app.py

import os
import openai
import gradio as gr
import time

# Set up password
username = os.getenv('username')
password = os.getenv('password')

# Set up OpenAI client
openai_api_key = os.getenv('openai_api_key')
openai.api_key = openai_api_key
client = openai.Client(api_key=openai.api_key)

# Set up assistant
assistant_id = os.getenv('assistant_id')
assistant = client.beta.assistants.retrieve(assistant_id)
thread = client.beta.threads.create()

# Get avatar link
avatar_link = os.getenv('avatar_link')

def chat_with_assistant(message, history):
    # Add the user's message to the thread
    client.beta.threads.messages.create(
        thread_id=thread.id,
        role="user",
        content=message
    )

    # Run the assistant with streaming
    response = client.chat.completions.create(
        model='gpt-4o-mini',
        messages=[{'role': 'user', 'content': message}],
        stream=True
    )
    
    # Handle the streaming response
    response_text = ""
    for chunk in response:
        delta = chunk['choices'][0]['delta']
        if 'content' in delta:
            response_text += delta['content']
            yield response_text

# Custom CSS for chat bubbles and colors
custom_css = """
.user-message { background-color: #DCF8C6; }
.assistant-message { background-color: #E2E2E2; }
"""

# Create the Gradio interface
with gr.Blocks(css=custom_css) as demo:
    gr.HTML(f"""<img src = "https://i.postimg.cc/L6mbcC7F/DASH.png" width="150" height="112">""")
    gr.Markdown("# **Learning Scales Assistant**🐬")
    chatbot = gr.Chatbot(
        [],
        elem_id="chatbot",
        bubble_full_width=False,
        avatar_images=(None, avatar_link)
    )
    msg = gr.Textbox(
        show_label=False,
        placeholder="Enter text and press enter",
    )

    def user(user_message, history):
        return "", history + [[user_message, None]]

    def bot(history):
        user_message = history[-1][0]
        bot_message = ""
        for response_text in chat_with_assistant(user_message, history):
            history[-1][1] = response_text
            chatbot.update(history)
        return history

    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot, chatbot, chatbot
    )

demo.launch(auth=(username, password))