Update app.py

app.py

@@ -7,6 +7,7 @@ import re
 import numpy as np
 from sklearn.feature_extraction.text import CountVectorizer
 from sklearn.naive_bayes import MultinomialNB
+import asyncio
 
 # Set up logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -73,9 +74,9 @@ def check_confidence(response):
     uncertain_phrases = ["I'm not sure", "It's possible", "I don't have enough information"]
     return not any(phrase.lower() in response.lower() for phrase in uncertain_phrases)
 
-def generate_response(prompt):
+async def generate_response(prompt):
     try:
-        return client.text_generation(prompt, max_new_tokens=500, temperature=0.7)
+        return await client.text_generation(prompt, max_new_tokens=500, temperature=0.7)
     except Exception as e:
         logger.error(f"Error generating response: {e}")
         return "I apologize, but I'm having trouble generating a response at the moment. Please try again later."
@@ -92,7 +93,7 @@ def post_process_response(response):
     return response
 
 # Main function
-def zerodha_support(message, history):
+async def zerodha_support(message, history):
     try:
         sanitized_message = sanitize_input(message)
         
@@ -102,7 +103,8 @@ def zerodha_support(message, history):
         sanitized_message = redact_sensitive_info(sanitized_message)
 
         # Generate response
-        response = generate_response(f"{SHARED_CONTEXT}\n\nUser: {sanitized_message}\nAssistant:")
+        prompt = f"{SHARED_CONTEXT}\n\nUser: {sanitized_message}\nAssistant:"
+        response = await generate_response(prompt)
 
         if not check_response_content(response):
             response += "\n\nPlease note that I cannot provide specific investment advice or guarantee returns. For personalized guidance, please consult with a qualified financial advisor."