi6cze+l0zhw5G3lGkAvoae9l4bp5LLlyIacPtUWecUFqDWRfB2P54Uapd4G/HVtDEZm0j5/2mdsyi0Xr53PS0rwUAFJXjPRw2JmTAkBiPoR9EEb//hoLFQMiEpmuMz1s//nzXH7zKrb9bBddbT2RZ5jQ8OC3HmPuqtl0t/Xw6tOvRZ4B0H9+gMd/8Cyr37OCPY+9ypkj7VZytn73CabNm0oum2PXA3usZOS7H3LgP23p9d+UzM8byO63lhDmQh781mMsvHwe7cc72L/jgKUkD8LzkLV8x+6UQWYrxf4Sr75NICKRMcaw99nX2fvs61Zz2o938MuvPmw1AwMHdx/h4O4jVmO62nrY8o2tVjPAgP+U5QyALGQesp5y5kg7v/iK7Zxg5CJtmaVvXIyVegZERERiTsWAiIhIzKkYEBERiTkVAyJSkJ5NIFL6VAyISEF6hLFI6VMxICIiEnMqBkRERGJOxYCIiEjMWV10yBhNPRot7Sux6Xd5e735p3qPXlyMKZ1jYhifbRmvHNuMMZFP7LVSDHRyFr+IT1+61PSfyfI//q4HzdsWW4bThv7Bd/akum7aCchF3CL5XT26b5jBe3qL3YxInD46yFHS1nN+sW2AI8GA9RzbMr6hty/aJ086ZpRlkuNoTrGIiMilZjSXec0ZEBERiTkVAyIiIjGnYkBERCTmVAyIiIjEnIoBERGRmFMxICIiEnOjXmegFBZqEBERkV+nngEREZGYUzEgIiIScyoGREREYk7FgIiISMypGBAREYk5FQMiIiIxp2JAREQk5lQMiIiIxJyKARERkZj7/wGSSr7YMRAB+gAAAABJRU5ErkJggg=="},"metadata":{}}]},{"cell_type":"code","source":"# Load the image\nimage = mpimg.imread(df['file_paths'].iloc[1200])\n\n# Display the image\nplt.imshow(image)\nplt.axis('off')  # Hide the axes\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-07-10T19:44:33.293863Z","iopub.execute_input":"2024-07-10T19:44:33.294351Z","iopub.status.idle":"2024-07-10T19:44:33.472792Z","shell.execute_reply.started":"2024-07-10T19:44:33.294315Z","shell.execute_reply":"2024-07-10T19:44:33.471505Z"},"trusted":true},"execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"model_path = \"liuhaotian/llava-v1.5-7b\"\n\nmodel_path = \"liuhaotian/llava-v1.5-7b\"\nprompt = \"What is the puzzle described in the image ?\"\nimage_file = df['file_paths'].iloc[1]\n\nargs = type('Args', (), {\n    \"model_path\": model_path,\n    \"model_base\": None,\n    \"model_name\": get_model_name_from_path(model_path),\n    \"query\": prompt,\n    \"conv_mode\": None,\n    \"image_file\": image_file,\n    \"sep\": \",\",\n    \"temperature\": 0,\n    \"top_p\": None,\n    \"num_beams\": 1,\n    \"max_new_tokens\": 512\n})()\n\neval_model(args)\n","metadata":{"execution":{"iopub.status.busy":"2024-07-10T18:35:47.318202Z","iopub.execute_input":"2024-07-10T18:35:47.319624Z","iopub.status.idle":"2024-07-10T18:46:08.128229Z","shell.execute_reply.started":"2024-07-10T18:35:47.319587Z","shell.execute_reply":"2024-07-10T18:46:08.127171Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n  warnings.warn(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/749 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"76fd4841232b4cffa7b664465897981d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"731cf3f0210c44cf94808870ebde39dd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/438 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"682f6221fa224758a4b8ee2adca9172c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/1.16k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b2c70ccf6a014c949b5796aafec5c5ef"}},"metadata":{}},{"name":"stderr","text":"You are using a model of type llava to instantiate a model of type llava_llama. This is not supported for all configurations of models and can yield errors.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"pytorch_model.bin.index.json:   0%|          | 0.00/27.1k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c0f4066e1bf443f1821ee5f31d057f16"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"70d960916bee4442874c5dea8986c17b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"pytorch_model-00001-of-00002.bin:   0%|          | 0.00/9.98G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ed22af6456454240b25b3ea6f978dd77"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"pytorch_model-00002-of-00002.bin:   0%|          | 0.00/3.54G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7fdfc9071c3b4156b762936f39cb9ea6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/4.76k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5f497cfcd7eb451695d1c1d27acdfa7f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9fb0555c09da4d54ab372c61b9998eb9"}},"metadata":{}},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n  return self.fget.__get__(instance, owner)()\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fb0f9293f24646af9ae9523aecef036b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"preprocessor_config.json:   0%|          | 0.00/316 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ec3d6721b7d14750b1b998fda66c221f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"pytorch_model.bin:   0%|          | 0.00/1.71G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a640f23f4a1a4a52a7551a4bb9c0712f"}},"metadata":{}},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:397: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `None` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"The puzzle in the image is a two-dimensional puzzle, consisting of a grid of squares, where each square is either yellow or purple. The puzzle is designed to be solved by rearranging the squares to create a specific pattern or configuration.\n","output_type":"stream"}]},{"cell_type":"code","source":"prompt = \"\"\"\nGiven two matrices as in the image, an input matrix and an output matrix, our task is to identify and formalize the transformation rules that convert the input into the output. These matrices are marked by the values '7' and '0', which have specific roles in the transformation. Please analyze the matrices according to the following guidelines and provide detailed insights:\n\n1. Identification of Matrix Structure\n    Input Matrix Characteristics:\n        What are the dimensions of the input matrix?\n        What values are present in the matrix, and how are they arranged?\n    Output Matrix Characteristics:\n        What are the dimensions of the output matrix?\n        How are the values arranged differently from the input matrix?\n        \n2. Analysis of Transformation Patterns\n    Relationship Between Elements:\n        How do the positions of '7's in the input relate to their positions in the output?\n        Are there any observable patterns in how '7's influence the surrounding space in the output matrix?\n    Transformation Types:\n        Is the transformation characterized by copying, expanding, or filling of elements?\n        How does the transformation affect the distribution and arrangement of '0's?\n    Role of Placeholders:\n        How are the '0's treated during the transformation? Are they merely placeholders, or do they shift or change in the process?\n        \n3. Formulation of Transformation Rules\n    Direct Mapping:\n        Define any direct mappings observed. For example, does a '7' in the input consistently lead to a specific pattern in the output, such as a '+' shape?\n\n    Handling of Inert Elements:\n        Determine how non-active elements like '0's are managed within the transformation—are they static, or do they adjust based on the transformation of nearby '7's?\n\n4. Development and Testing of Transformation Code\n    Function to Read Input:\n        Create a function to process input matrices of varying sizes and compositions. Ensure it can read and interpret the matrix data accurately.\n    Transformation Rules Implementation:\n        Develop a function that dynamically applies identified transformation rules to the input matrix to generate the output matrix.\n    Adaptability and Testing:\n        Test the transformation function with different matrix sizes and patterns. Provide a couple of test cases with expected outputs to verify the accuracy and robustness of the transformation rules.\n\"\"\"\n\nargs = type('Args', (), {\n    \"model_path\": model_path,\n    \"model_base\": None,\n    \"model_name\": get_model_name_from_path(model_path),\n    \"query\": prompt,\n    \"conv_mode\": None,\n    \"image_file\": image_file,\n    \"sep\": \",\",\n    \"temperature\": 0,\n    \"top_p\": None,\n    \"num_beams\": 1,\n    \"max_new_tokens\": 2048\n})()\n\neval_model(args)\n","metadata":{"execution":{"iopub.status.busy":"2024-07-11T00:30:45.386857Z","iopub.execute_input":"2024-07-11T00:30:45.387245Z","iopub.status.idle":"2024-07-11T00:30:45.904336Z","shell.execute_reply.started":"2024-07-11T00:30:45.387217Z","shell.execute_reply":"2024-07-11T00:30:45.902966Z"},"trusted":true},"execution_count":4,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[4], line 39\u001b[0m\n\u001b[1;32m      1\u001b[0m prompt \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;124mGiven two matrices as in the image, an input matrix and an output matrix, our task is to identify and formalize the transformation rules that convert the input into the output. These matrices are marked by the values \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m7\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m0\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, which have specific roles in the transformation. Please analyze the matrices according to the following guidelines and provide detailed insights:\u001b[39m\n\u001b[1;32m      3\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;124m        Test the transformation function with different matrix sizes and patterns. Provide a couple of test cases with expected outputs to verify the accuracy and robustness of the transformation rules.\u001b[39m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m     38\u001b[0m args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mArgs\u001b[39m\u001b[38;5;124m'\u001b[39m, (), {\n\u001b[0;32m---> 39\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_path\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[43mmodel_path\u001b[49m,\n\u001b[1;32m     40\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_base\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     41\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_name\u001b[39m\u001b[38;5;124m\"\u001b[39m: get_model_name_from_path(model_path),\n\u001b[1;32m     42\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquery\u001b[39m\u001b[38;5;124m\"\u001b[39m: prompt,\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconv_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     44\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimage_file\u001b[39m\u001b[38;5;124m\"\u001b[39m: image_file,\n\u001b[1;32m     45\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msep\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     46\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtemperature\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtop_p\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_beams\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m     49\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_new_tokens\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m2048\u001b[39m\n\u001b[1;32m     50\u001b[0m })()\n\u001b[1;32m     52\u001b[0m eval_model(args)\n","\u001b[0;31mNameError\u001b[0m: name 'model_path' is not defined"],"ename":"NameError","evalue":"name 'model_path' is not defined","output_type":"error"}]},{"cell_type":"code","source":"!pip install langchain langchain_openai\n","metadata":{"execution":{"iopub.status.busy":"2024-07-11T00:30:50.376378Z","iopub.execute_input":"2024-07-11T00:30:50.377261Z","iopub.status.idle":"2024-07-11T00:30:56.130478Z","shell.execute_reply.started":"2024-07-11T00:30:50.377226Z","shell.execute_reply":"2024-07-11T00:30:56.129335Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n  pid, fd = os.forkpty()\n","output_type":"stream"},{"name":"stdout","text":"Requirement already satisfied: langchain in /opt/conda/lib/python3.10/site-packages (0.2.7)\nRequirement already satisfied: langchain_openai in /opt/conda/lib/python3.10/site-packages (0.1.15)\nRequirement already satisfied: PyYAML>=5.3 in /opt/conda/lib/python3.10/site-packages (from langchain) (6.0.1)\nRequirement already satisfied: SQLAlchemy<3,>=1.4 in /opt/conda/lib/python3.10/site-packages (from langchain) (2.0.25)\nRequirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /opt/conda/lib/python3.10/site-packages (from langchain) (3.9.1)\nRequirement already satisfied: async-timeout<5.0.0,>=4.0.0 in /opt/conda/lib/python3.10/site-packages (from langchain) (4.0.3)\nRequirement already satisfied: langchain-core<0.3.0,>=0.2.12 in /opt/conda/lib/python3.10/site-packages (from langchain) (0.2.13)\nRequirement already satisfied: 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attrs>=17.3.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (23.2.0)\nRequirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (6.0.4)\nRequirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.9.3)\nRequirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.4.1)\nRequirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\nRequirement already satisfied: jsonpatch<2.0,>=1.33 in /opt/conda/lib/python3.10/site-packages (from langchain-core<0.3.0,>=0.2.12->langchain) (1.33)\nRequirement already satisfied: packaging<25,>=23.2 in /opt/conda/lib/python3.10/site-packages (from langchain-core<0.3.0,>=0.2.12->langchain) (24.1)\nRequirement already satisfied: 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regex>=2022.1.18 in /opt/conda/lib/python3.10/site-packages (from tiktoken<1,>=0.7->langchain_openai) (2023.12.25)\nRequirement already satisfied: exceptiongroup>=1.0.2 in /opt/conda/lib/python3.10/site-packages (from anyio<5,>=3.5.0->openai<2.0.0,>=1.32.0->langchain_openai) (1.2.0)\nRequirement already satisfied: httpcore<0.18.0,>=0.15.0 in /opt/conda/lib/python3.10/site-packages (from httpx<1,>=0.23.0->openai<2.0.0,>=1.32.0->langchain_openai) (0.17.3)\nRequirement already satisfied: jsonpointer>=1.9 in /opt/conda/lib/python3.10/site-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.3.0,>=0.2.12->langchain) (2.4)\nRequirement already satisfied: h11<0.15,>=0.13 in /opt/conda/lib/python3.10/site-packages (from httpcore<0.18.0,>=0.15.0->httpx<1,>=0.23.0->openai<2.0.0,>=1.32.0->langchain_openai) (0.14.0)\n","output_type":"stream"}]},{"cell_type":"code","source":"from langchain_core.pydantic_v1 import BaseModel, Field\nfrom langchain.chains import TransformChain\nfrom langchain_core.messages import HumanMessage\nfrom langchain_openai import ChatOpenAI\nfrom langchain import globals\nfrom langchain_core.runnables import chain\nfrom langchain_core.output_parsers import JsonOutputParser\nimport os\nimport langchain\nimport base64\n\n# Setting the OpenAI API Key\nos.environ[\"OPENAI_API_KEY\"] = \"sk-proj-coC00pN0VTBUhXQSIgwMT3BlbkFJKXzpLehcVAVcxWjiryny\"\n\ndef encode_image_to_base64(image_path):\n    \"\"\"Load an image file and convert it to a base64 encoded string.\"\"\"\n    with open(image_path, \"rb\") as image_file:\n        return base64.b64encode(image_file.read()).decode('utf-8')\n\ndef load_image(inputs: dict) -> dict:\n    \"\"\"Encode the image at the provided path to base64.\"\"\"\n    image_base64 = encode_image_to_base64(inputs[\"image_path\"])\n    return {\"image\": image_base64}\n\nload_image_chain = TransformChain(\n    input_variables=[\"image_path\"],\n    output_variables=[\"image\"],\n    transform=load_image\n)\n\nclass ImageTransformationDetails(BaseModel):\n    \"\"\"Model for capturing the transformation rules of matrices in images.\"\"\"\n    structure: str = Field(description=\"Describe the size and content of the matrices.\")\n    #pattern_analysis: str = Field(description=\"Analyze how elements transform from input to output.\")\n    #rules: str = Field(description=\"Define rules for transforming matrix elements.\")\n    #code: str = Field(description=\"Outline functions for reading and transforming matrices.\")\n\n# Enable debugging\nglobals.set_debug(True)\n\n@chain\ndef process_image(inputs: dict):\n    \"\"\"Processes an image to model transformation rules.\"\"\"\n    model = ChatOpenAI(temperature=0.5, \n                       model=\"gpt-4o\", \n                       max_tokens=2048)\n    msg = model.invoke([HumanMessage(content=[\n        {\"type\": \"text\", \"text\": inputs[\"prompt\"]},\n        {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{inputs['image']}\"}}\n    ])])\n    return msg.content\n\ndef analyze_image_transformation(image_path: str, prompt: str) -> dict:\n    \"\"\"Analyzes image to identify transformation rules between matrices.\"\"\"\n    #parser = JsonOutputParser(pydantic_object=ImageTransformationDetails)\n    vision_chain = load_image_chain | process_image# | parser\n    return vision_chain.invoke({'image_path': image_path, 'prompt': prompt})\n\nstructure_prompt = \"\"\"\nPlease provide a detailed description of the matrix structure in the image. Focus on the size (number of rows and columns), the type of elements it contains, and the overall arrangement of these elements. Note any unusual patterns or alignments that might suggest a non-standard matrix structure. For example, are there any nested matrices or blocks within the main matrix? Are the elements numerical, textual, or mixed? This analysis will help in understanding the foundational layout before applying any transformations.\n\"\"\"\n\npattern_prompt = \"\"\"\nAnalyze and identify any recurring patterns or sequences in the matrix elements from the image. Describe how these patterns change from one part of the matrix to another, such as gradual increases, periodic repetitions, or symmetrical arrangements. Mention if certain rows or columns follow specific arithmetic or geometric progressions. Understanding these patterns will assist in predicting the matrix transformations or manipulations that might be applicable.\n\"\"\"\n\ntransformation_prompt = \"\"\"\nFormulate comprehensive rules for transforming the matrix based on the observed structures and patterns. Describe step-by-step how to apply these transformations to convert an input matrix into its output form. For instance, if the matrix needs to be rotated, specify the angle and direction; if elements need to be scaled or shifted, provide the scaling factor or shift magnitude. Additionally, propose a generic Python function that can implement these rules dynamically on any given matrix, adjusting to different sizes and patterns.\n\"\"\"\n\nstructure = analyze_image_transformation(df['file_paths'].iloc[1], structure_prompt)\npattern = analyze_image_transformation(df['file_paths'].iloc[1], pattern_prompt)\ntransformation = analyze_image_transformation(df['file_paths'].iloc[1], transformation_prompt)\n\ncode_generation_prompt = f\"\"\"\nGiven the following information about the puzzle in the image, generate code to solve the puzzle in python.\nStructural Information: {structure}\nPattern Based Information: {pattern}\nTransformation Information: {transformation}\n\nGiven the structural, pattern-based, and transformational information, generate a Python script to solve the matrix puzzle depicted in the image. Include functions for:\n- Reading and processing input matrices of varying sizes and compositions, ensuring accurate interpretation of data.\n- Dynamically applying the identified transformation rules to the input matrix to produce the output matrix.\n- Testing the transformation function with different matrix sizes and patterns. Provide test cases with expected outputs to verify the accuracy and robustness of the transformation rules.\nFocus on adaptability and error handling to manage different matrix configurations and potential data anomalies effectively.\n\"\"\"\n\ncode = analyze_image_transformation(df['file_paths'].iloc[1], code_generation_prompt)\n","metadata":{"_kg_hide-output":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-11T00:42:10.916085Z","iopub.execute_input":"2024-07-11T00:42:10.916741Z","iopub.status.idle":"2024-07-11T00:43:22.595487Z","shell.execute_reply.started":"2024-07-11T00:42:10.916709Z","shell.execute_reply":"2024-07-11T00:43:22.594486Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nPlease provide a detailed description of the matrix structure in the image. Focus on the size (number of rows and columns), the type of elements it contains, and the overall arrangement of these elements. Note any unusual patterns or alignments that might suggest a non-standard matrix structure. For example, are there any nested matrices or blocks within the main matrix? Are the elements numerical, textual, or mixed? This analysis will help in understanding the foundational layout before applying any transformations.\\n\"\n}\n\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:TransformChain] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nPlease provide a detailed description of the matrix structure in the image. Focus on the size (number of rows and columns), the type of elements it contains, and the overall arrangement of these elements. Note any unusual patterns or alignments that might suggest a non-standard matrix structure. For example, are there any nested matrices or blocks within the main matrix? Are the elements numerical, textual, or mixed? This analysis will help in understanding the foundational layout before applying any transformations.\\n\"\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:TransformChain] [1ms] Exiting Chain run with output:\n\u001b[0m{\n  \"image\": \"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\"\n}\n\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nPlease provide a detailed description of the matrix structure in the image. Focus on the size (number of rows and columns), the type of elements it contains, and the overall arrangement of these elements. Note any unusual patterns or alignments that might suggest a non-standard matrix structure. For example, are there any nested matrices or blocks within the main matrix? Are the elements numerical, textual, or mixed? This analysis will help in understanding the foundational layout before applying any transformations.\\n\",\n  \"image\": 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\"\n}\n\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image > llm:ChatOpenAI] Entering LLM run with input:\n\u001b[0m{\n  \"prompts\": [\n    \"Human: [{'type': 'text', 'text': '\\\\nPlease provide a detailed description of the matrix structure in the image. Focus on the size (number of rows and columns), the type of elements it contains, and the overall arrangement of these elements. Note any unusual patterns or alignments that might suggest a non-standard matrix structure. For example, are there any nested matrices or blocks within the main matrix? Are the elements numerical, textual, or mixed? This analysis will help in understanding the foundational layout before applying any transformations.\\\\n'}, {'type': 'image_url', 'image_url': {'url': 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AAAogIIOAAAABVDQAQAAoAAKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACqCgAwAAQAEUdAAAACiAgg4AAAAFUNABAACgAAo6AAAAFEBBBwAAgAIo6AAAAFAABR0AAAAKoKADAABAAXrbHgAAgPot37y67RF4k7DXGM+8gg4AAAAFUNABAACgAAo6AAAAFEBBBwAAgAIo6AAAAFAABR0AAAAKoKADAABAARR0AAAAKICCDgAAAAVQ0AEAAKAACjoAAAAUQEEHAACAAijoAAAAUAAFHQAAAAqgoAMAAEABFHQAAAAogIIOAAAABVDQAQAAoAAKOgAAABSgt+0BeMmCi+bnnMsX5KhZ07P+kU259rM35vEH1rU9FpXozFyRTs/bRxxvdt6SZmBxCxNRK3uNV1PTNa6mLEkdeWp73pGnXDVlSerKU1MWBb0Ap3/ilHzmmkX55oXX5bEfr8vvX3pmvnzXFTn/PZfkuad3tD0eFWi2LkzT2ecNM739Oeyov0+z+872hqJK9hqvVNM1rqYsST15anvekadcNWVJ6spTUxZvcS/Awss+ljtv+EGW37wyP3/syXzjguuy+/kXMv/8D7c9GrVonk2Gtg6vzsQPpdmzKXlhVduTURt7jVeo6RpXU5akojy1Pe/IU66asiR15akoi4Lest4Jvek/aU4evnvN8LGmafLw3Wty3Mn9LU5GvSYkkxekGby97UGonr32ZlfTNa6mLEl9eX6ltucdecpVU5akrjzjO4uC3rJpM6akp7cn257avt/xbVu2p2/W9HaGom6Tzkg6U5PBpW1PQu3stTe9mq5xNWVJ6sszrLbnHXnKVVOWpK484zyLgg5vMp3J5yS7702GtrQ9CpWz14BDrbbnHXnKVVOWpK484z2Lgt6y7VsHsnfP3vQdM22/431HT8u2Xz7XzlDU67DZyeGnpBlc0vYk1M5eI3Vd42rKktSXJ0l9zzvylKumLEldeSrIoqC3bM+Le7L2oQ05cd7c4WOdTicnzpubR+9f2+Jk1KhzxMJk6Jlk98q2R6Fy9hpJXde4mrIk9eVJ6nvekadcNWVJ6spTQxYfs1aA733tjnz+5ouz9sH1eXzVupx96ZmZdOTELL9pRdujUZVOMnlhMrgsyd62h6Fq9hq/UtM1rqYsSW15anvekadcNWVJ6spTRxYFvQD3LLkv02dOzaLF56Zv1vSsX70xX/zIVXluy/bX/2I4UIefmk7P2zI0Tn+jJeOIvcY+arrG1ZQlqSxPbc878pSrpixJXXkqydJJ0nTr5B/MWentTOjW6SFJsnzz6rZHABgTOwaG0te/4ZDfr+s1h4LrNVCLbl6v/Qw6AAAAFEBBBwAAgAIo6AAAAFAABR0AAAAKoKADAABAARR0AAAAKICCDgAAAAVQ0AEAAKAACjoAAAAUQEEHAACAAijoAAAAUAAFHQAAAAqgoAMAAEABFHQAAAAogIIOAAAABVDQAQAAoAAKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACtDb9gAAANRv/uwT2h5hTC3fvLrtEXgN9hrjmVfQAQAAoAAKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACqCgAwAAQAEUdAAAACiAgg4AAAAFUNABAACgAAo6AAAAFEBBBwAAgAIo6AAAAFAABR0AAAAKoKADAABAARR0AAAAKICCDgAAAAVQ0AEAAKAACjoAAAAUQEEHAACAAijoAAAAUIDetgcAAOq24KL5OefyBTlq1vSsf2RTrv3sjXn8gXVtj3VQasqS1JWnliydmSvS6Xn7iOPNzlvSDCxuYaI3prY8ib1WopqyeAUdAOia0z9xSj5zzaLc8qV/yYUn/WU2rNmUL991RabPnNr2aKNWU5akrjw1ZWm2LszQlvf/aj276KXju+9sebKDU1see61MNWVR0AGArll42cdy5w0/yPKbV+bnjz2Zb1xwXXY//0Lmn//htkcbtZqyJHXlqSlLmmeToa3DqzPxQ2n2bEpeWNX2ZAensjz2WqEqyqKgAwBd0TuhN/0nzcnDd68ZPtY0TR6+e02OO7m/xclGr6YsSV15asoy0oRk8oI0g7e3PcgYGd957LXxYnxnUdABgK6YNmNKenp7su2p7fsd37Zle/pmTW9nqINUU5akrjw1ZRlh0hlJZ2oyuLTtScbGOM9jr40T4zyLgg4AAAXqTD4n2X1vMrSl7VHGRG15alLTYzPesyjoAEBXbN86kL179qbvmGn7He87elq2/fK5doY6SDVlSerKU1OW/Rw2Ozn8lDSDS9qeZGxUkMdeGwcqyKKgAwBdsefFPVn70IacOG/u8LFOp5MT583No/evbXGy0aspS1JXnpqy7KtzxMJk6Jlk98q2RxkTNeSx18pXQxafgw4AdM33vnZHPn/zxVn74Po8vmpdzr70zEw6cmKW37Si7dFGraYsSV15asrykk4yeWEyuCzJ3raHGQP15LHXSlZHFgUdAOiae5bcl+kzp2bR4nPTN2t61q/emC9+5Ko8t2X7639xYWrKktSVp6YsSZLDT02n520ZGqe/hXqEivLYawWrJEsnSdOtk38wZ6W3M6Fbp4ckyfLNq9seAWBM7BgYSl//hkN+v67XMHq+/yjX/NkntD3CmLLXytPN67WfQQcAAIACKOgAAABQAAUdAAAACqCgAwAAQAEUdAAAACiAgg4AAAAFUNABAACgAAo6AAAAFEBBBwAAgAIo6AAAAFAABR0AAAAKoKADAABAARR0AAAAKICCDgAAAAVQ0AEAAKAACjoAAAAUQEEHAACAAijoAAAAUAAFHQAAAAqgoAMAAEABert58mVr/ztTp/g3AADg0Fi+eXXbI/Aa5s8+oe0RAIqnPQMAAEABFHQAAAAogIIOAAAABVDQAQAAoAAKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACqCgAwAAQAEUdAAAACiAgg4AAAAFUNABAACgAAo6AAAAFEBBBwAAgAIo6AAAAFAABR0AAAAKoKADAABAARR0AAAAKICCDgAAAAXobXsAAKBuCy6an3MuX5CjZk3P+kc25drP3pjHH1jX9lij0pm5Ip2et4843uy8Jc3A4hYmemNqy5PUsc+S+h6b2vIk9lqJasqioAMAXXP6J07JZ65ZlG9eeF0e+/G6/P6lZ+bLd12R899zSZ57ekfb4x2wZuvCNJ193njY25/Djvr7NLvvbG+oN6C2PLXss6S+x6a2PPZamWrK4i3uAEDXLLzsY7nzhh9k+c0r8/PHnsw3Lrguu59/IfPP/3Dbo41O82wytHV4dSZ+KM2eTckLq9qe7OBUlqeafZZU99jUlsdeK1RFWRR0AKAreif0pv+kOXn47jXDx5qmycN3r8lxJ/e3ONkbNSGZvCDN4O1tDzJGxneeevdZMt4fm5HGdx57bbwY31kUdACgK6bNmJKe3p5se2r7fse3bdmevlnT2xlqLEw6I+lMTQaXtj3J2BjneardZ8m4f2xGGOd57LVxYpxnUdABAEahM/mcZPe9ydCWtkcZE7XlqUltj01teWpS02Mz3rMo6ABAV2zfOpC9e/am75hp+x3vO3patv3yuXaGeqMOm50cfkqawSVtTzI2KshT5T5Lqnhs9lNBHnttHKggi4IOAHTFnhf3ZO1DG3LivLnDxzqdTk6cNzeP3r+2xckOXueIhcnQM8nulW2PMiZqyFPjPkvqeGz2VUMee618NWTxMWsAQNd872t35PM3X5y1D67P46vW5exLz8ykIydm+U0r2h7tIHSSyQuTwWVJ9rY9zBioJ09d+yyp6bF5ST157LWS1ZFFQQcAuuaeJfdl+sypWbT43PTNmp71qzfmix+5Ks9t2f76X1yaw09Np+dtGRqnvxl4hIryVLXPkqoemyRV5bHXClZJlk6Splsn37Z2TqZO8S56ADgQOwaG0te/4ZDf7wdzVno7Ew75/XbD8s2r2x6B1zB/9gltjzCm7LVy2Wt0Wzev19ozAAAAFEBBBwAAgAIo6AAAAFAABR0AAAAKoKADAABAARR0AAAAKICCDgAAAAVQ0AEAAKAACjoAAAAUQEEHAACAAijoAAAAUAAFHQAAAAqgoAMAAEABFHQAAAAogIIOAAAABVDQAQAAoAAKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACqCgAwAAQAE6SZpunXzb2jmZOsW/AQDAgdgxMJS+/g2H/H5drwHgwHXzeu1qDAAAAAVQ0AEAAKAACjoAAAAUQEEHAACAAijoAAAAUAAFHQAAAAqgoAMAAEABFHQAAAAogIIOAAAABVDQAQAAoAAKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACqCgAwAAQAEUdAAAACiAgg4AAAAFUNABAACgAAo6AAAAFKC37QHe7DozV6TT8/YRx5udt6QZWNzCRNTIPuNQsdfYV037oaYsSV15asqSyFOymrIkdeWpKYuC3rJm68I0nX3eyNDbn8OO+vs0u+9sbyiqY59xqNhr7Kum/VBTlqSuPDVlSeQpWU1Zkrry1JRFQW9b82zS/OqPnYmfSbNnU/LCqvZmoj72GYeKvca+atoPNWVJ6spTU5ZEnpLVlCWpK09FWfwMelEmJJMXpBm8ve1BqJp9xqFir7GvmvZDTVmSuvLUlCWRp2Q1ZUnqyjO+syjoJZl0RtKZmgwubXsSamafcajYa+yrpv1QU5akrjw1ZUnkKVlNWZK68ozzLAp6QTqTz0l235sMbWl7FCpmn3Go2Gvsq6b9UFOWpK48NWVJ5ClZTVmSuvKM9ywKeikOm50cfkqawSVtT0LN7DMOFXuNfdW0H2rKktSVp6YsiTwlqylLUleeCrIo6IXoHLEwGXom2b2y7VGomH3GoWKvsa+a9kNNWZK68tSUJZGnZDVlSerKU0MWBb0InWTywmRwWZK9bQ9DtewzDhV7jX3VtB9qypLUlaemLIk8JaspS1JXnjqyKOglOPzUdHreNm5/0yDjhH3GoWKvsa+a9kNNWZK68tSUJZGnZDVlSerKU0mWTvb7xLixtW3tnEyd4t8AAOBA7BgYSl//hkN+v67XAHDgunm9djUGAACAAijoAAAAUAAFHQAAAAqgoAMAAEABFHQAAAAogIIOAAAABVDQAQAAoAAKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACqCgAwAAQAEUdAAAACiAgg4AAAAFUNABAACgAAo6AAAAFEBBBwAAgAIo6AAAAFAABR0AAAAKoKADAABAAXrbHgAAaNfZ/XPT25nQ9hhjYvnm1W2PwGuYP/uEtkcYU/Zauew1xjOvoAMAAEABFHQAAAAogIIOAAAABVDQAQAAoAAKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACqCgAwAAQAEUdAAAACiAgg4AAAAFUNABAACgAAo6AAAAFEBBBwAAgAIo6AAAAFAABR0AAAAKoKADAABAARR0AAAAKICCDgAAAAVQ0AEAAKAAvW0PAADUbcFF83PO5Qty1KzpWf/Iplz72Rvz+APr2h5rVDozV6TT8/YRx5udt6QZWNzCRG9MbXmSOvZZUt9jU1uexF4rUU1ZFHQAoGtO/8Qp+cw1i/LNC6/LYz9el9+/9Mx8+a4rcv57LslzT+9oe7wD1mxdmKazzxsPe/tz2FF/n2b3ne0N9QbUlqeWfZbU99jUlsdeK1NNWbzFHQDomoWXfSx33vCDLL95ZX7+2JP5xgXXZffzL2T++R9ue7TRaZ5NhrYOr87ED6XZsyl5YVXbkx2cyvJUs8+S6h6b2vLYa4WqKIuCDgB0Re+E3vSfNCcP371m+FjTNHn47jU57uT+Fid7oyYkkxekGby97UHGyPjOU+8+S8b7YzPS+M5jr40X4zuLgg4AdMW0GVPS09uTbU9t3+/4ti3b0zdrejtDjYVJZySdqcng0rYnGRvjPE+1+ywZ94/NCOM8j702TozzLAo6AMAodCafk+y+Nxna0vYoY6K2PDWp7bGpLU9NanpsxnsWBR0A6IrtWweyd8/e9B0zbb/jfUdPy7ZfPtfOUG/UYbOTw09JM7ik7UnGRgV5qtxnSRWPzX4qyGOvjQMVZFHQAYCu2PPinqx9aENOnDd3+Fin08mJ8+bm0fvXtjjZwescsTAZeibZvbLtUcZEDXlq3GdJHY/NvmrIY6+Vr4YsPmYNAOia733tjnz+5ouz9sH1eXzVupx96ZmZdOTELL9pRdujHYROMnlhMrgsyd62hxkD9eSpa58lNT02L6knj71WsjqyKOgAQNfcs+S+TJ85NYsWn5u+WdOzfvXGfPEjV+W5Ldtf/4tLc/ip6fS8LUPj9DcDj1BRnqr2WVLVY5Okqjz2WsEqydJJ0nTr5NvWzsnUKd5FDwAHYsfAUPr6Nxzy+/1gzkpvZ8Ihv99uWL55ddsj8Brmzz6h7RHGlL1WLnuNbuvm9Vp7BgAAgAIo6AAAAFAABR0AAAAKoKADAABAARR0AAAAKICCDgAAAAVQ0AEAAKAACjoAAAAUQEEHAACAAijoAAAAUAAFHQAAAAqgoAMAAEABFHQAAAAogIIOAAAABVDQAQAAoAAKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACqCgAwAAQAF6u3nys/vnprczoZt3AVm+eXXbIwBQiPmzT2h7BAA4aF5BBwAAgAIo6AAAAFAABR0AAAAKoKADAABAARR0AAAAKICCDgAAAAVQ0AEAAKAACjoAAAAUQEEHAACAAijoAAAAUAAFHQAAAAqgoAMAAEABFHQAAAAogIIOAAAABVDQAQAAoAAKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACtDb9gAAQN0WXDQ/51y+IEfNmp71j2zKtZ+9MY8/sK7tsQ5KTVmSuvLUkqUzc0U6PW8fcbzZeUuagcUtTPTG1JYnsddKVFMWr6ADAF1z+idOyWeuWZRbvvQvufCkv8yGNZvy5buuyPSZU9sebdRqypLUlaemLM3WhRna8v5frWcXvXR8950tT3Zwastjr5WppiwKOgDQNQsv+1juvOEHWX7zyvz8sSfzjQuuy+7nX8j88z/c9mijVlOWpK48NWVJ82wytHV4dSZ+KM2eTckLq9qe7OBUlsdeK1RFWRR0AKAreif0pv+kOXn47jXDx5qmycN3r8lxJ/e3ONno1ZQlqStPTVlGmpBMXpBm8Pa2Bxkj4zuPvTZejO8sCjoA0BXTZkxJT29Ptj21fb/j27ZsT9+s6e0MdZBqypLUlaemLCNMOiPpTE0Gl7Y9ydgY53nstXFinGdR0AEAoECdyecku+9Nhra0PcqYqC1PTWp6bMZ7FgUdAOiK7VsHsnfP3vQdM22/431HT8u2Xz7XzlAHqaYsSV15asqyn8NmJ4efkmZwSduTjI0K8thr40AFWRR0AKAr9ry4J2sf2pAT580dPtbpdHLivLl59P61LU42ejVlSerKU1OWfXWOWJgMPZPsXtn2KGOihjz2WvlqyOJz0AGArvne1+7I52++OGsfXJ/HV63L2ZeemUlHTszym1a0Pdqo1ZQlqStPTVle0kkmL0wGlyXZ2/YwY6CePPZayerIoqADAF1zz5L7Mn3m1CxafG76Zk3P+tUb88WPXJXntmx//S8uTE1Zkrry1JQlSXL4qen0vC1D4/S3UI9QUR57rWCVZOkkabp18g/mrPR2JnTr9JAkWb55ddsjAIyJHQND6evfcMjv1/UaRs/3H+WaP/uEtkcYU/Zaebp5vfYz6AAAAFAABR0AAAAKoKADAABAARR0AAAAKICCDgAAAAVQ0AEAAKAACjoAAAAUQEEHAACAAijoAAAAUAAFHQAAAAqgoAMAAEABFHQAAAAogIIOAAAABVDQAQAAoAAKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACqCgAwAAQAEUdAAAACiAgg4AAAAF6G17AAAA6rd88+q2R+BNwl5jPPMKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACqCgAwAAQAEUdAAAACiAgg4AAAAFUNABAACgAAo6AAAAFEBBBwAAgAIo6AAAAFAABR0AAAAKoKADAABAARR0AAAAKICCDgAAAAVQ0AEAAKAACjoAAAAUQEEHAACAAijoAAAAUIDetgfgJQsump9zLl+Qo2ZNz/pHNuXaz96Yxx9Y1/ZYVKIzc0U6PW8fcbzZeUuagcUtTESt7DVeTU3XuJqyJHXkqe15R55y1ZQlqStPTVkU9AKc/olT8plrFuWbF16Xx368Lr9/6Zn58l1X5Pz3XJLnnt7R9nhUoNm6ME1nnzfM9PbnsKP+Ps3uO9sbiirZa7xSTde4mrIk9eSp7XlHnnLVlCWpK09NWbzFvQALL/tY7rzhB1l+88r8/LEn840Lrsvu51/I/PM/3PZo1KJ5NhnaOrw6Ez+UZs+m5IVVbU9Gbew1XqGma1xNWZKK8tT2vCNPuWrKktSVp6IsCnrLeif0pv+kOXn47jXDx5qmycN3r8lxJ/e3OBn1mpBMXpBm8Pa2B6F69tqbXU3XuJqyJPXl+ZXannfkKVdNWZK68ozvLAp6y6bNmJKe3p5se2r7fse3bdmevlnT2xmKuk06I+lMTQaXtj0JtbPX3vRqusbVlCWpL8+w2p535ClXTVmSuvKM8ywKOrzJdCafk+y+Nxna0vYoVM5eAw612p535ClXTVmSuvKM9ywKesu2bx3I3j1703fMtP2O9x09Ldt++Vw7Q1Gvw2Ynh5+SZnBJ25NQO3uN1HWNqylLUl+eJPU978hTrpqyJHXlqSCLgt6yPS/uydqHNuTEeXOHj3U6nZw4b24evX9ti5NRo84RC5OhZ5LdK9sehcrZayR1XeNqypLUlyep73lHnnLVlCWpK08NWXzMWgG+97U78vmbL87aB9fn8VXrcvalZ2bSkROz/KYVbY9GVTrJ5IXJ4LIke9sehqrZa/xKTde4mrIkteWp7XlHnnLVlCWpK08dWRT0Atyz5L5Mnzk1ixafm75Z07N+9cZ88SNX5bkt21//i+FAHX5qOj1vy9A4/Y2WjCP2Gvuo6RpXU5aksjy1Pe/IU66asiR15akkSydJ062TfzBnpbczoVunhyTJ8s2r2x4BYEzsGBhKX/+GQ36/rtccCq7XQC26eb32M+gAAABQAAUdAAAACqCgAwAAQAEUdAAAACiAgg4AAAAFUNABAACgAAo6AAAAFEBBBwAAgAIo6AAAAFAABR0AAAAKoKADAABAARR0AAAAKICCDgAAAAVQ0AEAAKAACjoAAAAUQEEHAACAAijoAAAAUAAFHQAAAAqgoAMAAEABFHQAAAAoQG/bAwAAUL/5s09oe4QxtXzz6rZH4DXYa4xnXkEHAACAAijoAAAAUAAFHQAAAAqgoAMAAEABFHQAAAAogIIOAAAABVDQAQAAoAAKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACqCgAwAAQAEUdAAAACiAgg4AAAAFUNABAACgAAo6AAAAFEBBBwAAgAIo6AAAAFAABR0AAAAK0Nv2AABA3RZcND/nXL4gR82anvWPbMq1n70xjz+wru2xDkpNWZK68tSSpTNzRTo9bx9xvNl5S5qBxS1M9MbUliex10pUUxavoAMAXXP6J07JZ65ZlFu+9C+58KS/zIY1m/Llu67I9JlT2x5t1GrKktSVp6YszdaFGdry/l+tZxe9dHz3nS1PdnBqy2OvlammLAo6ANA1Cy/7WO684QdZfvPK/PyxJ/ONC67L7udfyPzzP9z2aKNWU5akrjw1ZUnzbDK0dXh1Jn4ozZ5NyQur2p7s4FSWx14rVEVZFHQAoCt6J/Sm/6Q5efjuNcPHmqbJw3evyXEn97c42ejVlCWpK09NWUaakExekGbw9rYHGSPjO4+9Nl6M7ywKOgDQFdNmTElPb0+2PbV9v+PbtmxP36zp7Qx1kGrKktSVp6YsI0w6I+lMTQaXtj3J2Bjneey1cWKcZ1HQAQCgQJ3J5yS7702GtrQ9ypioLU9NanpsxnsWBR0A6IrtWweyd8/e9B0zbb/jfUdPy7ZfPtfOUAeppixJXXlqyrKfw2Ynh5+SZnBJ25OMjQry2GvjQAVZFHQAoCv2vLgnax/akBPnzR0+1ul0cuK8uXn0/rUtTjZ6NWVJ6spTU5Z9dY5YmAw9k+xe2fYoY6KGPPZa+WrI4nPQAYCu+d7X7sjnb744ax9cn8dXrcvZl56ZSUdOzPKbVrQ92qjVlCWpK09NWV7SSSYvTAaXJdnb9jBjoJ489lrJ6siioAMAXXPPkvsyfebULFp8bvpmTc/61RvzxY9clee2bH/9Ly5MTVmSuvLUlCVJcvip6fS8LUPj9LdQj1BRHnutYJVk6SRpunXyD+as9HYmdOv0kCRZvnl12yMAjIkdA0Pp699wyO/X9RpGz/cf5Zo/+4S2RxhT9lp5unm99jPoAAAAUAAFHQAAAAqgoAMAAEABFHQAAAAogIIOAAAABVDQAQAAoAAKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACqCgAwAAQAEUdAAAACiAgg4AAAAFUNABAACgAAo6AAAAFEBBBwAAgAIo6AAAAFAABR0AAAAKoKADAABAARR0AAAAKEBvN0++Jy8mTTfvAZIdA0NtjwAwJnb8XzvPZ67XMHq+/yjXnubFtkcYU/Zaebp5ve7EJRkAAABa5y3uAAAAUAAFHQAAAAqgoAMAAEABFHQAAAAogIIOAAAABVDQAQAAoAAKOgAAABRAQQcAAIACKOgAAABQAAUdAAAACqCgAwAAQAEUdAAAACiAgg4AAAAFUNABAACgAP8fecy8TaZ5cmgAAAAASUVORK5CYII='}}]\"\n  ]\n}\n\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image > llm:ChatOpenAI] [13.59s] Exiting LLM run with output:\n\u001b[0m{\n  \"generations\": [\n    [\n      {\n        \"text\": \"The image displays two matrices labeled \\\"Input 2\\\" and \\\"Output 2.\\\" Below is a detailed description of each matrix:\\n\\n### Input 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a large block of 7s, with specific areas filled with 0s.\\n   - The top-left corner contains a 4x4 block of 0s.\\n   - The bottom-left corner contains a 4x4 block of 0s.\\n   - The rest of the matrix is filled with 7s.\\n\\n4. **Patterns and Alignments:**\\n   - The 0s form two distinct rectangular blocks within the matrix, located at the top-left and bottom-left corners.\\n   - The rest of the matrix is uniformly filled with 7s.\\n\\n### Output 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is also a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a more complex pattern of 0s and 7s compared to the Input 2 matrix.\\n   - The 0s and 7s are arranged in a checkerboard-like pattern in certain areas, creating a more intricate design.\\n\\n4. **Patterns and Alignments:**\\n   - The top-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - The middle of the matrix has a large cross-like structure made of 7s, with 0s filling the spaces around it.\\n   - The bottom-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - There is a noticeable symmetry in the distribution of 0s and 7s, particularly around the central cross-like structure of 7s.\\n\\n### Summary\\n\\n- Both matrices are 10x10 grids with numerical elements (0 and 7).\\n- The Input 2 matrix has large, uniform blocks of 0s and 7s, while the Output 2 matrix has a more complex, patterned arrangement of 0s and 7s.\\n- The transformation from Input 2 to Output 2 involves redistributing the 0s and 7s into a more intricate pattern, suggesting a specific rule or algorithm applied to achieve this transformation.\",\n        \"generation_info\": {\n          \"finish_reason\": \"stop\",\n          \"logprobs\": null\n        },\n        \"type\": \"ChatGeneration\",\n        \"message\": {\n          \"lc\": 1,\n          \"type\": \"constructor\",\n          \"id\": [\n            \"langchain\",\n            \"schema\",\n            \"messages\",\n            \"AIMessage\"\n          ],\n          \"kwargs\": {\n            \"content\": \"The image displays two matrices labeled \\\"Input 2\\\" and \\\"Output 2.\\\" Below is a detailed description of each matrix:\\n\\n### Input 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a large block of 7s, with specific areas filled with 0s.\\n   - The top-left corner contains a 4x4 block of 0s.\\n   - The bottom-left corner contains a 4x4 block of 0s.\\n   - The rest of the matrix is filled with 7s.\\n\\n4. **Patterns and Alignments:**\\n   - The 0s form two distinct rectangular blocks within the matrix, located at the top-left and bottom-left corners.\\n   - The rest of the matrix is uniformly filled with 7s.\\n\\n### Output 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is also a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a more complex pattern of 0s and 7s compared to the Input 2 matrix.\\n   - The 0s and 7s are arranged in a checkerboard-like pattern in certain areas, creating a more intricate design.\\n\\n4. **Patterns and Alignments:**\\n   - The top-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - The middle of the matrix has a large cross-like structure made of 7s, with 0s filling the spaces around it.\\n   - The bottom-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - There is a noticeable symmetry in the distribution of 0s and 7s, particularly around the central cross-like structure of 7s.\\n\\n### Summary\\n\\n- Both matrices are 10x10 grids with numerical elements (0 and 7).\\n- The Input 2 matrix has large, uniform blocks of 0s and 7s, while the Output 2 matrix has a more complex, patterned arrangement of 0s and 7s.\\n- The transformation from Input 2 to Output 2 involves redistributing the 0s and 7s into a more intricate pattern, suggesting a specific rule or algorithm applied to achieve this transformation.\",\n            \"response_metadata\": {\n              \"token_usage\": {\n                \"completion_tokens\": 597,\n                \"prompt_tokens\": 527,\n                \"total_tokens\": 1124\n              },\n              \"model_name\": \"gpt-4o-2024-05-13\",\n              \"system_fingerprint\": \"fp_298125635f\",\n              \"finish_reason\": \"stop\",\n              \"logprobs\": null\n            },\n            \"type\": \"ai\",\n            \"id\": \"run-7df38ef4-c13a-44d0-acd6-3e104c372bbd-0\",\n            \"usage_metadata\": {\n              \"input_tokens\": 527,\n              \"output_tokens\": 597,\n              \"total_tokens\": 1124\n            },\n            \"tool_calls\": [],\n            \"invalid_tool_calls\": []\n          }\n        }\n      }\n    ]\n  ],\n  \"llm_output\": {\n    \"token_usage\": {\n      \"completion_tokens\": 597,\n      \"prompt_tokens\": 527,\n      \"total_tokens\": 1124\n    },\n    \"model_name\": \"gpt-4o-2024-05-13\",\n    \"system_fingerprint\": \"fp_298125635f\"\n  },\n  \"run\": null\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image] [13.62s] Exiting Chain run with output:\n\u001b[0m{\n  \"output\": \"The image displays two matrices labeled \\\"Input 2\\\" and \\\"Output 2.\\\" Below is a detailed description of each matrix:\\n\\n### Input 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a large block of 7s, with specific areas filled with 0s.\\n   - The top-left corner contains a 4x4 block of 0s.\\n   - The bottom-left corner contains a 4x4 block of 0s.\\n   - The rest of the matrix is filled with 7s.\\n\\n4. **Patterns and Alignments:**\\n   - The 0s form two distinct rectangular blocks within the matrix, located at the top-left and bottom-left corners.\\n   - The rest of the matrix is uniformly filled with 7s.\\n\\n### Output 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is also a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a more complex pattern of 0s and 7s compared to the Input 2 matrix.\\n   - The 0s and 7s are arranged in a checkerboard-like pattern in certain areas, creating a more intricate design.\\n\\n4. **Patterns and Alignments:**\\n   - The top-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - The middle of the matrix has a large cross-like structure made of 7s, with 0s filling the spaces around it.\\n   - The bottom-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - There is a noticeable symmetry in the distribution of 0s and 7s, particularly around the central cross-like structure of 7s.\\n\\n### Summary\\n\\n- Both matrices are 10x10 grids with numerical elements (0 and 7).\\n- The Input 2 matrix has large, uniform blocks of 0s and 7s, while the Output 2 matrix has a more complex, patterned arrangement of 0s and 7s.\\n- The transformation from Input 2 to Output 2 involves redistributing the 0s and 7s into a more intricate pattern, suggesting a specific rule or algorithm applied to achieve this transformation.\"\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence] [13.62s] Exiting Chain run with output:\n\u001b[0m{\n  \"output\": \"The image displays two matrices labeled \\\"Input 2\\\" and \\\"Output 2.\\\" Below is a detailed description of each matrix:\\n\\n### Input 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a large block of 7s, with specific areas filled with 0s.\\n   - The top-left corner contains a 4x4 block of 0s.\\n   - The bottom-left corner contains a 4x4 block of 0s.\\n   - The rest of the matrix is filled with 7s.\\n\\n4. **Patterns and Alignments:**\\n   - The 0s form two distinct rectangular blocks within the matrix, located at the top-left and bottom-left corners.\\n   - The rest of the matrix is uniformly filled with 7s.\\n\\n### Output 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is also a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a more complex pattern of 0s and 7s compared to the Input 2 matrix.\\n   - The 0s and 7s are arranged in a checkerboard-like pattern in certain areas, creating a more intricate design.\\n\\n4. **Patterns and Alignments:**\\n   - The top-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - The middle of the matrix has a large cross-like structure made of 7s, with 0s filling the spaces around it.\\n   - The bottom-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - There is a noticeable symmetry in the distribution of 0s and 7s, particularly around the central cross-like structure of 7s.\\n\\n### Summary\\n\\n- Both matrices are 10x10 grids with numerical elements (0 and 7).\\n- The Input 2 matrix has large, uniform blocks of 0s and 7s, while the Output 2 matrix has a more complex, patterned arrangement of 0s and 7s.\\n- The transformation from Input 2 to Output 2 involves redistributing the 0s and 7s into a more intricate pattern, suggesting a specific rule or algorithm applied to achieve this transformation.\"\n}\n\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nAnalyze and identify any recurring patterns or sequences in the matrix elements from the image. Describe how these patterns change from one part of the matrix to another, such as gradual increases, periodic repetitions, or symmetrical arrangements. Mention if certain rows or columns follow specific arithmetic or geometric progressions. Understanding these patterns will assist in predicting the matrix transformations or manipulations that might be applicable.\\n\"\n}\n\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:TransformChain] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nAnalyze and identify any recurring patterns or sequences in the matrix elements from the image. Describe how these patterns change from one part of the matrix to another, such as gradual increases, periodic repetitions, or symmetrical arrangements. Mention if certain rows or columns follow specific arithmetic or geometric progressions. Understanding these patterns will assist in predicting the matrix transformations or manipulations that might be applicable.\\n\"\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:TransformChain] [0ms] Exiting Chain run with output:\n\u001b[0m{\n  \"image\": \"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\"\n}\n\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nAnalyze and identify any recurring patterns or sequences in the matrix elements from the image. Describe how these patterns change from one part of the matrix to another, such as gradual increases, periodic repetitions, or symmetrical arrangements. Mention if certain rows or columns follow specific arithmetic or geometric progressions. Understanding these patterns will assist in predicting the matrix transformations or manipulations that might be applicable.\\n\",\n  \"image\": 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\"\n}\n\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image > llm:ChatOpenAI] Entering LLM run with input:\n\u001b[0m{\n  \"prompts\": [\n    \"Human: [{'type': 'text', 'text': '\\\\nAnalyze and identify any recurring patterns or sequences in the matrix elements from the image. Describe how these patterns change from one part of the matrix to another, such as gradual increases, periodic repetitions, or symmetrical arrangements. Mention if certain rows or columns follow specific arithmetic or geometric progressions. Understanding these patterns will assist in predicting the matrix transformations or manipulations that might be applicable.\\\\n'}, {'type': 'image_url', 'image_url': {'url': 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'}}]\"\n  ]\n}\n\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image > llm:ChatOpenAI] [19.56s] Exiting LLM run with output:\n\u001b[0m{\n  \"generations\": [\n    [\n      {\n        \"text\": \"### Analysis of the Input Matrix\\n\\nThe input matrix is a 7x7 grid with two distinct elements: 0 and 7. The matrix is divided into two main regions:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains mostly 0s except for the bottom-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 7\\n     ```\\n\\n2. **Remaining part of the matrix:**\\n   - The rest of the matrix (excluding the top-left 3x3 block and the bottom-left 3x3 block) is filled with 7s.\\n   - The bottom-left 3x3 block contains mostly 0s except for the top-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     0 0 0 7 7 7 7\\n     0 0 0 0 0 0 0\\n     0 0 0 0 0 0 0\\n     ```\\n\\n### Analysis of the Output Matrix\\n\\nThe output matrix is also a 7x7 grid with two distinct elements: 0 and 7. The matrix appears to be a transformation of the input matrix, with the following noticeable patterns:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains only 0s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 0\\n     ```\\n\\n2. **Middle section:**\\n   - The middle section of the matrix forms a cross pattern with 0s, while the rest are filled with 7s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0 7 7 0 7\\n     0 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     7 7 7 0 7 7 7\\n     7 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     0 0 0 7 7 0 7\\n     ```\\n\\n### Pattern Changes and Transformations\\n\\n- **Top-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Bottom-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Middle Section:**\\n  - Input: Filled with 7s.\\n  - Output: Forms a cross pattern with 0s and the rest are filled with 7s.\\n\\n### Observations\\n\\n1. The transformation involves converting specific regions (3x3 blocks in the corners) to 0s.\\n2. The middle section of the matrix forms a cross pattern with 0s in the output matrix, while the rest of the elements remain 7s.\\n\\n### Conclusion\\n\\nThe transformation from the input matrix to the output matrix involves:\\n1. Converting the top-left and bottom-left 3x3 blocks entirely to 0s.\\n2. Creating a cross pattern in the middle section of the matrix with 0s, while the rest of the elements remain 7s.\\n\\nThis pattern suggests a specific rule for transforming the matrix, which could be useful for predicting future transformations or manipulations of similar matrices.\",\n        \"generation_info\": {\n          \"finish_reason\": \"stop\",\n          \"logprobs\": null\n        },\n        \"type\": \"ChatGeneration\",\n        \"message\": {\n          \"lc\": 1,\n          \"type\": \"constructor\",\n          \"id\": [\n            \"langchain\",\n            \"schema\",\n            \"messages\",\n            \"AIMessage\"\n          ],\n          \"kwargs\": {\n            \"content\": \"### Analysis of the Input Matrix\\n\\nThe input matrix is a 7x7 grid with two distinct elements: 0 and 7. The matrix is divided into two main regions:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains mostly 0s except for the bottom-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 7\\n     ```\\n\\n2. **Remaining part of the matrix:**\\n   - The rest of the matrix (excluding the top-left 3x3 block and the bottom-left 3x3 block) is filled with 7s.\\n   - The bottom-left 3x3 block contains mostly 0s except for the top-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     0 0 0 7 7 7 7\\n     0 0 0 0 0 0 0\\n     0 0 0 0 0 0 0\\n     ```\\n\\n### Analysis of the Output Matrix\\n\\nThe output matrix is also a 7x7 grid with two distinct elements: 0 and 7. The matrix appears to be a transformation of the input matrix, with the following noticeable patterns:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains only 0s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 0\\n     ```\\n\\n2. **Middle section:**\\n   - The middle section of the matrix forms a cross pattern with 0s, while the rest are filled with 7s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0 7 7 0 7\\n     0 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     7 7 7 0 7 7 7\\n     7 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     0 0 0 7 7 0 7\\n     ```\\n\\n### Pattern Changes and Transformations\\n\\n- **Top-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Bottom-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Middle Section:**\\n  - Input: Filled with 7s.\\n  - Output: Forms a cross pattern with 0s and the rest are filled with 7s.\\n\\n### Observations\\n\\n1. The transformation involves converting specific regions (3x3 blocks in the corners) to 0s.\\n2. The middle section of the matrix forms a cross pattern with 0s in the output matrix, while the rest of the elements remain 7s.\\n\\n### Conclusion\\n\\nThe transformation from the input matrix to the output matrix involves:\\n1. Converting the top-left and bottom-left 3x3 blocks entirely to 0s.\\n2. Creating a cross pattern in the middle section of the matrix with 0s, while the rest of the elements remain 7s.\\n\\nThis pattern suggests a specific rule for transforming the matrix, which could be useful for predicting future transformations or manipulations of similar matrices.\",\n            \"response_metadata\": {\n              \"token_usage\": {\n                \"completion_tokens\": 854,\n                \"prompt_tokens\": 507,\n                \"total_tokens\": 1361\n              },\n              \"model_name\": \"gpt-4o-2024-05-13\",\n              \"system_fingerprint\": \"fp_298125635f\",\n              \"finish_reason\": \"stop\",\n              \"logprobs\": null\n            },\n            \"type\": \"ai\",\n            \"id\": \"run-f2e6d1a1-fbaa-40a7-af2d-d5292c189af0-0\",\n            \"usage_metadata\": {\n              \"input_tokens\": 507,\n              \"output_tokens\": 854,\n              \"total_tokens\": 1361\n            },\n            \"tool_calls\": [],\n            \"invalid_tool_calls\": []\n          }\n        }\n      }\n    ]\n  ],\n  \"llm_output\": {\n    \"token_usage\": {\n      \"completion_tokens\": 854,\n      \"prompt_tokens\": 507,\n      \"total_tokens\": 1361\n    },\n    \"model_name\": \"gpt-4o-2024-05-13\",\n    \"system_fingerprint\": \"fp_298125635f\"\n  },\n  \"run\": null\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image] [19.58s] Exiting Chain run with output:\n\u001b[0m{\n  \"output\": \"### Analysis of the Input Matrix\\n\\nThe input matrix is a 7x7 grid with two distinct elements: 0 and 7. The matrix is divided into two main regions:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains mostly 0s except for the bottom-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 7\\n     ```\\n\\n2. **Remaining part of the matrix:**\\n   - The rest of the matrix (excluding the top-left 3x3 block and the bottom-left 3x3 block) is filled with 7s.\\n   - The bottom-left 3x3 block contains mostly 0s except for the top-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     0 0 0 7 7 7 7\\n     0 0 0 0 0 0 0\\n     0 0 0 0 0 0 0\\n     ```\\n\\n### Analysis of the Output Matrix\\n\\nThe output matrix is also a 7x7 grid with two distinct elements: 0 and 7. The matrix appears to be a transformation of the input matrix, with the following noticeable patterns:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains only 0s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 0\\n     ```\\n\\n2. **Middle section:**\\n   - The middle section of the matrix forms a cross pattern with 0s, while the rest are filled with 7s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0 7 7 0 7\\n     0 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     7 7 7 0 7 7 7\\n     7 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     0 0 0 7 7 0 7\\n     ```\\n\\n### Pattern Changes and Transformations\\n\\n- **Top-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Bottom-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Middle Section:**\\n  - Input: Filled with 7s.\\n  - Output: Forms a cross pattern with 0s and the rest are filled with 7s.\\n\\n### Observations\\n\\n1. The transformation involves converting specific regions (3x3 blocks in the corners) to 0s.\\n2. The middle section of the matrix forms a cross pattern with 0s in the output matrix, while the rest of the elements remain 7s.\\n\\n### Conclusion\\n\\nThe transformation from the input matrix to the output matrix involves:\\n1. Converting the top-left and bottom-left 3x3 blocks entirely to 0s.\\n2. Creating a cross pattern in the middle section of the matrix with 0s, while the rest of the elements remain 7s.\\n\\nThis pattern suggests a specific rule for transforming the matrix, which could be useful for predicting future transformations or manipulations of similar matrices.\"\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence] [19.59s] Exiting Chain run with output:\n\u001b[0m{\n  \"output\": \"### Analysis of the Input Matrix\\n\\nThe input matrix is a 7x7 grid with two distinct elements: 0 and 7. The matrix is divided into two main regions:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains mostly 0s except for the bottom-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 7\\n     ```\\n\\n2. **Remaining part of the matrix:**\\n   - The rest of the matrix (excluding the top-left 3x3 block and the bottom-left 3x3 block) is filled with 7s.\\n   - The bottom-left 3x3 block contains mostly 0s except for the top-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     0 0 0 7 7 7 7\\n     0 0 0 0 0 0 0\\n     0 0 0 0 0 0 0\\n     ```\\n\\n### Analysis of the Output Matrix\\n\\nThe output matrix is also a 7x7 grid with two distinct elements: 0 and 7. The matrix appears to be a transformation of the input matrix, with the following noticeable patterns:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains only 0s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 0\\n     ```\\n\\n2. **Middle section:**\\n   - The middle section of the matrix forms a cross pattern with 0s, while the rest are filled with 7s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0 7 7 0 7\\n     0 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     7 7 7 0 7 7 7\\n     7 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     0 0 0 7 7 0 7\\n     ```\\n\\n### Pattern Changes and Transformations\\n\\n- **Top-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Bottom-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Middle Section:**\\n  - Input: Filled with 7s.\\n  - Output: Forms a cross pattern with 0s and the rest are filled with 7s.\\n\\n### Observations\\n\\n1. The transformation involves converting specific regions (3x3 blocks in the corners) to 0s.\\n2. The middle section of the matrix forms a cross pattern with 0s in the output matrix, while the rest of the elements remain 7s.\\n\\n### Conclusion\\n\\nThe transformation from the input matrix to the output matrix involves:\\n1. Converting the top-left and bottom-left 3x3 blocks entirely to 0s.\\n2. Creating a cross pattern in the middle section of the matrix with 0s, while the rest of the elements remain 7s.\\n\\nThis pattern suggests a specific rule for transforming the matrix, which could be useful for predicting future transformations or manipulations of similar matrices.\"\n}\n\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nFormulate comprehensive rules for transforming the matrix based on the observed structures and patterns. Describe step-by-step how to apply these transformations to convert an input matrix into its output form. For instance, if the matrix needs to be rotated, specify the angle and direction; if elements need to be scaled or shifted, provide the scaling factor or shift magnitude. Additionally, propose a generic Python function that can implement these rules dynamically on any given matrix, adjusting to different sizes and patterns.\\n\"\n}\n\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:TransformChain] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nFormulate comprehensive rules for transforming the matrix based on the observed structures and patterns. Describe step-by-step how to apply these transformations to convert an input matrix into its output form. For instance, if the matrix needs to be rotated, specify the angle and direction; if elements need to be scaled or shifted, provide the scaling factor or shift magnitude. Additionally, propose a generic Python function that can implement these rules dynamically on any given matrix, adjusting to different sizes and patterns.\\n\"\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:TransformChain] [0ms] Exiting Chain run with output:\n\u001b[0m{\n  \"image\": 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\"\n}\n\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nFormulate comprehensive rules for transforming the matrix based on the observed structures and patterns. Describe step-by-step how to apply these transformations to convert an input matrix into its output form. For instance, if the matrix needs to be rotated, specify the angle and direction; if elements need to be scaled or shifted, provide the scaling factor or shift magnitude. Additionally, propose a generic Python function that can implement these rules dynamically on any given matrix, adjusting to different sizes and patterns.\\n\",\n  \"image\": 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\"\n}\n\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image > llm:ChatOpenAI] Entering LLM run with input:\n\u001b[0m{\n  \"prompts\": [\n    \"Human: [{'type': 'text', 'text': '\\\\nFormulate comprehensive rules for transforming the matrix based on the observed structures and patterns. Describe step-by-step how to apply these transformations to convert an input matrix into its output form. For instance, if the matrix needs to be rotated, specify the angle and direction; if elements need to be scaled or shifted, provide the scaling factor or shift magnitude. Additionally, propose a generic Python function that can implement these rules dynamically on any given matrix, adjusting to different sizes and patterns.\\\\n'}, {'type': 'image_url', 'image_url': {'url': 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'}}]\"\n  ]\n}\n\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image > llm:ChatOpenAI] [14.78s] Exiting LLM run with output:\n\u001b[0m{\n  \"generations\": [\n    [\n      {\n        \"text\": \"To transform the input matrix into the output matrix based on the observed structures and patterns, we need to follow these steps:\\n\\n1. **Identify the Pattern:**\\n   - The transformation involves creating a specific pattern where the values of `7` form a cross or plus sign in each quadrant of the matrix.\\n\\n2. **Divide the Matrix:**\\n   - Divide the input matrix into quadrants.\\n\\n3. **Create the Cross Pattern in Each Quadrant:**\\n   - For each quadrant, create a cross pattern with `7`s. This means setting the center column and center row of each quadrant to `7`.\\n\\n4. **Handle the Edges:**\\n   - Ensure that the edges of the quadrants are handled correctly to avoid overlapping or incorrect placement of `7`s.\\n\\n### Step-by-Step Transformation:\\n\\n1. **Divide the Matrix into Quadrants:**\\n   - Identify the size of the input matrix (let's assume it's a square matrix of size `n x n`).\\n   - Divide the matrix into four quadrants.\\n\\n2. **Create Cross Pattern in Each Quadrant:**\\n   - For each quadrant, determine the center row and center column.\\n   - Set the center row and center column of each quadrant to `7`.\\n\\n### Python Function Implementation:\\n\\nHere is a Python function that implements the above rules dynamically for any given matrix:\\n\\n```python\\nimport numpy as np\\n\\ndef transform_matrix(input_matrix):\\n    n = input_matrix.shape[0]\\n    output_matrix = np.zeros_like(input_matrix)\\n    \\n    half_n = n // 2\\n    \\n    def create_cross_pattern(matrix, top_left_row, top_left_col, size):\\n        center_row = top_left_row + size // 2\\n        center_col = top_left_col + size // 2\\n        for i in range(size):\\n            matrix[center_row, top_left_col + i] = 7\\n            matrix[top_left_row + i, center_col] = 7\\n    \\n    # Top-left quadrant\\n    create_cross_pattern(output_matrix, 0, 0, half_n)\\n    \\n    # Top-right quadrant\\n    create_cross_pattern(output_matrix, 0, half_n, half_n)\\n    \\n    # Bottom-left quadrant\\n    create_cross_pattern(output_matrix, half_n, 0, half_n)\\n    \\n    # Bottom-right quadrant\\n    create_cross_pattern(output_matrix, half_n, half_n, half_n)\\n    \\n    return output_matrix\\n\\n# Example usage:\\ninput_matrix = np.array([\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix = transform_matrix(input_matrix)\\nprint(output_matrix)\\n```\\n\\n### Explanation:\\n\\n- **create_cross_pattern Function:** This function creates a cross pattern of `7`s in a specified quadrant of the matrix.\\n- **transform_matrix Function:** This function divides the input matrix into four quadrants and applies the cross pattern to each quadrant.\\n\\nThis approach ensures that the transformation is applied dynamically to any given matrix, adjusting for different sizes and patterns.\",\n        \"generation_info\": {\n          \"finish_reason\": \"stop\",\n          \"logprobs\": null\n        },\n        \"type\": \"ChatGeneration\",\n        \"message\": {\n          \"lc\": 1,\n          \"type\": \"constructor\",\n          \"id\": [\n            \"langchain\",\n            \"schema\",\n            \"messages\",\n            \"AIMessage\"\n          ],\n          \"kwargs\": {\n            \"content\": \"To transform the input matrix into the output matrix based on the observed structures and patterns, we need to follow these steps:\\n\\n1. **Identify the Pattern:**\\n   - The transformation involves creating a specific pattern where the values of `7` form a cross or plus sign in each quadrant of the matrix.\\n\\n2. **Divide the Matrix:**\\n   - Divide the input matrix into quadrants.\\n\\n3. **Create the Cross Pattern in Each Quadrant:**\\n   - For each quadrant, create a cross pattern with `7`s. This means setting the center column and center row of each quadrant to `7`.\\n\\n4. **Handle the Edges:**\\n   - Ensure that the edges of the quadrants are handled correctly to avoid overlapping or incorrect placement of `7`s.\\n\\n### Step-by-Step Transformation:\\n\\n1. **Divide the Matrix into Quadrants:**\\n   - Identify the size of the input matrix (let's assume it's a square matrix of size `n x n`).\\n   - Divide the matrix into four quadrants.\\n\\n2. **Create Cross Pattern in Each Quadrant:**\\n   - For each quadrant, determine the center row and center column.\\n   - Set the center row and center column of each quadrant to `7`.\\n\\n### Python Function Implementation:\\n\\nHere is a Python function that implements the above rules dynamically for any given matrix:\\n\\n```python\\nimport numpy as np\\n\\ndef transform_matrix(input_matrix):\\n    n = input_matrix.shape[0]\\n    output_matrix = np.zeros_like(input_matrix)\\n    \\n    half_n = n // 2\\n    \\n    def create_cross_pattern(matrix, top_left_row, top_left_col, size):\\n        center_row = top_left_row + size // 2\\n        center_col = top_left_col + size // 2\\n        for i in range(size):\\n            matrix[center_row, top_left_col + i] = 7\\n            matrix[top_left_row + i, center_col] = 7\\n    \\n    # Top-left quadrant\\n    create_cross_pattern(output_matrix, 0, 0, half_n)\\n    \\n    # Top-right quadrant\\n    create_cross_pattern(output_matrix, 0, half_n, half_n)\\n    \\n    # Bottom-left quadrant\\n    create_cross_pattern(output_matrix, half_n, 0, half_n)\\n    \\n    # Bottom-right quadrant\\n    create_cross_pattern(output_matrix, half_n, half_n, half_n)\\n    \\n    return output_matrix\\n\\n# Example usage:\\ninput_matrix = np.array([\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix = transform_matrix(input_matrix)\\nprint(output_matrix)\\n```\\n\\n### Explanation:\\n\\n- **create_cross_pattern Function:** This function creates a cross pattern of `7`s in a specified quadrant of the matrix.\\n- **transform_matrix Function:** This function divides the input matrix into four quadrants and applies the cross pattern to each quadrant.\\n\\nThis approach ensures that the transformation is applied dynamically to any given matrix, adjusting for different sizes and patterns.\",\n            \"response_metadata\": {\n              \"token_usage\": {\n                \"completion_tokens\": 791,\n                \"prompt_tokens\": 527,\n                \"total_tokens\": 1318\n              },\n              \"model_name\": \"gpt-4o-2024-05-13\",\n              \"system_fingerprint\": \"fp_298125635f\",\n              \"finish_reason\": \"stop\",\n              \"logprobs\": null\n            },\n            \"type\": \"ai\",\n            \"id\": \"run-e303a730-7d2a-4088-973f-6f5bb1bf33b1-0\",\n            \"usage_metadata\": {\n              \"input_tokens\": 527,\n              \"output_tokens\": 791,\n              \"total_tokens\": 1318\n            },\n            \"tool_calls\": [],\n            \"invalid_tool_calls\": []\n          }\n        }\n      }\n    ]\n  ],\n  \"llm_output\": {\n    \"token_usage\": {\n      \"completion_tokens\": 791,\n      \"prompt_tokens\": 527,\n      \"total_tokens\": 1318\n    },\n    \"model_name\": \"gpt-4o-2024-05-13\",\n    \"system_fingerprint\": \"fp_298125635f\"\n  },\n  \"run\": null\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image] [14.80s] Exiting Chain run with output:\n\u001b[0m{\n  \"output\": \"To transform the input matrix into the output matrix based on the observed structures and patterns, we need to follow these steps:\\n\\n1. **Identify the Pattern:**\\n   - The transformation involves creating a specific pattern where the values of `7` form a cross or plus sign in each quadrant of the matrix.\\n\\n2. **Divide the Matrix:**\\n   - Divide the input matrix into quadrants.\\n\\n3. **Create the Cross Pattern in Each Quadrant:**\\n   - For each quadrant, create a cross pattern with `7`s. This means setting the center column and center row of each quadrant to `7`.\\n\\n4. **Handle the Edges:**\\n   - Ensure that the edges of the quadrants are handled correctly to avoid overlapping or incorrect placement of `7`s.\\n\\n### Step-by-Step Transformation:\\n\\n1. **Divide the Matrix into Quadrants:**\\n   - Identify the size of the input matrix (let's assume it's a square matrix of size `n x n`).\\n   - Divide the matrix into four quadrants.\\n\\n2. **Create Cross Pattern in Each Quadrant:**\\n   - For each quadrant, determine the center row and center column.\\n   - Set the center row and center column of each quadrant to `7`.\\n\\n### Python Function Implementation:\\n\\nHere is a Python function that implements the above rules dynamically for any given matrix:\\n\\n```python\\nimport numpy as np\\n\\ndef transform_matrix(input_matrix):\\n    n = input_matrix.shape[0]\\n    output_matrix = np.zeros_like(input_matrix)\\n    \\n    half_n = n // 2\\n    \\n    def create_cross_pattern(matrix, top_left_row, top_left_col, size):\\n        center_row = top_left_row + size // 2\\n        center_col = top_left_col + size // 2\\n        for i in range(size):\\n            matrix[center_row, top_left_col + i] = 7\\n            matrix[top_left_row + i, center_col] = 7\\n    \\n    # Top-left quadrant\\n    create_cross_pattern(output_matrix, 0, 0, half_n)\\n    \\n    # Top-right quadrant\\n    create_cross_pattern(output_matrix, 0, half_n, half_n)\\n    \\n    # Bottom-left quadrant\\n    create_cross_pattern(output_matrix, half_n, 0, half_n)\\n    \\n    # Bottom-right quadrant\\n    create_cross_pattern(output_matrix, half_n, half_n, half_n)\\n    \\n    return output_matrix\\n\\n# Example usage:\\ninput_matrix = np.array([\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix = transform_matrix(input_matrix)\\nprint(output_matrix)\\n```\\n\\n### Explanation:\\n\\n- **create_cross_pattern Function:** This function creates a cross pattern of `7`s in a specified quadrant of the matrix.\\n- **transform_matrix Function:** This function divides the input matrix into four quadrants and applies the cross pattern to each quadrant.\\n\\nThis approach ensures that the transformation is applied dynamically to any given matrix, adjusting for different sizes and patterns.\"\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence] [14.80s] Exiting Chain run with output:\n\u001b[0m{\n  \"output\": \"To transform the input matrix into the output matrix based on the observed structures and patterns, we need to follow these steps:\\n\\n1. **Identify the Pattern:**\\n   - The transformation involves creating a specific pattern where the values of `7` form a cross or plus sign in each quadrant of the matrix.\\n\\n2. **Divide the Matrix:**\\n   - Divide the input matrix into quadrants.\\n\\n3. **Create the Cross Pattern in Each Quadrant:**\\n   - For each quadrant, create a cross pattern with `7`s. This means setting the center column and center row of each quadrant to `7`.\\n\\n4. **Handle the Edges:**\\n   - Ensure that the edges of the quadrants are handled correctly to avoid overlapping or incorrect placement of `7`s.\\n\\n### Step-by-Step Transformation:\\n\\n1. **Divide the Matrix into Quadrants:**\\n   - Identify the size of the input matrix (let's assume it's a square matrix of size `n x n`).\\n   - Divide the matrix into four quadrants.\\n\\n2. **Create Cross Pattern in Each Quadrant:**\\n   - For each quadrant, determine the center row and center column.\\n   - Set the center row and center column of each quadrant to `7`.\\n\\n### Python Function Implementation:\\n\\nHere is a Python function that implements the above rules dynamically for any given matrix:\\n\\n```python\\nimport numpy as np\\n\\ndef transform_matrix(input_matrix):\\n    n = input_matrix.shape[0]\\n    output_matrix = np.zeros_like(input_matrix)\\n    \\n    half_n = n // 2\\n    \\n    def create_cross_pattern(matrix, top_left_row, top_left_col, size):\\n        center_row = top_left_row + size // 2\\n        center_col = top_left_col + size // 2\\n        for i in range(size):\\n            matrix[center_row, top_left_col + i] = 7\\n            matrix[top_left_row + i, center_col] = 7\\n    \\n    # Top-left quadrant\\n    create_cross_pattern(output_matrix, 0, 0, half_n)\\n    \\n    # Top-right quadrant\\n    create_cross_pattern(output_matrix, 0, half_n, half_n)\\n    \\n    # Bottom-left quadrant\\n    create_cross_pattern(output_matrix, half_n, 0, half_n)\\n    \\n    # Bottom-right quadrant\\n    create_cross_pattern(output_matrix, half_n, half_n, half_n)\\n    \\n    return output_matrix\\n\\n# Example usage:\\ninput_matrix = np.array([\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix = transform_matrix(input_matrix)\\nprint(output_matrix)\\n```\\n\\n### Explanation:\\n\\n- **create_cross_pattern Function:** This function creates a cross pattern of `7`s in a specified quadrant of the matrix.\\n- **transform_matrix Function:** This function divides the input matrix into four quadrants and applies the cross pattern to each quadrant.\\n\\nThis approach ensures that the transformation is applied dynamically to any given matrix, adjusting for different sizes and patterns.\"\n}\n\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nGiven the following information about the puzzle in the image, generate code to solve the puzzle in python.\\nStructural Information: The image displays two matrices labeled \\\"Input 2\\\" and \\\"Output 2.\\\" Below is a detailed description of each matrix:\\n\\n### Input 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a large block of 7s, with specific areas filled with 0s.\\n   - The top-left corner contains a 4x4 block of 0s.\\n   - The bottom-left corner contains a 4x4 block of 0s.\\n   - The rest of the matrix is filled with 7s.\\n\\n4. **Patterns and Alignments:**\\n   - The 0s form two distinct rectangular blocks within the matrix, located at the top-left and bottom-left corners.\\n   - The rest of the matrix is uniformly filled with 7s.\\n\\n### Output 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is also a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a more complex pattern of 0s and 7s compared to the Input 2 matrix.\\n   - The 0s and 7s are arranged in a checkerboard-like pattern in certain areas, creating a more intricate design.\\n\\n4. **Patterns and Alignments:**\\n   - The top-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - The middle of the matrix has a large cross-like structure made of 7s, with 0s filling the spaces around it.\\n   - The bottom-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - There is a noticeable symmetry in the distribution of 0s and 7s, particularly around the central cross-like structure of 7s.\\n\\n### Summary\\n\\n- Both matrices are 10x10 grids with numerical elements (0 and 7).\\n- The Input 2 matrix has large, uniform blocks of 0s and 7s, while the Output 2 matrix has a more complex, patterned arrangement of 0s and 7s.\\n- The transformation from Input 2 to Output 2 involves redistributing the 0s and 7s into a more intricate pattern, suggesting a specific rule or algorithm applied to achieve this transformation.\\nPattern Based Information: ### Analysis of the Input Matrix\\n\\nThe input matrix is a 7x7 grid with two distinct elements: 0 and 7. The matrix is divided into two main regions:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains mostly 0s except for the bottom-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 7\\n     ```\\n\\n2. **Remaining part of the matrix:**\\n   - The rest of the matrix (excluding the top-left 3x3 block and the bottom-left 3x3 block) is filled with 7s.\\n   - The bottom-left 3x3 block contains mostly 0s except for the top-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     0 0 0 7 7 7 7\\n     0 0 0 0 0 0 0\\n     0 0 0 0 0 0 0\\n     ```\\n\\n### Analysis of the Output Matrix\\n\\nThe output matrix is also a 7x7 grid with two distinct elements: 0 and 7. The matrix appears to be a transformation of the input matrix, with the following noticeable patterns:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains only 0s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 0\\n     ```\\n\\n2. **Middle section:**\\n   - The middle section of the matrix forms a cross pattern with 0s, while the rest are filled with 7s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0 7 7 0 7\\n     0 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     7 7 7 0 7 7 7\\n     7 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     0 0 0 7 7 0 7\\n     ```\\n\\n### Pattern Changes and Transformations\\n\\n- **Top-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Bottom-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Middle Section:**\\n  - Input: Filled with 7s.\\n  - Output: Forms a cross pattern with 0s and the rest are filled with 7s.\\n\\n### Observations\\n\\n1. The transformation involves converting specific regions (3x3 blocks in the corners) to 0s.\\n2. The middle section of the matrix forms a cross pattern with 0s in the output matrix, while the rest of the elements remain 7s.\\n\\n### Conclusion\\n\\nThe transformation from the input matrix to the output matrix involves:\\n1. Converting the top-left and bottom-left 3x3 blocks entirely to 0s.\\n2. Creating a cross pattern in the middle section of the matrix with 0s, while the rest of the elements remain 7s.\\n\\nThis pattern suggests a specific rule for transforming the matrix, which could be useful for predicting future transformations or manipulations of similar matrices.\\nTransformation Information: To transform the input matrix into the output matrix based on the observed structures and patterns, we need to follow these steps:\\n\\n1. **Identify the Pattern:**\\n   - The transformation involves creating a specific pattern where the values of `7` form a cross or plus sign in each quadrant of the matrix.\\n\\n2. **Divide the Matrix:**\\n   - Divide the input matrix into quadrants.\\n\\n3. **Create the Cross Pattern in Each Quadrant:**\\n   - For each quadrant, create a cross pattern with `7`s. This means setting the center column and center row of each quadrant to `7`.\\n\\n4. **Handle the Edges:**\\n   - Ensure that the edges of the quadrants are handled correctly to avoid overlapping or incorrect placement of `7`s.\\n\\n### Step-by-Step Transformation:\\n\\n1. **Divide the Matrix into Quadrants:**\\n   - Identify the size of the input matrix (let's assume it's a square matrix of size `n x n`).\\n   - Divide the matrix into four quadrants.\\n\\n2. **Create Cross Pattern in Each Quadrant:**\\n   - For each quadrant, determine the center row and center column.\\n   - Set the center row and center column of each quadrant to `7`.\\n\\n### Python Function Implementation:\\n\\nHere is a Python function that implements the above rules dynamically for any given matrix:\\n\\n```python\\nimport numpy as np\\n\\ndef transform_matrix(input_matrix):\\n    n = input_matrix.shape[0]\\n    output_matrix = np.zeros_like(input_matrix)\\n    \\n    half_n = n // 2\\n    \\n    def create_cross_pattern(matrix, top_left_row, top_left_col, size):\\n        center_row = top_left_row + size // 2\\n        center_col = top_left_col + size // 2\\n        for i in range(size):\\n            matrix[center_row, top_left_col + i] = 7\\n            matrix[top_left_row + i, center_col] = 7\\n    \\n    # Top-left quadrant\\n    create_cross_pattern(output_matrix, 0, 0, half_n)\\n    \\n    # Top-right quadrant\\n    create_cross_pattern(output_matrix, 0, half_n, half_n)\\n    \\n    # Bottom-left quadrant\\n    create_cross_pattern(output_matrix, half_n, 0, half_n)\\n    \\n    # Bottom-right quadrant\\n    create_cross_pattern(output_matrix, half_n, half_n, half_n)\\n    \\n    return output_matrix\\n\\n# Example usage:\\ninput_matrix = np.array([\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix = transform_matrix(input_matrix)\\nprint(output_matrix)\\n```\\n\\n### Explanation:\\n\\n- **create_cross_pattern Function:** This function creates a cross pattern of `7`s in a specified quadrant of the matrix.\\n- **transform_matrix Function:** This function divides the input matrix into four quadrants and applies the cross pattern to each quadrant.\\n\\nThis approach ensures that the transformation is applied dynamically to any given matrix, adjusting for different sizes and patterns.\\n\\nGiven the structural, pattern-based, and transformational information, generate a Python script to solve the matrix puzzle depicted in the image. Include functions for:\\n- Reading and processing input matrices of varying sizes and compositions, ensuring accurate interpretation of data.\\n- Dynamically applying the identified transformation rules to the input matrix to produce the output matrix.\\n- Testing the transformation function with different matrix sizes and patterns. Provide test cases with expected outputs to verify the accuracy and robustness of the transformation rules.\\nFocus on adaptability and error handling to manage different matrix configurations and potential data anomalies effectively.\\n\"\n}\n\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:TransformChain] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nGiven the following information about the puzzle in the image, generate code to solve the puzzle in python.\\nStructural Information: The image displays two matrices labeled \\\"Input 2\\\" and \\\"Output 2.\\\" Below is a detailed description of each matrix:\\n\\n### Input 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a large block of 7s, with specific areas filled with 0s.\\n   - The top-left corner contains a 4x4 block of 0s.\\n   - The bottom-left corner contains a 4x4 block of 0s.\\n   - The rest of the matrix is filled with 7s.\\n\\n4. **Patterns and Alignments:**\\n   - The 0s form two distinct rectangular blocks within the matrix, located at the top-left and bottom-left corners.\\n   - The rest of the matrix is uniformly filled with 7s.\\n\\n### Output 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is also a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a more complex pattern of 0s and 7s compared to the Input 2 matrix.\\n   - The 0s and 7s are arranged in a checkerboard-like pattern in certain areas, creating a more intricate design.\\n\\n4. **Patterns and Alignments:**\\n   - The top-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - The middle of the matrix has a large cross-like structure made of 7s, with 0s filling the spaces around it.\\n   - The bottom-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - There is a noticeable symmetry in the distribution of 0s and 7s, particularly around the central cross-like structure of 7s.\\n\\n### Summary\\n\\n- Both matrices are 10x10 grids with numerical elements (0 and 7).\\n- The Input 2 matrix has large, uniform blocks of 0s and 7s, while the Output 2 matrix has a more complex, patterned arrangement of 0s and 7s.\\n- The transformation from Input 2 to Output 2 involves redistributing the 0s and 7s into a more intricate pattern, suggesting a specific rule or algorithm applied to achieve this transformation.\\nPattern Based Information: ### Analysis of the Input Matrix\\n\\nThe input matrix is a 7x7 grid with two distinct elements: 0 and 7. The matrix is divided into two main regions:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains mostly 0s except for the bottom-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 7\\n     ```\\n\\n2. **Remaining part of the matrix:**\\n   - The rest of the matrix (excluding the top-left 3x3 block and the bottom-left 3x3 block) is filled with 7s.\\n   - The bottom-left 3x3 block contains mostly 0s except for the top-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     0 0 0 7 7 7 7\\n     0 0 0 0 0 0 0\\n     0 0 0 0 0 0 0\\n     ```\\n\\n### Analysis of the Output Matrix\\n\\nThe output matrix is also a 7x7 grid with two distinct elements: 0 and 7. The matrix appears to be a transformation of the input matrix, with the following noticeable patterns:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains only 0s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 0\\n     ```\\n\\n2. **Middle section:**\\n   - The middle section of the matrix forms a cross pattern with 0s, while the rest are filled with 7s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0 7 7 0 7\\n     0 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     7 7 7 0 7 7 7\\n     7 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     0 0 0 7 7 0 7\\n     ```\\n\\n### Pattern Changes and Transformations\\n\\n- **Top-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Bottom-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Middle Section:**\\n  - Input: Filled with 7s.\\n  - Output: Forms a cross pattern with 0s and the rest are filled with 7s.\\n\\n### Observations\\n\\n1. The transformation involves converting specific regions (3x3 blocks in the corners) to 0s.\\n2. The middle section of the matrix forms a cross pattern with 0s in the output matrix, while the rest of the elements remain 7s.\\n\\n### Conclusion\\n\\nThe transformation from the input matrix to the output matrix involves:\\n1. Converting the top-left and bottom-left 3x3 blocks entirely to 0s.\\n2. Creating a cross pattern in the middle section of the matrix with 0s, while the rest of the elements remain 7s.\\n\\nThis pattern suggests a specific rule for transforming the matrix, which could be useful for predicting future transformations or manipulations of similar matrices.\\nTransformation Information: To transform the input matrix into the output matrix based on the observed structures and patterns, we need to follow these steps:\\n\\n1. **Identify the Pattern:**\\n   - The transformation involves creating a specific pattern where the values of `7` form a cross or plus sign in each quadrant of the matrix.\\n\\n2. **Divide the Matrix:**\\n   - Divide the input matrix into quadrants.\\n\\n3. **Create the Cross Pattern in Each Quadrant:**\\n   - For each quadrant, create a cross pattern with `7`s. This means setting the center column and center row of each quadrant to `7`.\\n\\n4. **Handle the Edges:**\\n   - Ensure that the edges of the quadrants are handled correctly to avoid overlapping or incorrect placement of `7`s.\\n\\n### Step-by-Step Transformation:\\n\\n1. **Divide the Matrix into Quadrants:**\\n   - Identify the size of the input matrix (let's assume it's a square matrix of size `n x n`).\\n   - Divide the matrix into four quadrants.\\n\\n2. **Create Cross Pattern in Each Quadrant:**\\n   - For each quadrant, determine the center row and center column.\\n   - Set the center row and center column of each quadrant to `7`.\\n\\n### Python Function Implementation:\\n\\nHere is a Python function that implements the above rules dynamically for any given matrix:\\n\\n```python\\nimport numpy as np\\n\\ndef transform_matrix(input_matrix):\\n    n = input_matrix.shape[0]\\n    output_matrix = np.zeros_like(input_matrix)\\n    \\n    half_n = n // 2\\n    \\n    def create_cross_pattern(matrix, top_left_row, top_left_col, size):\\n        center_row = top_left_row + size // 2\\n        center_col = top_left_col + size // 2\\n        for i in range(size):\\n            matrix[center_row, top_left_col + i] = 7\\n            matrix[top_left_row + i, center_col] = 7\\n    \\n    # Top-left quadrant\\n    create_cross_pattern(output_matrix, 0, 0, half_n)\\n    \\n    # Top-right quadrant\\n    create_cross_pattern(output_matrix, 0, half_n, half_n)\\n    \\n    # Bottom-left quadrant\\n    create_cross_pattern(output_matrix, half_n, 0, half_n)\\n    \\n    # Bottom-right quadrant\\n    create_cross_pattern(output_matrix, half_n, half_n, half_n)\\n    \\n    return output_matrix\\n\\n# Example usage:\\ninput_matrix = np.array([\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix = transform_matrix(input_matrix)\\nprint(output_matrix)\\n```\\n\\n### Explanation:\\n\\n- **create_cross_pattern Function:** This function creates a cross pattern of `7`s in a specified quadrant of the matrix.\\n- **transform_matrix Function:** This function divides the input matrix into four quadrants and applies the cross pattern to each quadrant.\\n\\nThis approach ensures that the transformation is applied dynamically to any given matrix, adjusting for different sizes and patterns.\\n\\nGiven the structural, pattern-based, and transformational information, generate a Python script to solve the matrix puzzle depicted in the image. Include functions for:\\n- Reading and processing input matrices of varying sizes and compositions, ensuring accurate interpretation of data.\\n- Dynamically applying the identified transformation rules to the input matrix to produce the output matrix.\\n- Testing the transformation function with different matrix sizes and patterns. Provide test cases with expected outputs to verify the accuracy and robustness of the transformation rules.\\nFocus on adaptability and error handling to manage different matrix configurations and potential data anomalies effectively.\\n\"\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:TransformChain] [0ms] Exiting Chain run with output:\n\u001b[0m{\n  \"image\": 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\"\n}\n\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nGiven the following information about the puzzle in the image, generate code to solve the puzzle in python.\\nStructural Information: The image displays two matrices labeled \\\"Input 2\\\" and \\\"Output 2.\\\" Below is a detailed description of each matrix:\\n\\n### Input 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a large block of 7s, with specific areas filled with 0s.\\n   - The top-left corner contains a 4x4 block of 0s.\\n   - The bottom-left corner contains a 4x4 block of 0s.\\n   - The rest of the matrix is filled with 7s.\\n\\n4. **Patterns and Alignments:**\\n   - The 0s form two distinct rectangular blocks within the matrix, located at the top-left and bottom-left corners.\\n   - The rest of the matrix is uniformly filled with 7s.\\n\\n### Output 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is also a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a more complex pattern of 0s and 7s compared to the Input 2 matrix.\\n   - The 0s and 7s are arranged in a checkerboard-like pattern in certain areas, creating a more intricate design.\\n\\n4. **Patterns and Alignments:**\\n   - The top-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - The middle of the matrix has a large cross-like structure made of 7s, with 0s filling the spaces around it.\\n   - The bottom-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - There is a noticeable symmetry in the distribution of 0s and 7s, particularly around the central cross-like structure of 7s.\\n\\n### Summary\\n\\n- Both matrices are 10x10 grids with numerical elements (0 and 7).\\n- The Input 2 matrix has large, uniform blocks of 0s and 7s, while the Output 2 matrix has a more complex, patterned arrangement of 0s and 7s.\\n- The transformation from Input 2 to Output 2 involves redistributing the 0s and 7s into a more intricate pattern, suggesting a specific rule or algorithm applied to achieve this transformation.\\nPattern Based Information: ### Analysis of the Input Matrix\\n\\nThe input matrix is a 7x7 grid with two distinct elements: 0 and 7. The matrix is divided into two main regions:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains mostly 0s except for the bottom-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 7\\n     ```\\n\\n2. **Remaining part of the matrix:**\\n   - The rest of the matrix (excluding the top-left 3x3 block and the bottom-left 3x3 block) is filled with 7s.\\n   - The bottom-left 3x3 block contains mostly 0s except for the top-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     0 0 0 7 7 7 7\\n     0 0 0 0 0 0 0\\n     0 0 0 0 0 0 0\\n     ```\\n\\n### Analysis of the Output Matrix\\n\\nThe output matrix is also a 7x7 grid with two distinct elements: 0 and 7. The matrix appears to be a transformation of the input matrix, with the following noticeable patterns:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains only 0s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 0\\n     ```\\n\\n2. **Middle section:**\\n   - The middle section of the matrix forms a cross pattern with 0s, while the rest are filled with 7s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0 7 7 0 7\\n     0 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     7 7 7 0 7 7 7\\n     7 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     0 0 0 7 7 0 7\\n     ```\\n\\n### Pattern Changes and Transformations\\n\\n- **Top-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Bottom-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Middle Section:**\\n  - Input: Filled with 7s.\\n  - Output: Forms a cross pattern with 0s and the rest are filled with 7s.\\n\\n### Observations\\n\\n1. The transformation involves converting specific regions (3x3 blocks in the corners) to 0s.\\n2. The middle section of the matrix forms a cross pattern with 0s in the output matrix, while the rest of the elements remain 7s.\\n\\n### Conclusion\\n\\nThe transformation from the input matrix to the output matrix involves:\\n1. Converting the top-left and bottom-left 3x3 blocks entirely to 0s.\\n2. Creating a cross pattern in the middle section of the matrix with 0s, while the rest of the elements remain 7s.\\n\\nThis pattern suggests a specific rule for transforming the matrix, which could be useful for predicting future transformations or manipulations of similar matrices.\\nTransformation Information: To transform the input matrix into the output matrix based on the observed structures and patterns, we need to follow these steps:\\n\\n1. **Identify the Pattern:**\\n   - The transformation involves creating a specific pattern where the values of `7` form a cross or plus sign in each quadrant of the matrix.\\n\\n2. **Divide the Matrix:**\\n   - Divide the input matrix into quadrants.\\n\\n3. **Create the Cross Pattern in Each Quadrant:**\\n   - For each quadrant, create a cross pattern with `7`s. This means setting the center column and center row of each quadrant to `7`.\\n\\n4. **Handle the Edges:**\\n   - Ensure that the edges of the quadrants are handled correctly to avoid overlapping or incorrect placement of `7`s.\\n\\n### Step-by-Step Transformation:\\n\\n1. **Divide the Matrix into Quadrants:**\\n   - Identify the size of the input matrix (let's assume it's a square matrix of size `n x n`).\\n   - Divide the matrix into four quadrants.\\n\\n2. **Create Cross Pattern in Each Quadrant:**\\n   - For each quadrant, determine the center row and center column.\\n   - Set the center row and center column of each quadrant to `7`.\\n\\n### Python Function Implementation:\\n\\nHere is a Python function that implements the above rules dynamically for any given matrix:\\n\\n```python\\nimport numpy as np\\n\\ndef transform_matrix(input_matrix):\\n    n = input_matrix.shape[0]\\n    output_matrix = np.zeros_like(input_matrix)\\n    \\n    half_n = n // 2\\n    \\n    def create_cross_pattern(matrix, top_left_row, top_left_col, size):\\n        center_row = top_left_row + size // 2\\n        center_col = top_left_col + size // 2\\n        for i in range(size):\\n            matrix[center_row, top_left_col + i] = 7\\n            matrix[top_left_row + i, center_col] = 7\\n    \\n    # Top-left quadrant\\n    create_cross_pattern(output_matrix, 0, 0, half_n)\\n    \\n    # Top-right quadrant\\n    create_cross_pattern(output_matrix, 0, half_n, half_n)\\n    \\n    # Bottom-left quadrant\\n    create_cross_pattern(output_matrix, half_n, 0, half_n)\\n    \\n    # Bottom-right quadrant\\n    create_cross_pattern(output_matrix, half_n, half_n, half_n)\\n    \\n    return output_matrix\\n\\n# Example usage:\\ninput_matrix = np.array([\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix = transform_matrix(input_matrix)\\nprint(output_matrix)\\n```\\n\\n### Explanation:\\n\\n- **create_cross_pattern Function:** This function creates a cross pattern of `7`s in a specified quadrant of the matrix.\\n- **transform_matrix Function:** This function divides the input matrix into four quadrants and applies the cross pattern to each quadrant.\\n\\nThis approach ensures that the transformation is applied dynamically to any given matrix, adjusting for different sizes and patterns.\\n\\nGiven the structural, pattern-based, and transformational information, generate a Python script to solve the matrix puzzle depicted in the image. Include functions for:\\n- Reading and processing input matrices of varying sizes and compositions, ensuring accurate interpretation of data.\\n- Dynamically applying the identified transformation rules to the input matrix to produce the output matrix.\\n- Testing the transformation function with different matrix sizes and patterns. Provide test cases with expected outputs to verify the accuracy and robustness of the transformation rules.\\nFocus on adaptability and error handling to manage different matrix configurations and potential data anomalies effectively.\\n\",\n  \"image\": 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\"\n}\n\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image > llm:ChatOpenAI] Entering LLM run with input:\n\u001b[0m{\n  \"prompts\": [\n    \"Human: [{'type': 'text', 'text': '\\\\nGiven the following information about the puzzle in the image, generate code to solve the puzzle in python.\\\\nStructural Information: The image displays two matrices labeled \\\"Input 2\\\" and \\\"Output 2.\\\" Below is a detailed description of each matrix:\\\\n\\\\n### Input 2 Matrix\\\\n\\\\n1. **Size and Dimensions:**\\\\n   - The matrix is a 10x10 grid (10 rows and 10 columns).\\\\n\\\\n2. **Type of Elements:**\\\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\\\n\\\\n3. **Arrangement of Elements:**\\\\n   - The matrix has a large block of 7s, with specific areas filled with 0s.\\\\n   - The top-left corner contains a 4x4 block of 0s.\\\\n   - The bottom-left corner contains a 4x4 block of 0s.\\\\n   - The rest of the matrix is filled with 7s.\\\\n\\\\n4. **Patterns and Alignments:**\\\\n   - The 0s form two distinct rectangular blocks within the matrix, located at the top-left and bottom-left corners.\\\\n   - The rest of the matrix is uniformly filled with 7s.\\\\n\\\\n### Output 2 Matrix\\\\n\\\\n1. **Size and Dimensions:**\\\\n   - The matrix is also a 10x10 grid (10 rows and 10 columns).\\\\n\\\\n2. **Type of Elements:**\\\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\\\n\\\\n3. **Arrangement of Elements:**\\\\n   - The matrix has a more complex pattern of 0s and 7s compared to the Input 2 matrix.\\\\n   - The 0s and 7s are arranged in a checkerboard-like pattern in certain areas, creating a more intricate design.\\\\n\\\\n4. **Patterns and Alignments:**\\\\n   - The top-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\\\n   - The middle of the matrix has a large cross-like structure made of 7s, with 0s filling the spaces around it.\\\\n   - The bottom-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\\\n   - There is a noticeable symmetry in the distribution of 0s and 7s, particularly around the central cross-like structure of 7s.\\\\n\\\\n### Summary\\\\n\\\\n- Both matrices are 10x10 grids with numerical elements (0 and 7).\\\\n- The Input 2 matrix has large, uniform blocks of 0s and 7s, while the Output 2 matrix has a more complex, patterned arrangement of 0s and 7s.\\\\n- The transformation from Input 2 to Output 2 involves redistributing the 0s and 7s into a more intricate pattern, suggesting a specific rule or algorithm applied to achieve this transformation.\\\\nPattern Based Information: ### Analysis of the Input Matrix\\\\n\\\\nThe input matrix is a 7x7 grid with two distinct elements: 0 and 7. The matrix is divided into two main regions:\\\\n\\\\n1. **Top-left corner (3x3 block):**\\\\n   - The top-left 3x3 block contains mostly 0s except for the bottom-right cell, which contains a 7.\\\\n   - The elements are arranged as follows:\\\\n     ```\\\\n     0 0 0\\\\n     0 0 0\\\\n     0 0 7\\\\n     ```\\\\n\\\\n2. **Remaining part of the matrix:**\\\\n   - The rest of the matrix (excluding the top-left 3x3 block and the bottom-left 3x3 block) is filled with 7s.\\\\n   - The bottom-left 3x3 block contains mostly 0s except for the top-right cell, which contains a 7.\\\\n   - The elements are arranged as follows:\\\\n     ```\\\\n     7 7 7 7 7 7 7\\\\n     7 7 7 7 7 7 7\\\\n     7 7 7 7 7 7 7\\\\n     7 7 7 7 7 7 7\\\\n     0 0 0 7 7 7 7\\\\n     0 0 0 0 0 0 0\\\\n     0 0 0 0 0 0 0\\\\n     ```\\\\n\\\\n### Analysis of the Output Matrix\\\\n\\\\nThe output matrix is also a 7x7 grid with two distinct elements: 0 and 7. The matrix appears to be a transformation of the input matrix, with the following noticeable patterns:\\\\n\\\\n1. **Top-left corner (3x3 block):**\\\\n   - The top-left 3x3 block contains only 0s.\\\\n   - The elements are arranged as follows:\\\\n     ```\\\\n     0 0 0\\\\n     0 0 0\\\\n     0 0 0\\\\n     ```\\\\n\\\\n2. **Middle section:**\\\\n   - The middle section of the matrix forms a cross pattern with 0s, while the rest are filled with 7s.\\\\n   - The elements are arranged as follows:\\\\n     ```\\\\n     0 0 0 7 7 0 7\\\\n     0 7 7 7 7 7 7\\\\n     0 7 7 7 7 7 7\\\\n     7 7 7 0 7 7 7\\\\n     7 7 7 7 7 7 7\\\\n     0 7 7 7 7 7 7\\\\n     0 0 0 7 7 0 7\\\\n     ```\\\\n\\\\n### Pattern Changes and Transformations\\\\n\\\\n- **Top-left 3x3 Block:**\\\\n  - Input: Contains mostly 0s with one 7.\\\\n  - Output: Completely filled with 0s.\\\\n\\\\n- **Bottom-left 3x3 Block:**\\\\n  - Input: Contains mostly 0s with one 7.\\\\n  - Output: Completely filled with 0s.\\\\n\\\\n- **Middle Section:**\\\\n  - Input: Filled with 7s.\\\\n  - Output: Forms a cross pattern with 0s and the rest are filled with 7s.\\\\n\\\\n### Observations\\\\n\\\\n1. The transformation involves converting specific regions (3x3 blocks in the corners) to 0s.\\\\n2. The middle section of the matrix forms a cross pattern with 0s in the output matrix, while the rest of the elements remain 7s.\\\\n\\\\n### Conclusion\\\\n\\\\nThe transformation from the input matrix to the output matrix involves:\\\\n1. Converting the top-left and bottom-left 3x3 blocks entirely to 0s.\\\\n2. Creating a cross pattern in the middle section of the matrix with 0s, while the rest of the elements remain 7s.\\\\n\\\\nThis pattern suggests a specific rule for transforming the matrix, which could be useful for predicting future transformations or manipulations of similar matrices.\\\\nTransformation Information: To transform the input matrix into the output matrix based on the observed structures and patterns, we need to follow these steps:\\\\n\\\\n1. **Identify the Pattern:**\\\\n   - The transformation involves creating a specific pattern where the values of `7` form a cross or plus sign in each quadrant of the matrix.\\\\n\\\\n2. **Divide the Matrix:**\\\\n   - Divide the input matrix into quadrants.\\\\n\\\\n3. **Create the Cross Pattern in Each Quadrant:**\\\\n   - For each quadrant, create a cross pattern with `7`s. This means setting the center column and center row of each quadrant to `7`.\\\\n\\\\n4. **Handle the Edges:**\\\\n   - Ensure that the edges of the quadrants are handled correctly to avoid overlapping or incorrect placement of `7`s.\\\\n\\\\n### Step-by-Step Transformation:\\\\n\\\\n1. **Divide the Matrix into Quadrants:**\\\\n   - Identify the size of the input matrix (let\\\\'s assume it\\\\'s a square matrix of size `n x n`).\\\\n   - Divide the matrix into four quadrants.\\\\n\\\\n2. **Create Cross Pattern in Each Quadrant:**\\\\n   - For each quadrant, determine the center row and center column.\\\\n   - Set the center row and center column of each quadrant to `7`.\\\\n\\\\n### Python Function Implementation:\\\\n\\\\nHere is a Python function that implements the above rules dynamically for any given matrix:\\\\n\\\\n```python\\\\nimport numpy as np\\\\n\\\\ndef transform_matrix(input_matrix):\\\\n    n = input_matrix.shape[0]\\\\n    output_matrix = np.zeros_like(input_matrix)\\\\n    \\\\n    half_n = n // 2\\\\n    \\\\n    def create_cross_pattern(matrix, top_left_row, top_left_col, size):\\\\n        center_row = top_left_row + size // 2\\\\n        center_col = top_left_col + size // 2\\\\n        for i in range(size):\\\\n            matrix[center_row, top_left_col + i] = 7\\\\n            matrix[top_left_row + i, center_col] = 7\\\\n    \\\\n    # Top-left quadrant\\\\n    create_cross_pattern(output_matrix, 0, 0, half_n)\\\\n    \\\\n    # Top-right quadrant\\\\n    create_cross_pattern(output_matrix, 0, half_n, half_n)\\\\n    \\\\n    # Bottom-left quadrant\\\\n    create_cross_pattern(output_matrix, half_n, 0, half_n)\\\\n    \\\\n    # Bottom-right quadrant\\\\n    create_cross_pattern(output_matrix, half_n, half_n, half_n)\\\\n    \\\\n    return output_matrix\\\\n\\\\n# Example usage:\\\\ninput_matrix = np.array([\\\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\\\n    [0, 0, 7, 7, 7, 7, 7, 7]\\\\n])\\\\n\\\\noutput_matrix = transform_matrix(input_matrix)\\\\nprint(output_matrix)\\\\n```\\\\n\\\\n### Explanation:\\\\n\\\\n- **create_cross_pattern Function:** This function creates a cross pattern of `7`s in a specified quadrant of the matrix.\\\\n- **transform_matrix Function:** This function divides the input matrix into four quadrants and applies the cross pattern to each quadrant.\\\\n\\\\nThis approach ensures that the transformation is applied dynamically to any given matrix, adjusting for different sizes and patterns.\\\\n\\\\nGiven the structural, pattern-based, and transformational information, generate a Python script to solve the matrix puzzle depicted in the image. Include functions for:\\\\n- Reading and processing input matrices of varying sizes and compositions, ensuring accurate interpretation of data.\\\\n- Dynamically applying the identified transformation rules to the input matrix to produce the output matrix.\\\\n- Testing the transformation function with different matrix sizes and patterns. Provide test cases with expected outputs to verify the accuracy and robustness of the transformation rules.\\\\nFocus on adaptability and error handling to manage different matrix configurations and potential data anomalies effectively.\\\\n'}, {'type': 'image_url', 'image_url': {'url': 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'}}]\"\n  ]\n}\n\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image > llm:ChatOpenAI] [23.59s] Exiting LLM run with output:\n\u001b[0m{\n  \"generations\": [\n    [\n      {\n        \"text\": \"To solve the matrix puzzle depicted in the image, we need to transform the input matrix into the output matrix based on the identified patterns and transformation rules. Here is a step-by-step approach to achieve this using Python:\\n\\n### Step-by-Step Approach:\\n\\n1. **Read and Process Input Matrix:**\\n   - Read the input matrix from a given source (e.g., file, direct input, etc.).\\n   - Ensure the matrix is correctly interpreted as a 2D array.\\n\\n2. **Apply Transformation Rules:**\\n   - Identify the quadrants of the matrix.\\n   - Apply the cross pattern of `7`s to each quadrant.\\n\\n3. **Test the Transformation Function:**\\n   - Verify the function with different matrix sizes and patterns.\\n   - Include error handling for potential data anomalies.\\n\\nHere's the Python script that implements the above approach:\\n\\n```python\\nimport numpy as np\\n\\ndef create_cross_pattern(matrix, top_left_row, top_left_col, size):\\n    center_row = top_left_row + size // 2\\n    center_col = top_left_col + size // 2\\n    for i in range(size):\\n        matrix[center_row, top_left_col + i] = 7\\n        matrix[top_left_row + i, center_col] = 7\\n\\ndef transform_matrix(input_matrix):\\n    n = input_matrix.shape[0]\\n    output_matrix = np.zeros_like(input_matrix)\\n    \\n    half_n = n // 2\\n    \\n    # Top-left quadrant\\n    create_cross_pattern(output_matrix, 0, 0, half_n)\\n    \\n    # Top-right quadrant\\n    create_cross_pattern(output_matrix, 0, half_n, half_n)\\n    \\n    # Bottom-left quadrant\\n    create_cross_pattern(output_matrix, half_n, 0, half_n)\\n    \\n    # Bottom-right quadrant\\n    create_cross_pattern(output_matrix, half_n, half_n, half_n)\\n    \\n    return output_matrix\\n\\ndef print_matrix(matrix):\\n    for row in matrix:\\n        print(\\\" \\\".join(map(str, row)))\\n\\n# Example usage\\ninput_matrix = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix = transform_matrix(input_matrix)\\nprint(\\\"Input Matrix:\\\")\\nprint_matrix(input_matrix)\\nprint(\\\"\\\\nOutput Matrix:\\\")\\nprint_matrix(output_matrix)\\n```\\n\\n### Explanation:\\n\\n1. **create_cross_pattern Function:** This function creates a cross pattern of `7`s in a specified quadrant of the matrix.\\n2. **transform_matrix Function:** This function divides the input matrix into four quadrants and applies the cross pattern to each quadrant.\\n3. **print_matrix Function:** This function prints the matrix in a readable format.\\n\\n### Testing the Transformation Function:\\n\\nTo verify the accuracy and robustness of the transformation rules, you can test the function with different input matrices. Here are some test cases:\\n\\n```python\\n# Test Case 1: 10x10 Matrix as in the example\\ninput_matrix_10x10 = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix_10x10 = transform_matrix(input_matrix_10x10)\\nprint(\\\"Output Matrix for 10x10 Test Case:\\\")\\nprint_matrix(output_matrix_10x10)\\n\\n# Test Case 2: 8x8 Matrix\\ninput_matrix_8x8 = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7]\\n])\\n\\noutput_matrix_8x8 = transform_matrix(input_matrix_8x8)\\nprint(\\\"Output Matrix for 8x8 Test Case:\\\")\\nprint_matrix(output_matrix_8x8)\\n```\\n\\n### Error Handling:\\n\\n- Ensure that the input matrix is a square matrix.\\n- Handle cases where the matrix size is not divisible by 2.\\n\\nBy following this approach, you can dynamically apply the identified transformation rules to the input matrix and produce the desired output matrix, ensuring adaptability and robustness.\",\n        \"generation_info\": {\n          \"finish_reason\": \"stop\",\n          \"logprobs\": null\n        },\n        \"type\": \"ChatGeneration\",\n        \"message\": {\n          \"lc\": 1,\n          \"type\": \"constructor\",\n          \"id\": [\n            \"langchain\",\n            \"schema\",\n            \"messages\",\n            \"AIMessage\"\n          ],\n          \"kwargs\": {\n            \"content\": \"To solve the matrix puzzle depicted in the image, we need to transform the input matrix into the output matrix based on the identified patterns and transformation rules. Here is a step-by-step approach to achieve this using Python:\\n\\n### Step-by-Step Approach:\\n\\n1. **Read and Process Input Matrix:**\\n   - Read the input matrix from a given source (e.g., file, direct input, etc.).\\n   - Ensure the matrix is correctly interpreted as a 2D array.\\n\\n2. **Apply Transformation Rules:**\\n   - Identify the quadrants of the matrix.\\n   - Apply the cross pattern of `7`s to each quadrant.\\n\\n3. **Test the Transformation Function:**\\n   - Verify the function with different matrix sizes and patterns.\\n   - Include error handling for potential data anomalies.\\n\\nHere's the Python script that implements the above approach:\\n\\n```python\\nimport numpy as np\\n\\ndef create_cross_pattern(matrix, top_left_row, top_left_col, size):\\n    center_row = top_left_row + size // 2\\n    center_col = top_left_col + size // 2\\n    for i in range(size):\\n        matrix[center_row, top_left_col + i] = 7\\n        matrix[top_left_row + i, center_col] = 7\\n\\ndef transform_matrix(input_matrix):\\n    n = input_matrix.shape[0]\\n    output_matrix = np.zeros_like(input_matrix)\\n    \\n    half_n = n // 2\\n    \\n    # Top-left quadrant\\n    create_cross_pattern(output_matrix, 0, 0, half_n)\\n    \\n    # Top-right quadrant\\n    create_cross_pattern(output_matrix, 0, half_n, half_n)\\n    \\n    # Bottom-left quadrant\\n    create_cross_pattern(output_matrix, half_n, 0, half_n)\\n    \\n    # Bottom-right quadrant\\n    create_cross_pattern(output_matrix, half_n, half_n, half_n)\\n    \\n    return output_matrix\\n\\ndef print_matrix(matrix):\\n    for row in matrix:\\n        print(\\\" \\\".join(map(str, row)))\\n\\n# Example usage\\ninput_matrix = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix = transform_matrix(input_matrix)\\nprint(\\\"Input Matrix:\\\")\\nprint_matrix(input_matrix)\\nprint(\\\"\\\\nOutput Matrix:\\\")\\nprint_matrix(output_matrix)\\n```\\n\\n### Explanation:\\n\\n1. **create_cross_pattern Function:** This function creates a cross pattern of `7`s in a specified quadrant of the matrix.\\n2. **transform_matrix Function:** This function divides the input matrix into four quadrants and applies the cross pattern to each quadrant.\\n3. **print_matrix Function:** This function prints the matrix in a readable format.\\n\\n### Testing the Transformation Function:\\n\\nTo verify the accuracy and robustness of the transformation rules, you can test the function with different input matrices. Here are some test cases:\\n\\n```python\\n# Test Case 1: 10x10 Matrix as in the example\\ninput_matrix_10x10 = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix_10x10 = transform_matrix(input_matrix_10x10)\\nprint(\\\"Output Matrix for 10x10 Test Case:\\\")\\nprint_matrix(output_matrix_10x10)\\n\\n# Test Case 2: 8x8 Matrix\\ninput_matrix_8x8 = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7]\\n])\\n\\noutput_matrix_8x8 = transform_matrix(input_matrix_8x8)\\nprint(\\\"Output Matrix for 8x8 Test Case:\\\")\\nprint_matrix(output_matrix_8x8)\\n```\\n\\n### Error Handling:\\n\\n- Ensure that the input matrix is a square matrix.\\n- Handle cases where the matrix size is not divisible by 2.\\n\\nBy following this approach, you can dynamically apply the identified transformation rules to the input matrix and produce the desired output matrix, ensuring adaptability and robustness.\",\n            \"response_metadata\": {\n              \"token_usage\": {\n                \"completion_tokens\": 1563,\n                \"prompt_tokens\": 2816,\n                \"total_tokens\": 4379\n              },\n              \"model_name\": \"gpt-4o-2024-05-13\",\n              \"system_fingerprint\": \"fp_298125635f\",\n              \"finish_reason\": \"stop\",\n              \"logprobs\": null\n            },\n            \"type\": \"ai\",\n            \"id\": \"run-79a555f7-2d14-4e75-b28c-bc2d4f6412de-0\",\n            \"usage_metadata\": {\n              \"input_tokens\": 2816,\n              \"output_tokens\": 1563,\n              \"total_tokens\": 4379\n            },\n            \"tool_calls\": [],\n            \"invalid_tool_calls\": []\n          }\n        }\n      }\n    ]\n  ],\n  \"llm_output\": {\n    \"token_usage\": {\n      \"completion_tokens\": 1563,\n      \"prompt_tokens\": 2816,\n      \"total_tokens\": 4379\n    },\n    \"model_name\": \"gpt-4o-2024-05-13\",\n    \"system_fingerprint\": \"fp_298125635f\"\n  },\n  \"run\": null\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image] [23.61s] Exiting Chain run with output:\n\u001b[0m{\n  \"output\": \"To solve the matrix puzzle depicted in the image, we need to transform the input matrix into the output matrix based on the identified patterns and transformation rules. Here is a step-by-step approach to achieve this using Python:\\n\\n### Step-by-Step Approach:\\n\\n1. **Read and Process Input Matrix:**\\n   - Read the input matrix from a given source (e.g., file, direct input, etc.).\\n   - Ensure the matrix is correctly interpreted as a 2D array.\\n\\n2. **Apply Transformation Rules:**\\n   - Identify the quadrants of the matrix.\\n   - Apply the cross pattern of `7`s to each quadrant.\\n\\n3. **Test the Transformation Function:**\\n   - Verify the function with different matrix sizes and patterns.\\n   - Include error handling for potential data anomalies.\\n\\nHere's the Python script that implements the above approach:\\n\\n```python\\nimport numpy as np\\n\\ndef create_cross_pattern(matrix, top_left_row, top_left_col, size):\\n    center_row = top_left_row + size // 2\\n    center_col = top_left_col + size // 2\\n    for i in range(size):\\n        matrix[center_row, top_left_col + i] = 7\\n        matrix[top_left_row + i, center_col] = 7\\n\\ndef transform_matrix(input_matrix):\\n    n = input_matrix.shape[0]\\n    output_matrix = np.zeros_like(input_matrix)\\n    \\n    half_n = n // 2\\n    \\n    # Top-left quadrant\\n    create_cross_pattern(output_matrix, 0, 0, half_n)\\n    \\n    # Top-right quadrant\\n    create_cross_pattern(output_matrix, 0, half_n, half_n)\\n    \\n    # Bottom-left quadrant\\n    create_cross_pattern(output_matrix, half_n, 0, half_n)\\n    \\n    # Bottom-right quadrant\\n    create_cross_pattern(output_matrix, half_n, half_n, half_n)\\n    \\n    return output_matrix\\n\\ndef print_matrix(matrix):\\n    for row in matrix:\\n        print(\\\" \\\".join(map(str, row)))\\n\\n# Example usage\\ninput_matrix = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix = transform_matrix(input_matrix)\\nprint(\\\"Input Matrix:\\\")\\nprint_matrix(input_matrix)\\nprint(\\\"\\\\nOutput Matrix:\\\")\\nprint_matrix(output_matrix)\\n```\\n\\n### Explanation:\\n\\n1. **create_cross_pattern Function:** This function creates a cross pattern of `7`s in a specified quadrant of the matrix.\\n2. **transform_matrix Function:** This function divides the input matrix into four quadrants and applies the cross pattern to each quadrant.\\n3. **print_matrix Function:** This function prints the matrix in a readable format.\\n\\n### Testing the Transformation Function:\\n\\nTo verify the accuracy and robustness of the transformation rules, you can test the function with different input matrices. Here are some test cases:\\n\\n```python\\n# Test Case 1: 10x10 Matrix as in the example\\ninput_matrix_10x10 = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix_10x10 = transform_matrix(input_matrix_10x10)\\nprint(\\\"Output Matrix for 10x10 Test Case:\\\")\\nprint_matrix(output_matrix_10x10)\\n\\n# Test Case 2: 8x8 Matrix\\ninput_matrix_8x8 = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7]\\n])\\n\\noutput_matrix_8x8 = transform_matrix(input_matrix_8x8)\\nprint(\\\"Output Matrix for 8x8 Test Case:\\\")\\nprint_matrix(output_matrix_8x8)\\n```\\n\\n### Error Handling:\\n\\n- Ensure that the input matrix is a square matrix.\\n- Handle cases where the matrix size is not divisible by 2.\\n\\nBy following this approach, you can dynamically apply the identified transformation rules to the input matrix and produce the desired output matrix, ensuring adaptability and robustness.\"\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence] [23.62s] Exiting Chain run with output:\n\u001b[0m{\n  \"output\": \"To solve the matrix puzzle depicted in the image, we need to transform the input matrix into the output matrix based on the identified patterns and transformation rules. Here is a step-by-step approach to achieve this using Python:\\n\\n### Step-by-Step Approach:\\n\\n1. **Read and Process Input Matrix:**\\n   - Read the input matrix from a given source (e.g., file, direct input, etc.).\\n   - Ensure the matrix is correctly interpreted as a 2D array.\\n\\n2. **Apply Transformation Rules:**\\n   - Identify the quadrants of the matrix.\\n   - Apply the cross pattern of `7`s to each quadrant.\\n\\n3. **Test the Transformation Function:**\\n   - Verify the function with different matrix sizes and patterns.\\n   - Include error handling for potential data anomalies.\\n\\nHere's the Python script that implements the above approach:\\n\\n```python\\nimport numpy as np\\n\\ndef create_cross_pattern(matrix, top_left_row, top_left_col, size):\\n    center_row = top_left_row + size // 2\\n    center_col = top_left_col + size // 2\\n    for i in range(size):\\n        matrix[center_row, top_left_col + i] = 7\\n        matrix[top_left_row + i, center_col] = 7\\n\\ndef transform_matrix(input_matrix):\\n    n = input_matrix.shape[0]\\n    output_matrix = np.zeros_like(input_matrix)\\n    \\n    half_n = n // 2\\n    \\n    # Top-left quadrant\\n    create_cross_pattern(output_matrix, 0, 0, half_n)\\n    \\n    # Top-right quadrant\\n    create_cross_pattern(output_matrix, 0, half_n, half_n)\\n    \\n    # Bottom-left quadrant\\n    create_cross_pattern(output_matrix, half_n, 0, half_n)\\n    \\n    # Bottom-right quadrant\\n    create_cross_pattern(output_matrix, half_n, half_n, half_n)\\n    \\n    return output_matrix\\n\\ndef print_matrix(matrix):\\n    for row in matrix:\\n        print(\\\" \\\".join(map(str, row)))\\n\\n# Example usage\\ninput_matrix = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix = transform_matrix(input_matrix)\\nprint(\\\"Input Matrix:\\\")\\nprint_matrix(input_matrix)\\nprint(\\\"\\\\nOutput Matrix:\\\")\\nprint_matrix(output_matrix)\\n```\\n\\n### Explanation:\\n\\n1. **create_cross_pattern Function:** This function creates a cross pattern of `7`s in a specified quadrant of the matrix.\\n2. **transform_matrix Function:** This function divides the input matrix into four quadrants and applies the cross pattern to each quadrant.\\n3. **print_matrix Function:** This function prints the matrix in a readable format.\\n\\n### Testing the Transformation Function:\\n\\nTo verify the accuracy and robustness of the transformation rules, you can test the function with different input matrices. Here are some test cases:\\n\\n```python\\n# Test Case 1: 10x10 Matrix as in the example\\ninput_matrix_10x10 = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix_10x10 = transform_matrix(input_matrix_10x10)\\nprint(\\\"Output Matrix for 10x10 Test Case:\\\")\\nprint_matrix(output_matrix_10x10)\\n\\n# Test Case 2: 8x8 Matrix\\ninput_matrix_8x8 = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7]\\n])\\n\\noutput_matrix_8x8 = transform_matrix(input_matrix_8x8)\\nprint(\\\"Output Matrix for 8x8 Test Case:\\\")\\nprint_matrix(output_matrix_8x8)\\n```\\n\\n### Error Handling:\\n\\n- Ensure that the input matrix is a square matrix.\\n- Handle cases where the matrix size is not divisible by 2.\\n\\nBy following this approach, you can dynamically apply the identified transformation rules to the input matrix and produce the desired output matrix, ensuring adaptability and robustness.\"\n}\n","output_type":"stream"}]},{"cell_type":"code","source":"structure","metadata":{"execution":{"iopub.status.busy":"2024-07-11T00:43:22.603582Z","iopub.execute_input":"2024-07-11T00:43:22.603869Z","iopub.status.idle":"2024-07-11T00:43:22.609647Z","shell.execute_reply.started":"2024-07-11T00:43:22.603843Z","shell.execute_reply":"2024-07-11T00:43:22.608752Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"'The image displays two matrices labeled \"Input 2\" and \"Output 2.\" Below is a detailed description of each matrix:\\n\\n### Input 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a large block of 7s, with specific areas filled with 0s.\\n   - The top-left corner contains a 4x4 block of 0s.\\n   - The bottom-left corner contains a 4x4 block of 0s.\\n   - The rest of the matrix is filled with 7s.\\n\\n4. **Patterns and Alignments:**\\n   - The 0s form two distinct rectangular blocks within the matrix, located at the top-left and bottom-left corners.\\n   - The rest of the matrix is uniformly filled with 7s.\\n\\n### Output 2 Matrix\\n\\n1. **Size and Dimensions:**\\n   - The matrix is also a 10x10 grid (10 rows and 10 columns).\\n\\n2. **Type of Elements:**\\n   - The elements are numerical, consisting of two distinct values: 0 and 7.\\n\\n3. **Arrangement of Elements:**\\n   - The matrix has a more complex pattern of 0s and 7s compared to the Input 2 matrix.\\n   - The 0s and 7s are arranged in a checkerboard-like pattern in certain areas, creating a more intricate design.\\n\\n4. **Patterns and Alignments:**\\n   - The top-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - The middle of the matrix has a large cross-like structure made of 7s, with 0s filling the spaces around it.\\n   - The bottom-left corner contains a 3x3 block of 0s, followed by a 1x1 block of 7s, and then another 3x3 block of 0s.\\n   - There is a noticeable symmetry in the distribution of 0s and 7s, particularly around the central cross-like structure of 7s.\\n\\n### Summary\\n\\n- Both matrices are 10x10 grids with numerical elements (0 and 7).\\n- The Input 2 matrix has large, uniform blocks of 0s and 7s, while the Output 2 matrix has a more complex, patterned arrangement of 0s and 7s.\\n- The transformation from Input 2 to Output 2 involves redistributing the 0s and 7s into a more intricate pattern, suggesting a specific rule or algorithm applied to achieve this transformation.'"},"metadata":{}}]},{"cell_type":"code","source":"pattern","metadata":{"execution":{"iopub.status.busy":"2024-07-11T00:43:22.611394Z","iopub.execute_input":"2024-07-11T00:43:22.611748Z","iopub.status.idle":"2024-07-11T00:43:22.620863Z","shell.execute_reply.started":"2024-07-11T00:43:22.611711Z","shell.execute_reply":"2024-07-11T00:43:22.620082Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"'### Analysis of the Input Matrix\\n\\nThe input matrix is a 7x7 grid with two distinct elements: 0 and 7. The matrix is divided into two main regions:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains mostly 0s except for the bottom-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 7\\n     ```\\n\\n2. **Remaining part of the matrix:**\\n   - The rest of the matrix (excluding the top-left 3x3 block and the bottom-left 3x3 block) is filled with 7s.\\n   - The bottom-left 3x3 block contains mostly 0s except for the top-right cell, which contains a 7.\\n   - The elements are arranged as follows:\\n     ```\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     7 7 7 7 7 7 7\\n     0 0 0 7 7 7 7\\n     0 0 0 0 0 0 0\\n     0 0 0 0 0 0 0\\n     ```\\n\\n### Analysis of the Output Matrix\\n\\nThe output matrix is also a 7x7 grid with two distinct elements: 0 and 7. The matrix appears to be a transformation of the input matrix, with the following noticeable patterns:\\n\\n1. **Top-left corner (3x3 block):**\\n   - The top-left 3x3 block contains only 0s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0\\n     0 0 0\\n     0 0 0\\n     ```\\n\\n2. **Middle section:**\\n   - The middle section of the matrix forms a cross pattern with 0s, while the rest are filled with 7s.\\n   - The elements are arranged as follows:\\n     ```\\n     0 0 0 7 7 0 7\\n     0 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     7 7 7 0 7 7 7\\n     7 7 7 7 7 7 7\\n     0 7 7 7 7 7 7\\n     0 0 0 7 7 0 7\\n     ```\\n\\n### Pattern Changes and Transformations\\n\\n- **Top-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Bottom-left 3x3 Block:**\\n  - Input: Contains mostly 0s with one 7.\\n  - Output: Completely filled with 0s.\\n\\n- **Middle Section:**\\n  - Input: Filled with 7s.\\n  - Output: Forms a cross pattern with 0s and the rest are filled with 7s.\\n\\n### Observations\\n\\n1. The transformation involves converting specific regions (3x3 blocks in the corners) to 0s.\\n2. The middle section of the matrix forms a cross pattern with 0s in the output matrix, while the rest of the elements remain 7s.\\n\\n### Conclusion\\n\\nThe transformation from the input matrix to the output matrix involves:\\n1. Converting the top-left and bottom-left 3x3 blocks entirely to 0s.\\n2. Creating a cross pattern in the middle section of the matrix with 0s, while the rest of the elements remain 7s.\\n\\nThis pattern suggests a specific rule for transforming the matrix, which could be useful for predicting future transformations or manipulations of similar matrices.'"},"metadata":{}}]},{"cell_type":"code","source":"transformation","metadata":{"execution":{"iopub.status.busy":"2024-07-11T00:43:22.622697Z","iopub.execute_input":"2024-07-11T00:43:22.622977Z","iopub.status.idle":"2024-07-11T00:43:22.631911Z","shell.execute_reply.started":"2024-07-11T00:43:22.622947Z","shell.execute_reply":"2024-07-11T00:43:22.630994Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"\"To transform the input matrix into the output matrix based on the observed structures and patterns, we need to follow these steps:\\n\\n1. **Identify the Pattern:**\\n   - The transformation involves creating a specific pattern where the values of `7` form a cross or plus sign in each quadrant of the matrix.\\n\\n2. **Divide the Matrix:**\\n   - Divide the input matrix into quadrants.\\n\\n3. **Create the Cross Pattern in Each Quadrant:**\\n   - For each quadrant, create a cross pattern with `7`s. This means setting the center column and center row of each quadrant to `7`.\\n\\n4. **Handle the Edges:**\\n   - Ensure that the edges of the quadrants are handled correctly to avoid overlapping or incorrect placement of `7`s.\\n\\n### Step-by-Step Transformation:\\n\\n1. **Divide the Matrix into Quadrants:**\\n   - Identify the size of the input matrix (let's assume it's a square matrix of size `n x n`).\\n   - Divide the matrix into four quadrants.\\n\\n2. **Create Cross Pattern in Each Quadrant:**\\n   - For each quadrant, determine the center row and center column.\\n   - Set the center row and center column of each quadrant to `7`.\\n\\n### Python Function Implementation:\\n\\nHere is a Python function that implements the above rules dynamically for any given matrix:\\n\\n```python\\nimport numpy as np\\n\\ndef transform_matrix(input_matrix):\\n    n = input_matrix.shape[0]\\n    output_matrix = np.zeros_like(input_matrix)\\n    \\n    half_n = n // 2\\n    \\n    def create_cross_pattern(matrix, top_left_row, top_left_col, size):\\n        center_row = top_left_row + size // 2\\n        center_col = top_left_col + size // 2\\n        for i in range(size):\\n            matrix[center_row, top_left_col + i] = 7\\n            matrix[top_left_row + i, center_col] = 7\\n    \\n    # Top-left quadrant\\n    create_cross_pattern(output_matrix, 0, 0, half_n)\\n    \\n    # Top-right quadrant\\n    create_cross_pattern(output_matrix, 0, half_n, half_n)\\n    \\n    # Bottom-left quadrant\\n    create_cross_pattern(output_matrix, half_n, 0, half_n)\\n    \\n    # Bottom-right quadrant\\n    create_cross_pattern(output_matrix, half_n, half_n, half_n)\\n    \\n    return output_matrix\\n\\n# Example usage:\\ninput_matrix = np.array([\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix = transform_matrix(input_matrix)\\nprint(output_matrix)\\n```\\n\\n### Explanation:\\n\\n- **create_cross_pattern Function:** This function creates a cross pattern of `7`s in a specified quadrant of the matrix.\\n- **transform_matrix Function:** This function divides the input matrix into four quadrants and applies the cross pattern to each quadrant.\\n\\nThis approach ensures that the transformation is applied dynamically to any given matrix, adjusting for different sizes and patterns.\""},"metadata":{}}]},{"cell_type":"code","source":"code","metadata":{"execution":{"iopub.status.busy":"2024-07-11T00:43:22.633073Z","iopub.execute_input":"2024-07-11T00:43:22.633406Z","iopub.status.idle":"2024-07-11T00:43:22.641766Z","shell.execute_reply.started":"2024-07-11T00:43:22.633381Z","shell.execute_reply":"2024-07-11T00:43:22.640871Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"'To solve the matrix puzzle depicted in the image, we need to transform the input matrix into the output matrix based on the identified patterns and transformation rules. Here is a step-by-step approach to achieve this using Python:\\n\\n### Step-by-Step Approach:\\n\\n1. **Read and Process Input Matrix:**\\n   - Read the input matrix from a given source (e.g., file, direct input, etc.).\\n   - Ensure the matrix is correctly interpreted as a 2D array.\\n\\n2. **Apply Transformation Rules:**\\n   - Identify the quadrants of the matrix.\\n   - Apply the cross pattern of `7`s to each quadrant.\\n\\n3. **Test the Transformation Function:**\\n   - Verify the function with different matrix sizes and patterns.\\n   - Include error handling for potential data anomalies.\\n\\nHere\\'s the Python script that implements the above approach:\\n\\n```python\\nimport numpy as np\\n\\ndef create_cross_pattern(matrix, top_left_row, top_left_col, size):\\n    center_row = top_left_row + size // 2\\n    center_col = top_left_col + size // 2\\n    for i in range(size):\\n        matrix[center_row, top_left_col + i] = 7\\n        matrix[top_left_row + i, center_col] = 7\\n\\ndef transform_matrix(input_matrix):\\n    n = input_matrix.shape[0]\\n    output_matrix = np.zeros_like(input_matrix)\\n    \\n    half_n = n // 2\\n    \\n    # Top-left quadrant\\n    create_cross_pattern(output_matrix, 0, 0, half_n)\\n    \\n    # Top-right quadrant\\n    create_cross_pattern(output_matrix, 0, half_n, half_n)\\n    \\n    # Bottom-left quadrant\\n    create_cross_pattern(output_matrix, half_n, 0, half_n)\\n    \\n    # Bottom-right quadrant\\n    create_cross_pattern(output_matrix, half_n, half_n, half_n)\\n    \\n    return output_matrix\\n\\ndef print_matrix(matrix):\\n    for row in matrix:\\n        print(\" \".join(map(str, row)))\\n\\n# Example usage\\ninput_matrix = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix = transform_matrix(input_matrix)\\nprint(\"Input Matrix:\")\\nprint_matrix(input_matrix)\\nprint(\"\\\\nOutput Matrix:\")\\nprint_matrix(output_matrix)\\n```\\n\\n### Explanation:\\n\\n1. **create_cross_pattern Function:** This function creates a cross pattern of `7`s in a specified quadrant of the matrix.\\n2. **transform_matrix Function:** This function divides the input matrix into four quadrants and applies the cross pattern to each quadrant.\\n3. **print_matrix Function:** This function prints the matrix in a readable format.\\n\\n### Testing the Transformation Function:\\n\\nTo verify the accuracy and robustness of the transformation rules, you can test the function with different input matrices. Here are some test cases:\\n\\n```python\\n# Test Case 1: 10x10 Matrix as in the example\\ninput_matrix_10x10 = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7, 7, 7]\\n])\\n\\noutput_matrix_10x10 = transform_matrix(input_matrix_10x10)\\nprint(\"Output Matrix for 10x10 Test Case:\")\\nprint_matrix(output_matrix_10x10)\\n\\n# Test Case 2: 8x8 Matrix\\ninput_matrix_8x8 = np.array([\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [7, 7, 7, 7, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7],\\n    [0, 0, 0, 0, 7, 7, 7, 7]\\n])\\n\\noutput_matrix_8x8 = transform_matrix(input_matrix_8x8)\\nprint(\"Output Matrix for 8x8 Test Case:\")\\nprint_matrix(output_matrix_8x8)\\n```\\n\\n### Error Handling:\\n\\n- Ensure that the input matrix is a square matrix.\\n- Handle cases where the matrix size is not divisible by 2.\\n\\nBy following this approach, you can dynamically apply the identified transformation rules to the input matrix and produce the desired output matrix, ensuring adaptability and robustness.'"},"metadata":{}}]},{"cell_type":"code","source":"ip_op_matrix = exec(code.split(\"```python\")[1].split(\"\\n```\")[0])\nip_op_matrix","metadata":{"execution":{"iopub.status.busy":"2024-07-11T00:43:22.642732Z","iopub.execute_input":"2024-07-11T00:43:22.642993Z","iopub.status.idle":"2024-07-11T00:43:22.651908Z","shell.execute_reply.started":"2024-07-11T00:43:22.642970Z","shell.execute_reply":"2024-07-11T00:43:22.651030Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"Input Matrix:\n0 0 0 0 7 7 7 7 7 7\n0 0 0 0 7 7 7 7 7 7\n0 0 0 0 7 7 7 7 7 7\n0 0 0 0 7 7 7 7 7 7\n7 7 7 7 7 7 7 7 7 7\n7 7 7 7 7 7 7 7 7 7\n0 0 0 0 7 7 7 7 7 7\n0 0 0 0 7 7 7 7 7 7\n0 0 0 0 7 7 7 7 7 7\n0 0 0 0 7 7 7 7 7 7\n\nOutput Matrix:\n0 0 7 0 0 0 0 7 0 0\n0 0 7 0 0 0 0 7 0 0\n7 7 7 7 7 7 7 7 7 7\n0 0 7 0 0 0 0 7 0 0\n0 0 7 0 0 0 0 7 0 0\n0 0 7 0 0 0 0 7 0 0\n0 0 7 0 0 0 0 7 0 0\n7 7 7 7 7 7 7 7 7 7\n0 0 7 0 0 0 0 7 0 0\n0 0 7 0 0 0 0 7 0 0\n","output_type":"stream"}]},{"cell_type":"code","source":"# Load the image\nimage = mpimg.imread(df['file_paths'].iloc[1])\n\n# Display the image\nplt.imshow(image)\nplt.axis('off')  # Hide the axes\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-07-11T00:39:04.422410Z","iopub.execute_input":"2024-07-11T00:39:04.423244Z","iopub.status.idle":"2024-07-11T00:39:04.601109Z","shell.execute_reply.started":"2024-07-11T00:39:04.423202Z","shell.execute_reply":"2024-07-11T00:39:04.597377Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"validation_prompt = f\"\"\"\nGiven the image of an input matrix and a transformed output matrix, is the input output matrix in text format below same as the screenshot ?\n{ip_op_matrix}..\nRespond with yes or no and a short reasoning\n\"\"\"\n\nvalidation = analyze_image_transformation(df['file_paths'].iloc[1], structure_prompt)\nvalidation","metadata":{"execution":{"iopub.status.busy":"2024-07-10T20:14:41.070731Z","iopub.execute_input":"2024-07-10T20:14:41.071664Z","iopub.status.idle":"2024-07-10T20:14:54.234567Z","shell.execute_reply.started":"2024-07-10T20:14:41.071628Z","shell.execute_reply":"2024-07-10T20:14:54.233656Z"},"trusted":true},"execution_count":61,"outputs":[{"name":"stdout","text":"\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nPlease provide a detailed description of the matrix structure in the image. Focus on the size (number of rows and columns), the type of elements it contains, and the overall arrangement of these elements. Note any unusual patterns or alignments that might suggest a non-standard matrix structure. For example, are there any nested matrices or blocks within the main matrix? Are the elements numerical, textual, or mixed? This analysis will help in understanding the foundational layout before applying any transformations.\\n\"\n}\n\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:TransformChain] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nPlease provide a detailed description of the matrix structure in the image. Focus on the size (number of rows and columns), the type of elements it contains, and the overall arrangement of these elements. Note any unusual patterns or alignments that might suggest a non-standard matrix structure. For example, are there any nested matrices or blocks within the main matrix? Are the elements numerical, textual, or mixed? This analysis will help in understanding the foundational layout before applying any transformations.\\n\"\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:TransformChain] [1ms] Exiting Chain run with output:\n\u001b[0m{\n  \"image\": 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\"\n}\n\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image] Entering Chain run with input:\n\u001b[0m{\n  \"image_path\": \"/kaggle/working/train/007bbfb7_1.png\",\n  \"prompt\": \"\\nPlease provide a detailed description of the matrix structure in the image. Focus on the size (number of rows and columns), the type of elements it contains, and the overall arrangement of these elements. Note any unusual patterns or alignments that might suggest a non-standard matrix structure. For example, are there any nested matrices or blocks within the main matrix? Are the elements numerical, textual, or mixed? This analysis will help in understanding the foundational layout before applying any transformations.\\n\",\n  \"image\": 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\"\n}\n\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image > llm:ChatOpenAI] Entering LLM run with input:\n\u001b[0m{\n  \"prompts\": [\n    \"Human: [{'type': 'text', 'text': '\\\\nPlease provide a detailed description of the matrix structure in the image. Focus on the size (number of rows and columns), the type of elements it contains, and the overall arrangement of these elements. Note any unusual patterns or alignments that might suggest a non-standard matrix structure. For example, are there any nested matrices or blocks within the main matrix? Are the elements numerical, textual, or mixed? This analysis will help in understanding the foundational layout before applying any transformations.\\\\n'}, {'type': 'image_url', 'image_url': {'url': 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'}}]\"\n  ]\n}\n\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image > llm:ChatOpenAI] [13.12s] Exiting LLM run with output:\n\u001b[0m{\n  \"generations\": [\n    [\n      {\n        \"text\": \"The image contains two matrices labeled \\\"Input 2\\\" and \\\"Output 2\\\". Below is a detailed description of each:\\n\\n### Input 2 Matrix\\n- **Size**: 8 rows x 8 columns.\\n- **Elements**: Numerical, specifically the numbers 0 and 7.\\n- **Arrangement**:\\n  - The matrix is divided into distinct blocks:\\n    - The top left quadrant (4x4) is mostly filled with 0s, with the exception of some 7s in the lower part of this quadrant.\\n    - The bottom left quadrant (4x4) is similar, with a block of 0s and a few 7s.\\n    - The right half (4x8) is entirely filled with the number 7.\\n  - This creates a pattern where the left side has two blocks of 0s in the top and bottom quadrants, and the right side is uniformly filled with 7s.\\n\\n### Output 2 Matrix\\n- **Size**: 8 rows x 8 columns.\\n- **Elements**: Numerical, specifically the numbers 0 and 7.\\n- **Arrangement**:\\n  - The matrix is more complex in its structure compared to the Input matrix.\\n  - The 0s and 7s form a pattern that resembles a maze or a grid with pathways:\\n    - The 0s are arranged in vertical and horizontal lines, creating a framework or grid-like pattern.\\n    - The 7s fill in the spaces between the 0s, forming contiguous blocks or regions.\\n  - The pattern appears to be symmetric along the vertical axis of the matrix, creating a balanced and structured look.\\n\\n### Observations:\\n- **Input 2**:\\n  - The matrix has a clear block structure with a stark contrast between the left and right halves.\\n  - The left half contains blocks of 0s with some 7s, while the right half is completely filled with 7s.\\n- **Output 2**:\\n  - The matrix transforms the input into a more intricate pattern.\\n  - The 0s form a grid-like structure, and the 7s fill the spaces within this grid.\\n  - There is a noticeable symmetry in the arrangement of the elements.\\n\\n### Unusual Patterns:\\n- **Input 2**:\\n  - The clear division between blocks of 0s and 7s is notable.\\n- **Output 2**:\\n  - The maze-like pattern and symmetry suggest a non-standard matrix structure, possibly derived from applying a specific transformation or algorithm to the Input matrix.\\n\\nIn summary, the Input 2 matrix has a simpler block-based structure, while the Output 2 matrix exhibits a more complex, grid-like pattern with symmetry, suggesting a transformation that reorganizes the elements into a more intricate layout.\",\n        \"generation_info\": {\n          \"finish_reason\": \"stop\",\n          \"logprobs\": null\n        },\n        \"type\": \"ChatGeneration\",\n        \"message\": {\n          \"lc\": 1,\n          \"type\": \"constructor\",\n          \"id\": [\n            \"langchain\",\n            \"schema\",\n            \"messages\",\n            \"AIMessage\"\n          ],\n          \"kwargs\": {\n            \"content\": \"The image contains two matrices labeled \\\"Input 2\\\" and \\\"Output 2\\\". Below is a detailed description of each:\\n\\n### Input 2 Matrix\\n- **Size**: 8 rows x 8 columns.\\n- **Elements**: Numerical, specifically the numbers 0 and 7.\\n- **Arrangement**:\\n  - The matrix is divided into distinct blocks:\\n    - The top left quadrant (4x4) is mostly filled with 0s, with the exception of some 7s in the lower part of this quadrant.\\n    - The bottom left quadrant (4x4) is similar, with a block of 0s and a few 7s.\\n    - The right half (4x8) is entirely filled with the number 7.\\n  - This creates a pattern where the left side has two blocks of 0s in the top and bottom quadrants, and the right side is uniformly filled with 7s.\\n\\n### Output 2 Matrix\\n- **Size**: 8 rows x 8 columns.\\n- **Elements**: Numerical, specifically the numbers 0 and 7.\\n- **Arrangement**:\\n  - The matrix is more complex in its structure compared to the Input matrix.\\n  - The 0s and 7s form a pattern that resembles a maze or a grid with pathways:\\n    - The 0s are arranged in vertical and horizontal lines, creating a framework or grid-like pattern.\\n    - The 7s fill in the spaces between the 0s, forming contiguous blocks or regions.\\n  - The pattern appears to be symmetric along the vertical axis of the matrix, creating a balanced and structured look.\\n\\n### Observations:\\n- **Input 2**:\\n  - The matrix has a clear block structure with a stark contrast between the left and right halves.\\n  - The left half contains blocks of 0s with some 7s, while the right half is completely filled with 7s.\\n- **Output 2**:\\n  - The matrix transforms the input into a more intricate pattern.\\n  - The 0s form a grid-like structure, and the 7s fill the spaces within this grid.\\n  - There is a noticeable symmetry in the arrangement of the elements.\\n\\n### Unusual Patterns:\\n- **Input 2**:\\n  - The clear division between blocks of 0s and 7s is notable.\\n- **Output 2**:\\n  - The maze-like pattern and symmetry suggest a non-standard matrix structure, possibly derived from applying a specific transformation or algorithm to the Input matrix.\\n\\nIn summary, the Input 2 matrix has a simpler block-based structure, while the Output 2 matrix exhibits a more complex, grid-like pattern with symmetry, suggesting a transformation that reorganizes the elements into a more intricate layout.\",\n            \"response_metadata\": {\n              \"token_usage\": {\n                \"completion_tokens\": 570,\n                \"prompt_tokens\": 527,\n                \"total_tokens\": 1097\n              },\n              \"model_name\": \"gpt-4o-2024-05-13\",\n              \"system_fingerprint\": \"fp_4008e3b719\",\n              \"finish_reason\": \"stop\",\n              \"logprobs\": null\n            },\n            \"type\": \"ai\",\n            \"id\": \"run-21ecfb79-85e6-4b63-b2fe-6eab65b6554d-0\",\n            \"usage_metadata\": {\n              \"input_tokens\": 527,\n              \"output_tokens\": 570,\n              \"total_tokens\": 1097\n            },\n            \"tool_calls\": [],\n            \"invalid_tool_calls\": []\n          }\n        }\n      }\n    ]\n  ],\n  \"llm_output\": {\n    \"token_usage\": {\n      \"completion_tokens\": 570,\n      \"prompt_tokens\": 527,\n      \"total_tokens\": 1097\n    },\n    \"model_name\": \"gpt-4o-2024-05-13\",\n    \"system_fingerprint\": \"fp_4008e3b719\"\n  },\n  \"run\": null\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:process_image] [13.15s] Exiting Chain run with output:\n\u001b[0m{\n  \"output\": \"The image contains two matrices labeled \\\"Input 2\\\" and \\\"Output 2\\\". Below is a detailed description of each:\\n\\n### Input 2 Matrix\\n- **Size**: 8 rows x 8 columns.\\n- **Elements**: Numerical, specifically the numbers 0 and 7.\\n- **Arrangement**:\\n  - The matrix is divided into distinct blocks:\\n    - The top left quadrant (4x4) is mostly filled with 0s, with the exception of some 7s in the lower part of this quadrant.\\n    - The bottom left quadrant (4x4) is similar, with a block of 0s and a few 7s.\\n    - The right half (4x8) is entirely filled with the number 7.\\n  - This creates a pattern where the left side has two blocks of 0s in the top and bottom quadrants, and the right side is uniformly filled with 7s.\\n\\n### Output 2 Matrix\\n- **Size**: 8 rows x 8 columns.\\n- **Elements**: Numerical, specifically the numbers 0 and 7.\\n- **Arrangement**:\\n  - The matrix is more complex in its structure compared to the Input matrix.\\n  - The 0s and 7s form a pattern that resembles a maze or a grid with pathways:\\n    - The 0s are arranged in vertical and horizontal lines, creating a framework or grid-like pattern.\\n    - The 7s fill in the spaces between the 0s, forming contiguous blocks or regions.\\n  - The pattern appears to be symmetric along the vertical axis of the matrix, creating a balanced and structured look.\\n\\n### Observations:\\n- **Input 2**:\\n  - The matrix has a clear block structure with a stark contrast between the left and right halves.\\n  - The left half contains blocks of 0s with some 7s, while the right half is completely filled with 7s.\\n- **Output 2**:\\n  - The matrix transforms the input into a more intricate pattern.\\n  - The 0s form a grid-like structure, and the 7s fill the spaces within this grid.\\n  - There is a noticeable symmetry in the arrangement of the elements.\\n\\n### Unusual Patterns:\\n- **Input 2**:\\n  - The clear division between blocks of 0s and 7s is notable.\\n- **Output 2**:\\n  - The maze-like pattern and symmetry suggest a non-standard matrix structure, possibly derived from applying a specific transformation or algorithm to the Input matrix.\\n\\nIn summary, the Input 2 matrix has a simpler block-based structure, while the Output 2 matrix exhibits a more complex, grid-like pattern with symmetry, suggesting a transformation that reorganizes the elements into a more intricate layout.\"\n}\n\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence] [13.15s] Exiting Chain run with output:\n\u001b[0m{\n  \"output\": \"The image contains two matrices labeled \\\"Input 2\\\" and \\\"Output 2\\\". Below is a detailed description of each:\\n\\n### Input 2 Matrix\\n- **Size**: 8 rows x 8 columns.\\n- **Elements**: Numerical, specifically the numbers 0 and 7.\\n- **Arrangement**:\\n  - The matrix is divided into distinct blocks:\\n    - The top left quadrant (4x4) is mostly filled with 0s, with the exception of some 7s in the lower part of this quadrant.\\n    - The bottom left quadrant (4x4) is similar, with a block of 0s and a few 7s.\\n    - The right half (4x8) is entirely filled with the number 7.\\n  - This creates a pattern where the left side has two blocks of 0s in the top and bottom quadrants, and the right side is uniformly filled with 7s.\\n\\n### Output 2 Matrix\\n- **Size**: 8 rows x 8 columns.\\n- **Elements**: Numerical, specifically the numbers 0 and 7.\\n- **Arrangement**:\\n  - The matrix is more complex in its structure compared to the Input matrix.\\n  - The 0s and 7s form a pattern that resembles a maze or a grid with pathways:\\n    - The 0s are arranged in vertical and horizontal lines, creating a framework or grid-like pattern.\\n    - The 7s fill in the spaces between the 0s, forming contiguous blocks or regions.\\n  - The pattern appears to be symmetric along the vertical axis of the matrix, creating a balanced and structured look.\\n\\n### Observations:\\n- **Input 2**:\\n  - The matrix has a clear block structure with a stark contrast between the left and right halves.\\n  - The left half contains blocks of 0s with some 7s, while the right half is completely filled with 7s.\\n- **Output 2**:\\n  - The matrix transforms the input into a more intricate pattern.\\n  - The 0s form a grid-like structure, and the 7s fill the spaces within this grid.\\n  - There is a noticeable symmetry in the arrangement of the elements.\\n\\n### Unusual Patterns:\\n- **Input 2**:\\n  - The clear division between blocks of 0s and 7s is notable.\\n- **Output 2**:\\n  - The maze-like pattern and symmetry suggest a non-standard matrix structure, possibly derived from applying a specific transformation or algorithm to the Input matrix.\\n\\nIn summary, the Input 2 matrix has a simpler block-based structure, while the Output 2 matrix exhibits a more complex, grid-like pattern with symmetry, suggesting a transformation that reorganizes the elements into a more intricate layout.\"\n}\n","output_type":"stream"},{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"'The image contains two matrices labeled \"Input 2\" and \"Output 2\". Below is a detailed description of each:\\n\\n### Input 2 Matrix\\n- **Size**: 8 rows x 8 columns.\\n- **Elements**: Numerical, specifically the numbers 0 and 7.\\n- **Arrangement**:\\n  - The matrix is divided into distinct blocks:\\n    - The top left quadrant (4x4) is mostly filled with 0s, with the exception of some 7s in the lower part of this quadrant.\\n    - The bottom left quadrant (4x4) is similar, with a block of 0s and a few 7s.\\n    - The right half (4x8) is entirely filled with the number 7.\\n  - This creates a pattern where the left side has two blocks of 0s in the top and bottom quadrants, and the right side is uniformly filled with 7s.\\n\\n### Output 2 Matrix\\n- **Size**: 8 rows x 8 columns.\\n- **Elements**: Numerical, specifically the numbers 0 and 7.\\n- **Arrangement**:\\n  - The matrix is more complex in its structure compared to the Input matrix.\\n  - The 0s and 7s form a pattern that resembles a maze or a grid with pathways:\\n    - The 0s are arranged in vertical and horizontal lines, creating a framework or grid-like pattern.\\n    - The 7s fill in the spaces between the 0s, forming contiguous blocks or regions.\\n  - The pattern appears to be symmetric along the vertical axis of the matrix, creating a balanced and structured look.\\n\\n### Observations:\\n- **Input 2**:\\n  - The matrix has a clear block structure with a stark contrast between the left and right halves.\\n  - The left half contains blocks of 0s with some 7s, while the right half is completely filled with 7s.\\n- **Output 2**:\\n  - The matrix transforms the input into a more intricate pattern.\\n  - The 0s form a grid-like structure, and the 7s fill the spaces within this grid.\\n  - There is a noticeable symmetry in the arrangement of the elements.\\n\\n### Unusual Patterns:\\n- **Input 2**:\\n  - The clear division between blocks of 0s and 7s is notable.\\n- **Output 2**:\\n  - The maze-like pattern and symmetry suggest a non-standard matrix structure, possibly derived from applying a specific transformation or algorithm to the Input matrix.\\n\\nIn summary, the Input 2 matrix has a simpler block-based structure, while the Output 2 matrix exhibits a more complex, grid-like pattern with symmetry, suggesting a transformation that reorganizes the elements into a more intricate layout.'"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}