app.py

import logging
import gradio as gr
from queue import Queue
import time
from prometheus_client import start_http_server, Counter, Histogram
import threading
import psutil
import random
from transformers import pipeline, AutoConfig

# Load the model and its configuration
model_name = "Sevixdd/roberta-base-finetuned-ner"  # Make sure this model is available
ner_pipeline = pipeline("ner", model=model_name)
config = AutoConfig.from_pretrained(model_name)

# --- Prometheus Metrics ---
REQUEST_COUNT = Counter('gradio_request_count', 'Total requests')
REQUEST_LATENCY = Histogram('gradio_request_latency_seconds', 'Request latency (s)')

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

# --- Chat Queue ---
chat_queue = Queue(maxsize=1)  # Allow only one request at a time

# --- Chat Function ---
def chat_function(message, history):
    with REQUEST_LATENCY.time():
        REQUEST_COUNT.inc()
        try:
            if chat_queue.full():
                return "The model is busy. Please wait..."  # More user-friendly message

            chat_queue.put(message)
            logging.info(f"User: {message}")

            ner_result = ner_pipeline(message)
            response = f"Response from NER model: {ner_result}"
            logging.info(f"Bot: {response}")

            time.sleep(random.uniform(0.5, 2.5))  # Simulate processing (adjust as needed)

            chat_queue.get()
            return response
        except Exception as e:
            logging.error(f"Error: {e}")
            return "An error occurred. Please try again later."  # More helpful error message

# --- Gradio Interface ---
with gr.Blocks(
    css="""
    body {
        background-image: url("stag.jpeg"); 
        background-size: cover; 
        background-repeat: no-repeat;
    }
    """, 
    title="PLOD Filtered with Monitoring"
) as demo:
    with gr.Tab("Chat"):
        gr.Markdown("## Chat with the Bot")
        chatbot = gr.ChatInterface(fn=chat_function)

    with gr.Tab("Model Details"):
        gr.Markdown("## Model Configuration")
        gr.JSON(value=config.to_dict(), interactive=False)

    # ... other tabs (Performance Metrics, Infrastructure, Logs) ...

    # --- Update Functions ---
    # ... (Implement update functions for metrics, usage, and logs here)

# --- Background Threads ---
threading.Thread(target=start_http_server, args=(8000,), daemon=True).start()
# ... (Threads for metrics, usage, and logs update)

# Launch the app
demo.launch(share=True)