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To explore the real-time weather alert chat application in detail and try it out for yourself, please visit our GitHub repository. The repository contains all the necessary code, detailed setup instructions, and additional documentation to help you get started. Once the setup is complete, you can start the Streamlit frontend and the Python backend. Set your weather alert criteria, and see how the system processes real-time weather data to keep you informed.
The code segment in the main() function in the backend.py file demonstrates the integration of NATS for even-driven messaging, continuous weather monitoring, and alerting. We use the nats.py library to integrate NATS within Python code. First, we establish a connection to the NATs server running in Docker at nats://localhost:4222.
Imagine you have an AI-powered personal alerting chat assistant that interacts using up-to-date data. Whether it's a big move in the stock market that affects your investments, any significant change on your shared SharePoint documents, or discounts on Amazon you were waiting for, the application is designed to keep you informed and alert you about any significant changes based on the criteria you set in advance using your natural language. In this post, we will learn how to build a full-stack event-driven weather alert chat application in Python using pretty cool tools: Streamlit, NATS, and OpenAI. The app can collect real-time weather information, understand your criteria for alerts using AI, and deliver these alerts to the user interface.
The backend service integrates with both external services like Weather API and Open AI Chat Completion API. If both weather data and user alert criteria are present, the app constructs a prompt for OpenAI's GPT model to determine if the weather meets the user's criteria. The prompt asks the AI to analyze the current weather against the user's criteria and respond with "YES" or "NO" and a brief weather summary. Once the AI determines that the incoming weather data matches a user's alert criteria, it crafts a personalized alert message and publishes a weather alert to the chat_response subject on the NATS server to update the frontend app with the latest changes. This message contains user-friendly notifications designed to inform and advise the user. For example, it might say, "Heads up! Rain is expected in Estonia tomorrow. Don't forget to bring an umbrella!"
Imagine you have an AI-powered personal alerting chat assistant that interacts using up-to-date data. Whether it's a big move in the stock market that affects your investments, any significant change on your shared SharePoint documents, or discounts on Amazon you were waiting for, the application is designed to keep you informed and alert you about any significant changes based on the criteria you set in advance using your natural language. In this post, we will learn how to build a full-stack event-driven weather alert chat application in Python using pretty cool tools: Streamlit, NATS, and OpenAI. The app can collect real-time weather information, understand your criteria for alerts using AI, and deliver these alerts to the user interface.
Imagine you have an AI-powered personal alerting chat assistant that interacts using up-to-date data. Whether it's a big move in the stock market that affects your investments, any significant change on your shared SharePoint documents, or discounts on Amazon you were waiting for, the application is designed to keep you informed and alert you about any significant changes based on the criteria you set in advance using your natural language. In this post, we will learn how to build a full-stack event-driven weather alert chat application in Python using pretty cool tools: Streamlit, NATS, and OpenAI. The app can collect real-time weather information, understand your criteria for alerts using AI, and deliver these alerts to the user interface.