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=|\/)([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):
    try:
        thumbnail = None
        summary = "No transcript available"
        sentiment_label = "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:
            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"

        return thumbnail, summary, sentiment_label

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

# 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()