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| 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() | |