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# 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"])
# subprocess.check_call(["pip", "install", "google-api-python-client>=2.0.0"])
# 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
# 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():
#     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",
#         "google-api-python-client": ">=2.0.0"
#     }


#     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 extract_video_id(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):
#     try:
#         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 clean_text_for_analysis(text):
#     return " ".join(text.split())

# def extract_subtitle_info(text):
#     try:
#         sentences = text.split(". ")
#         words = text.split()
#         common_words = Counter(words).most_common(10)
#         key_topics = ", ".join([word for word, count in common_words])
#         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 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)}"

# 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
#     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"])
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"])
subprocess.check_call(["pip", "install", "google-api-python-client>=2.0.0"])
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
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():
    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",
        "google-api-python-client": ">=2.0.0"
    }


    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.")


YOUTUBE_API_KEY = "AIzaSyD_SDF4lC3vpHVAMnBOcN2ZCTz7dRjUc98"  # Replace with your YouTube API Key

USER_CREDENTIALS = {"admin": "password"}  # Example user credentials

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 extract_video_id(url):
    match = re.search(r"(?:v=|\/|be\/|embed\/|watch\?v=)([0-9A-Za-z_-]{11})", url)
    return match.group(1) if match else None

def get_video_metadata(video_id):
    try:
        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 clean_text_for_analysis(text):
    return " ".join(text.split())

def process_youtube_video(url):
    try:
        video_id = extract_video_id(url)
        if not video_id:
            return None, "Invalid YouTube URL"

        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)
            summary = f"Summary: {cleaned_text[:400]}..."
            return thumbnail, summary

        except (TranscriptsDisabled, NoTranscriptFound):
            metadata = get_video_metadata(video_id)
            summary = metadata.get("description", "No subtitles available")
            return thumbnail, summary

    except Exception as e:
        return None, f"Error: {str(e)}"

def analyze_sentiment(url):
    try:
        video_id = extract_video_id(url)
        if not video_id:
            return "Invalid YouTube URL", "N/A"

        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})"

            return "Sentiment Analysis Completed", sentiment_label

        except (TranscriptsDisabled, NoTranscriptFound):
            return "No transcript available", "N/A"

    except Exception as e:
        return f"Error: {str(e)}", "N/A"

# Gradio Interface
# 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_youtube = gr.Button("🎥 YouTube Tool")
                logout_btn = gr.Button("🚪 Logout", variant="stop")

            with gr.Column(scale=3):
                dashboard_page = gr.Group()
                with dashboard_page:
                    gr.Markdown("## 📊 Dashboard")

                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=..."
                            )
                            analyze_btn = gr.Button("🔍 Analyze Video", variant="primary")
                            sentiment_btn = gr.Button("😊 Analyze Sentiment", 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")


    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),
            youtube_page: gr.update(visible=False)
        }
        updates[page_name] = gr.update(visible=True)
        return updates

    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_youtube.click(lambda: show_page(youtube_page), outputs=list(show_page(youtube_page).keys()))

    analyze_btn.click(
        process_youtube_video,
        inputs=[video_url],
        outputs=[video_thumbnail, summary]
    )

    sentiment_btn.click(
        analyze_sentiment,
        inputs=[video_url],
        outputs=[summary, sentiment]
    )

    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()








# def extract_video_id(url):
#     # Improved regex to handle various YouTube URL formats
#     match = re.search(r"(?:v=|\/|be\/|embed\/|watch\?v=)([0-9A-Za-z_-]{11})", url)
#     return match.group(1) if match else None

# def get_video_metadata(video_id):
#     try:
#         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 clean_text_for_analysis(text):
#     return " ".join(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()

#         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):
#     import re
#     from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound
#     from textblob import TextBlob

#     try:
#         # Extract video ID
#         video_id = extract_video_id(url)
#         if not video_id:
#             return None, "Invalid YouTube URL", "N/A"

#         # Generate thumbnail URL
#         thumbnail = f"https://img.youtube.com/vi/{video_id}/maxresdefault.jpg"

#         # Initialize default values
#         summary = "No transcript available"
#         sentiment_label = "N/A"

#         try:
#             # Fetch transcript
#             print(f"Fetching transcript for video ID: {video_id}")
#             transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
#             transcript = None
#             try:
#                 transcript = transcript_list.find_transcript(['en'])
#             except:
#                 transcript = transcript_list.find_generated_transcript(['en'])

#             # Combine transcript into text
#             text = " ".join([t['text'] for t in transcript.fetch()])
#             if not text.strip():
#                 raise ValueError("Transcript is empty")

#             # Clean and analyze text
#             print(f"Transcript fetched successfully. Length: {len(text)} characters")
#             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})"

#             # Summarize text
#             summary = f"Summary: {cleaned_text[:400]}..."
#             print(f"Sentiment analysis completed: {sentiment_label}")

#         except (TranscriptsDisabled, NoTranscriptFound):
#             # Fall back to metadata if no transcript
#             print(f"No transcript found for video ID: {video_id}")
#             metadata = get_video_metadata(video_id)
#             summary = metadata.get("description", "No subtitles available")
#             sentiment_label = "N/A"

#         return thumbnail, summary, sentiment_label

#     except Exception as e:
#         print(f"Error processing video: {e}")
#         return None, f"Error: {str(e)}", "N/A"

# # Test the function
# url = "https://www.youtube.com/watch?v=q1XFm21I-VQ"
# thumbnail, summary, sentiment = process_youtube_video(url)
# print(f"Thumbnail: {thumbnail}\n")
# print(f"Summary:\n{summary}\n")
# print(f"Sentiment: {sentiment}")


# # 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")
#                             recommend_btn = gr.Button("🔎 Get Recommendations", 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],
#         outputs=[video_thumbnail, summary, sentiment]
#     )

#     recommend_btn.click(
#         get_recommendations,
#         inputs=[keywords],
#         outputs=[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()