Unleash Your Creativity with Qwik: A ChatGPT Alternative

Unleash Your Creativity with Qwik: A ChatGPT Alternative

Table of Contents:

1. Introduction
2. The Quick Chat Application
3. The Code Structure
4. Front-End Implementation
   1. Main File - index.tsx
   2. Chat Messages Component
   3. Select Dropdown and Text Area
   4. Clear Button
5. Back-End Implementation
   1. API Endpoints
   2. Fetching Models
   3. Chat Message Endpoint
6. Connecting Front-End and Back-End
7. Conclusion

Introduction

The combination of Quick and Open AI brings us Quick Chat, a user-friendly application that allows users to query different AI models and receive responses within seconds. In this article, we will Delve into the code structure and implementation of Quick Chat, exploring both the front-end and back-end aspects. By the end of this article, You will have a comprehensive understanding of how Quick Chat works and how it connects with the Open AI API.

The Quick Chat Application

Quick Chat is a small chat application with a sleek Dark theme. Users have the option to select a specific AI model to query and provide a prompt. Within seconds, the application will fetch a response Based on the chosen model and prompt. The chat messages are stored locally in the browser's storage to ensure continuity even after a page refresh. Additionally, the application features a clear button to reset the prompt and messages.

The Code Structure

The codebase of Quick Chat is organized into two main sections: front-end and back-end. The front-end handles the user interface and communication with the Open AI API, while the back-end consists of API endpoints for retrieving models and processing chat messages.

Front-End Implementation

Main File - index.tsx

The index.tsx file serves as the entry point for the Quick Chat application. It initializes the application's state, including the selected model, prompt, and messages. The main file also fetches and displays the available AI models in a dropdown menu. When the user selects a model or updates the prompt, a new message is fetched from the back-end API.

Chat Messages Component

The chat messages component is responsible for rendering the chat interface. It loops over the messages array and displays the prompt and corresponding response using a chat message component. The component also includes an icon slot and a default slot for additional customization.

Select Dropdown and Text Area

Quick Chat provides a select dropdown for choosing the desired AI model. The dropdown is populated with the available models fetched from the back-end API. The text area allows users to enter their Prompts. Upon pressing the Enter key, the value of the text area is assigned to the prompt, triggering a new message fetch.

Clear Button

To reset the prompt and clear all chat messages, Quick Chat offers a clear button. Clicking the clear button removes the prompt and messages from the application's state.

Back-End Implementation

API Endpoints

The back-end of Quick Chat is built using the Quick API framework. It exposes two API endpoints: one for retrieving the available AI models and another for processing chat messages. These endpoints handle GET and POST requests, respectively.

Fetching Models

The get models endpoint fetches information about the AI models available for Quick Chat. It utilizes the Open AI object and its functions to retrieve the models' names. The fetched data is transformed into a list of model names.

Chat Message Endpoint

The chat endpoint handles incoming chat messages. It receives the prompt and model information from the request and creates a payload for processing. The Open AI Helper function Create completion is used to generate a response based on the provided prompt and chosen model.

Connecting Front-End and Back-End

Quick Chat bridges the front-end and back-end by utilizing helper functions. These functions facilitate the communication between the user interface and the API endpoints. For example, the get models helper function uses the fetch function to call the models' endpoint, while the get message function fetches new messages based on user prompts.

Conclusion

Quick Chat serves as a powerful demonstration of combining Quick and Open AI. By providing a streamlined user interface and efficient communication with the back-end, Quick Chat offers seamless interaction with AI models. Understanding the code structure and implementation of Quick Chat gives developers the knowledge to build their own AI-powered chat applications. So, get ready to unlock the potential of Quick and Open AI with Quick Chat!

Highlights:

• Quick Chat is a user-friendly application that combines Quick and Open AI.
• Users can select an AI model, provide a prompt, and receive responses within seconds.
• The chat messages are stored locally, ensuring continuity even after a page refresh.
• The code structure of Quick Chat is organized into front-end and back-end sections.
• The front-end handles the user interface and communication with the Open AI API.
• The back-end consists of API endpoints for fetching models and processing chat messages.
• Quick Chat bridges the front-end and back-end through helper functions.
• The application offers a clear button to reset prompts and messages.
• By understanding Quick Chat's code structure, developers can build their own AI Chat applications.
• Quick Chat showcases the potential of Quick and Open AI in creating interactive user experiences.

FAQ

Q: Can I use my own AI models with Quick Chat? A: Yes, Quick Chat allows you to select any AI model that is compatible with the Open AI API.

Q: Are the chat messages stored securely? A: The chat messages are stored locally in the browser's storage, ensuring privacy and security.

Q: Can I customize the appearance of Quick Chat? A: Yes, Quick Chat utilizes Tailwind CSS for styling, making it easy to customize the application's appearance.

Q: Is Quick Chat compatible with different programming languages? A: Quick Chat is built using Quick, a framework for TypeScript and JavaScript. However, it can be adapted to work with other programming languages with appropriate modifications.