Update app.py

app.py

@@ -1,449 +1,3 @@
-# import subprocess
-# subprocess.check_call(["pip", "install", "transformers==4.34.0"])
-# subprocess.check_call(["pip", "install", "torch>=1.7.1"])
-# subprocess.check_call(["pip", "install", "youtube_transcript_api>=0.6.3"])
-# subprocess.check_call(["pip", "install", "pytube"])
-# subprocess.check_call(["pip", "install", "huggingface_hub>=0.19.0"])
-# subprocess.check_call(["pip", "install", "PyPDF2>=3.0.1"])
-# subprocess.check_call(["pip", "install", "google-generativeai"])
-# subprocess.check_call(["pip", "install", "textblob>=0.17.1"])
-# subprocess.check_call(["pip", "install", "python-dotenv>=1.0.0"])
-# subprocess.check_call(["pip", "install", "genai"])
-# subprocess.check_call(["pip", "install", "google-cloud-aiplatform==1.34.0"])
-# import transformers
-# import torch
-# import os 
-# import youtube_transcript_api
-# import pytube
-# import gradio
-# import PyPDF2
-# import pathlib
-# import pandas
-# import numpy
-# import textblob
-# import gradio as gr
-# from youtube_transcript_api import YouTubeTranscriptApi
-# import google.generativeai as genai
-# import requests
-# from textblob import TextBlob
-# import re
-# #from google.cloud import generativeai
-# from huggingface_hub import login
-# from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound
-# def install_missing_packages():
-#     required_packages = {
-#          "torch":">=1.11.0",
-#         "transformers":">=4.34.0",
-#         "youtube_transcript_api" :">=0.6.3" ,
-#         "pytube":None,
-#         "huggingface_hub": ">=0.19.0",
-#         "PyPDF2": ">=3.0.1",
-#         "textblob":">=0.17.1",
-#         "python-dotenv":">=1.0.0",
-#         "genai":None,
-#         "google-generativeai": None,
-#         "google-cloud-aiplatform":"==1.34.0"
-#     }
-
-
-#     for package, version in required_packages.items():
-#         try:
-#             __import__(package)
-#         except ImportError:
-#             package_name = f"{package}{version}" if version else package
-#             subprocess.check_call(["pip", "install", package_name])
-
-# install_missing_packages()
-# # Configuration
-
-# hf_token = os.getenv("HF_TOKEN")
-# if hf_token:
-#     login(hf_token)
-# else:
-#     raise ValueError("HF_TOKEN environment variable not set.")
-
-# # Configuration
-# USER_CREDENTIALS = {
-#     "admin": "password123",
-#     "teacher": "teach2024",
-#     "student": "learn2024"
-# }
-
-# import os
-# from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound
-
-# # Use environment variables
-# GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-# YOUTUBE_API_KEY = os.getenv("YOUTUBE_API_KEY")
-
-# if not GOOGLE_API_KEY or not YOUTUBE_API_KEY:
-#     raise ValueError("Please set GOOGLE_API_KEY and YOUTUBE_API_KEY environment variables")
-
-# genai.configure(api_key=GOOGLE_API_KEY)
-
-# # Database
-# students_data = [
-#     (1, "Alice", "A", "Computer Science"),
-#     (2, "Aliaa", "B", "Mathematics"),
-#     (3, "Charlie", "A", "Machine Learning"),
-#     (4, "Daan", "A", "Physics"),
-#     (5, "Jhon", "C", "Math"),
-#     (6, "Emma", "A+", "Computer Science")
-# ]
-
-# teachers_data = [
-#     (1, "Dr. Smith", "Math", "MS Mathematics"),
-#     (2, "Ms. Johnson", "Science", "MSc Physics"),
-#     (3, "Ms. Jack", "Artificial Intelligence Engineer", "MSc AI"),
-#     (4, "Ms. Evelyn", "Computer Science", "MSc Computer Science"),
-# ]
-
-# courses_data = [
-#     (1, "Algebra", "Dr. Smith", "Advanced"),
-#     (2, "Biology", "Ms. Mia", "Intermediate"),
-#     (3, "Machine Learning", "Ms. Jack", "Intermediate"),
-#     (4, "Computer Science", "Ms. Evelyn", "Intermediate"),
-#     (5, "Mathematics", "Ms. Smith", "Intermediate")
-# ]
-
-# def sanitize_text(text):
-#     """Remove invalid Unicode characters."""
-#     return text.encode("utf-8", "replace").decode("utf-8")
-
-# def extract_video_id(url):
-#     if not url:
-#         return None
-#     patterns = [
-#         r'(?:v=|\/videos\/|embed\/|youtu.be\/|\/v\/|\/e\/|watch\?v=|\/watch\?v=)([^#\&\?]*)'
-#     ]
-#     for pattern in patterns:
-#         match = re.search(pattern, url)
-#         if match:
-#             return match.group(1)
-#     return None
-
-
-
-# from textblob import TextBlob
-# from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound
-# import re
-# from collections import Counter
-# from googleapiclient.discovery import build
-
-# def process_youtube_video(url="", keywords=""):
-#     try:
-#         #Initialize variables
-#         thumbnail = None
-#         summary = "No transcript available"
-#         sentiment_label = "N/A"
-#         recommendations = ""
-#         subtitle_info = "No additional information available"
-
-#         if not url.strip():
-#             return None, "Please enter a YouTube URL", "N/A", "", ""
-
-#         video_id = extract_video_id(url)
-#         if not video_id:
-#             return None, "Invalid YouTube URL", "N/A", "", ""
-
-#         thumbnail = f"https://img.youtube.com/vi/{video_id}/maxresdefault.jpg"
-
-#         try:
-#             # Fetch transcript
-#             transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
-#             transcript = None
-#             try:
-#                 transcript = transcript_list.find_transcript(['en'])
-#             except:
-#                 transcript = transcript_list.find_generated_transcript(['en'])
-
-#             text = " ".join([t['text'] for t in transcript.fetch()])
-#             if not text.strip():
-#                 raise ValueError("Transcript is empty")
-
-#             # Clean up the text for sentiment analysis
-#             cleaned_text = clean_text_for_analysis(text)
-
-#             # Sentiment analysis
-#             sentiment = TextBlob(cleaned_text).sentiment  # Use cleaned text for sentiment analysis
-#             sentiment_label = f"{'Positive' if sentiment.polarity > 0 else 'Negative' if sentiment.polarity < 0 else 'Neutral'} ({sentiment.polarity:.2f})"
-
-#             # Generate summary
-#             model = genai.GenerativeModel("gemini-pro")
-#             summary = model.generate_content(f"Summarize this: {cleaned_text[:4000]}").text
-
-#             # Extract subtitle information
-#             subtitle_info = extract_subtitle_info(cleaned_text)
-
-#         except TranscriptsDisabled:
-#             metadata = get_video_metadata(video_id)
-#             summary = metadata.get("description", "⚠️ This video has disabled subtitles.")
-#             sentiment_label = "N/A"
-#             subtitle_info = "No subtitles available for analysis."
-#         except NoTranscriptFound:
-#             metadata = get_video_metadata(video_id)
-#             summary = metadata.get("description", "⚠️ No English transcript available.")
-#             sentiment_label = "N/A"
-#             subtitle_info = "No subtitles available for analysis."
-#         except Exception as e:
-#             return thumbnail, f"⚠️ Error processing transcript: {str(e)}", "N/A", "", ""
-
-#         # Get recommendations
-#         if keywords.strip():
-#             recommendations = get_recommendations(keywords)
-
-#         return thumbnail, summary, sentiment_label, subtitle_info, recommendations
-
-#     except Exception as e:
-#         return None, f"Error: {str(e)}", "N/A", "", ""
-
-
-# def extract_video_id(url):
-#     """
-#     Extracts the video ID from a YouTube URL.
-#     """
-#     match = re.search(r"(?:v=|\/)([0-9A-Za-z_-]{11})", url)
-#     return match.group(1) if match else None
-
-
-# def get_video_metadata(video_id):
-#     """
-#     Fetches video metadata such as title and description using the YouTube Data API.
-#     """
-#     try:
-#         YOUTUBE_API_KEY = "AIzaSyD_SDF4lC3vpHVAMnBOcN2ZCTz7dRjUc98"  # Replace with your YouTube Data API key
-#         youtube = build("youtube", "v3", developerKey=YOUTUBE_API_KEY)
-#         request = youtube.videos().list(part="snippet", id=video_id)
-#         response = request.execute()
-
-#         if "items" in response and len(response["items"]) > 0:
-#             snippet = response["items"][0]["snippet"]
-#             return {
-#                 "title": snippet.get("title", "No title available"),
-#                 "description": snippet.get("description", "No description available"),
-#             }
-#         return {}
-
-#     except Exception as e:
-#         return {"title": "Error fetching metadata", "description": str(e)}
-
-
-# def extract_subtitle_info(text):
-#     """
-#     Extracts meaningful information from the subtitles.
-#     This could include topics, key insights, or a breakdown of the content.
-#     """
-#     try:
-#         # Split text into sentences for better analysis
-#         sentences = text.split(". ")
-        
-#         # Example: Extract key topics or keywords
-#         words = text.split()
-#         common_words = Counter(words).most_common(10)
-#         key_topics = ", ".join([word for word, count in common_words])
-
-#         # Example: Provide a breakdown of the content
-#         info = f"Key topics discussed: {key_topics}. \nNumber of sentences: {len(sentences)}. \nTotal words: {len(words)}."
-
-#         return info
-#     except Exception as e:
-#         return f"Error extracting subtitle information: {str(e)}"
-
-
-# def clean_text_for_analysis(text):
-#     """
-#     Cleans the transcript text by removing extra spaces, line breaks, and non-text elements.
-#     """
-#     # Remove extra spaces and line breaks
-#     cleaned_text = " ".join(text.split())
-#     return cleaned_text
-
-
-# def get_recommendations(keywords):
-#     """
-#     Fetches related video recommendations based on the provided keywords.
-#     This function can be expanded with a proper API or custom logic.
-#     """
-#     # Placeholder for fetching recommendations based on keywords
-#     return f"Recommendations for: {keywords}"  # Dummy return for now
-
-
-# def get_recommendations(keywords, max_results=5):
-#     if not keywords:
-#         return "Please provide search keywords"
-#     try:
-#         response = requests.get(
-#             "https://www.googleapis.com/youtube/v3/search",
-#             params={
-#                 "part": "snippet",
-#                 "q": f"educational {keywords}",
-#                 "type": "video",
-#                 "maxResults": max_results,
-#                 "relevanceLanguage": "en",
-#                 "key": YOUTUBE_API_KEY
-#             }
-#         ).json()
-        
-#         results = []
-#         for item in response.get("items", []):
-#             title = item["snippet"]["title"]
-#             channel = item["snippet"]["channelTitle"]
-#             video_id = item["id"]["videoId"]
-#             results.append(f"📺 {title}\n👤 {channel}\n🔗 https://youtube.com/watch?v={video_id}\n")
-        
-#         return "\n".join(results) if results else "No recommendations found"
-#     except Exception as e:
-#         return f"Error: {str(e)}"
-
-# # Gradio Interface
-# with gr.Blocks(theme=gr.themes.Soft()) as app:
-#     # Login Page
-#     with gr.Group() as login_page:
-#         gr.Markdown("# 🎓 Educational Learning Management System")
-#         username = gr.Textbox(label="Username")
-#         password = gr.Textbox(label="Password", type="password")
-#         login_btn = gr.Button("Login", variant="primary")
-#         login_msg = gr.Markdown()
-    
-#     # Main Interface
-#     with gr.Group(visible=False) as main_page:
-#         with gr.Row():
-#             with gr.Column(scale=1):
-#                 gr.Markdown("### 📋 Navigation")
-#                 nav_dashboard = gr.Button("📊 Dashboard", variant="primary")
-#                 nav_students = gr.Button("👥 Students")
-#                 nav_teachers = gr.Button("👨‍🏫 Teachers")
-#                 nav_courses = gr.Button("📚 Courses")
-#                 nav_youtube = gr.Button("🎥 YouTube Tool")
-#                 logout_btn = gr.Button("🚪 Logout", variant="stop")
-            
-#             with gr.Column(scale=3):
-#                 # Dashboard Content
-#                 dashboard_page = gr.Group()
-#                 with dashboard_page:
-#                     gr.Markdown("## 📊 Dashboard")
-#                     gr.Markdown(f"""
-#                     ### System Overview
-#                     - 👥 Total Students: {len(students_data)}
-#                     - 👨‍🏫 Total Teachers: {len(teachers_data)}
-#                     - 📚 Total Courses: {len(courses_data)}
-                    
-#                     ### Quick Actions
-#                     - View student performance
-#                     - Access course materials
-#                     - Generate learning insights
-#                     """)
-                
-#                 # Students Content
-#                 students_page = gr.Group(visible=False)
-#                 with students_page:
-#                     gr.Markdown("## 👥 Students")
-#                     gr.DataFrame(
-#                         value=students_data,
-#                         headers=["ID", "Name", "Grade", "Program"]
-#                     )
-                
-#                 # Teachers Content
-#                 teachers_page = gr.Group(visible=False)
-#                 with teachers_page:
-#                     gr.Markdown("## 👨‍🏫 Teachers")
-#                     gr.DataFrame(
-#                         value=teachers_data,
-#                         headers=["ID", "Name", "Subject", "Qualification"]
-#                     )
-                
-#                 # Courses Content
-#                 courses_page = gr.Group(visible=False)
-#                 with courses_page:
-#                     gr.Markdown("## 📚 Courses")
-#                     gr.DataFrame(
-#                         value=courses_data,
-#                         headers=["ID", "Name", "Instructor", "Level"]
-#                     )
-                
-#                 # YouTube Tool Content
-#                 youtube_page = gr.Group(visible=False)
-#                 with youtube_page:
-#                     gr.Markdown("## Agent for YouTube Content Exploration")
-#                     with gr.Row():
-#                         with gr.Column(scale=2):
-#                             video_url = gr.Textbox(
-#                                 label="YouTube URL",
-#                                 placeholder="https://youtube.com/watch?v=..."
-#                             )
-#                             keywords = gr.Textbox(
-#                                 label="Keywords for Recommendations",
-#                                 placeholder="e.g., python programming, machine learning"
-#                             )
-#                             analyze_btn = gr.Button("🔍 Analyze Video", variant="primary")
-                        
-#                         with gr.Column(scale=1):
-#                             video_thumbnail = gr.Image(label="Video Preview")
-                    
-#                     with gr.Row():
-#                         with gr.Column():
-#                             summary = gr.Textbox(label="📝 Summary", lines=8)
-#                             sentiment = gr.Textbox(label="😊 Content Sentiment")
-#                         with gr.Column():
-#                             recommendations = gr.Textbox(label="🎯 Related Videos", lines=10)
-
-#     def login_check(user, pwd):
-#         if USER_CREDENTIALS.get(user) == pwd:
-#             return {
-#                 login_page: gr.update(visible=False),
-#                 main_page: gr.update(visible=True),
-#                 login_msg: ""
-#             }
-#         return {
-#             login_page: gr.update(visible=True),
-#             main_page: gr.update(visible=False),
-#             login_msg: "❌ Invalid credentials"
-#         }
-    
-#     def show_page(page_name):
-#         updates = {
-#             dashboard_page: gr.update(visible=False),
-#             students_page: gr.update(visible=False),
-#             teachers_page: gr.update(visible=False),
-#             courses_page: gr.update(visible=False),
-#             youtube_page: gr.update(visible=False)
-#         }
-#         updates[page_name] = gr.update(visible=True)
-#         return updates
-    
-#     # Event Handlers
-#     login_btn.click(
-#         login_check,
-#         inputs=[username, password],
-#         outputs=[login_page, main_page, login_msg]
-#     )
-    
-#     nav_dashboard.click(lambda: show_page(dashboard_page), outputs=list(show_page(dashboard_page).keys()))
-#     nav_students.click(lambda: show_page(students_page), outputs=list(show_page(students_page).keys()))
-#     nav_teachers.click(lambda: show_page(teachers_page), outputs=list(show_page(teachers_page).keys()))
-#     nav_courses.click(lambda: show_page(courses_page), outputs=list(show_page(courses_page).keys()))
-#     nav_youtube.click(lambda: show_page(youtube_page), outputs=list(show_page(youtube_page).keys()))
-    
-#     analyze_btn.click(
-#         process_youtube_video,
-#         inputs=[video_url, keywords],
-#         outputs=[video_thumbnail, summary, sentiment, recommendations]
-#     )
-    
-#     logout_btn.click(
-#         lambda: {
-#             login_page: gr.update(visible=True),
-#             main_page: gr.update(visible=False)
-#         },
-#         outputs=[login_page, main_page]
-#     )
-
-# if __name__ == "__main__":
-#     app.launch()
-
-
-
-#############################
 import subprocess
 subprocess.check_call(["pip", "install", "transformers==4.34.0"])
 subprocess.check_call(["pip", "install", "torch>=1.7.1"])
@@ -456,7 +10,7 @@ subprocess.check_call(["pip", "install", "textblob>=0.17.1"])
 subprocess.check_call(["pip", "install", "python-dotenv>=1.0.0"])
 subprocess.check_call(["pip", "install", "genai"])
 subprocess.check_call(["pip", "install", "google-cloud-aiplatform==1.34.0"])
-subprocess.check_call(["pip", "install", "google-api-python-client"])
+subprocess.check_call(["pip", "install", "google-api-python-client>=2.0.0"])
 import transformers
 import torch
 import os 
@@ -471,10 +25,12 @@ import textblob
 import gradio as gr
 from youtube_transcript_api import YouTubeTranscriptApi
 import google.generativeai as genai
+from googleapiclient.discovery import build
 import requests
 from textblob import TextBlob
 import re
 #from google.cloud import generativeai
+from googleapiclient.discovery import build
 from huggingface_hub import login
 from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound
 def install_missing_packages():
@@ -489,7 +45,8 @@ def install_missing_packages():
         "python-dotenv":">=1.0.0",
         "genai":None,
         "google-generativeai": None,
-        "google-cloud-aiplatform":"==1.34.0"
+        "google-cloud-aiplatform":"==1.34.0",
+        "google-api-python-client": ">=2.0.0"
     }
 
 
@@ -570,316 +127,19 @@ def extract_video_id(url):
             return match.group(1)
     return None
 
-
-# def process_youtube_video(url="", keywords=""):
-#     try:
-#         # Initialize variables
-#         thumbnail = None  # Default value for thumbnail
-#         summary = "No transcript available"
-#         sentiment_label = "N/A"
-
-#         if not url.strip():
-#             return None, "Please enter a YouTube URL", "N/A", ""
-            
-#         video_id = extract_video_id(url)
-#         if not video_id:
-#             return None, "Invalid YouTube URL", "N/A", ""
-
-#         thumbnail = f"https://img.youtube.com/vi/{video_id}/maxresdefault.jpg"
-
-#         try:
-#             # Try multiple transcript options
-#             transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
-            
-#             try:
-#                 transcript = transcript_list.find_transcript(['en'])
-#             except:
-#                 try:
-#                     transcript = transcript_list.find_transcript(['en-US'])
-#                 except:
-#                     try:
-#                         # Try auto-generated
-#                         transcript = transcript_list.find_generated_transcript(['en'])
-#                     except:
-#                         raise NoTranscriptFound()
-
-#             text = " ".join([t['text'] for t in transcript.fetch()])
-            
-#             # Generate summary
-#             model = genai.GenerativeModel("gemini-pro")
-#             summary = model.generate_content(f"Summarize this: {text[:4000]}").text
-            
-#             # Analysis
-#             sentiment = TextBlob(text[:1000]).sentiment
-#             sentiment_label = f"{'Positive' if sentiment.polarity > 0 else 'Negative' if sentiment.polarity < 0 else 'Neutral'} ({sentiment.polarity:.2f})"
-
-#         except TranscriptsDisabled:
-#             # Fallback: Use video metadata if available
-#             metadata = get_video_metadata(video_id)
-#             summary = metadata.get("description", "⚠️ This video has disabled subtitles. No transcript available.")
-#         except NoTranscriptFound:
-#             # Fallback: Use video metadata if available
-#             metadata = get_video_metadata(video_id)
-#             summary = metadata.get("description", "⚠️ No English transcript available. No transcript available.")
-#         except Exception as e:
-#             return thumbnail, f"⚠️ Error: {str(e)}", "N/A", ""
-
-#         # Get recommendations
-#         if keywords.strip():
-#             recommendations = get_recommendations(keywords)
-#         else:
-#             recommendations = ""
-
-#         return thumbnail, summary, sentiment_label, recommendations
-
-#     except Exception as e:
-#         return None, f"Error: {str(e)}", "N/A", ""
-
-
-# def get_video_metadata(video_id):
-#     """
-#     Fetches video metadata such as title and description using the YouTube Data API.
-#     """
-#     try:
-#         from googleapiclient.discovery import build
-
-#         # Replace with your YouTube Data API key
-#         API_KEY = "AIzaSyB7X-RYjZmUuDSMTQsvCfyzURw5bhqOto4"
-#         youtube = build("youtube", "v3", developerKey=API_KEY)
-#         request = youtube.videos().list(part="snippet", id=video_id)
-#         response = request.execute()
-
-#         if "items" in response and len(response["items"]) > 0:
-#             snippet = response["items"][0]["snippet"]
-#             return {
-#                 "title": snippet.get("title", "No title available"),
-#                 "description": snippet.get("description", "No description available"),
-#             }
-#         return {}
-
-#     except Exception as e:
-#         return {"title": "Error fetching metadata", "description": str(e)}
-
-#         # Get recommendations
-#         if keywords.strip():
-#             recommendations = get_recommendations(keywords)
-#         else:
-#             recommendations = ""
-
-#         return thumbnail, summary, sentiment_label, recommendations
-
-#     except Exception as e:
-#         return None, f"Error: {str(e)}", "N/A", ""
-
 from textblob import TextBlob
 from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound
 import re
 from collections import Counter
 from googleapiclient.discovery import build
-# def process_youtube_video(url="", keywords=""):
-#     try:
-#         # Initialize variables
-#         thumbnail = None
-#         summary = "No transcript available"
-#         sentiment_label = "N/A"
-#         recommendations = ""
-#         subtitle_info = "No additional information available"
-
-#         if not url.strip():
-#             return None, "Please enter a YouTube URL", "N/A", "", ""
-
-#         video_id = extract_video_id(url)
-#         if not video_id:
-#             return None, "Invalid YouTube URL", "N/A", "", ""
-
-#         thumbnail = f"https://img.youtube.com/vi/{video_id}/maxresdefault.jpg"
-
-#         try:
-#             # Fetch transcript
-#             transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
-#             transcript = None
-#             try:
-#                 transcript = transcript_list.find_transcript(['en'])
-#             except:
-#                 transcript = transcript_list.find_generated_transcript(['en'])
-
-#             text = " ".join([t['text'] for t in transcript.fetch()])
-#             if not text.strip():
-#                 raise ValueError("Transcript is empty")
-
-#             # Generate summary
-#             model = genai.GenerativeModel("gemini-pro")
-#             summary = model.generate_content(f"Summarize this: {text[:4000]}").text
-
-#             # Extract subtitle information
-#             subtitle_info = extract_subtitle_info(text)
-
-#             # Sentiment analysis
-#             sentiment = TextBlob(text[:1000]).sentiment
-#             sentiment_label = f"{'Positive' if sentiment.polarity > 0 else 'Negative' if sentiment.polarity < 0 else 'Neutral'} ({sentiment.polarity:.2f})"
-
-#         except TranscriptsDisabled:
-#             metadata = get_video_metadata(video_id)
-#             summary = metadata.get("description", "⚠️ This video has disabled subtitles.")
-#             sentiment_label = "N/A"
-#             subtitle_info = "No subtitles available for analysis."
-#         except NoTranscriptFound:
-#             metadata = get_video_metadata(video_id)
-#             summary = metadata.get("description", "⚠️ No English transcript available.")
-#             sentiment_label = "N/A"
-#             subtitle_info = "No subtitles available for analysis."
-#         except Exception as e:
-#             return thumbnail, f"⚠️ Error processing transcript: {str(e)}", "N/A", "", ""
-
-#         # Get recommendations
-#         if keywords.strip():
-#             recommendations = get_recommendations(keywords)
-
-#         return thumbnail, summary, sentiment_label, subtitle_info, recommendations
-
-#     except Exception as e:
-#         return None, f"Error: {str(e)}", "N/A", "", ""
-
-
-# def extract_video_id(url):
-#     """
-#     Extracts the video ID from a YouTube URL.
-#     """
-#     import re
-#     match = re.search(r"(?:v=|\/)([0-9A-Za-z_-]{11})", url)
-#     return match.group(1) if match else None
-
-
-# def get_video_metadata(video_id):
-#     """
-#     Fetches video metadata such as title and description using the YouTube Data API.
-#     """
-#     try:
-#         from googleapiclient.discovery import build
-
-#         # Replace with your YouTube Data API key
-#         YOUTUBE_API_KEY = "AIzaSyD_SDF4lC3vpHVAMnBOcN2ZCTz7dRjUc98"
-#         youtube = build("youtube", "v3", developerKey=YOUTUBE_API_KEY)
-#         request = youtube.videos().list(part="snippet", id=video_id)
-#         response = request.execute()
-
-#         if "items" in response and len(response["items"]) > 0:
-#             snippet = response["items"][0]["snippet"]
-#             return {
-#                 "title": snippet.get("title", "No title available"),
-#                 "description": snippet.get("description", "No description available"),
-#             }
-#         return {}
-
-#     except Exception as e:
-#         return {"title": "Error fetching metadata", "description": str(e)}
-
-
-# def extract_subtitle_info(text):
-#     """
-#     Extracts meaningful information from the subtitles.
-#     This could include topics, key insights, or a breakdown of the content.
-#     """
-#     try:
-#         # Split text into sentences for better analysis
-#         sentences = text.split(". ")
-        
-#         # Example: Extract key topics or keywords
-#         from collections import Counter
-#         words = text.split()
-#         common_words = Counter(words).most_common(10)
-#         key_topics = ", ".join([word for word, count in common_words])
-
-#         # Example: Provide a breakdown of the content
-#         info = f"Key topics discussed: {key_topics}. \nNumber of sentences: {len(sentences)}. \nTotal words: {len(words)}."
-
-#         return info
-#     except Exception as e:
-#         return f"Error extracting subtitle information: {str(e)}"
-
-##########
-def process_youtube_video(url="", keywords=""):
-    try:
-        # Initialize variables
-        thumbnail = None
-        summary = "No transcript available"
-        sentiment_label = "N/A"
-        recommendations = ""
-        subtitle_info = "No additional information available"
-
-        if not url.strip():
-            return None, "Please enter a YouTube URL", "N/A", "", ""
-
-        video_id = extract_video_id(url)
-        if not video_id:
-            return None, "Invalid YouTube URL", "N/A", "", ""
-
-        thumbnail = f"https://img.youtube.com/vi/{video_id}/maxresdefault.jpg"
-
-        try:
-            # Fetch transcript
-            transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
-            transcript = None
-            try:
-                transcript = transcript_list.find_transcript(['en'])
-            except:
-                transcript = transcript_list.find_generated_transcript(['en'])
-
-            text = " ".join([t['text'] for t in transcript.fetch()])
-            if not text.strip():
-                raise ValueError("Transcript is empty")
-
-            # Clean up the text for sentiment analysis
-            cleaned_text = clean_text_for_analysis(text)
-
-            # Sentiment analysis
-            sentiment = TextBlob(cleaned_text).sentiment  # Use cleaned text for sentiment analysis
-            sentiment_label = f"{'Positive' if sentiment.polarity > 0 else 'Negative' if sentiment.polarity < 0 else 'Neutral'} ({sentiment.polarity:.2f})"
-
-            # Generate summary
-            model = genai.GenerativeModel("gemini-pro")
-            summary = model.generate_content(f"Summarize this: {cleaned_text[:4000]}").text
-
-            # Extract subtitle information
-            subtitle_info = extract_subtitle_info(cleaned_text)
-
-        except TranscriptsDisabled:
-            metadata = get_video_metadata(video_id)
-            summary = metadata.get("description", "⚠️ This video has disabled subtitles.")
-            sentiment_label = "N/A"
-            subtitle_info = "No subtitles available for analysis."
-        except NoTranscriptFound:
-            metadata = get_video_metadata(video_id)
-            summary = metadata.get("description", "⚠️ No English transcript available.")
-            sentiment_label = "N/A"
-            subtitle_info = "No subtitles available for analysis."
-        except Exception as e:
-            return thumbnail, f"⚠️ Error processing transcript: {str(e)}", "N/A", "", ""
-
-        # Get recommendations
-        if keywords.strip():
-            recommendations = get_recommendations(keywords)
-
-        return thumbnail, summary, sentiment_label, subtitle_info, recommendations
-
-    except Exception as e:
-        return None, f"Error: {str(e)}", "N/A", "", ""
 
 
 def extract_video_id(url):
-    """
-    Extracts the video ID from a YouTube URL.
-    """
     match = re.search(r"(?:v=|\/)([0-9A-Za-z_-]{11})", url)
     return match.group(1) if match else None
 
-
 def get_video_metadata(video_id):
-    """
-    Fetches video metadata such as title and description using the YouTube Data API.
-    """
     try:
-        YOUTUBE_API_KEY = "AIzaSyD_SDF4lC3vpHVAMnBOcN2ZCTz7dRjUc98"  # Replace with your YouTube Data API key
         youtube = build("youtube", "v3", developerKey=YOUTUBE_API_KEY)
         request = youtube.videos().list(part="snippet", id=video_id)
         response = request.execute()
@@ -895,48 +155,20 @@ def get_video_metadata(video_id):
     except Exception as e:
         return {"title": "Error fetching metadata", "description": str(e)}
 
+def clean_text_for_analysis(text):
+    return " ".join(text.split())
 
 def extract_subtitle_info(text):
-    """
-    Extracts meaningful information from the subtitles.
-    This could include topics, key insights, or a breakdown of the content.
-    """
     try:
-        # Split text into sentences for better analysis
         sentences = text.split(". ")
-        
-        # Example: Extract key topics or keywords
         words = text.split()
         common_words = Counter(words).most_common(10)
         key_topics = ", ".join([word for word, count in common_words])
-
-        # Example: Provide a breakdown of the content
         info = f"Key topics discussed: {key_topics}. \nNumber of sentences: {len(sentences)}. \nTotal words: {len(words)}."
-
         return info
     except Exception as e:
         return f"Error extracting subtitle information: {str(e)}"
 
-
-def clean_text_for_analysis(text):
-    """
-    Cleans the transcript text by removing extra spaces, line breaks, and non-text elements.
-    """
-    # Remove extra spaces and line breaks
-    cleaned_text = " ".join(text.split())
-    return cleaned_text
-
-
-def get_recommendations(keywords):
-    """
-    Fetches related video recommendations based on the provided keywords.
-    This function can be expanded with a proper API or custom logic.
-    """
-    # Placeholder for fetching recommendations based on keywords
-    return f"Recommendations for: {keywords}"  # Dummy return for now
-
-
-
 def get_recommendations(keywords, max_results=5):
     if not keywords:
         return "Please provide search keywords"
@@ -952,18 +184,91 @@ def get_recommendations(keywords, max_results=5):
                 "key": YOUTUBE_API_KEY
             }
         ).json()
-        
+
         results = []
         for item in response.get("items", []):
             title = item["snippet"]["title"]
             channel = item["snippet"]["channelTitle"]
             video_id = item["id"]["videoId"]
             results.append(f"📺 {title}\n👤 {channel}\n🔗 https://youtube.com/watch?v={video_id}\n")
-        
+
         return "\n".join(results) if results else "No recommendations found"
     except Exception as e:
         return f"Error: {str(e)}"
 
+def process_youtube_video(url, keywords):
+    try:
+        thumbnail = None
+        summary = "No transcript available"
+        sentiment_label = "N/A"
+        recommendations = ""
+
+        video_id = extract_video_id(url)
+        if not video_id:
+            return None, "Invalid YouTube URL", "N/A", "", ""
+
+        thumbnail = f"https://img.youtube.com/vi/{video_id}/maxresdefault.jpg"
+
+        try:
+            transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
+            transcript = None
+            try:
+                transcript = transcript_list.find_transcript(['en'])
+            except:
+                transcript = transcript_list.find_generated_transcript(['en'])
+
+            text = " ".join([t['text'] for t in transcript.fetch()])
+            if not text.strip():
+                raise ValueError("Transcript is empty")
+
+            cleaned_text = clean_text_for_analysis(text)
+
+            sentiment = TextBlob(cleaned_text).sentiment
+            sentiment_label = f"{'Positive' if sentiment.polarity > 0 else 'Negative' if sentiment.polarity < 0 else 'Neutral'} ({sentiment.polarity:.2f})"
+
+            summary = f"Summary: {cleaned_text[:400]}..."
+
+        except (TranscriptsDisabled, NoTranscriptFound):
+            metadata = get_video_metadata(video_id)
+            summary = metadata.get("description", "No subtitles available")
+            sentiment_label = "N/A"
+
+        if keywords.strip():
+            recommendations = get_recommendations(keywords)
+
+        return thumbnail, summary, sentiment_label, recommendations
+
+    except Exception as e:
+        return None, f"Error: {str(e)}", "N/A", ""
+
+
+# def get_recommendations(keywords, max_results=5):
+#     if not keywords:
+#         return "Please provide search keywords"
+#     try:
+#         response = requests.get(
+#             "https://www.googleapis.com/youtube/v3/search",
+#             params={
+#                 "part": "snippet",
+#                 "q": f"educational {keywords}",
+#                 "type": "video",
+#                 "maxResults": max_results,
+#                 "relevanceLanguage": "en",
+#                 "key": YOUTUBE_API_KEY
+#             }
+#         ).json()
+        
+#         results = []
+#         for item in response.get("items", []):
+#             title = item["snippet"]["title"]
+#             channel = item["snippet"]["channelTitle"]
+#             video_id = item["id"]["videoId"]
+#             results.append(f"📺 {title}\n👤 {channel}\n🔗 https://youtube.com/watch?v={video_id}\n")
+        
+#         return "\n".join(results) if results else "No recommendations found"
+#     except Exception as e:
+#         return f"Error: {str(e)}"
+
 # Gradio Interface
 with gr.Blocks(theme=gr.themes.Soft()) as app:
     # Login Page