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import gradio as gr
import logging
import gradio as gr
from queue import Queue
import time
from prometheus_client import start_http_server, Counter, Histogram
# --- Prometheus Metrics Setup ---
REQUEST_COUNT = Counter('gradio_request_count', 'Total number of requests')
REQUEST_LATENCY = Histogram('gradio_request_latency_seconds', 'Request latency in seconds')
# --- Logging Setup ---
logging.basicConfig(filename="chat_log.txt", level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
# --- Queue and Metrics ---
chat_queue = Queue()
# --- Chat Function with Monitoring ---
def chat_function(message, history):
with REQUEST_LATENCY.time():
REQUEST_COUNT.inc()
try:
start_time = time.time()
chat_queue.put(message)
logging.info(f"User: {message}")
# ... (Your chatbot processing logic here) ...
time.sleep(2) # Simulate processing delay
response = chat_queue.get()
logging.info(f"Bot: {response}")
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)
# --- Start Prometheus Metrics Server ---
start_http_server(8000) # Expose metrics on port 8000
gr.load("models/Sevixdd/roberta-base-finetuned-ner").launch()
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