![]() However like most AI related innovations, it has its pros and cons. Additionally, ChatGPT is able to generate responses to a wide range of prompts, making it a versatile choice for chatbot applications, content writing and many more. ChatGPT has been trained on a large dataset of human-human conversation, making it well-suited for generating responses that feel natural and authentic. One major advantage of ChatGPT is its ability to generate human-like responses. If you have not pushed the commits to a remote repository, you can use the "reset" command. Let’s use one of the most asked questions on Stackoverflow Enter your question: How do I undo the most recent local commits in Git? Note how the response uses the actual Weather API endpoint, moreover it knows exactly where the response is in the returned JSON object! Json_data = requests.get(api_address).json()įormatted_data = json_data Let’s say we want ChatGPT to create a function to retrieve data from an API endpoint: Enter your question: write python code to get data from the weather api You can find the source code in this git repository. The final code should look like the following: import openai Save your code in a python file, let’s call it app.py. For this purpose, we will rewrite our script to accept user import then print the result. Let’s take a look at some advanced examples. More ExamplesĬhatGPT is not limited to just answering simple questions. In this example, the chatbot will continue to generate responses as long as the user doesn’t input the word “exit”. Here’s an example of how you might use the generate_response() function in a simple chatbot application: while True: A higher temperature will result in more diverse and unpredictable responses, while a lower temperature will produce more conservative and predictable responses. You can also customize the behavior of the ChatGPT model by adjusting the temperature parameter. The prompt parameter is the input that the user has provided to the chatbot, and the max_tokens parameter specifies the maximum number of tokens (i.e. In this example, we’re using the () method to generate a response to a given prompt. You can find more details about the different engines here. Davinci is the most capable model, and Ada is the fastest. There are four main models with different levels of power suitable for different tasks. GPT-3 models can understand and generate natural language. Here’s an example of how to use the openai library to generate a response using ChatGPT: import openaiįor this demo, we are using the “ text-davinci-003″ model engine. You can then import and use the openai module in your Python code. This will install the latest version of the openai package and its dependencies. Once pip is installed, you can use it to install the openai package by running the following command (as of the writing of this article, note that the pandaspackage is required in order to use openai): pip install pandas openai If pip is not installed, you can install it by running the following command: python -m ensurepip -upgrade You can check if you have pip installed by running the following command in your terminal: pip -version ![]() To install openai with pip, first ensure that you have pip installed on your system. ![]() This will give you access to the various language models, including ChatGPT, that are available through the API.īefore we start writing code, we need to install openai with pip: Once you have created an account, you can obtain an API key from here. To get started, you’ll need to sign up for an O p enAI API key. In this post, we’ll take a look at how to use ChatGPT in a Python application and provide some code snippets as examples. One of the great things about ChatGPT is that it can be easily integrated into Python applications using the OpenAI API. Using the OpenAI API to Access ChatGPT in Python
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