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Wednesday, 31 May 2023

OpenAI for Laravel: Using chatGPT/GPT 3/3.5 with PHP and Laravel

 OpenAI PHP for Laravel is a powerful tool for developers looking to use the OpenAI chatGPT / GPT-3 API within their Laravel projects. This library allows you to easily interact with the OpenAI API and use its various features within your Laravel application.

Installing OpenAI for Laravel

To install OpenAI PHP for Laravel, you will first need to make sure you have PHP 8.1 or higher installed on your system. Once you have the required version of PHP, you can use the Composer package manager to install the OpenAI PHP for Laravel library by running the following command:

composer require openai-php/laravel

After installing the library, you will need to publish the configuration file by running the following artisan command:

php artisan vendor:publish --provider="OpenAI\Laravel\ServiceProvider"

This will create a config/openai.php configuration file in your project that you can modify to your needs using environment variables. You will need to provide your OpenAI API key in this configuration file in order to use the library.

Using OpenAI for Laravel

Once you have installed and configured OpenAI PHP for Laravel, you can use the OpenAI facade to access the various features of the OpenAI chatGPT / GPT-3 API within your Laravel application. For example, you can use the completions method to generate completions for a given prompt, as shown in the following example:

use OpenAI\Laravel\Facades\OpenAI;

$result = OpenAI::completions()->create([ 'model' => 'text-davinci-003', 'prompt' => 'PHP is', ]);

echo $result['choices'][0]['text']; // an open-source, widely-used, server-side scripting language.

Conclusion

OpenAI PHP for Laravel is a powerful tool for developers looking to use the OpenAI API within their Laravel projects. With its easy installation and integration, it makes it simple to use the various features of the OpenAI API within your Laravel application.

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AI:Create a frameworks using various humanoid robotics with the help of various neural networks and LLMs on the occasiun of Diwali night from late night to peak hours to earley morning detection of fireworks reside, the pollution noice, voice and various traces of scraps found there along with fire broken and taming calming the fire broken in real time in the various neural networks of remote backward areas tow, cities, states countries and continents of the US,UK, Canada, Midle East, Africa, Korea, China, Europe, Asia and Oceania in real time. IoT-Based Automated Fire Detection and Alert System Overview This project automates fire detection and alert systems using IoT technology. It replaces traditional methods of fire reporting, ensuring quick and reliable emergency responses even in the absence of human intervention. The system leverages NodeMCU microcontrollers, environmental sensors, and the MQTT protocol to monitor conditions and trigger alarms in case of abnormalities. Features Automated Fire Detection: Monitors temperature and gas levels using DHT11 and gas sensors. Real-Time Alerts: Sends location-based notifications to receivers for immediate action. Scalable MQTT Communication: Efficient communication using a single MQTT topic for all nodes. Localized Alarm System: Receiver nodes trigger buzzers for the corresponding locations. System Architecture Source Nodes: NodeMCUs (Source_Beleghata, Source_London) equipped with: DHT11 Sensor for temperature monitoring. Gas Sensor for detecting smoke or hazardous gases. Publishes location-based messages upon detecting abnormal conditions. Receiver Nodes: NodeMCUs (Receiver_Beleghata, Receiver_London) equipped with buzzers. Subscribes to a shared MQTT topic and triggers the buzzer if the location matches incoming data. Communication Protocol MQTT Protocol: All nodes are connected to the internet for real-time communication. A shared MQTT topic ensures scalability and simplicity. Demonstration Setup Nodes: Source_Beleghata (with sensors) Receiver_Beleghata (with buzzer) Source_London (with sensors) Receiver_London (with buzzer) Workflow: Source nodes detect abnormal conditions and publish the affected location ("Beleghata" or "London") to the MQTT topic. Receiver nodes subscribe to the topic and trigger an alarm if the message matches their location. Requirements Hardware: 4 NodeMCU boards DHT11 sensors (2 units) Gas sensors (2 units) Buzzers (2 units) Software: Arduino IDE MQTT broker (e.g., Mosquitto) Internet connection for NodeMCU devices Installation and Usage Hardware Setup: Connect sensors to the Source NodeMCUs. Connect buzzers to the Receiver NodeMCUs. Software Configuration: Flash the provided code onto the NodeMCU devices using the Arduino IDE. Set up an MQTT broker and configure the nodes to publish/subscribe to the shared topic. Testing: Simulate abnormal conditions to test the system. Verify that alarms are triggered at the appropriate receiver nodes. Future Enhancements Integration with mobile applications for remote monitoring and alerts. Expansion to support additional locations and sensors. Advanced machine learning algorithms for anomaly detection.

  Global AI–IoT Humanoid Robotics Framework for Diwali Night Fireworks, Pollution, Waste and Fire Emergency Detection Proposed...