Update app.py

app.py

@@ -3,18 +3,17 @@ import pandas as pd
 import numpy as np
 from datetime import datetime, timedelta
 import nltk
-import os
-from io import BytesIO
-import base64
-import plotly.express as px
-from wordcloud import WordCloud
+from nltk.sentiment import SentimentIntensityAnalyzer
 from textblob import TextBlob
-from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
 from transformers import pipeline
-from googleapiclient.discovery import build
-from googleapiclient.errors import HttpError
+from wordcloud import WordCloud
+import base64
+from io import BytesIO
+import plotly.express as px
 import praw
+from googleapiclient.discovery import build
 from sklearn.linear_model import Ridge
+import os
 
 # --------------------------
 # Initial Setup
@@ -33,7 +32,7 @@ st.set_page_config(
 # --------------------------
 
 class Config:
-    # Replace these with your actual API keys or use environment variables
+    # API Keys - Replace with your actual keys or use environment variables
     YOUTUBE_API_KEY = os.getenv("YT_API_KEY", "your_youtube_api_key_here")
     REDDIT_CLIENT_ID = os.getenv("REDDIT_CLIENT_ID", "your_reddit_client_id")
     REDDIT_CLIENT_SECRET = os.getenv("REDDIT_CLIENT_SECRET", "your_reddit_secret")
@@ -52,9 +51,8 @@ class Config:
 
 def initialize_resources():
     """Initialize all required resources with proper error handling"""
-    
-    # Setup NLTK
     try:
+        # Setup NLTK data
         os.makedirs(Config.NLTK_DATA_PATH, exist_ok=True)
         nltk.data.path.append(Config.NLTK_DATA_PATH)
         
@@ -195,13 +193,6 @@ def fetch_youtube_data(keyword, max_results=25):
         
         return pd.DataFrame(data)
     
-    except HttpError as e:
-        if e.resp.status == 403:
-            st.error("YouTube API quota exceeded. Try again later.")
-        else:
-            st.error(f"YouTube API error: {str(e)}")
-        return pd.DataFrame()
-    
     except Exception as e:
         st.error(f"Error fetching YouTube data: {str(e)}")
         return pd.DataFrame()