media-summarizer 2025.12.21160330

A new package designed to interpret and summarize user-provided textual descriptions of multimedia content, such as videos or audio transcripts, to generate structured overviews or insights. It allows

media-summarizer

A lightweight Python package that interprets and summarizes user‑provided textual descriptions of multimedia content (e.g., video or audio transcripts). 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.

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Installation

pip install media_summarizer

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Quick Start

from media_summarizer import media_summarizer

# Example raw transcript or description
user_input = """
In this video the presenter explains the difference between supervised and unsupervised learning,
covers examples of classification, regression, clustering, and ends with a short Q&A.
"""

# Call the summarizer with default LLM (ChatLLM7)
summary = media_summarizer(user_input)

print(summary)
# -> ['The video covers supervised vs unsupervised learning', 
#     'Examples: classification, regression, clustering', 
#     'Ends with a short Q&A']

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Parameters

Name	Type	Description
user_input	str	Textual content (e.g., transcript, description) to be summarized.
llm (optional)	BaseChatModel	A LangChain chat model instance. If omitted, the package creates a ChatLLM7 instance automatically.
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).

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Using a Custom LLM

You can pass any LangChain‑compatible chat model instead of the default ChatLLM7.

OpenAI

from langchain_openai import ChatOpenAI
from media_summarizer import media_summarizer

llm = ChatOpenAI(model="gpt-4o-mini")
summary = media_summarizer(user_input, llm=llm)

Anthropic

from langchain_anthropic import ChatAnthropic
from media_summarizer import media_summarizer

llm = ChatAnthropic(model="claude-3-sonnet-20240229")
summary = media_summarizer(user_input, llm=llm)

Google Gemini

from langchain_google_genai import ChatGoogleGenerativeAI
from media_summarizer import media_summarizer

llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
summary = media_summarizer(user_input, llm=llm)

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API Key & Rate Limits

• Default LLM: ChatLLM7 from the langchain_llm7 package (PyPI link).
• Free Tier: The default rate limits of the LLM7 free tier are sufficient for most use cases of this package.
• Higher Limits: Provide your own API key via the LLM7_API_KEY environment variable or directly:

summary = media_summarizer(user_input, api_key="your_personal_api_key")

• Get a free API key: Register at https://token.llm7.io/.

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License

Distributed under the MIT License. See the LICENSE file for details.

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Contributing & Support

• Issues & feature requests: https://github.com/chigwell/media-summarizer/issues
• Author: Eugene Evstafev – [e-mail]
• GitHub: https://github.com/chigwell

Feel free to open issues, submit pull requests, or contact the author for any questions.