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 app tracks the growth of a Discord server and posts updates to Twitter. This app creates a tweet to your twitter account every time another hundred users join your discord server. Make sure you have twitter access tokens created with Read, Write, and Direct Messages permissions to run this app.
This app uses the Stripe API to set a default payment method for customers. It includes a Flask web service with an endpoint to create the default payment method. The backend makes an API call to set the payment method using the Stripe API. The app displays whether the API call was successful or not after submission.
This app integrates a custom Stripe payment page in WordPress. It includes both a backend and a frontend. The backend service is set up using FastAPI and is compatible with any price point established through the Stripe API. The backend service creates a Stripe checkout session and retrieves the status of a checkout session. It also allows all CORS and logs sent requests and checkout session statuses. The price ID is fetched during the request from the user. After adding the Stripe API key and directing the backend service to the price ID, the backend service can be activated by clicking the test button. The frontend code can be integrated into a WordPress page to create a custom payment page in WordPress. This method can be used to set up various types of payment pages in WordPress, including one-time payments and subscriptions. The required environment secrets for this app are STRIPE_SECRET_KEY and YOUR_DOMAIN.
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.