Update app.py

app.py

@@ -1,20 +1,23 @@
 import numpy as np
 import pandas as pd
-import logging
+
 from sentence_transformers import SentenceTransformer
 
+from sklearn.decomposition import PCA
 from sklearn.metrics import DistanceMetric
 
+import matplotlib.pyplot as plt
+import matplotlib as mpl
 import warnings
 warnings.filterwarnings("ignore")
 import gradio as gr
 # from transformers.utils.hub import move_cache
 #
 # move_cache()
-from telegram import Bot
+from mailersend import emails
 
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+import asyncio
+from telegram import Bot
 
 token = '7370905765:AAHvmlw68cW7RxWzsJE1yxTzgf3xQFiokDo'
 chat_id = '7431171535'
@@ -25,13 +28,10 @@ print(df_resume.head())
 model = SentenceTransformer("all-MiniLM-L6-v2")
 # file_name = 'questions.txt'
 
-def send_telegram_message(token, chat_id, message):
-    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}")
+async def send_telegram_message(token, chat_id, message):
+    bot = Bot(token=token)
+    await bot.send_message(chat_id=chat_id, text=message)
+    print("Message sent successfully.")
 
 
 def savevectorstore():
@@ -85,10 +85,8 @@ 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}"
-    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)
+    asyncio.run(send_telegram_message(token, chat_id, message))
+    print('Successfully sent!')
     return que,ans
 # rag_chain('I am very hungry.')