import gradio as gr
import json, openai, os, time
from openai import OpenAI

client = None
assistant = None
thread = None

def show_json(str, obj):
    print(f"### {str}")
    print(json.loads(obj.model_dump_json()))

def init_assistant():
    client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
    
    assistant = client.beta.assistants.create(
        name="Math Tutor",
        instructions="You are a personal math tutor. Answer questions briefly, in a sentence or less.",
        model="gpt-4-1106-preview",
    )
    
    thread = client.beta.threads.create()

    return client, assistant, thread

def wait_on_run(client, run, thread):
    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)
    return run

def extract_content_value(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
    global assistant
    global thread     
       
    history_openai_format = []
    
    for human, assistant in history:
        history_openai_format.append({"role": "user", "content": human})
        history_openai_format.append({"role": "assistant", "content":assistant})

    history_openai_format.append({"role": "user", "content": message})

    if len(history_openai_format) == 1:
        client, assistant, thread = init_assistant()

    client = client
    assistant = assistant
    thread = thread
    
    show_json("assistant", assistant)
    show_json("thread", thread)
    
    #print("### history")
    #print(len(history_openai_format))
    #print(history_openai_format)
    
    message = client.beta.threads.messages.create(
        thread_id=thread.id,
        role="user",
        content=message,
    )
    
    #show_json("message", message)
    
    run = client.beta.threads.runs.create(
        thread_id=thread.id,
        assistant_id=assistant.id,
    )
    
    #show_json("run", run)

    run = wait_on_run(client, run, thread)
    
    #show_json("run", run)

    messages = client.beta.threads.messages.list(thread_id=thread.id)
    
    #show_json("messages", messages)

    return extract_content_value(messages)[0]

gr.ChatInterface(
    chat,
    chatbot=gr.Chatbot(height=300),
    textbox=gr.Textbox(placeholder="Ask Math Tutor any question", container=False, scale=7),
    title="Math Tutor",
    description="Question",
    theme="soft",
    examples=["I need to solve the equation `3x + 13 = 11`. Can you help me?"],
    cache_examples=False,
    retry_btn=None,
    undo_btn=None,
    clear_btn="Clear",
    #multimodal=True,
    #additional_inputs=[
    #    gr.Textbox("You are a personal math tutor. Answer questions briefly, in a sentence or less.", label="System Prompt"),
    #],
).launch()