Update app.py

app.py

@@ -1,6 +1,6 @@
 import numpy as np
 import pandas as pd
-
+import logging
 from sentence_transformers import SentenceTransformer
 
 from sklearn.metrics import DistanceMetric
@@ -13,6 +13,9 @@ import gradio as gr
 # move_cache()
 from telegram import Bot
 
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
 token = '7370905765:AAHvmlw68cW7RxWzsJE1yxTzgf3xQFiokDo'
 chat_id = '7431171535'
 pd.set_option('display.max_colwidth', None)
@@ -23,9 +26,12 @@ model = SentenceTransformer("all-MiniLM-L6-v2")
 # file_name = 'questions.txt'
 
 def send_telegram_message(token, chat_id, message):
-    bot = Bot(token=token)
-    bot.send_message(chat_id=chat_id, text=message)
-    print("Message sent successfully.")
+    try:
+        bot = Bot(token=token)
+        bot.send_message(chat_id=chat_id, text=message)
+        logger.info("Message sent successfully.")
+    except TelegramError as e:
+        logger.error(f"Failed to send message: {e}")
 
 
 def savevectorstore():
@@ -79,7 +85,10 @@ def rag_chain(question,name):
     # with open('questions.txt', 'a',encoding='utf-8') as file:
     #     file.write(f"\n\n{question}\n\n{country}\n\n{whatsapp}\n\n{name}||{sales}||{profit}||{product}")
     message = f"{name}\n\n{question}\n\n{ans}"
-    send_telegram_message(token, chat_id, message)
+    if not token or not chat_id:
+        logger.error("Please set the TELEGRAM_BOT_TOKEN and TELEGRAM_CHAT_ID environment variables.")
+    else:
+        send_telegram_message(token, chat_id, message)
     return que,ans
 # rag_chain('I am very hungry.')