![]() ![]() The first div holds an input element, and a button. Then add the following html elements to the elements: After creating an account, create a New Project and name it with a fake company name of your choosing. We are using Qoom so that we can create a login system without writing any backend code. If you haven't already, create an account on and follow along. Let's implement a basic structure to send input and recieve ouput using HTML, and we will design the bot later using CSS. It might be difficult to build the chatbot and test its functionality without starting with a basic page for sending inputs and recieving outputs. Create basic page to display input and output Get help from the Qoom team and our community members. ![]() Connect API functionality to the chatbot page.Create a basic input and output on chatbot page.The HTML/CSS necessary to create a popup chatbot page.Using JavaScript functions and Flask to send and recieve data from the page to the API.Submitting data and recieving generated responses using the OPENAI API.Design a page that displays the popup chatbot and allows users to open and close the popup.Use Flask and JavaScript to communicate data between the frontend and backend code.Develop Python code to implement the OpenAI API that recieves user questions and develops a response.Want to learn more? Try our complete WEB-BASED CHATBOT WITH PYTHON AND CHATGPT course. As you continue your learning journey, we hope this tutorial serves as a helpful starting point for integrating the power of OpenAI’s chatbot capabilities into your projects. You might want to dive deeper into OpenAI’s offerings, learn more about natural language processing, or experiment with other AI frameworks. There is so much more to explore in the world of AI and chatbot development. Your new chatbot can serve as a foundation for more complex projects, such as adding additional features, training the model for different languages, or even integrating it with other platforms such as messaging apps, websites, or voice-activated systems. By now, you should have a good understanding of how to set up a project, interact with the library, and process user input to generate meaningful responses. Running our code, we see that’s working as expected:Ĭongratulations! You’ve successfully built a simple chatbot using the OpenAI library. Note that the role here is set to ‘assistant’ as it’s a response from the chatbot. To check the structure of the response (e.g., how we receive our responses back from the bot), you can go to the documentation link above: We will access the first choice from the response and get the content of that message: # share response in console Lastly, we’re going to print out the response. Note that by the time you watch this lesson, the model may change changed. Response = (model='gpt-3.5-turbo', messages=messages) To send the API call, we have two required parameters, the model and the messages list as per the documentation (available at ): # send the api call We save this in a variable and add it to the messages list. To capture user input, we’re calling the inbuilt input function. User_input = input('Enter your prompt: ') We’ll then capture user input and send the API call: messages = In order to use the Chat Completion API, we will need to pass a messages list which will start empty. Replace the YOUR_KEY_HERE string with your secret key. We’ll also add the API key, which can be generated from the OpenAI website: import openai Next, we’re going to import the OpenAI library so that we have access to the API. Name the file “ app.py” and save it in the newly created folder. In this lesson, we will be creating a simple chatbot using the OpenAI library.įirst, create a new folder and file in Visual Studio Code. AVAILABLE FOR A LIMITED TIME ONLY Creating the Project ![]()
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