# 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"šŸ“ŗ {title}\nšŸ‘¤ {channel}\nšŸ”— 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"šŸ“ŗ {title}\nšŸ‘¤ {channel}\nšŸ”— 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("# šŸŽ“ 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("### šŸ“‹ Navigation") # nav_dashboard = gr.Button("šŸ“Š Dashboard", variant="primary") # nav_students = gr.Button("šŸ‘„ Students") # nav_teachers = gr.Button("šŸ‘Øā€šŸ« Teachers") # nav_courses = gr.Button("šŸ“š Courses") # nav_youtube = gr.Button("šŸŽ„ YouTube Tool") # logout_btn = gr.Button("šŸšŖ Logout", variant="stop") # with gr.Column(scale=3): # # Dashboard Content # dashboard_page = gr.Group() # with dashboard_page: # gr.Markdown("## šŸ“Š Dashboard") # gr.Markdown(f""" # ### System Overview # - šŸ‘„ Total Students: {len(students_data)} # - šŸ‘Øā€šŸ« Total Teachers: {len(teachers_data)} # - šŸ“š 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("## šŸ‘„ Students") # gr.DataFrame( # value=students_data, # headers=["ID", "Name", "Grade", "Program"] # ) # # Teachers Content # teachers_page = gr.Group(visible=False) # with teachers_page: # gr.Markdown("## šŸ‘Øā€šŸ« Teachers") # gr.DataFrame( # value=teachers_data, # headers=["ID", "Name", "Subject", "Qualification"] # ) # # Courses Content # courses_page = gr.Group(visible=False) # with courses_page: # gr.Markdown("## šŸ“š 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("šŸ” 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="šŸ“ Summary", lines=8) # sentiment = gr.Textbox(label="šŸ˜Š Content Sentiment") # with gr.Column(): # recommendations = gr.Textbox(label="šŸŽÆ 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: "āŒ 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("# šŸŽ“ 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("### šŸ“‹ Navigation") nav_dashboard = gr.Button("šŸ“Š Dashboard", variant="primary") nav_youtube = gr.Button("šŸŽ„ YouTube Tool") logout_btn = gr.Button("šŸšŖ Logout", variant="stop") with gr.Column(scale=3): dashboard_page = gr.Group() with dashboard_page: gr.Markdown("## šŸ“Š 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("šŸ” Analyze Video", variant="primary") sentiment_btn = gr.Button("šŸ˜Š 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="šŸ“ Summary", lines=8) sentiment = gr.Textbox(label="šŸ˜Š 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: "āŒ 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"šŸ“ŗ {title}\nšŸ‘¤ {channel}\nšŸ”— 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("# šŸŽ“ 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("### šŸ“‹ Navigation") # nav_dashboard = gr.Button("šŸ“Š Dashboard", variant="primary") # nav_students = gr.Button("šŸ‘„ Students") # nav_teachers = gr.Button("šŸ‘Øā€šŸ« Teachers") # nav_courses = gr.Button("šŸ“š Courses") # nav_youtube = gr.Button("šŸŽ„ YouTube Tool") # logout_btn = gr.Button("šŸšŖ Logout", variant="stop") # with gr.Column(scale=3): # # Dashboard Content # dashboard_page = gr.Group() # with dashboard_page: # gr.Markdown("## šŸ“Š Dashboard") # gr.Markdown(f""" # ### System Overview # - šŸ‘„ Total Students: {len(students_data)} # - šŸ‘Øā€šŸ« Total Teachers: {len(teachers_data)} # - šŸ“š 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("## šŸ‘„ Students") # gr.DataFrame( # value=students_data, # headers=["ID", "Name", "Grade", "Program"] # ) # # Teachers Content # teachers_page = gr.Group(visible=False) # with teachers_page: # gr.Markdown("## šŸ‘Øā€šŸ« Teachers") # gr.DataFrame( # value=teachers_data, # headers=["ID", "Name", "Subject", "Qualification"] # ) # # Courses Content # courses_page = gr.Group(visible=False) # with courses_page: # gr.Markdown("## šŸ“š 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("šŸ” Analyze Video", variant="primary") # recommend_btn = gr.Button("šŸ”Ž 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="šŸ“ Summary", lines=8) # sentiment = gr.Textbox(label="šŸ˜Š Content Sentiment") # with gr.Column(): # recommendations = gr.Textbox(label="šŸŽÆ 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: "āŒ 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()