second_text_to_video_new_approach_genai.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":896,"sourceType":"datasetVersion","datasetId":435}],"dockerImageVersionId":30804,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:11:01.768958Z","iopub.execute_input":"2024-12-17T13:11:01.769316Z","iopub.status.idle":"2024-12-17T13:11:02.092672Z","shell.execute_reply.started":"2024-12-17T13:11:01.769266Z","shell.execute_reply":"2024-12-17T13:11:02.091782Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/sample-sales-data/sales_data_sample.csv\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"# Import necessary libraries\n# Installing necessary libraries\n!pip install moviepy\n!pip install moviepy\n!pip install gradio\n!pip install langchain\n!pip install transformers\n!pip install pandas\n!pip install numpy \n!pip install langchain_community\n!pip install moviepy\n!pip install openai\n!pip install os\n!pip install numpy\n!pip install dateime\n!pip install os\n!pip install diffusers\n!pip install torch\n!pip install editor\n!pip install dalle","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:11:02.094083Z","iopub.execute_input":"2024-12-17T13:11:02.094440Z","iopub.status.idle":"2024-12-17T13:13:22.558663Z","shell.execute_reply.started":"2024-12-17T13:11:02.094413Z","shell.execute_reply":"2024-12-17T13:13:22.557562Z"}},"outputs":[{"name":"stdout","text":"Collecting moviepy\n  Downloading moviepy-2.1.1-py3-none-any.whl.metadata (6.9 kB)\nRequirement already satisfied: decorator<6.0,>=4.0.2 in /opt/conda/lib/python3.10/site-packages (from moviepy) (5.1.1)\nRequirement already satisfied: imageio<3.0,>=2.5 in /opt/conda/lib/python3.10/site-packages (from moviepy) (2.34.1)\nCollecting imageio_ffmpeg>=0.2.0 (from moviepy)\n  Downloading imageio_ffmpeg-0.5.1-py3-none-manylinux2010_x86_64.whl.metadata (1.6 kB)\nRequirement already satisfied: numpy>=1.25.0 in /opt/conda/lib/python3.10/site-packages (from moviepy) (1.26.4)\nCollecting proglog<=1.0.0 (from moviepy)\n  Downloading proglog-0.1.10-py3-none-any.whl.metadata (639 bytes)\nRequirement already satisfied: python-dotenv>=0.10 in /opt/conda/lib/python3.10/site-packages (from moviepy) (1.0.1)\nRequirement already satisfied: pillow<11.0,>=9.2.0 in /opt/conda/lib/python3.10/site-packages (from moviepy) (10.3.0)\nRequirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from imageio_ffmpeg>=0.2.0->moviepy) (70.0.0)\nRequirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from proglog<=1.0.0->moviepy) (4.66.4)\nDownloading moviepy-2.1.1-py3-none-any.whl (123 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m123.5/123.5 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n\u001b[?25hDownloading imageio_ffmpeg-0.5.1-py3-none-manylinux2010_x86_64.whl (26.9 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m26.9/26.9 MB\u001b[0m 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tenacity!=8.4.0,<10,>=8.1.0 in /opt/conda/lib/python3.10/site-packages (from langchain) (8.3.0)\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: 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: 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: multidict<7.0,>=4.5 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (6.0.5)\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) (1.9.4)\nRequirement already satisfied: jsonpatch<2.0,>=1.33 in /opt/conda/lib/python3.10/site-packages (from langchain-core<0.4.0,>=0.3.25->langchain) (1.33)\nCollecting packaging<25,>=23.2 (from langchain-core<0.4.0,>=0.3.25->langchain)\n  Downloading packaging-24.2-py3-none-any.whl.metadata (3.2 kB)\nRequirement already satisfied: typing-extensions>=4.7 in /opt/conda/lib/python3.10/site-packages (from langchain-core<0.4.0,>=0.3.25->langchain) (4.12.2)\nRequirement already satisfied: httpx<1,>=0.23.0 in /opt/conda/lib/python3.10/site-packages (from langsmith<0.3,>=0.1.17->langchain) (0.27.0)\nRequirement already satisfied: orjson<4.0.0,>=3.9.14 in /opt/conda/lib/python3.10/site-packages (from langsmith<0.3,>=0.1.17->langchain) (3.10.4)\nCollecting requests-toolbelt<2.0.0,>=1.0.0 (from langsmith<0.3,>=0.1.17->langchain)\n  Downloading requests_toolbelt-1.0.0-py2.py3-none-any.whl.metadata (14 kB)\nRequirement already satisfied: annotated-types>=0.6.0 in /opt/conda/lib/python3.10/site-packages (from pydantic<3.0.0,>=2.7.4->langchain) (0.7.0)\nRequirement already satisfied: pydantic-core==2.27.1 in /opt/conda/lib/python3.10/site-packages (from pydantic<3.0.0,>=2.7.4->langchain) (2.27.1)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2->langchain) (3.3.2)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2->langchain) (3.7)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2->langchain) (1.26.18)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2->langchain) (2024.6.2)\nRequirement already satisfied: greenlet!=0.4.17 in /opt/conda/lib/python3.10/site-packages (from SQLAlchemy<3,>=1.4->langchain) (3.0.3)\nRequirement already satisfied: anyio in /opt/conda/lib/python3.10/site-packages (from httpx<1,>=0.23.0->langsmith<0.3,>=0.1.17->langchain) (4.4.0)\nRequirement already satisfied: httpcore==1.* in /opt/conda/lib/python3.10/site-packages (from httpx<1,>=0.23.0->langsmith<0.3,>=0.1.17->langchain) (1.0.5)\nRequirement already satisfied: sniffio in /opt/conda/lib/python3.10/site-packages (from httpx<1,>=0.23.0->langsmith<0.3,>=0.1.17->langchain) (1.3.1)\nRequirement already satisfied: h11<0.15,>=0.13 in /opt/conda/lib/python3.10/site-packages (from httpcore==1.*->httpx<1,>=0.23.0->langsmith<0.3,>=0.1.17->langchain) (0.14.0)\nRequirement already satisfied: jsonpointer>=1.9 in /opt/conda/lib/python3.10/site-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.4.0,>=0.3.25->langchain) (2.4)\nRequirement already satisfied: exceptiongroup>=1.0.2 in /opt/conda/lib/python3.10/site-packages (from anyio->httpx<1,>=0.23.0->langsmith<0.3,>=0.1.17->langchain) (1.2.0)\nDownloading langchain-0.3.12-py3-none-any.whl (1.0 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m0:01\u001b[0m\n\u001b[?25hDownloading langchain_core-0.3.25-py3-none-any.whl (411 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m411.2/411.2 kB\u001b[0m \u001b[31m26.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading langchain_text_splitters-0.3.3-py3-none-any.whl (27 kB)\nDownloading langsmith-0.2.3-py3-none-any.whl (320 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m320.7/320.7 kB\u001b[0m \u001b[31m21.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading packaging-24.2-py3-none-any.whl (65 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.5/65.5 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading requests_toolbelt-1.0.0-py2.py3-none-any.whl (54 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hInstalling collected packages: packaging, requests-toolbelt, langsmith, langchain-core, langchain-text-splitters, langchain\n  Attempting uninstall: packaging\n    Found existing installation: packaging 21.3\n    Uninstalling packaging-21.3:\n      Successfully uninstalled packaging-21.3\n  Attempting uninstall: requests-toolbelt\n    Found existing installation: requests-toolbelt 0.10.1\n    Uninstalling requests-toolbelt-0.10.1:\n      Successfully uninstalled requests-toolbelt-0.10.1\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 24.10.1 requires cubinlinker, which is not installed.\ncudf 24.10.1 requires cupy-cuda11x>=12.0.0, which is not installed.\ncudf 24.10.1 requires libcudf==24.10.*, which is not installed.\ncudf 24.10.1 requires ptxcompiler, which is not installed.\ncuml 24.10.0 requires cupy-cuda11x>=12.0.0, which is not installed.\ncuml 24.10.0 requires cuvs==24.10.*, which is not installed.\ncuml 24.10.0 requires nvidia-cublas, which is not installed.\ncuml 24.10.0 requires nvidia-cufft, which is not installed.\ncuml 24.10.0 requires nvidia-curand, which is not installed.\ncuml 24.10.0 requires nvidia-cusolver, which is not installed.\ncuml 24.10.0 requires nvidia-cusparse, which is not installed.\ndask-cudf 24.10.1 requires cupy-cuda11x>=12.0.0, which is not installed.\npylibcudf 24.10.1 requires libcudf==24.10.*, which is not installed.\ncudf 24.10.1 requires cuda-python<12.0a0,>=11.7.1, but you have cuda-python 12.6.2.post1 which is incompatible.\ncudf 24.10.1 requires pandas<2.2.3dev0,>=2.0, but you have pandas 2.2.3 which is incompatible.\ndask-cudf 24.10.1 requires pandas<2.2.3dev0,>=2.0, but you have pandas 2.2.3 which is incompatible.\ndistributed 2024.9.0 requires dask==2024.9.0, but you have dask 2024.11.2 which is incompatible.\ngoogle-cloud-bigquery 2.34.4 requires packaging<22.0dev,>=14.3, but you have packaging 24.2 which is incompatible.\njupyterlab 4.3.1 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.\nkfp 2.5.0 requires google-cloud-storage<3,>=2.2.1, but you have google-cloud-storage 1.44.0 which is incompatible.\nkfp 2.5.0 requires requests-toolbelt<1,>=0.8.0, but you have requests-toolbelt 1.0.0 which is incompatible.\nlibpysal 4.9.2 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.\nmlxtend 0.23.3 requires scikit-learn>=1.3.1, but you have scikit-learn 1.2.2 which is incompatible.\nplotnine 0.14.3 requires matplotlib>=3.8.0, but you have matplotlib 3.7.5 which is incompatible.\npylibcudf 24.10.1 requires cuda-python<12.0a0,>=11.7.1, but you have cuda-python 12.6.2.post1 which is incompatible.\nrapids-dask-dependency 24.10.0a0 requires dask==2024.9.0, but you have dask 2024.11.2 which is incompatible.\nrapids-dask-dependency 24.10.0a0 requires dask-expr==1.1.14, but you have dask-expr 1.1.19 which is incompatible.\nthinc 8.3.2 requires numpy<2.1.0,>=2.0.0; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\nydata-profiling 4.12.0 requires scipy<1.14,>=1.4.1, but you have scipy 1.14.1 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed langchain-0.3.12 langchain-core-0.3.25 langchain-text-splitters-0.3.3 langsmith-0.2.3 packaging-24.2 requests-toolbelt-1.0.0\nRequirement already satisfied: transformers in /opt/conda/lib/python3.10/site-packages (4.46.3)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from transformers) (3.15.1)\nRequirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /opt/conda/lib/python3.10/site-packages (from transformers) (0.26.2)\nRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from transformers) (1.26.4)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from transformers) (24.2)\nRequirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from transformers) (6.0.2)\nRequirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers) (2024.5.15)\nRequirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from transformers) (2.32.3)\nRequirement already satisfied: tokenizers<0.21,>=0.20 in /opt/conda/lib/python3.10/site-packages (from transformers) (0.20.3)\nRequirement already satisfied: safetensors>=0.4.1 in /opt/conda/lib/python3.10/site-packages (from transformers) (0.4.5)\nRequirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers) (4.66.4)\nRequirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.6.0)\nRequirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->transformers) (3.3.2)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->transformers) (3.7)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->transformers) (1.26.18)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->transformers) (2024.6.2)\nRequirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (2.2.3)\nRequirement already satisfied: numpy>=1.22.4 in /opt/conda/lib/python3.10/site-packages (from pandas) (1.26.4)\nRequirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas) (2.9.0.post0)\nRequirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas) (2024.1)\nRequirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.10/site-packages (from pandas) (2024.1)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (1.26.4)\nCollecting langchain_community\n  Downloading langchain_community-0.3.12-py3-none-any.whl.metadata (2.9 kB)\nRequirement already satisfied: PyYAML>=5.3 in /opt/conda/lib/python3.10/site-packages (from langchain_community) (6.0.2)\nRequirement already satisfied: SQLAlchemy<3,>=1.4 in /opt/conda/lib/python3.10/site-packages (from langchain_community) (2.0.30)\nRequirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /opt/conda/lib/python3.10/site-packages (from langchain_community) (3.9.5)\nRequirement already satisfied: dataclasses-json<0.7,>=0.5.7 in /opt/conda/lib/python3.10/site-packages (from langchain_community) (0.6.7)\nCollecting httpx-sse<0.5.0,>=0.4.0 (from langchain_community)\n  Downloading httpx_sse-0.4.0-py3-none-any.whl.metadata (9.0 kB)\nRequirement already satisfied: langchain<0.4.0,>=0.3.12 in /opt/conda/lib/python3.10/site-packages (from langchain_community) (0.3.12)\nRequirement already satisfied: langchain-core<0.4.0,>=0.3.25 in /opt/conda/lib/python3.10/site-packages (from langchain_community) (0.3.25)\nRequirement already satisfied: langsmith<0.3,>=0.1.125 in /opt/conda/lib/python3.10/site-packages (from langchain_community) (0.2.3)\nRequirement already satisfied: numpy<2,>=1.22.4 in /opt/conda/lib/python3.10/site-packages (from langchain_community) (1.26.4)\nCollecting pydantic-settings<3.0.0,>=2.4.0 (from langchain_community)\n  Downloading pydantic_settings-2.7.0-py3-none-any.whl.metadata (3.5 kB)\nRequirement already satisfied: requests<3,>=2 in /opt/conda/lib/python3.10/site-packages (from langchain_community) (2.32.3)\nRequirement already satisfied: tenacity!=8.4.0,<10,>=8.1.0 in /opt/conda/lib/python3.10/site-packages (from langchain_community) (8.3.0)\nRequirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (1.3.1)\nRequirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (23.2.0)\nRequirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (1.4.1)\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_community) (6.0.5)\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_community) (1.9.4)\nRequirement already satisfied: async-timeout<5.0,>=4.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain_community) (4.0.3)\nRequirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /opt/conda/lib/python3.10/site-packages (from dataclasses-json<0.7,>=0.5.7->langchain_community) (3.23.1)\nRequirement already satisfied: typing-inspect<1,>=0.4.0 in /opt/conda/lib/python3.10/site-packages (from dataclasses-json<0.7,>=0.5.7->langchain_community) (0.9.0)\nRequirement already satisfied: langchain-text-splitters<0.4.0,>=0.3.3 in /opt/conda/lib/python3.10/site-packages (from langchain<0.4.0,>=0.3.12->langchain_community) (0.3.3)\nRequirement already satisfied: pydantic<3.0.0,>=2.7.4 in /opt/conda/lib/python3.10/site-packages (from langchain<0.4.0,>=0.3.12->langchain_community) (2.10.1)\nRequirement already satisfied: jsonpatch<2.0,>=1.33 in /opt/conda/lib/python3.10/site-packages (from langchain-core<0.4.0,>=0.3.25->langchain_community) (1.33)\nRequirement already satisfied: packaging<25,>=23.2 in /opt/conda/lib/python3.10/site-packages (from langchain-core<0.4.0,>=0.3.25->langchain_community) (24.2)\nRequirement already satisfied: typing-extensions>=4.7 in /opt/conda/lib/python3.10/site-packages (from langchain-core<0.4.0,>=0.3.25->langchain_community) (4.12.2)\nRequirement already satisfied: httpx<1,>=0.23.0 in /opt/conda/lib/python3.10/site-packages (from langsmith<0.3,>=0.1.125->langchain_community) (0.27.0)\nRequirement already satisfied: orjson<4.0.0,>=3.9.14 in /opt/conda/lib/python3.10/site-packages (from langsmith<0.3,>=0.1.125->langchain_community) (3.10.4)\nRequirement already satisfied: requests-toolbelt<2.0.0,>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from langsmith<0.3,>=0.1.125->langchain_community) (1.0.0)\nRequirement already satisfied: python-dotenv>=0.21.0 in /opt/conda/lib/python3.10/site-packages (from pydantic-settings<3.0.0,>=2.4.0->langchain_community) (1.0.1)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2->langchain_community) (3.3.2)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2->langchain_community) (3.7)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2->langchain_community) (1.26.18)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2->langchain_community) (2024.6.2)\nRequirement already satisfied: greenlet!=0.4.17 in /opt/conda/lib/python3.10/site-packages (from SQLAlchemy<3,>=1.4->langchain_community) (3.0.3)\nRequirement already satisfied: anyio in /opt/conda/lib/python3.10/site-packages (from httpx<1,>=0.23.0->langsmith<0.3,>=0.1.125->langchain_community) (4.4.0)\nRequirement already satisfied: httpcore==1.* in /opt/conda/lib/python3.10/site-packages (from httpx<1,>=0.23.0->langsmith<0.3,>=0.1.125->langchain_community) (1.0.5)\nRequirement already satisfied: sniffio in /opt/conda/lib/python3.10/site-packages (from httpx<1,>=0.23.0->langsmith<0.3,>=0.1.125->langchain_community) (1.3.1)\nRequirement already satisfied: h11<0.15,>=0.13 in /opt/conda/lib/python3.10/site-packages (from httpcore==1.*->httpx<1,>=0.23.0->langsmith<0.3,>=0.1.125->langchain_community) (0.14.0)\nRequirement already satisfied: jsonpointer>=1.9 in /opt/conda/lib/python3.10/site-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.4.0,>=0.3.25->langchain_community) (2.4)\nRequirement already satisfied: annotated-types>=0.6.0 in /opt/conda/lib/python3.10/site-packages (from pydantic<3.0.0,>=2.7.4->langchain<0.4.0,>=0.3.12->langchain_community) (0.7.0)\nRequirement already satisfied: pydantic-core==2.27.1 in /opt/conda/lib/python3.10/site-packages (from pydantic<3.0.0,>=2.7.4->langchain<0.4.0,>=0.3.12->langchain_community) (2.27.1)\nRequirement already satisfied: mypy-extensions>=0.3.0 in /opt/conda/lib/python3.10/site-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain_community) (1.0.0)\nRequirement already satisfied: exceptiongroup>=1.0.2 in /opt/conda/lib/python3.10/site-packages (from anyio->httpx<1,>=0.23.0->langsmith<0.3,>=0.1.125->langchain_community) (1.2.0)\nDownloading langchain_community-0.3.12-py3-none-any.whl (2.5 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hDownloading 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xmod-1.8.1-py3-none-any.whl (4.6 kB)\nInstalling collected packages: xmod, runs, editor\nSuccessfully installed editor-1.6.6 runs-1.2.2 xmod-1.8.1\n\u001b[31mERROR: Could not find a version that satisfies the requirement dalle (from versions: none)\u001b[0m\u001b[31m\n\u001b[0m\u001b[31mERROR: No matching distribution found for dalle\u001b[0m\u001b[31m\n\u001b[0m","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"!pip install gtts\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport numpy as np\nimport os\nimport torch\nfrom transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoModelForTokenClassification\nimport gtts  # For text-to-speech audio generation\n# from langchain_community import LangChain  # For implementing langchain and other NLP tasks\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:13:22.560228Z","iopub.execute_input":"2024-12-17T13:13:22.560539Z","iopub.status.idle":"2024-12-17T13:13:35.629130Z","shell.execute_reply.started":"2024-12-17T13:13:22.560511Z","shell.execute_reply":"2024-12-17T13:13:35.628153Z"}},"outputs":[{"name":"stdout","text":"Collecting gtts\n  Downloading gTTS-2.5.4-py3-none-any.whl.metadata (4.1 kB)\nRequirement already satisfied: requests<3,>=2.27 in /opt/conda/lib/python3.10/site-packages (from gtts) (2.32.3)\nRequirement already satisfied: click<8.2,>=7.1 in /opt/conda/lib/python3.10/site-packages (from gtts) (8.1.7)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2.27->gtts) (3.3.2)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2.27->gtts) (3.7)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2.27->gtts) (1.26.18)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2.27->gtts) (2024.6.2)\nDownloading gTTS-2.5.4-py3-none-any.whl (29 kB)\nInstalling collected packages: gtts\nSuccessfully installed gtts-2.5.4\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"def nlp_pipeline(text, data):\n    # Use T5 for summarization\n    summary_model = AutoModelForSeq2SeqLM.from_pretrained('t5-base')\n    summary_tokenizer = AutoTokenizer.from_pretrained('t5-base')\n    \n    # Prepare input\n    input_text = f\"summarize: {text} {data}\"\n    inputs = summary_tokenizer.encode(input_text, return_tensors='pt', max_length=512, truncation=True)\n    \n    # Generate summary\n    outputs = summary_model.generate(inputs, max_length=100)\n    summary_text = summary_tokenizer.decode(outputs[0], skip_special_tokens=True)\n    \n    # Extract percentages and categories using simpler regex patterns\n    import re\n    percentages = [int(x.strip('%')) for x in re.findall(r'\\d+%', text)]\n    words = text.split()\n    categories = []\n    \n    # Find words after \"use\" or \"uses\"\n    for i, word in enumerate(words):\n        if word.lower() in ['use', 'uses'] and i + 1 < len(words):\n            categories.append(words[i + 1])\n    \n    if not percentages or not categories:\n        percentages = [100]\n        categories = ['Summary']\n    \n    # Generate audio\n    tts = gtts.gTTS(summary_text, lang='en')\n    tts.save('summary_audio.mp3')\n    \n    return {\n        'categories': categories,\n        'values': percentages,\n        'text': summary_text\n    }","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:13:35.631134Z","iopub.execute_input":"2024-12-17T13:13:35.631580Z","iopub.status.idle":"2024-12-17T13:13:35.639870Z","shell.execute_reply.started":"2024-12-17T13:13:35.631524Z","shell.execute_reply":"2024-12-17T13:13:35.639009Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"# Define a function to generate infographics from CSV file with dynamic visualization selection\ndef generate_infographics_from_csv(csv_file):\n    # Read the CSV file with error handling for UnicodeDecodeError\n    try:\n        df = pd.read_csv(csv_file)\n    except UnicodeDecodeError:\n        df = pd.read_csv(csv_file, encoding='utf-8', errors='replace')\n    \n    # Determine the best visualization method based on data characteristics\n    if len(df) <= 10:  # For small datasets, a pie chart is suitable\n        visualization_method = 'pie_chart'\n    elif len(df) <= 50:  # For medium-sized datasets, a bar chart is suitable\n        visualization_method = 'bar_chart'\n    elif len(df) <= 100:  # For larger datasets, a line chart is suitable\n        visualization_method = 'line_chart'\n    else:  # For even larger datasets, consider using more advanced visualization tools or libraries\n        visualization_method = 'advanced_visualization'\n    \n    # Generate the selected visualization\n    if visualization_method == 'bar_chart':\n        fig, ax = plt.subplots()\n        ax.bar(df['Category'], df['Value'])\n        ax.set_title('Bar Chart')\n        ax.set_xlabel('Category')\n        ax.set_ylabel('Value')\n        plt.savefig('bar_chart.png')\n    elif visualization_method == 'pie_chart':\n        fig, ax = plt.subplots()\n        ax.pie(df['Value'], labels=df['Category'], autopct='%1.1f%%')\n        ax.set_title('Pie Chart')\n        ax.axis('equal')\n        plt.savefig('pie_chart.png')\n    elif visualization_method == 'line_chart':\n        fig, ax = plt.subplots()\n        ax.plot(df['Category'], df['Value'])\n        ax.set_title('Line Chart')\n        ax.set_xlabel('Category')\n        ax.set_ylabel('Value')\n        plt.savefig('line_chart.png')\n    elif visualization_method == 'advanced_visualization':\n        # For larger datasets, consider using more advanced visualization tools or libraries\n        # such as seaborn, plotly, or bokeh to handle large datasets and process the data\n        # Example using seaborn\n        import seaborn as sns\n        sns.set_theme(style=\"whitegrid\")\n        sns.barplot(data=df, x='Category', y='Value')\n        plt.title('Advanced Visualization')\n        plt.xlabel('Category')\n        plt.ylabel('Value')\n        plt.savefig('advanced_visualization.png')\n    \n    # Save the generated image\n    plt.savefig(f'{visualization_method}.png')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:13:35.640878Z","iopub.execute_input":"2024-12-17T13:13:35.641133Z","iopub.status.idle":"2024-12-17T13:13:35.695389Z","shell.execute_reply.started":"2024-12-17T13:13:35.641109Z","shell.execute_reply":"2024-12-17T13:13:35.694576Z"}},"outputs":[],"execution_count":6},{"cell_type":"code","source":"# Define a function to generate infographics from custom prompt and CSV file\ndef generate_infographics_from_prompt_and_csv(prompt, csv_file):\n    # Use the NLP pipeline to process the prompt and CSV file\n    summary = nlp_pipeline(prompt, pd.read_csv(csv_file)['Category'].tolist())\n    \n    # Integrate with AI models like GPT-3 for more dynamic and interactive visualizations\n    # Assuming GPT-3 is available and can generate images based on the summary\n    # For demonstration, we'll use matplotlib for a simple visualization\n    fig, ax = plt.subplots()\n    ax.bar(summary['categories'], summary['values'])\n    ax.set_title('Infographics from CSV and Prompt')\n    ax.set_xlabel('Category')\n    ax.set_ylabel('Value')\n    plt.savefig('infographics_image.png')\n    images = ['infographics_image.png']\n    \n    # Create a video from the images using moviepy\n    from moviepy.editor import ImageSequenceClip\n    clip = ImageSequenceClip(images, fps=1)\n    clip.write_videofile('infographics_video.mp4')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:13:35.696398Z","iopub.execute_input":"2024-12-17T13:13:35.696680Z","iopub.status.idle":"2024-12-17T13:13:35.713227Z","shell.execute_reply.started":"2024-12-17T13:13:35.696656Z","shell.execute_reply":"2024-12-17T13:13:35.712599Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"def create_animated_gif(text):\n    import os\n    from PIL import Image\n    import matplotlib.pyplot as plt\n    \n    # Use the NLP pipeline to process the text\n    summary = nlp_pipeline(text, '')\n    categories = summary['categories']\n    values = summary['values']\n    \n    # Create frames directory\n    frames_dir = 'animation_frames'\n    os.makedirs(frames_dir, exist_ok=True)\n    \n    def create_frame(frame_number, value_multiplier, categories=categories, values=values):  # Pass values as parameter\n        fig, ax = plt.subplots(figsize=(12, 7))\n        \n        # Calculate current height of bars\n        current_values = [v * value_multiplier for v in values]\n        \n        # Create bars with current height\n        bars = ax.bar(categories, current_values, color='skyblue')\n        \n        # Styling\n        ax.set_title('Market Share Analysis', fontsize=20, pad=20)\n        ax.set_xlabel('Brands', fontsize=14)\n        ax.set_ylabel('Percentage (%)', fontsize=14)\n        ax.set_ylim(0, max(values) * 1.2)\n        \n        # Add value labels\n        for bar, value in zip(bars, current_values):\n            if value > 0:\n                height = bar.get_height()\n                ax.text(bar.get_x() + bar.get_width()/2., height,\n                       f'{int(value)}%',\n                       ha='center', va='bottom', fontsize=12)\n        \n        ax.grid(True, axis='y', linestyle='--', alpha=0.7)\n        plt.tight_layout()\n        \n        # Save frame\n        frame_path = os.path.join(frames_dir, f'frame_{frame_number:03d}.png')\n        plt.savefig(frame_path, dpi=300, bbox_inches='tight')\n        plt.close()\n        return frame_path\n    \n    # Generate frames\n    frames = []\n    num_frames = 20  # Number of frames for animation\n    \n    print(\"Generating frames...\")\n    for i in range(num_frames + 1):\n        multiplier = i / num_frames\n        frame_path = create_frame(i, multiplier)\n        frames.append(frame_path)\n    \n    # Create GIF\n    print(\"Creating GIF...\")\n    images = [Image.open(f) for f in frames]\n    \n    gif_path = 'animated_infographic.gif'\n    images[0].save(\n        gif_path,\n        save_all=True,\n        append_images=images[1:],\n        duration=100,  # 100ms between frames\n        loop=0\n    )\n    \n    # Clean up frames\n    for frame in frames:\n        os.remove(frame)\n    os.rmdir(frames_dir)\n    \n    print(f\"Animation saved as GIF: {gif_path}\")\n    return gif_path","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:13:35.714378Z","iopub.execute_input":"2024-12-17T13:13:35.714674Z","iopub.status.idle":"2024-12-17T13:13:35.731467Z","shell.execute_reply.started":"2024-12-17T13:13:35.714649Z","shell.execute_reply":"2024-12-17T13:13:35.730854Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"def create_animated_gif(text):\n    import os\n    from PIL import Image\n    import matplotlib.pyplot as plt\n    import shutil  # For directory removal\n    \n    # Use the NLP pipeline to process the text\n    summary = nlp_pipeline(text, '')\n    categories = summary['categories']\n    values = summary['values']\n    \n    # Create frames directory\n    frames_dir = 'animation_frames'\n    if os.path.exists(frames_dir):\n        shutil.rmtree(frames_dir)  # Remove directory if it exists\n    os.makedirs(frames_dir)\n    \n    def create_frame(frame_number, value_multiplier, categories=categories, values=values):\n        fig, ax = plt.subplots(figsize=(12, 7))\n        \n        # Calculate current height of bars\n        current_values = [v * value_multiplier for v in values]\n        \n        # Create bars with current height\n        bars = ax.bar(categories, current_values, color='skyblue')\n        \n        # Styling\n        ax.set_title('Market Share Analysis', fontsize=20, pad=20)\n        ax.set_xlabel('Brands', fontsize=14)\n        ax.set_ylabel('Percentage (%)', fontsize=14)\n        ax.set_ylim(0, max(values) * 1.2)\n        \n        # Add value labels\n        for bar, value in zip(bars, current_values):\n            if value > 0:\n                height = bar.get_height()\n                ax.text(bar.get_x() + bar.get_width()/2., height,\n                       f'{int(value)}%',\n                       ha='center', va='bottom', fontsize=12)\n        \n        ax.grid(True, axis='y', linestyle='--', alpha=0.7)\n        plt.tight_layout()\n        \n        # Save frame\n        frame_path = os.path.join(frames_dir, f'frame_{frame_number:03d}.png')\n        plt.savefig(frame_path, dpi=300, bbox_inches='tight')\n        plt.close()\n        return frame_path\n    \n    # Generate frames\n    frames = []\n    num_frames = 20  # Number of frames for animation\n    \n    print(\"Generating frames...\")\n    for i in range(num_frames + 1):\n        multiplier = i / num_frames\n        frame_path = create_frame(i, multiplier)\n        frames.append(frame_path)\n    \n    # Create GIF\n    print(\"Creating GIF...\")\n    images = [Image.open(f) for f in frames]\n    \n    gif_path = 'animated_infographic.gif'\n    images[0].save(\n        gif_path,\n        save_all=True,\n        append_images=images[1:],\n        duration=100,  # 100ms between frames\n        loop=0\n    )\n    \n    # Clean up frames directory\n    try:\n        shutil.rmtree(frames_dir)\n        print(\"Cleanup completed successfully\")\n    except Exception as e:\n        print(f\"Cleanup error: {e}\")\n    \n    print(f\"Animation saved as GIF: {gif_path}\")\n    return gif_path","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:13:35.732449Z","iopub.execute_input":"2024-12-17T13:13:35.732770Z","iopub.status.idle":"2024-12-17T13:13:35.750095Z","shell.execute_reply.started":"2024-12-17T13:13:35.732746Z","shell.execute_reply":"2024-12-17T13:13:35.749241Z"}},"outputs":[],"execution_count":9},{"cell_type":"code","source":"\n\ndef create_detailed_infographic(text):\n    \"\"\"\n    Creates a static detailed infographic for data storytelling\n    \"\"\"\n    import matplotlib.pyplot as plt\n    \n    # Process text\n    summary = nlp_pipeline(text, '')\n    categories = summary['categories']\n    values = summary['values']\n    \n    # Create figure with subplots\n    fig = plt.figure(figsize=(15, 10))\n    \n    # Main bar plot\n    ax1 = plt.subplot2grid((2, 2), (0, 0), colspan=2)\n    bars = ax1.bar(categories, values, color='skyblue')\n    ax1.set_title('Market Share Distribution', fontsize=16)\n    ax1.set_ylabel('Percentage (%)')\n    \n    # Add value labels\n    for bar in bars:\n        height = bar.get_height()\n        ax1.text(bar.get_x() + bar.get_width()/2., height,\n                f'{int(height)}%',\n                ha='center', va='bottom')\n    \n    # Pie chart\n    ax2 = plt.subplot2grid((2, 2), (1, 0))\n    ax2.pie(values, labels=categories, autopct='%1.1f%%')\n    ax2.set_title('Market Share Proportion')\n    \n    # Additional insights text\n    ax3 = plt.subplot2grid((2, 2), (1, 1))\n    ax3.axis('off')\n    total = sum(values)\n    insights_text = f\"\"\"Key Insights:\n    \n    • Total market coverage: {total}%\n    • Leading brand: {categories[values.index(max(values))]}\n    • Market share gap: {max(values)-min(values)}%\n    \"\"\"\n    ax3.text(0, 0.5, insights_text, fontsize=12, va='center')\n    \n    plt.tight_layout()\n    \n    # Save high-quality image\n    output_path = 'detailed_infographic.png'\n    plt.savefig(output_path, dpi=300, bbox_inches='tight')\n    plt.close()\n    \n    print(f\"Detailed infographic saved as: {output_path}\")\n    return output_path\n    print(f\"Detailed infographic saved as: {output_path}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:13:35.751126Z","iopub.execute_input":"2024-12-17T13:13:35.751391Z","iopub.status.idle":"2024-12-17T13:13:35.764725Z","shell.execute_reply.started":"2024-12-17T13:13:35.751367Z","shell.execute_reply":"2024-12-17T13:13:35.763949Z"}},"outputs":[],"execution_count":10},{"cell_type":"code","source":"def convert_gif_to_storytelling_video(gif_path, text):\n    \"\"\"\n    Converts a GIF into a storytelling video using imageio\n    \"\"\"\n    import os\n    from PIL import Image, ImageDraw, ImageFont\n    import numpy as np\n    import imageio\n    \n    # Process text for insights\n    summary = nlp_pipeline(text, '')\n    categories = summary['categories']\n    values = summary['values']\n    \n    def create_text_frame(text, size=(1920, 1080), bg_color='white'):\n        img = Image.new('RGB', size, color=bg_color)\n        draw = ImageDraw.Draw(img)\n        \n        try:\n            font = ImageFont.truetype(\"arial.ttf\", 60)\n        except:\n            font = ImageFont.load_default()\n        \n        # Get text bbox\n        bbox = draw.textbbox((0, 0), text, font=font)\n        text_width = bbox[2] - bbox[0]\n        text_height = bbox[3] - bbox[1]\n        \n        # Center text\n        x = (size[0] - text_width) // 2\n        y = (size[1] - text_height) // 2\n        \n        draw.text((x, y), text, fill='black' if bg_color == 'white' else 'white', font=font)\n        # Convert to RGB numpy array\n        return np.array(img.convert('RGB'))\n    \n    # Prepare frames\n    frames = []\n    fps = 30\n    \n    # 1. Title sequence (2 seconds)\n    title_frame = create_text_frame(\"Market Share Analysis\", bg_color='black')\n    for _ in range(2 * fps):\n        frames.append(title_frame)\n    \n    # 2. GIF sequence (4 seconds)\n    gif = Image.open(gif_path)\n    gif_frames = []\n    try:\n        while True:\n            frame = gif.copy()\n            # Resize frame and ensure RGB\n            frame = frame.convert('RGB').resize((1920, 1080), Image.LANCZOS)\n            # Convert to numpy array\n            frame_array = np.array(frame)\n            gif_frames.append(frame_array)\n            gif.seek(len(gif_frames))\n    except EOFError:\n        pass\n    \n    # Extend gif frames to 4 seconds\n    frames_needed = 4 * fps\n    while len(gif_frames) < frames_needed:\n        gif_frames.extend(gif_frames)\n    frames.extend(gif_frames[:frames_needed])\n    \n    # 3. Explanation sequence (4 seconds)\n    explanations = [\n        \"Analyzing market share data...\",\n        f\"Main competitor: {categories[values.index(max(values))]} leads with {max(values)}%\",\n        f\"Market gap analysis shows {max(values)-min(values)}% difference\",\n        f\"Total market coverage: {sum(values)}%\",\n        \"Generating insights and recommendations...\"\n    ]\n    \n    frames_per_explanation = int((4 * fps) / len(explanations))\n    for exp in explanations:\n        exp_frame = create_text_frame(exp)\n        for _ in range(frames_per_explanation):\n            frames.append(exp_frame)\n    \n    # Verify all frames have same shape and channels\n    frame_shape = frames[0].shape\n    frames = [frame.reshape(frame_shape) if frame.shape != frame_shape else frame \n             for frame in frames]\n    \n    # Save as MP4\n    output_path = 'data_storytelling_video.mp4'\n    \n    print(\"Writing video...\")\n    writer = imageio.get_writer(output_path, fps=fps)\n    for frame in frames:\n        writer.append_data(frame)\n    writer.close()\n    \n    print(f\"Data storytelling video saved as: {output_path}\")\n    return output_path","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:13:35.767279Z","iopub.execute_input":"2024-12-17T13:13:35.767541Z","iopub.status.idle":"2024-12-17T13:13:35.782809Z","shell.execute_reply.started":"2024-12-17T13:13:35.767510Z","shell.execute_reply":"2024-12-17T13:13:35.782118Z"}},"outputs":[],"execution_count":11},{"cell_type":"code","source":"# Create the animated GIF\ngif_path = create_animated_gif('30% dogs use nokia and 90% use iphones')\n\n# Convert to storytelling video\nvideo_path = convert_gif_to_storytelling_video(gif_path, '30% dogs use nokia and 90% use iphones')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:32:38.262487Z","iopub.execute_input":"2024-12-17T13:32:38.262931Z","iopub.status.idle":"2024-12-17T13:33:12.210707Z","shell.execute_reply.started":"2024-12-17T13:32:38.262895Z","shell.execute_reply":"2024-12-17T13:33:12.209389Z"}},"outputs":[{"name":"stdout","text":"Generating frames...\nCreating GIF...\nCleanup completed successfully\nAnimation saved as GIF: animated_infographic.gif\nWriting video...\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.10/subprocess.py:1796: 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  self.pid = _posixsubprocess.fork_exec(\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n","output_type":"stream"},{"name":"stdout","text":"Data storytelling video saved as: data_storytelling_video.mp4\n","output_type":"stream"}],"execution_count":20},{"cell_type":"code","source":"# # First create the animated GIF\n# gif_path = create_animated_gif('20% users use iphone and 40% uses samsung')\n\n# # Then convert it to storytelling video\n# video_path = convert_gif_to_storytelling_video(gif_path, '20% users use iphone and 40% uses samsung')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:14:29.221836Z","iopub.execute_input":"2024-12-17T13:14:29.223169Z","iopub.status.idle":"2024-12-17T13:14:29.227296Z","shell.execute_reply.started":"2024-12-17T13:14:29.223135Z","shell.execute_reply":"2024-12-17T13:14:29.226473Z"}},"outputs":[],"execution_count":13},{"cell_type":"code","source":"# print(output_path)\n# print(f\"Detailed infographic saved as: {output_path}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:14:29.228374Z","iopub.execute_input":"2024-12-17T13:14:29.228665Z","iopub.status.idle":"2024-12-17T13:14:29.241446Z","shell.execute_reply.started":"2024-12-17T13:14:29.228639Z","shell.execute_reply":"2024-12-17T13:14:29.240722Z"}},"outputs":[],"execution_count":14},{"cell_type":"code","source":"\n\n# # Test the functions\n# gif_path = create_animated_gif('20% users use iphone and 40% uses samsung')\n# detailed_path = create_detailed_infographic('20% users use iphone and 40% uses samsung')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:14:29.242523Z","iopub.execute_input":"2024-12-17T13:14:29.242859Z","iopub.status.idle":"2024-12-17T13:14:29.251642Z","shell.execute_reply.started":"2024-12-17T13:14:29.242832Z","shell.execute_reply":"2024-12-17T13:14:29.250820Z"}},"outputs":[],"execution_count":15},{"cell_type":"code","source":"# # Test the function\n# gif_path, video_path = generate_animated_infographics('20% users use iphone and 40% uses samsung')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:14:29.252746Z","iopub.execute_input":"2024-12-17T13:14:29.253082Z","iopub.status.idle":"2024-12-17T13:14:29.263008Z","shell.execute_reply.started":"2024-12-17T13:14:29.253044Z","shell.execute_reply":"2024-12-17T13:14:29.262235Z"}},"outputs":[],"execution_count":16},{"cell_type":"code","source":"# generate_infographics_from_csv('/kaggle/input/sample-sales-data/sales_data_sample.csv')\n# # generate_infographics_from_prompt_and_csv('This is a custom prompt', 'data.csv')\n# # generate_high_quality_infographics_from_text('20% users use iphone and 40% uses samsung')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:14:29.264046Z","iopub.execute_input":"2024-12-17T13:14:29.264304Z","iopub.status.idle":"2024-12-17T13:14:29.278051Z","shell.execute_reply.started":"2024-12-17T13:14:29.264278Z","shell.execute_reply":"2024-12-17T13:14:29.277127Z"}},"outputs":[],"execution_count":17},{"cell_type":"code","source":"# for today its done date 3 Dec","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-17T13:14:29.279176Z","iopub.execute_input":"2024-12-17T13:14:29.279462Z","iopub.status.idle":"2024-12-17T13:14:29.288276Z","shell.execute_reply.started":"2024-12-17T13:14:29.279434Z","shell.execute_reply":"2024-12-17T13:14:29.287616Z"}},"outputs":[],"execution_count":18}]}