app.py

from dotenv import load_dotenv

load_dotenv()  # take environment variables from .env.
import gradio as gr
import openai

# Define a function to get the AI's reply using the OpenAI API
def get_ai_reply(message, model="gpt-3.5-turbo", system_message=None, temperature=0, message_history=[]):
    # Initialize the messages list
    messages = []
    
    # Add the system message to the messages list
    if system_message is not None:
        messages += [{"role": "system", "content": system_message}]

    # Add the message history to the messages list
    if message_history is not None:
        messages += message_history
    
    # Add the user's message to the messages list
    messages += [{"role": "user", "content": message}]
    
    # Make an API call to the OpenAI ChatCompletion endpoint with the model and messages
    completion = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature
    )
    
    # Extract and return the AI's response from the API response
    return completion.choices[0].message.content.strip()

# Define a function to handle the chat interaction with the AI model
def chat(message, chatbot_messages, history_state):
    # Initialize chatbot_messages and history_state if they are not provided
    chatbot_messages = chatbot_messages or []
    history_state = history_state or []
    
    # Try to get the AI's reply using the get_ai_reply function
    try:
        prompt = """
        You are bot created to simulate commands.

        Simulate doing a command using this notation:
        :: <command> ::

        Simulate doing nothing with this notation:
        :: does nothing ::
        """
        ai_reply = get_ai_reply(message, model="gpt-3.5-turbo", system_message=prompt.strip(), message_history=history_state)
            
        # Append the user's message and the AI's reply to the chatbot_messages list
        chatbot_messages.append((message, ai_reply))

        # Append the user's message and the AI's reply to the history_state list
        history_state.append({"role": "user", "content": message})
        history_state.append({"role": "assistant", "content": ai_reply})

        # Return None (empty out the user's message textbox), the updated chatbot_messages, and the updated history_state
    except Exception as e:
        # If an error occurs, raise a Gradio error
        raise gr.Error(e)
        
    return None, chatbot_messages, history_state

# Define a function to launch the chatbot interface using Gradio
def get_chatbot_app():
    # Create the Gradio interface using the Blocks layout
    with gr.Blocks() as app:
        # Create a chatbot interface for the conversation
        chatbot = gr.Chatbot(label="Conversation")
        # Create a textbox for the user's message
        message = gr.Textbox(label="Message")
        # Create a state object to store the conversation history
        history_state = gr.State()
        # Create a button to send the user's message
        btn = gr.Button(value="Send")

        # Connect the send button to the chat function
        btn.click(chat, inputs=[message, chatbot, history_state], outputs=[message, chatbot, history_state])
        # Return the app
        return app
        
# Call the launch_chatbot function to start the chatbot interface using Gradio
app = get_chatbot_app()
app.queue()  # this is to be able to queue multiple requests at once
app.launch()