Viroscope / API-to-API-conversations

This code implements a conversational AI chatbot using the OpenAI GPT-3.5 Turbo model. The chatbot simulates a conversation between two characters, Karen and Charles.

API to API Conversations

This code implements a conversational AI chatbot using the OpenAI GPT-3.5 Turbo model. The chatbot simulates a conversation between two characters, Karen and Charles, who have distinct personalities. Karen is sarcastic, while Charles is also sarcastic but in a different way. The chatbot alternates between Karen and Charles, generating responses based on the conversation history.

Prerequisites

Before running the code, make sure you have the following dependencies installed:

• Python 3.x
• requests library
• dotenv library

You also need to obtain an API key from OpenAI to access the GPT-3.5 Turbo model.

Setup

1. Clone the repository and navigate to the project directory.

2. Install the required dependencies using the following command:

   pip install requests python-dotenv
   

3. Create a file named .env in the project directory and add your OpenAI API key to it in the following format:

   OpenAIKey=YOUR_API_KEY
   

Usage

The main part of the code simulates a conversation between Karen and Charles. Each character takes turns generating responses based on the conversation history.

To run the code, execute the following command:

python chatbot.py

Code Explanation

Importing Dependencies

The required libraries are imported at the beginning of the code:

• requests is used to make HTTP requests to the OpenAI API.
• os is used to access the environment variables.
• json is used to parse and format JSON data.
• time is used to introduce delays between conversation turns.
• dotenv is used to load environment variables from the .env file.

Loading Environment Variables

The code uses the dotenv_values function from the dotenv library to load environment variables from the .env file. The API key is stored in the apiKey variable.

Defining Conversation Roles

Two functions, Charles and Karen, are defined to generate responses from the respective characters. These functions take the API key and the conversation history as input.

The conversation history is a list of messages, where each message has a role (user, assistant, or system) and content (the text of the message).

Generating Responses

Inside the Charles and Karen functions, the API endpoint URL and headers are set. The conversation history is modified to include system messages that provide role-playing instructions to the characters. The conversation history is then sent as a JSON payload to the OpenAI API using a POST request.

The response from the API is parsed as JSON, and the generated response from the character is extracted from the response data.

Running the Conversation Loop

The main part of the code executes a loop that simulates the conversation. In each iteration, Karen and Charles take turns generating responses.

• Karen generates a response using the Karen function and prints it in pink color.
• The generated response from Karen is added to the conversation history with the role set as user.
• A 1-second pause is introduced.
• Charles generates a response using the Charles function and prints it in blue color.
• The generated response from Charles is added to the conversation history with the role set as assistant.
• Another 1-second pause is introduced.
• A new line is printed to separate the outputs of each iteration.

Note

The code includes commented-out lines that print the JSON responses from the OpenAI API. Uncommenting these lines can be useful for debugging or inspecting the response data structure.