Learn to build modular Flask applications using Jinja and templates. This article covers what Jinja is, how to create templates, and the benefits of using templates and the Jinja template engine. The article includes code examples and a GitHub link to the source code.
This article provides a guide on how to build routes in Flask, a Python web framework. It covers topics such as URL routing, the features of Flask, and its suitability for production-level web applications.
"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.
This article provides a guide on how to run a Flask application with Gunicorn, including installing Gunicorn, setting up a Flask application, and running the application with Gunicorn.
Key quote "When you run the server via Gunicorn, you need to specify the module name and the variable name of the app for Gunicorn to access it. Note that the variable should be a WSGI callable object for e.g a flask app object. "
Key quote "When you run the server via Gunicorn, you need to specify the module name and the variable name of the app for Gunicorn to access it. Note that the variable should be a WSGI callable object for e.g a flask app object. "
