Update app.py

app.py

@@ -1,34 +1,78 @@
-# -*- coding: utf-8 -*-
+
+
+# Standard Library Imports
+import os
+import io
+import re
+import base64
+import random
+from datetime import datetime, timedelta
+from collections import Counter
+from pathlib import Path  # Needed for cache_dir
+
+# Third-party Libraries
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import plotly.express as px
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
-import io
-import base64
-import random
-from datetime import datetime, timedelta
-from collections import Counter
 import gradio as gr
-from wordcloud import WordCloud
-import os
+from youtube_comment_downloader import YoutubeCommentDownloader
+
+# Lazy import helper functions
+def lazy_import_transformers():
+    try:
+        from transformers import pipeline
+        return pipeline
+    except ImportError:
+        print("transformers library not found.")
+        return None
 
-from transformers import pipeline
-from keybert import KeyBERT
+def lazy_import_keybert():
+    try:
+        from keybert import KeyBERT
+        return KeyBERT
+    except ImportError:
+        print("keybert library not found.")
+        return None
+
+# Initialize models with cache_dir for faster loading
+def initialize_models():
+    classifier = None
+    kw_model = None
+
+    try:
+        pipeline = lazy_import_transformers()
+        if pipeline:
+            cache_dir = Path.home() / ".cache/huggingface"
+            cache_dir.mkdir(parents=True, exist_ok=True)  
+
+            classifier = pipeline(
+                "sentiment-analysis",
+                model="cardiffnlp/twitter-roberta-base-sentiment-latest",
+                cache_dir=str(cache_dir)
+            )
+
+        KeyBERT = lazy_import_keybert()
+        if KeyBERT:
+            kw_model = KeyBERT()
+
+    except Exception as e:
+        print(f"Error initializing models: {e}")
+
+    return classifier, kw_model
+
+# Set transformers logging to error only
+import transformers
+transformers.logging.set_verbosity_error()
+
+# Load models
+classifier, kw_model = initialize_models()
 
-from transformers import pipeline
-from keybert import KeyBERT
-from youtube_comment_downloader import YoutubeCommentDownloader
-from datetime import datetime, timedelta
-import re
-import pandas as pd
 
 
 
-# Initialize models globally
-classifier = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
-kw_model = KeyBERT()
 
 # Label mapping - handling different model outputs
 sentiment_map = {
@@ -38,8 +82,6 @@ sentiment_map = {
 }
 color_map = {"Positive": "#2E8B57", "Neutral": "#4682B4", "Negative": "#CD5C5C"}
 
-
-
 # Default comments for when no file is uploaded
 comments = [
     "This new distance fare is really fair. I pay less for short trips!",
@@ -726,8 +768,6 @@ def export_data_to_csv(df_component):
 
 
 def analyze_youtube_comments(video_url):
-    from youtube_comment_downloader import YoutubeCommentDownloader
-    import re
 
     # Simple YouTube video URL validation
     youtube_pattern = r"(https?://)?(www\.)?(youtube\.com/watch\?v=|youtu\.be/)[\w-]{11}"
@@ -999,7 +1039,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
 # Launch the app
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=False)