gpt-based-DFA.py

from openai import OpenAI
from dotenv import load_dotenv

load_dotenv()


class GPT3DFA:
    def __init__(self, topic):
        self.current_state = 'start'
        self.client = OpenAI()
        self.states = ['start', 'content', 'conclusion', 'done']
        self.state_functions = {
            'start': self.start_state,
            'content': self.content_state,
            'conclusion': self.conclusion_state,
        }
        self.topic = topic
        self.context = ""  # To accumulate text across states

    def start_state(self):
        prompt = f"Write an introduction about {self.topic}."
        return self.generate_text(prompt)

    def content_state(self):
        prompt = f"Given the introduction: \"{self.context}\" Now, continue writing about {self.topic}."
        return self.generate_text(prompt)

    def conclusion_state(self):
        prompt = f"Given the content: \"{self.context}\" Now, conclude the text on {self.topic}."
        return self.generate_text(prompt)

    def generate_text(self, prompt):
        response = self.client.chat.completions.create(
                model="gpt-3.5-turbo",
                messages=[
                    {"role": "system", "content": "You are a helpful assistant."},
                    {"role": "user", "content": prompt},
                ],
                n=1,
                temperature=0.5
                )

        text = response.choices[0].message.content
        return text

    def run(self):
        for state in self.states:
            if state == 'done':
                break
            additional_text = self.state_functions[state]()
            self.context += additional_text
            print(additional_text, end='\n\n')


# Example usage
dfa = GPT3DFA(topic="the history of the internet")

dfa.run()