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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()
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