scoutLLM.py

import os
import datetime
import requests
import re
import pandas as pd
import gradio as gr
import threading
import uuid
import queue
import time
from transformers import AutoTokenizer
from mistralai import Mistral
from huggingface_hub import InferenceClient
import smtplib
import ssl
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart  # Add this line
from google_auth_oauthlib.flow import InstalledAppFlow
from googleapiclient.discovery import build
import base64
from google.oauth2.credentials import Credentials
from google.auth.transport.requests import Request
import openai  # Correct OpenAI import
from openai.error import RateLimitError  # Import rate limit error handling

# ------------------------------
# Helper functions and globals
# ------------------------------
sheet_data = None
file_name = None
sheet = None
slider_max_tokens = None

def debug_print(message: str):
    print(f"[{datetime.datetime.now().isoformat()}] {message}", flush=True)

def initialize_tokenizer():
    try:
        return AutoTokenizer.from_pretrained("gpt2")
    except Exception as e:
        debug_print("Failed to initialize tokenizer: " + str(e))
        return None

global_tokenizer = initialize_tokenizer()

def count_tokens(text: str) -> int:
    if global_tokenizer:
        try:
            return len(global_tokenizer.encode(text))
        except Exception:
            return len(text.split())
    return len(text.split())

def get_model_pricing(model_name: str):
    """Return pricing information for models."""
    model_pricing = {
        "GPT-3.5": {"USD": {"input": 0.0000005, "output": 0.0000015}, "RON": {"input": 0.0000023, "output": 0.0000069}},
        "GPT-4o": {"USD": {"input": 0.0000025, "output": 0.00001}, "RON": {"input": 0.0000115, "output": 0.000046}},
        "GPT-4o mini": {"USD": {"input": 0.00000015, "output": 0.0000006}, "RON": {"input": 0.0000007, "output": 0.0000028}},
        "o1-mini": {"USD": {"input": 0.0000011, "output": 0.0000044}, "RON": {"input": 0.0000051, "output": 0.0000204}},
        "o3-mini": {"USD": {"input": 0.0000011, "output": 0.0000044}, "RON": {"input": 0.0000051, "output": 0.0000204}},
        "Meta-Llama-3": {"USD": {"input": 0.00, "output": 0.00}, "RON": {"input": 0.00, "output": 0.00}},
        "Mistral-API": {"USD": {"input": 0.00, "output": 0.00}, "RON": {"input": 0.00, "output": 0.00}}
    }
    return model_pricing.get(model_name, {"USD": {"input": 0.00, "output": 0.00}, "RON": {"input": 0.00, "output": 0.00}})

def get_model_max_tokens(model_name: str) -> int:
    """Return the max context length for the selected model."""
    model_token_limits = {
        "GPT-3.5": 16385,
        "GPT-4o": 128000,
        "GPT-4o mini": 128000,
        "Meta-Llama-3": 4096,
        "Mistral-API": 128000,
        "o1-mini": 128000,
        "o3-mini": 128000
    }
    for key in model_token_limits:
        if key in model_name:
            return model_token_limits[key]
    return 4096  # Default safety limit


def generate_response(prompt: str, model_name: str, sheet_data: str = "") -> str:
    global slider_max_tokens
    
    full_prompt = f"{prompt}\n\nSheet Data:\n{sheet_data}" if sheet_data else prompt
    max_context_tokens = get_model_max_tokens(model_name)
    max_tokens = min(slider_max_tokens, max_context_tokens)
  # Extract base model name for API calls and pricing
    base_model_name = model_name.split()[1] if len(model_name.split()) > 1 else model_name
        
    
    try:
        if "Mistral" in model_name:
            mistral_api_key = os.getenv("MISTRAL_API_KEY")
            if not mistral_api_key:
                raise ValueError("MISTRAL_API_KEY environment variable not set.")
            mistral_client = Mistral(api_key=mistral_api_key)
            response = mistral_client.chat.complete(
                model="mistral-small-latest",
                messages=[{"role": "user", "content": full_prompt}],
                temperature=0.7,
                top_p=0.95
            )
            return f"[Model: {model_name}]" + response.choices[0].message.content
        
        elif "Meta-Llama" in model_name:
            hf_api_token = os.getenv("HF_API_TOKEN")
            if not hf_api_token:
                raise ValueError("HF_API_TOKEN environment variable not set.")
            client = InferenceClient(token=hf_api_token)
            response = client.text_generation(
                full_prompt,
                model="meta-llama/Meta-Llama-3-8B-Instruct",
                temperature=0.7,
                top_p=0.95,
                max_new_tokens=max_tokens
            )
            return f"[Model: {model_name}]" + response
        
        elif any(model in model_name for model in ["GPT-3.5", "GPT-4o", "o1-mini", "o3-mini"]):
            model_map = {
                "GPT-3.5": "gpt-3.5-turbo",
                "GPT-4o": "gpt-4o",
                "GPT-4o mini": "gpt-4o-mini",
                "o1-mini": "gpt-4o-mini",
                "o3-mini": "gpt-4o-mini"
            }
            model = next((model_map[key] for key in model_map if key in model_name), None)
            
            if not model:
                raise ValueError(f"Unsupported OpenAI model: {model_name}")
            
            openai.api_key = os.getenv("OPEN_API_KEY")
            
            response = openai.ChatCompletion.create(
                model=model,
                messages=[{"role": "user", "content": full_prompt}],
                temperature=0.7,
                max_tokens=max_tokens
            )
            
           # Count input tokens to estimate input cost
            input_tokens = count_tokens(full_prompt)
                        
            
            # Count output tokens
            output_tokens = count_tokens(response["choices"][0]["message"]["content"])
            
            # Get pricing information
            pricing = get_model_pricing(base_model_name)
            
            # Calculate per-token pricing
            per_token_pricing = (
                f" (${input_tokens * pricing['USD']['input']:.3f}/in, "
                f"${output_tokens * pricing['USD']['output']:.3f}/out | "
                f"{input_tokens * pricing['RON']['input']:.3f} RON/in, "
                f"{output_tokens * pricing['RON']['output']:.3f} RON/out)"
            )
            
            return f"[Model: {model_name}{per_token_pricing}]" + response["choices"][0]["message"]["content"]
    
    except Exception as e:
        debug_print(f"❌ Error generating response: {str(e)}")
        return f"[Model: {model_name}][Error] {str(e)}"

        
        
def process_query(prompt: str, model_name: str):
    global sheet_data
    
    # Handle the case where sheet_data might be None
    if sheet_data is None:
        sheet_data = get_sheet_data()
        
    full_prompt = f"{prompt}\n\nSheet Data:\n{sheet_data}"  # Append sheet data to prompt
    debug_print(f"Processing query with model {model_name}: {full_prompt}")

    # Generate the response using the specified model and sheet data
    response = generate_response(prompt, model_name, sheet_data)
    
    # Count the number of tokens for input and output
    input_tokens = count_tokens(prompt + "\n\n" + sheet_data)  # Include sheet data in the input token count
    output_tokens = count_tokens(response)
    
    # Return the response along with token counts
    return response, f"Input tokens: {input_tokens}", f"Output tokens: {output_tokens}"

# ------------------------------
# Global variables for background jobs
# ------------------------------
jobs = {}
results_queue = queue.Queue()
last_job_id = None

# ------------------------------
# Job management functions
# ------------------------------

def get_job_list():
    job_list_md = "### 📊 Submitted Jobs\n\n"
    
    if not jobs:
        return "No jobs found. Submit a query or load files to create jobs."
    
    # Sort jobs by start time (newest first)
    sorted_jobs = sorted(
        [(job_id, job_info) for job_id, job_info in jobs.items()],
        key=lambda x: x[1].get("start_time", 0),
        reverse=True
    )
    
    for job_id, job_info in sorted_jobs:
        status = job_info.get("status", "unknown")
        job_type = job_info.get("type", "unknown")
        query = job_info.get("query", "")
        start_time = job_info.get("start_time", 0)
        time_str = datetime.datetime.fromtimestamp(start_time).strftime("%Y-%m-%d %H:%M:%S")
        
        # Create a shortened query preview
        query_preview = query[:30] + "..." if query and len(query) > 30 else query or "N/A"
        
        # Color-code the status display
        if status == "processing":
            status_formatted = f"<span style='color: red'>⏳ {status}</span>"
        elif status == "completed":
            status_formatted = f"<span style='color: green'>✅ {status}</span>"
        else:
            status_formatted = f"<span style='color: orange'>❓ {status}</span>"
        
        if job_type == "query":
            job_list_md += f"- [{job_id}](javascript:void) - {time_str} - {status_formatted} - Query: {query_preview}\n"
        else:
            job_list_md += f"- [{job_id}](javascript:void) - {time_str} - {status_formatted} - File Load Job\n"
    
    return job_list_md

def get_sheet_data():
    global sheet_data
    global file_name  
    global sheet  
    file = file_name 
    sheet_name = sheet
    print ("file name: ",file," sheet name: ",sheet_name," ")
    
    if sheet_data is None: 
        try:
            df = pd.read_excel(file.name, sheet_name=sheet_name)
            sheet_data = df.to_string(index=False)  # Convert sheet data to string format
            return sheet_data  # Display sheet data in UI
        except Exception as e:
            return f"Error reading sheet: {str(e)}"
    else: 
        return sheet_data

# Assuming process_in_background is using threading to call process_query

def process_in_background(job_id, func, args):
    """Runs a function in the background and stores its result in a shared queue."""
    result = func(*args)
    results_queue.put((job_id, result))
    debug_print(f"Job {job_id} finished processing in background.")
    

def submit_query_async(query, model_choice, max_tokens_slider):
    """Asynchronous version of submit_query_updated to prevent timeouts."""
    global last_job_id
    global sheet_data 
    global slider_max_tokens 
    slider_max_tokens = max_tokens_slider
    
    if not query:
        return ("Please enter a non-empty query", "", "Input tokens: 0", "Output tokens: 0", "", "", get_job_list())
    
    job_id = str(uuid.uuid4())
    debug_print(f"Starting async job {job_id} for query: {query}")
    
    # Handle the case where sheet_data might be None
    if sheet_data is None:
        sheet_data = get_sheet_data()
        
    
    query = f"{query}\n\nSheet Data:\n{sheet_data}"  # Append sheet data to prompt
    
    # Start background thread to process the query
    threading.Thread(
        target=process_in_background,
        args=(job_id, process_query, [query, model_choice or "Mistral-API"])
    ).start()
    
    jobs[job_id] = {
        "status": "processing", 
        "type": "query",
        "start_time": time.time(),
        "query": query,
        "model": model_choice or "Mistral-API"
    }
    
    last_job_id = job_id
    
    return (
        f"🚀 Query submitted and processing in the background (Job ID: {job_id}).\n\n"
        f"Use the 'Check Job Status' section to view results.",
        f"Job ID: {job_id}",
        f"Input tokens: {count_tokens(query)}",
        "Output tokens: pending",
        job_id,  # For UI job id update
        query,  # For UI query display update
        get_job_list()  # Updated job list
    )

def job_selected(job_id):
    if job_id in jobs:
        return job_id, jobs[job_id].get("query", "No query for this job")
    return job_id, "Job not found"

def refresh_job_list():
    return get_job_list()

def sync_model_dropdown(value):
    return value    

def check_job_status(job_id):
    if not job_id:
        html_response = "<div style='font-family: monospace;'><p>Please enter a job ID.</p></div>"
        return html_response, "", "", "", ""
    
    # Process any completed jobs in the results queue
    try:
        while not results_queue.empty():
            completed_id, result = results_queue.get_nowait()
            if completed_id in jobs:
                jobs[completed_id]["status"] = "completed"
                jobs[completed_id]["result"] = result
                jobs[completed_id]["end_time"] = time.time()
                debug_print(f"Job {completed_id} completed and stored in jobs dictionary")
    except queue.Empty:
        pass
    
    if job_id not in jobs:
        html_response = "<div style='font-family: monospace;'><p>Job not found. Please check the ID and try again.</p></div>"
        return html_response, "", "", "", ""
    
    job = jobs[job_id]
    job_query = job.get("query", "No query available for this job")
    
    if job["status"] == "processing":
        elapsed_time = time.time() - job["start_time"]
        html_response = (
            f"<div style='font-family: monospace;'>"
            f"<p><strong>⏳ Query is still being processed</strong> (elapsed: {elapsed_time:.1f}s). Please check again shortly.</p>"
            f"</div>"
        )
        return (
            html_response,
            f"Job ID: {job_id}",
            f"Input tokens: {count_tokens(job.get('query', ''))}",
            "Output tokens: pending",
            job_query
        )
    
    if job["status"] == "completed":
        result = job["result"]
        processing_time = job["end_time"] - job["start_time"]
        html_response = (
            f"<div style='font-family: monospace;'>"
            f"<p><strong>✅ Response:</strong> {result[0]}</p>"
            f"<p>Processing time: {processing_time:.1f}s</p>"
            f"</div>"
        )
        return (
            html_response,
            f"Job ID: {job_id}",
            result[1],
            result[2],
            job_query
        )
    
    html_response = f"<div style='font-family: monospace;'><p>Job status: {job['status']}</p></div>"
    return html_response, "", "", "", job_query

def cleanup_old_jobs():
    current_time = time.time()
    to_delete = []
    
    for job_id, job in jobs.items():
        # Completed jobs older than 24 hours and processing jobs older than 48 hours will be removed.
        if job["status"] == "completed" and (current_time - job.get("end_time", 0)) > 86400:
            to_delete.append(job_id)
        elif job["status"] == "processing" and (current_time - job.get("start_time", 0)) > 172800:
            to_delete.append(job_id)
    
    for job_id in to_delete:
        del jobs[job_id]
    
    debug_print(f"Cleaned up {len(to_delete)} old jobs. {len(jobs)} jobs remaining.")
    return f"Cleaned up {len(to_delete)} old jobs", "", ""

# Function to run query (dummy function)
def run_query(max_value):
    # Simulate a data retrieval or processing function
    return [[i, i**2] for i in range(1, max_value + 1)]

# Function to call both refresh_job_list and check_job_status using the last job ID
def periodic_update(is_checked):
    interval = 3 if is_checked else None
    debug_print(f"Auto-refresh checkbox is {'checked' if is_checked else 'unchecked'}, every={interval}")
    if is_checked:
        global last_job_id
        job_list_md = refresh_job_list()
        job_status = check_job_status(last_job_id) if last_job_id else ("No job ID available", "", "", "", "")
        
        # Extract plain text from HTML for status_text
        from bs4 import BeautifulSoup
        html_content = job_status[0]
        plain_text = ""
        if html_content:
            soup = BeautifulSoup(html_content, "html.parser")
            plain_text = soup.get_text()
        
        # Return all expected outputs, including status_text
        return job_list_md, job_status[0], plain_text, job_status[1], job_status[2], job_status[3], job_status[4]
    else:
        # Return empty values to stop updates - make sure to match the number of expected outputs
        return "", "", "", "", "", "", ""
        
# OAuth 2.0 Scopes
SCOPES = ["https://www.googleapis.com/auth/gmail.send"]

from google_auth_oauthlib.flow import InstalledAppFlow
from google.oauth2.credentials import Credentials
from google.auth.transport.requests import Request
import os
import json


def get_gmail_credentials():
    global oauth_flow
    
    creds = None
    
    # Fetch client secrets from environment variables
    client_id = os.environ.get("HF_GOOGLE_CLIENT_ID")
    client_secret = os.environ.get("HF_GOOGLE_CLIENT_SECRET")
    
    if not client_id or not client_secret:
        raise ValueError("Missing Gmail OAuth credentials in environment variables.")
    
    # Define the redirect URI for your Hugging Face space
    redirect_uri = "https://huggingface.co/spaces/alx-d/scout/oauth2callback"
    
    # Load credentials from token.json if available
    if os.path.exists(token_path):
        creds = Credentials.from_authorized_user_file(token_path)
    
    # If no valid credentials, log in via OAuth
    if not creds or not creds.valid:
        if creds and creds.expired and creds.refresh_token:
            creds.refresh(Request())
        else:
            client_config = {
                "web": {
                    "client_id": client_id,
                    "project_id": "your_project_id",
                    "auth_uri": "https://accounts.google.com/o/oauth2/auth",
                    "token_uri": "https://oauth2.googleapis.com/token",
                    "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
                    "client_secret": client_secret,
                    "redirect_uris": [redirect_uri]
                }
            }
            
            oauth_flow = Flow.from_client_config(client_config, SCOPES, redirect_uri=redirect_uri)
            auth_url, _ = oauth_flow.authorization_url(
                prompt='consent',
                access_type='offline',
                include_granted_scopes='true'
            )
            
            return None, auth_url
    
    return creds, None

# Add email sending function  
def send_email(email_address, content, is_formatted=True):
    if not email_address or "@" not in email_address:
        return "Please enter a valid email address"

    try:
        creds = get_gmail_credentials()
        service = build("gmail", "v1", credentials=creds)

        # Create email message with appropriate MIME type
        msg = MIMEMultipart()
        msg["to"] = email_address
        msg["subject"] = "Scouting AI Report"
        msg.attach(MIMEText(content, "html" if is_formatted else "plain"))

        # Encode email message in base64
        encoded_msg = base64.urlsafe_b64encode(msg.as_bytes()).decode()
        send_message = {"raw": encoded_msg}

        # Send email using Gmail API
        service.users().messages().send(userId="me", body=send_message).execute()
        return "Email sent successfully via Gmail API!"
    
    except Exception as e:
        return f"Failed to send email: {str(e)}"

# Function to copy content to clipboard
def copy_to_clipboard(content):
    import pyperclip
    pyperclip.copy(content)
    return "Copied to clipboard!"


# Function to convert HTML to plain text using BeautifulSoup
def copy_plain_text(html_content):
    try:
        from bs4 import BeautifulSoup
    except ImportError:
        return "Error: BeautifulSoup is required to convert HTML to plain text. Please install it."
    soup = BeautifulSoup(html_content, "html.parser")
    plain_text = soup.get_text()
    import pyperclip
    pyperclip.copy(plain_text)
    
    return "Copied to clipboard!"


# Default prompt template
default_prompt = """
You are a scout who has played against this player, and you are analyzing the following statistics.
Create a scouting report for the head coach, detailing:

1) The player's strengths, along with a strategy to counter those strengths.
2) The player's weaknesses, and how we can exploit those weaknesses based on the stats.

Present the report in a way that is easy to read, combining each strength with its corresponding counter-strategy, and each weakness with an exploitation plan.

At the end of the report, include a “Key Points to Emphasize” section.

Use HTML formatting for the output, and apply a dark color palette (e.g., dark green, dark red, dark gray) for different sections to enhance visual readability.
"""

# ------------------------------
# Gradio UI Layout: Scouting AI App
# ------------------------------

with gr.Blocks() as app:
    # App Title and Description
    gr.Markdown("## 🏀 Scouting AI App")
    gr.Markdown("Welcome to the Scouting AI App! Upload your files, submit queries, and check job statuses easily. Game on! 😊")
    
    # Two-column layout for top section (File Load and Job Information)
    with gr.Row():
        # Left Column: File Load Section (50% width)
        with gr.Column(scale=1):
            gr.Markdown("### 📁 Load File Section")
            gr.Markdown("Upload your **.xlsm** file below, specify the sheet name, and click *Load Sheet* to process your file.")
            file_input = gr.File(label="Upload .xlsm File")
            sheet_input_file = gr.Textbox(label="Sheet Name")
            load_button_file = gr.Button("Load Sheet")
            sheet_output_file = gr.Textbox(label="Sheet Info", interactive=False)
        
        # Right Column: Job Information Section (50% width)
        with gr.Column(scale=1):
            gr.Markdown("### 📊 Job Information")
            gr.Markdown("View all submitted jobs, refresh the list, and check the status of individual jobs.")
            
            # Fixed-height job list with scrollbar
            job_list_display = gr.Markdown(
                get_job_list(),
                elem_id="job-list-display",
                elem_classes=["scrollable-job-list"]
            )
            
            # Add CSS for scrollable job list
            gr.HTML("""
            <style>
            .scrollable-job-list {
                height: 220px;
                overflow-y: auto;
                border: 1px solid #ccc;
                padding: 10px;
                margin-bottom: 10px;
            }
            </style>
            """)
            
            refresh_button = gr.Button("Refresh Job List")
                       
            gr.Markdown("#### 🔍 Check Job Status")
            job_id_input = gr.Textbox(label="Enter Job ID")
            check_status_button = gr.Button("Check Job Status")
            
    # Submit Query Section (left column, below File Load)
    with gr.Row():
        # Left Column: Submit Query Section
        with gr.Column(scale=1):
            gr.Markdown("### 🚀 Submit Query")
            gr.Markdown("Enter your prompt below and choose a model. Your query will be processed in the background.")
            # Update the model dropdown in the Gradio UI
            # Update the model dropdown in the Gradio UI
            model_dropdown = gr.Dropdown(
                choices=[
                    "🇺🇸 GPT-3.5",
                    "🇺🇸 GPT-4o",
                    "🇺🇸 GPT-4o mini",
                    "🇺🇸 o1-mini", 
                    "🇺🇸 o3-mini",
                    "🇺🇸 Remote Meta-Llama-3", 
                    "🇪🇺 Mistral-API",
                ],
                value="🇺🇸 GPT-4o mini",  # Default model set to Mistral
                label="Select Model"
            )
            max_tokens_slider = gr.Slider(minimum=200, maximum=4096, value=1200, label="🔢 Max Tokens", step=50)
                
            prompt_input = gr.Textbox(label="Enter your prompt", value=default_prompt, lines=6)
            with gr.Row():
                auto_refresh_checkbox = gr.Checkbox(
                    label="Enable Auto Refresh",
                    value=False  # Default to unchecked
                )                        
                submit_button = gr.Button("Submit Query ")
                # Use a Checkbox to control the periodic updates
                
            # Add a textarea to store the plain text version for copying
            status_text = gr.Textbox(label="Response Text ", visible=True)
            
            response_output = gr.Textbox(label="Response", interactive=False)
            token_info = gr.Textbox(label="Token Info", interactive=False)
            # Add buttons for copying and sending email
#            with gr.Row():
#                copy_btn = gr.Button("📋 Copy Text")

           
            # Add buttons for copying and sending email
#            with gr.Row():
#                copy_plain_button = gr.Button("📋 Copy Plain Text")
#                copy_formatted_button = gr.Button("📋 Copy Formatted")
                
#            with gr.Row():
#                email_input = gr.Textbox(label="Email Address")
#                send_email_button = gr.Button("📧 Send Report")
#                email_status = gr.Textbox(label="Status", interactive=False)
            
        # Job Status Output in right column
        with gr.Column(scale=1):
            # Change Job Status output to an HTML component for proper formatting
            status_output = gr.HTML(label="Job Status", interactive=False)
                
            job_id_display = gr.Textbox(label="Job ID", interactive=False)
            input_tokens_display = gr.Textbox(label="Input Tokens", interactive=False)
            output_tokens_display = gr.Textbox(label="Output Tokens", interactive=False)
            job_query_display = gr.Textbox(label="Job Query", interactive=False)
    
    # ------------------------------
    # Set up interactions
    # ------------------------------
    
    # Load file interaction (dummy function for now)
    def load_file(file, sheet_name):
        global sheet_data
        global file_name  
        file_name = file
        sheet = sheet_name
        
        if file is None or sheet_name.strip() == "":
            return "Please upload a file and enter a valid sheet name."
        
        try:
            df = pd.read_excel(file.name, sheet_name=sheet_name)
            sheet_data = df.to_string(index=False)  # Convert sheet data to string format
            return sheet_data  # Display sheet data in UI
        except Exception as e:
            return f"Error reading sheet: {str(e)}"
    
    load_button_file.click(
        fn=load_file,
        inputs=[file_input, sheet_input_file],
        outputs=sheet_output_file
    )
    
    # When submitting a query asynchronously
    submit_button.click(
        fn=submit_query_async,
        inputs=[prompt_input, model_dropdown, max_tokens_slider],
        outputs=[
            response_output, token_info, 
            input_tokens_display, output_tokens_display,
            job_id_input, job_query_display, job_list_display
        ]
    )
    
    # Check job status interaction
    check_status_button.click(
        fn=check_job_status,
        inputs=[job_id_input],
        outputs=[status_output, job_id_display, input_tokens_display,
                 output_tokens_display, job_query_display]
    )
    
    # Refresh the job list
    refresh_button.click(
        fn=refresh_job_list,
        inputs=[],
        outputs=job_list_display
    )
    
    # Use the Checkbox to control the periodic updates
    auto_refresh_checkbox.change(
        fn=periodic_update,
        inputs=[auto_refresh_checkbox],
        outputs=[job_list_display, status_output, status_text, job_id_display, input_tokens_display, output_tokens_display, job_query_display],
        every=3
    )
    

    # Connect the copy button to show the text in the textbox and make it visible temporarily
    def show_copy_text(text):
        # Simply return the text value and make the component visible
        return gr.update(value=text, visible=True)

    # Set up the event handlers
#    copy_btn.click(fn=show_copy_text, inputs=status_text, outputs=status_text)

    
    # Copy and email buttons
#    copy_plain_button.click(fn=copy_plain_text, inputs=[status_output], outputs=[email_status])
#    copy_formatted_button.click(fn=copy_to_clipboard, inputs=[status_output], outputs=[email_status])
#    send_email_button.click(fn=send_email, inputs=[email_input, status_output], outputs=[email_status])


if __name__ == "__main__":
    debug_print("Launching Gradio UI...")
    app.queue().launch(share=False)