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| import gradio as gr | |
| import openai | |
| import examples as chatbot_examples | |
| # Define a function to get the AI's reply using the OpenAI API | |
| def get_ai_reply(model, system_message, message, history_state): | |
| # Initialize the messages list with the system message | |
| messages = [{"role": "system", "content": system_message}] | |
| # Add the conversation history to the messages list | |
| messages += history_state | |
| # 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 | |
| ) | |
| # Extract and return the AI's response from the API response | |
| return completion.choices[0].message.content | |
| # Define a function to handle the chat interaction with the AI model | |
| def chat(model, system_message, 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: | |
| ai_reply = get_ai_reply(model, system_message, message, history_state) | |
| except: | |
| # If an error occurs, return None and the current chatbot_messages and history_state | |
| return None, chatbot_messages, 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 | |
| return None, chatbot_messages, history_state | |
| # Define a function to launch the chatbot interface using Gradio | |
| def launch_chatbot(additional_examples=[], share=False): | |
| # Load chatbot examples and merge with any additional examples provided | |
| examples = chatbot_examples.load_examples(additional=additional_examples) | |
| # Define a function to get the names of the examples | |
| def get_examples(): | |
| return [example["name"] for example in examples] | |
| # Define a function to choose an example based on the index | |
| def choose_example(index): | |
| system_message = examples[index]["system_message"].strip() | |
| user_message = examples[index]["message"].strip() | |
| return system_message, user_message, [], [] | |
| # Create the Gradio interface using the Blocks layout | |
| with gr.Blocks() as demo: | |
| with gr.Tab("Conversation"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| # Create a dropdown to select examples | |
| example_dropdown = gr.Dropdown(get_examples(), label="Examples", type="index") | |
| # Create a button to load the selected example | |
| example_load_btn = gr.Button(value="Load") | |
| # Create a textbox for the system message (prompt) | |
| system_message = gr.Textbox(label="System Message (Prompt)", value="You are a helpful assistant.") | |
| with gr.Column(): | |
| # Create a dropdown to select the AI model | |
| model_selector = gr.Dropdown( | |
| ["gpt-3.5-turbo", "gpt-4"], | |
| label="Model", | |
| value="gpt-3.5-turbo" | |
| ) | |
| # 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 example load button to the choose_example function | |
| example_load_btn.click(choose_example, inputs=[example_dropdown], outputs=[system_message, message, chatbot, history_state]) | |
| # Connect the send button to the chat function | |
| btn.click(chat, inputs=[model_selector, system_message, message, chatbot, history_state], outputs=[message, chatbot, history_state]) | |
| # Launch the Gradio interface | |
| demo.launch(share=share) | |
| # Call the launch_chatbot function to start the chatbot interface using Gradio | |
| # Set the share parameter to False, meaning the interface will not be publicly accessible | |
| launch_chatbot(share=False) | |