import gradio as gr
import logging
import gradio as gr
from queue import Queue
import time


gr.load("models/Sevixdd/roberta-base-finetuned-ner").launch()

# --- Logging Setup ---
logging.basicConfig(filename="chat_log.txt", level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# --- Queue and Metrics ---
chat_queue = Queue()
response_times = []

# --- Chat Function ---
def chat_function(message, history):
    try:
        start_time = time.time()  # Record start time for metrics
        chat_queue.put(message)  # Add message to queue
        logging.info(f"User: {message}")
    
        # ... (Your chatbot processing logic here) ...
        # Simulate processing delay
        time.sleep(2)  # Replace with actual processing
        response = chat_queue.get()
        logging.info(f"Bot: {response}")

        # --- Metrics Calculation ---
        end_time = time.time()
        response_time = end_time - start_time
        response_times.append(response_time)
        avg_response_time = sum(response_times) / len(response_times) if response_times else 0

        logging.info(f"Response Time: {response_time:.2f} seconds, Avg: {avg_response_time:.2f} seconds")
        return response
    except Exception as e:
        logging.error(f"Error in chat processing: {e}")
        return "An error occurred. Please try again."

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("## Chat with the Bot")
    chatbot = gr.ChatInterface(fn=chat_function)

demo.launch()

# --- Additional Monitoring (Optional) ---
def monitor_queue():
    while True:
        queue_size = chat_queue.qsize()
        logging.info(f"Queue Size: {queue_size}")
        time.sleep(60)  # Check every 60 seconds

# Uncomment to enable queue monitoring
# import threading
# threading.Thread(target=monitor_queue, daemon=True).start()