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import gradio as gr
import openai_secret_manager
# Function to get the secret values
def get_secrets():
secrets = openai_secret_manager.get_secret("your_api_key_secret_name")
instructor_prompt = openai_secret_manager.get_secret("your_instructor_prompt_secret_name")
return secrets, instructor_prompt
# Get the secrets
secrets, instructor_prompt = get_secrets()
# Set your API key
api_key = secrets["api_key"]
with gr.Blocks() as demo:
chat_history = gr.State(value=[])
def chat_with_instructor(message, history):
import openai
openai.api_key = api_key
chat_input = instructor_prompt + "\nUser: " + message + "\nInstructor:"
response = openai.Completion.create(
engine="text-davinci-002",
prompt=chat_input,
max_tokens=50 # Adjust this for response length
).choices[0].text.strip()
chat_history.value.append(
{
"user": message,
"instructor": instructor_prompt,
"bot": response,
}
)
return response
def generate_json(chat_history):
return chat_history.value
chatbox = gr.ChatInterface(
fn=chat_with_instructor,
title="Chat with Instructor",
description="Chat with the instructor using a predefined prompt.",
examples=["Hi, can you help me with this?"],
submit_btn="Send",
stop_btn="Stop",
retry_btn="Retry",
undo_btn="Undo last message",
clear_btn="Start a new conversation"
).queue()
chat_history_json = gr.JSON(generate_json(chat_history))
gr.Markdown("### 📩 Generate the JSON file for your chat history!")
gr.Interface(fn=generate_json,
inputs=None,
outputs=[chat_history_json])
demo.queue()
demo.launch()
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