Update app.py

app.py

@@ -1,727 +1,863 @@
-# 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"
-#     }
+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])
+    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
+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.")
+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"
-# }
+# Configuration
+USER_CREDENTIALS = {
+    "admin": "password123",
+    "teacher": "teach2024",
+    "student": "learn2024"
+}
 
-# import os
-# from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound
+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")
+# 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")
+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)
+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")
-# ]
+# 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"),
-# ]
+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")
-# ]
+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 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
+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
+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"
+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", "", ""
+        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", "", ""
+        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"
+        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'])
+        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")
+            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)
+            # 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})"
+            # 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
+            # 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)
+            # 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", "", ""
+        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)
+        # Get recommendations
+        if keywords.strip():
+            recommendations = get_recommendations(keywords)
 
-#         return thumbnail, summary, sentiment_label, subtitle_info, recommendations
+        return thumbnail, summary, sentiment_label, subtitle_info, recommendations
 
-#     except Exception as e:
-#         return None, f"Error: {str(e)}", "N/A", "", ""
+    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 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()
+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 {}
+        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)}
+    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(". ")
+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: 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)}."
+        # 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)}"
+        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 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):
+    """
+    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()
+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")
+        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)}"
+        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()
+# 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")
+    # 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)}
+            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
-#                     """)
+                    ### 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"]
-#                     )
+                # 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"]
-#                     )
+                # 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"]
-#                     )
+                # 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")
+                # 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.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)
+                    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 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
+    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]
-#     )
+    # 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()))
+    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]
-#     )
+    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]
-#     )
+    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()
+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"])
-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"
-    }
+# 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])
+#     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
+# 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.")
+# 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"
-}
+# # Configuration
+# USER_CREDENTIALS = {
+#     "admin": "password123",
+#     "teacher": "teach2024",
+#     "student": "learn2024"
+# }
 
-import os
-from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound
+# 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")
+# # 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")
+# 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)
+# 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")
-]
+# # 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"),
-]
+# 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")
-]
+# 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 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
+# 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
+
+
+# # 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)}"
+# from textblob import TextBlob
+# from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound
+# import re
+# from collections import Counter
+# from googleapiclient.discovery import build
 
 
+# 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  # Default value for thumbnail
+#         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", ""
-            
+#             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", ""
+#             return None, "Invalid YouTube URL", "N/A", "", ""
 
 #         thumbnail = f"https://img.youtube.com/vi/{video_id}/maxresdefault.jpg"
 
 #         try:
-#             # Try multiple transcript options
+#             # Fetch transcript
 #             transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
-            
+#             transcript = None
 #             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()
+#                 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: {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})"
+#             summary = model.generate_content(f"Summarize this: {cleaned_text[:4000]}").text
+
+#             # Extract subtitle information
+#             subtitle_info = extract_subtitle_info(cleaned_text)
 
 #         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."
+#             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.")
@@ -744,7 +880,6 @@ from googleapiclient.discovery import build
 #     """
 #     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
 
@@ -754,10 +889,7 @@ from googleapiclient.discovery import build
 #     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_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()
@@ -784,7 +916,6 @@ from googleapiclient.discovery import build
 #         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])
@@ -795,328 +926,197 @@ from googleapiclient.discovery import build
 #         return info
 #     except Exception as e:
 #         return f"Error extracting subtitle information: {str(e)}"
-from textblob import TextBlob
-from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound
-import re
-from collections import Counter
-from googleapiclient.discovery import build
-
-
-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 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_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 {}
+# 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
 
-    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(". ")
+# 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()
         
-        # 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)}"
-
+#         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 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()
+# # 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")
+#     # 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)}
+#             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
-                    """)
+#                     ### 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"]
-                    )
+#                 # 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"]
-                    )
+#                 # 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"]
-                    )
+#                 # 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")
+#                 # 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.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)
+#                     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 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
+#     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]
-    )
+#     # 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()))
+#     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]
-    )
+#     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]
-    )
+#     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()
+# if __name__ == "__main__":
+#     app.launch()