import gradio as gr
import time, os
from openai import OpenAI
api_key = os.environ["OPENAI_API_KEY_PUBLIC"]

class ask_me:
    def __init__(self):
        self.client = OpenAI(api_key=api_key)
        self.thread = self.client.beta.threads.create()
    
    def ask(self,question):
        run = self.client.beta.threads.runs.create(
              thread_id=self.thread.id,
              assistant_id='asst_cqL6gztTsqBGDdkKegpcQ32Y',
              instructions=question
            )
        while run.status in ['queued', 'in_progress', 'cancelling']:
            time.sleep(1) # Wait for 1 second
            run = self.client.beta.threads.runs.retrieve(
                thread_id=self.thread.id,
                run_id=run.id
            )
        if run.status == 'completed': 
            messages = self.client.beta.threads.messages.list(
                thread_id=self.thread.id
            )
            return messages.data[0].content[0].text.value
        else:
            return run.status
        
    def clear(self):
        self.thread = self.client.beta.threads.create()
        return "New search is there."
def wait():
    time.sleep(1)
    return "Waiting for the answer."

temp = ask_me()
with gr.Blocks(css="footer {visibility: hidden}") as demo:

    with gr.Row():
        with gr.Column(scale=5):
            search_box = gr.Textbox(label= "Your questions",value="How many MOF with Cu?",interactive = True)
        with gr.Column(scale=2):
            sub = gr.Button("Ask it!")
            clear = gr.Button("New search")
            
    with gr.Row():
            text = gr.Textbox(label= "Result",value="Answer is out there.")
        
    sub.click(temp.ask,inputs=search_box,outputs=text)
    clear.click(temp.clear,outputs=text)
demo.launch(debug=True)