"Summarizer Service" is a Flask web application designed for summarizing web pages using Large Language Models (LLMs). Here’s a summary:
**Core Functionality:** The application allows users to summarize web pages, leveraging LLMs. It supports summarization with or without a custom prompt, and also provides a Q&A functionality. It’s built around bookmarking services.
**Technical Details:**
* **Framework:** Flask (Python)
* **LLM Access:** Designed to work with llamafiles, but adaptable to other LLM access methods.
* **Dependencies:** Managed via `requirements.txt`.
* **Deployment:** Can be run with gunicorn, Python directly, or the Flask CLI.
* **Cards/Routes:** The application utilizes "cards" implemented in `app.py` with templates in the `templates/cards/` directory. Key routes include:
* `/`: Home
* `/scuttle?url=`: Scuttle bookmarking service integration.
* `/summarize?url=&prompt=`: Text summarization.
* `/ask?question=&context=`: Question answering.
* `/via-api-model?model_name=`: Model selection.
* **Bookmarklets:** The repository includes JavaScript bookmarklets to easily send web pages and selected text to the summarizer from a browser.
* **Sessions:** Flask sessions are used and store data in `flask_sessions/`. The documentation warns about the need to protect and periodically delete these files.
* **License:** MIT License.
**Installation:**
1. Clone the GitHub repository: `git clone https://github.com/leighklotz/summarizer-service.git`
2. Install dependencies: `pip install -r requirements.txt`
3. Configure: Copy and edit `config.py.example` to `config.py`.
**Overall:** The Summarizer Service provides a simple yet functional UI for utilizing LLMs for text summarization and Q&A, with a focus on integration with bookmarking workflows. It appears to be a good starting point or example for building similar applications.
**Core Functionality:** The application allows users to summarize web pages, leveraging LLMs. It supports summarization with or without a custom prompt, and also provides a Q&A functionality. It’s built around bookmarking services.
**Technical Details:**
* **Framework:** Flask (Python)
* **LLM Access:** Designed to work with llamafiles, but adaptable to other LLM access methods.
* **Dependencies:** Managed via `requirements.txt`.
* **Deployment:** Can be run with gunicorn, Python directly, or the Flask CLI.
* **Cards/Routes:** The application utilizes "cards" implemented in `app.py` with templates in the `templates/cards/` directory. Key routes include:
* `/`: Home
* `/scuttle?url=`: Scuttle bookmarking service integration.
* `/summarize?url=&prompt=`: Text summarization.
* `/ask?question=&context=`: Question answering.
* `/via-api-model?model_name=`: Model selection.
* **Bookmarklets:** The repository includes JavaScript bookmarklets to easily send web pages and selected text to the summarizer from a browser.
* **Sessions:** Flask sessions are used and store data in `flask_sessions/`. The documentation warns about the need to protect and periodically delete these files.
* **License:** MIT License.
**Installation:**
1. Clone the GitHub repository: `git clone https://github.com/leighklotz/summarizer-service.git`
2. Install dependencies: `pip install -r requirements.txt`
3. Configure: Copy and edit `config.py.example` to `config.py`.
**Overall:** The Summarizer Service provides a simple yet functional UI for utilizing LLMs for text summarization and Q&A, with a focus on integration with bookmarking workflows. It appears to be a good starting point or example for building similar applications.
