app.py

import gradio as gr
import datetime, openai, os, time

from openai import OpenAI
from utils import show_json

client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

assistant, thread = None, None

def create_assistant(client):
    assistant = client.beta.assistants.create(
        name="Python Code Generator",
        instructions=(
                         "You are a Python programming language expert that "
                         "generates Pylint-compliant code and explains it. "
                         "Only execute code when explicitly asked to."
                     ),
        model="gpt-4o",
        tools=[
                  {"type": "code_interpreter"}, 
              ],
    )
    
    show_json("assistant", assistant)
    
    return assistant

def load_assistant():
    return "TODO"

def create_thread(client):
    thread = client.beta.threads.create()
    
    show_json("thread", thread)
    
    return thread

def create_message(client, thread, msg):
    message = client.beta.threads.messages.create(
        role="user",
        thread_id=thread.id,
        content=msg,
    )
    
    show_json("message", message)
    
    return message

def create_run(client, assistant, thread):
    run = client.beta.threads.runs.create(
        assistant_id=assistant.id,
        thread_id=thread.id,
    )
    
    show_json("run", run)
    
    return run

def wait_on_run(client, thread, run):
    while run.status == "queued" or run.status == "in_progress":
        run = client.beta.threads.runs.retrieve(
            thread_id=thread.id,
            run_id=run.id,
        )
    
        time.sleep(0.25)
    
    show_json("run", run)
    
    return run

def get_run_steps(client, thread, run):
    run_steps = client.beta.threads.runs.steps.list(
        thread_id=thread.id,
        run_id=run.id,
        order="asc",
    )

    show_json("run_steps", run_steps)
    
    return run_steps

def get_run_step_details(run_steps):
    for step in run_steps.data:
        step_details = step.step_details
        
        show_json("step_details", step_details)

def get_messages(client, thread):
    messages = client.beta.threads.messages.list(
        thread_id=thread.id
    )
    
    show_json("messages", messages)
    
    return messages
    
def extract_content_values(data):
    content_values = []
    
    for item in data.data:
        for content in item.content:
            if content.type == "text":
                content_values.append(content.text.value)
    
    return content_values

def chat(message, history):
    global client, assistant, thread     
       
    if assistant == None:
        assistant = create_assistant(client)

    if thread == None:
        thread = create_thread(client)
        
    create_message(client, thread, message)

    run = create_run(client, assistant, thread)
    run = wait_on_run(client, thread, run)

    run_steps = get_run_steps(client, thread, run)

    get_step_details(run_steps)
    
    messages = get_messages(client, thread)

    content_values = extract_content_values(messages)
    
    return content_values[0]

gr.ChatInterface(
        fn=chat,
        chatbot=gr.Chatbot(height=500),
        textbox=gr.Textbox(placeholder="Ask anything", container=False, scale=7),
        title="Python Code Generator",
        description="The assistant can generate, explain, fix, optimize, document, test, and generally help with code. It can also search the internet and execute code.",
        #clear_btn="Clear",
        #retry_btn="Retry",
        #undo_btn="Undo",
        examples=[
                  ["Generate: NumPy/Pandas/Matplotlib & yfinance trading app"],
                  ["Explain: r'^(?=.*[A-Z])(?=.*[a-z])(?=.*[0-9])(?=.*[\\W]).{8,}$'"],
                  ["Fix: x = [5, 2, 1, 3, 4]; print(x.sort())"],
                  ["Optimize: x = []; for i in range(0, 10000): x.append(i)"],
                  ["Execute: Code to generate the first 20 fibbonaci numbers"],
                  [f"Today is {datetime.date.today()}. Create a plot showing stock gain YTD for NVDA, MSFT, AAPL, and GOOG, x-axis is 'Day' and y-axis is 'YTD Gain %'. Return the result, don't persist to storage."],
                 ],
    ).launch()