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It turns raw or pre‑processed text extracts into structured overviews or key‑point lists, making content analysis, cataloguing, and review straightforward—without handling the media files themselves.\n\n---\n\n## Installation\n\n```bash\npip install media_summarizer\n```\n\n---\n\n## Quick Start\n\n```python\nfrom media_summarizer import media_summarizer\n\n# Example raw transcript or description\nuser_input = \"\"\"\nIn this video the presenter explains the difference between supervised and unsupervised learning,\ncovers examples of classification, regression, clustering, and ends with a short Q\u0026A.\n\"\"\"\n\n# Call the summarizer with default LLM (ChatLLM7)\nsummary = media_summarizer(user_input)\n\nprint(summary)\n# -\u003e ['The video covers supervised vs unsupervised learning', \n#     'Examples: classification, regression, clustering', \n#     'Ends with a short Q\u0026A']\n```\n\n---\n\n## Parameters\n\n| Name      | Type                     | Description |\n|-----------|--------------------------|-------------|\n| `user_input` | `str` | Textual content (e.g., transcript, description) to be summarized. |\n| `llm` (optional) | `BaseChatModel` | A LangChain chat model instance. If omitted, the package creates a `ChatLLM7` instance automatically. |\n| `api_key` (optional) | `str` | API key for `ChatLLM7`. If omitted, the function reads the `LLM7_API_KEY` environment variable, and if that is missing it falls back to `\"None\"` (the default free‑tier key). |\n\n---\n\n## Using a Custom LLM\n\nYou can pass any LangChain‑compatible chat model instead of the default `ChatLLM7`.\n\n### OpenAI\n\n```python\nfrom langchain_openai import ChatOpenAI\nfrom media_summarizer import media_summarizer\n\nllm = ChatOpenAI(model=\"gpt-4o-mini\")\nsummary = media_summarizer(user_input, llm=llm)\n```\n\n### Anthropic\n\n```python\nfrom langchain_anthropic import ChatAnthropic\nfrom media_summarizer import media_summarizer\n\nllm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")\nsummary = media_summarizer(user_input, llm=llm)\n```\n\n### Google Gemini\n\n```python\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom media_summarizer import media_summarizer\n\nllm = ChatGoogleGenerativeAI(model=\"gemini-1.5-flash\")\nsummary = media_summarizer(user_input, llm=llm)\n```\n\n---\n\n## API Key \u0026 Rate Limits\n\n- **Default LLM**: `ChatLLM7` from the `langchain_llm7` package ([PyPI link](https://pypi.org/project/langchain-llm7/)).  \n- **Free Tier**: The default rate limits of the LLM7 free tier are sufficient for most use cases of this package.  \n- **Higher Limits**: Provide your own API key via the `LLM7_API_KEY` environment variable or directly:\n\n```python\nsummary = media_summarizer(user_input, api_key=\"your_personal_api_key\")\n```\n\n- **Get a free API key**: Register at \u003chttps://token.llm7.io/\u003e.\n\n---\n\n## License\n\nDistributed under the MIT License. See the `LICENSE` file for details.\n\n---\n\n## Contributing \u0026 Support\n\n- **Issues \u0026 feature requests**: \u003chttps://github.com/chigwell/media-summarizer/issues\u003e\n- **Author**: Eugene Evstafev – \u003chi@eugene.plus\u003e\n- **GitHub**: \u003chttps://github.com/chigwell\u003e\n\nFeel free to open issues, submit pull requests, or contact the author for any questions.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchigwell%2Fmedia-summarizer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchigwell%2Fmedia-summarizer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchigwell%2Fmedia-summarizer/lists"}