initial commit 💥 🍀 🌴

Author: KristopherKubicki

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Virtual environment
+venv/
+ENV/
+
+# OS generated files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Editor directories and files
+.vscode/
+.idea/
+*.sw[lmnopqrst]
+*~
+.project
+.settings/
+
+# Python cache and temporary files
+*.py[cod]
+*.pyc
+*.pyo
+*.pyd
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+*.manifest
+*.spec
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+.coverage
+instance/
+.webassets-cache
+
+# Flask stuff:
+instance/
+.web
+templates.json
+screenshots/
+data/
+glimpser.log
+.idea
+auth.py
+*.old
+*.tmp
+logs/*
+scratch/*
+*.tsv

Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.8-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents into the container at /app
+COPY . /app
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make port 5000 available to the world outside this container
+EXPOSE 5000
+
+# Define environment variable
+ENV FLASK_APP=main.py
+ENV FLASK_RUN_HOST=0.0.0.0
+
+# Run app.py when the container launches
+CMD ["flask", "run"]
+

LICENSE.md

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+MIT License
+
+Copyright (c) 2024 Kristopher Kubicki
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# Glimpser
+
+## Introduction
+Glimpser is a straightforward yet powerful real-time monitoring application designed to capture, analyze, and summarize live data from various sources such as cameras, dashboards, and video streams. Utilizing advanced image processing techniques and AI models, Glimpser provides insightful summaries and alerts. It’s highly configurable, allowing users to tailor it to their specific monitoring needs through an easy-to-use interface.
+
+![Peek2024-08-0721-25-ezgif com-optimize](https://github.com/user-attachments/assets/d0335f5a-7d80-4895-b3fd-a94ad0af276c)
+
+
+## Features
+- **Real-time Monitoring**: Continuously captures data from multiple sources. Whether it's a traffic camera or a weather dashboard, Glimpser ensures you’re always up-to-date with the latest information.
+
+- **Image Processing**: Employs advanced techniques to compare images and detect even subtle changes, making it ideal for monitoring evolving situations effectively.
+
+- **Motion Detection**: Automatically detects motion in the captured images and videos, triggering alerts and actions as configured by the user.
+
+- **AI Integration**: Integrates with models like LLaVA and ChatGPT to provide intelligent insights. It can summarize data, detect anomalies, and generate alerts based on predefined rules.
+
+- **Auto-captioning**: Automatically generates concise and informative captions for images and videos, providing quick insights into the content.
+
+- **Auto-summarization**: Summarizes data from multiple sources into a coherent and concise format, highlighting the most important information.
+
+- **Customizable Configuration**: Easily configure different data sources and processing rules through the user-friendly interface. Glimpser’s configuration is fully database-driven, ensuring flexibility and ease of use.
+
+- **Data Retention Policies**: Automatically manages storage by cleaning up old data, ensuring the system remains efficient without requiring constant manual intervention.
+
+- **Web Interface**: A user-friendly web interface allows for easy monitoring and configuration. Users can view live feeds, summaries, and configure settings without delving into the code.
+
+## Installation
+
+### Prerequisites
+- Python 3.8 or higher
+
+### Steps
+1. **Clone the Repository**
+    ```sh
+    git clone https://github.com/yourusername/glimpser.git
+    cd glimpser
+    ```
+
+2. **Install Dependencies**
+    ```sh
+    pip install -r requirements.txt
+    ```
+
+3. **Run the Application**
+    ```sh
+    python3 main.py
+    ```
+
+You will be prompted to create a secret key to initialize the local sqlite database.  Follow the rest of the guided setup and then direct your browser to http://127.0.0.1:8082 to finish the rest of the setup. 
+
+## Usage
+
+### Configuration
+Glimpser uses a database-driven configuration to manage data sources and processing rules. Users can easily add, update, or remove configurations through the web interface.
+
+### Capturing Screenshots
+The preferred method for capturing screenshots is through the Glimpser web interface. Simply navigate to the capture section, select your desired source, and click the capture button. This ensures a seamless and user-friendly experience.
+
+### Running Tests
+To ensure everything works as expected, you can run the included unit tests:
+```sh
+python -m unittest discover tests
+```
+
+### Motion Detection
+Glimpser automatically detects motion in the captured images and videos. When motion is detected, the system can trigger alerts, capture additional data, and generate relevant summaries and captions.
+
+### Auto-captioning
+Using advanced AI models, Glimpser generates concise and informative captions for images and videos. This feature helps users quickly understand the content and context of the captured data.
+
+### Auto-summarization
+Glimpser can summarize data from multiple sources into a coherent and concise format. The summaries highlight the most important information, making it easier for users to stay informed.
+
+## Contributing
+Contributions are always welcome. If you have an idea to improve Glimpser, feel free to fork the repository and submit a pull request. 
+
+### Steps to Contribute
+1. Fork the repository.
+2. Create a feature branch.
+    ```sh
+    git checkout -b feature-branch
+    ```
+3. Commit your changes.
+    ```sh
+    git commit -m "Description of changes"
+    ```
+4. Push to the branch.
+    ```sh
+    git push origin feature-branch
+    ```
+5. Open a pull request.
+
+## License
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
+
+## Acknowledgements
+We are grateful to the contributors and the open-source community. Special thanks to OpenAI for their powerful models that enable Glimpser's advanced features.

app/__init__.py

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+# app/__init__.py
+
+import logging
+import os
+
+from flask import Flask, current_app
+from flask_apscheduler import APScheduler
+
+from config import DevelopmentConfig, SCREENSHOT_DIRECTORY, VIDEO_DIRECTORY, SECRET_KEY
+from .utils.video_archiver import archive_screenshots, compile_to_teaser
+from .utils.video_compressor import compress_and_cleanup
+from .utils.retention_policy import retention_cleanup
+from .utils.scheduling import scheduler, schedule_crawlers, schedule_summarization
+
+# needed for the llava compare
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+def create_app():
+    app = Flask(__name__)
+    app.config.from_object(DevelopmentConfig)
+    app.secret_key = SECRET_KEY
+
+    # Ensure the screenshot directory exists
+    os.makedirs(SCREENSHOT_DIRECTORY, exist_ok=True)
+    os.makedirs(VIDEO_DIRECTORY, exist_ok=True)
+
+    from .routes import init_routes
+    init_routes(app)
+
+    # Set the executor configuration in the Flask app's config
+    # should be at least as many sources
+    #app.config['SCHEDULER_EXECUTORS'] = {
+    #    'default': {'type': 'threadpool', 'max_workers': 100}  # Increase the number of threads here
+    #}
+
+    app.config['SCHEDULER_EXECUTORS'] = {
+            'default': {'type': 'processpool', 'max_workers': 8} # todo: read this from a config.  by default, each thread is 6gb.  So don't do more than what the machine can hande
+            #  allow to be configurable by the database
+    }
+
+    scheduler.init_app(app)
+
+    # Scheduler setup
+    if os.environ.get('WERKZEUG_RUN_MAIN') == 'true' or not app.debug:
+        scheduler.start()
+        logging.info('initializing...')
+
+        # Schedule the crawlers upon app start
+        with app.app_context():
+
+            # remove existing schedules, particularly if the app reloads (which it does in debug mode)
+            scheduler.remove_all_jobs()
+
+            schedule_crawlers()
+            # Additional scheduler setup for video archiving
+            scheduler.add_job(id='compile_to_teaser', func=compile_to_teaser, trigger='interval', minutes=3)
+            scheduler.add_job(id='archive_screenshots', func=archive_screenshots, trigger='interval', minutes=1)
+            #scheduler.add_job(id='compress_and_cleanup', func=compress_and_cleanup, trigger='interval', hours=1)
+            scheduler.add_job(id='retention_cleanup', func=retention_cleanup, trigger='cron', day='*')
+            schedule_summarization()
+
+        # one time cleanup..
+        retention_cleanup()
+        logging.info('initialization complete')
+
+    return app