Create testapp.py

testapp.py

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+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)