This app allows users to interact with a Slack bot, ask a question about the data in a table or request the table schema, and then uses the latest ChatGPT to generate a query that is executed on BigQuery to return the results. The app includes a retry mechanism for query generation in case of an error (up to two retries) and provides the LLM with the table info to generate more accurate queries. The table schema is only printed if it is successfully retrieved. All errors from retries are now passed to the LLM. The generated query is printed before the results, and the results are displayed in a pretty table format. The bot uses the Slack API to send and receive messages and parses the user's message to determine the action to take. The bot always responds in a thread to the original message.
The bot requires certain permissions to function properly. These include the ability to read message history, send messages, and react to messages. The bot will generate stats such as This bot will provide ticker stats, commodity stats, Stock News and other AI Stock Trading Advice Please provide the Discord bot token in the Env Secrets tab under the name 'DISCORD_BOT_TOKEN' and your API Key for the Alpha Advantage
A customizable Streamlit dashboard template for evaluating machine learning models with interactive elements and real-time visualizations. This comprehensive dashboard allows you to upload your dataset and evaluate it using various pre-trained machine learning models. You can select from models like Random Forest, SVM, and Logistic Regression. Adjust model parameters using interactive sliders and buttons. The dashboard provides real-time visualizations, including dynamic charts and confusion matrices, to help you interpret the results effectively. Ideal for data scientists and ML enthusiasts looking to quickly assess model performance.
This application employs Flask for the backend and JavaScript for the frontend. It enables users to generate custom prompts by providing details and selecting a prompt type. The backend receives the user input, constructs a prompt, and sends it to a language model (LLM) for further processing. The generated prompt is then returned to the frontend and displayed for the user. The interface allows users to copy the generated prompt for their use. Additionally, error handling ensures smooth operation even in case of failures during prompt generation. Made by BaranDev[https://github.com/BaranDev]
This is a FastAPI application that interacts with the Google Calendar API to create events. It requires three environment secrets: CLIENT_ID, CLIENT_SECRET, and REDIRECT_URI. The application has three main endpoints: POST /create_event: This endpoint accepts a dictionary representing an event and attempts to create this event on the primary calendar of the authenticated user. GET /oauth2callback: This endpoint handles the OAuth2 callback from Google. If no code is provided, it redirects the user to the Google authorization page. If a code is provided, it exchanges the code for an access token and stores the token. GET /clear_credentials: This endpoint clears the stored credentials. Please note that the application must be authenticated with Google before events can be created. This is done by accessing the /oauth2callback endpoint and following the Google authorization process.
This application is a web server built with FastAPI. It provides an endpoint /get_location that fetches location data based on latitude and longitude using the Google Maps API. When a POST request is made to the /get_location endpoint with latitude and longitude as form data, the application returns the location data in JSON format. The application requires a Google Maps API key to function. This key should be provided via the environment variable GOOGLE_MAPS_API_KEY
This app uses the Stripe API to create payouts for Connect Stripe Accouts and allows users to modify the payout schedule. It includes a Flask web service with an endpoint for this purpose. The backend makes API calls to create a transfer of funds and update the payout schedule using the Stripe API and the submitted form data.
Lazy apps can be helpful in the AI category by automating tasks and simplifying processes for users. These apps use artificial intelligence algorithms to understand user preferences and behavior, and then proactively suggest and perform tasks on behalf of the user. This can include tasks such as organizing emails, scheduling appointments, managing to-do lists, and even making recommendations based on user preferences.
Lazy apps can save users time and effort by taking care of mundane and repetitive tasks, allowing them to focus on more important and meaningful activities. Additionally, these apps can learn from user interactions and improve their suggestions and performance over time, providing a personalized and efficient experience.
In summary, lazy apps in the AI category can enhance productivity, streamline workflows, and provide a more convenient and personalized user experience.