Update app.py

app.py

@@ -130,65 +130,374 @@ import re
 from collections import Counter
 from googleapiclient.discovery import build
 
+# def process_youtube_video(url="", keywords=""):
+#     try:
+#         #Initialize variables
+#         thumbnail = None
+#         summary = "No transcript available"
+#         sentiment_label = "N/A"
+#         recommendations = ""
+#         subtitle_info = "No additional information available"
+
+#         if not url.strip():
+#             return None, "Please enter a YouTube URL", "N/A", "", ""
+
+#         video_id = extract_video_id(url)
+#         if not video_id:
+#             return None, "Invalid YouTube URL", "N/A", "", ""
+
+#         thumbnail = f"https://img.youtube.com/vi/{video_id}/maxresdefault.jpg"
+
+#         try:
+#             # Fetch transcript
+#             transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
+#             transcript = None
+#             try:
+#                 transcript = transcript_list.find_transcript(['en'])
+#             except:
+#                 transcript = transcript_list.find_generated_transcript(['en'])
+
+#             text = " ".join([t['text'] for t in transcript.fetch()])
+#             if not text.strip():
+#                 raise ValueError("Transcript is empty")
+
+#             # Clean up the text for sentiment analysis
+#             cleaned_text = clean_text_for_analysis(text)
+
+#             # Sentiment analysis
+#             sentiment = TextBlob(cleaned_text).sentiment  # Use cleaned text for sentiment analysis
+#             sentiment_label = f"{'Positive' if sentiment.polarity > 0 else 'Negative' if sentiment.polarity < 0 else 'Neutral'} ({sentiment.polarity:.2f})"
+
+#             # Generate summary
+#             model = genai.GenerativeModel("gemini-pro")
+#             summary = model.generate_content(f"Summarize this: {cleaned_text[:4000]}").text
+
+#             # Extract subtitle information
+#             subtitle_info = extract_subtitle_info(cleaned_text)
+
+#         except TranscriptsDisabled:
+#             metadata = get_video_metadata(video_id)
+#             summary = metadata.get("description", "⚠️ This video has disabled subtitles.")
+#             sentiment_label = "N/A"
+#             subtitle_info = "No subtitles available for analysis."
+#         except NoTranscriptFound:
+#             metadata = get_video_metadata(video_id)
+#             summary = metadata.get("description", "⚠️ No English transcript available.")
+#             sentiment_label = "N/A"
+#             subtitle_info = "No subtitles available for analysis."
+#         except Exception as e:
+#             return thumbnail, f"⚠️ Error processing transcript: {str(e)}", "N/A", "", ""
+
+#         # Get recommendations
+#         if keywords.strip():
+#             recommendations = get_recommendations(keywords)
+
+#         return thumbnail, summary, sentiment_label, subtitle_info, recommendations
+
+#     except Exception as e:
+#         return None, f"Error: {str(e)}", "N/A", "", ""
+
+
+# def extract_video_id(url):
+#     """
+#     Extracts the video ID from a YouTube URL.
+#     """
+#     match = re.search(r"(?:v=|\/)([0-9A-Za-z_-]{11})", url)
+#     return match.group(1) if match else None
+
+
+# def get_video_metadata(video_id):
+#     """
+#     Fetches video metadata such as title and description using the YouTube Data API.
+#     """
+#     try:
+#         YOUTUBE_API_KEY = "AIzaSyD_SDF4lC3vpHVAMnBOcN2ZCTz7dRjUc98"  # Replace with your YouTube Data API key
+#         youtube = build("youtube", "v3", developerKey=YOUTUBE_API_KEY)
+#         request = youtube.videos().list(part="snippet", id=video_id)
+#         response = request.execute()
+
+#         if "items" in response and len(response["items"]) > 0:
+#             snippet = response["items"][0]["snippet"]
+#             return {
+#                 "title": snippet.get("title", "No title available"),
+#                 "description": snippet.get("description", "No description available"),
+#             }
+#         return {}
+
+#     except Exception as e:
+#         return {"title": "Error fetching metadata", "description": str(e)}
+
+
+# def extract_subtitle_info(text):
+#     """
+#     Extracts meaningful information from the subtitles.
+#     This could include topics, key insights, or a breakdown of the content.
+#     """
+#     try:
+#         # Split text into sentences for better analysis
+#         sentences = text.split(". ")
+        
+#         # Example: Extract key topics or keywords
+#         words = text.split()
+#         common_words = Counter(words).most_common(10)
+#         key_topics = ", ".join([word for word, count in common_words])
+
+#         # Example: Provide a breakdown of the content
+#         info = f"Key topics discussed: {key_topics}. \nNumber of sentences: {len(sentences)}. \nTotal words: {len(words)}."
+
+#         return info
+#     except Exception as e:
+#         return f"Error extracting subtitle information: {str(e)}"
+
+
+# def clean_text_for_analysis(text):
+#     """
+#     Cleans the transcript text by removing extra spaces, line breaks, and non-text elements.
+#     """
+#     # Remove extra spaces and line breaks
+#     cleaned_text = " ".join(text.split())
+#     return cleaned_text
+
+
+# def get_recommendations(keywords):
+#     """
+#     Fetches related video recommendations based on the provided keywords.
+#     This function can be expanded with a proper API or custom logic.
+#     """
+#     # Placeholder for fetching recommendations based on keywords
+#     return f"Recommendations for: {keywords}"  # Dummy return for now
+
+
+# def get_recommendations(keywords, max_results=5):
+#     if not keywords:
+#         return "Please provide search keywords"
+#     try:
+#         response = requests.get(
+#             "https://www.googleapis.com/youtube/v3/search",
+#             params={
+#                 "part": "snippet",
+#                 "q": f"educational {keywords}",
+#                 "type": "video",
+#                 "maxResults": max_results,
+#                 "relevanceLanguage": "en",
+#                 "key": YOUTUBE_API_KEY
+#             }
+#         ).json()
+        
+#         results = []
+#         for item in response.get("items", []):
+#             title = item["snippet"]["title"]
+#             channel = item["snippet"]["channelTitle"]
+#             video_id = item["id"]["videoId"]
+#             results.append(f"📺 {title}\n👤 {channel}\n🔗 https://youtube.com/watch?v={video_id}\n")
+        
+#         return "\n".join(results) if results else "No recommendations found"
+#     except Exception as e:
+#         return f"Error: {str(e)}"
+
+# # Gradio Interface
+# with gr.Blocks(theme=gr.themes.Soft()) as app:
+#     # Login Page
+#     with gr.Group() as login_page:
+#         gr.Markdown("# 🎓 Educational Learning Management System")
+#         username = gr.Textbox(label="Username")
+#         password = gr.Textbox(label="Password", type="password")
+#         login_btn = gr.Button("Login", variant="primary")
+#         login_msg = gr.Markdown()
+    
+#     # Main Interface
+#     with gr.Group(visible=False) as main_page:
+#         with gr.Row():
+#             with gr.Column(scale=1):
+#                 gr.Markdown("### 📋 Navigation")
+#                 nav_dashboard = gr.Button("📊 Dashboard", variant="primary")
+#                 nav_students = gr.Button("👥 Students")
+#                 nav_teachers = gr.Button("👨‍🏫 Teachers")
+#                 nav_courses = gr.Button("📚 Courses")
+#                 nav_youtube = gr.Button("🎥 YouTube Tool")
+#                 logout_btn = gr.Button("🚪 Logout", variant="stop")
+            
+#             with gr.Column(scale=3):
+#                 # Dashboard Content
+#                 dashboard_page = gr.Group()
+#                 with dashboard_page:
+#                     gr.Markdown("## 📊 Dashboard")
+#                     gr.Markdown(f"""
+#                     ### System Overview
+#                     - 👥 Total Students: {len(students_data)}
+#                     - 👨‍🏫 Total Teachers: {len(teachers_data)}
+#                     - 📚 Total Courses: {len(courses_data)}
+                    
+#                     ### Quick Actions
+#                     - View student performance
+#                     - Access course materials
+#                     - Generate learning insights
+#                     """)
+                
+#                 # Students Content
+#                 students_page = gr.Group(visible=False)
+#                 with students_page:
+#                     gr.Markdown("## 👥 Students")
+#                     gr.DataFrame(
+#                         value=students_data,
+#                         headers=["ID", "Name", "Grade", "Program"]
+#                     )
+                
+#                 # Teachers Content
+#                 teachers_page = gr.Group(visible=False)
+#                 with teachers_page:
+#                     gr.Markdown("## 👨‍🏫 Teachers")
+#                     gr.DataFrame(
+#                         value=teachers_data,
+#                         headers=["ID", "Name", "Subject", "Qualification"]
+#                     )
+                
+#                 # Courses Content
+#                 courses_page = gr.Group(visible=False)
+#                 with courses_page:
+#                     gr.Markdown("## 📚 Courses")
+#                     gr.DataFrame(
+#                         value=courses_data,
+#                         headers=["ID", "Name", "Instructor", "Level"]
+#                     )
+                
+#                 # YouTube Tool Content
+#                 youtube_page = gr.Group(visible=False)
+#                 with youtube_page:
+#                     gr.Markdown("## Agent for YouTube Content Exploration")
+#                     with gr.Row():
+#                         with gr.Column(scale=2):
+#                             video_url = gr.Textbox(
+#                                 label="YouTube URL",
+#                                 placeholder="https://youtube.com/watch?v=..."
+#                             )
+#                             keywords = gr.Textbox(
+#                                 label="Keywords for Recommendations",
+#                                 placeholder="e.g., python programming, machine learning"
+#                             )
+#                             analyze_btn = gr.Button("🔍 Analyze Video", variant="primary")
+                        
+#                         with gr.Column(scale=1):
+#                             video_thumbnail = gr.Image(label="Video Preview")
+                    
+#                     with gr.Row():
+#                         with gr.Column():
+#                             summary = gr.Textbox(label="📝 Summary", lines=8)
+#                             sentiment = gr.Textbox(label="😊 Content Sentiment")
+#                         with gr.Column():
+#                             recommendations = gr.Textbox(label="🎯 Related Videos", lines=10)
+
+#     def login_check(user, pwd):
+#         if USER_CREDENTIALS.get(user) == pwd:
+#             return {
+#                 login_page: gr.update(visible=False),
+#                 main_page: gr.update(visible=True),
+#                 login_msg: ""
+#             }
+#         return {
+#             login_page: gr.update(visible=True),
+#             main_page: gr.update(visible=False),
+#             login_msg: "❌ Invalid credentials"
+#         }
+    
+#     def show_page(page_name):
+#         updates = {
+#             dashboard_page: gr.update(visible=False),
+#             students_page: gr.update(visible=False),
+#             teachers_page: gr.update(visible=False),
+#             courses_page: gr.update(visible=False),
+#             youtube_page: gr.update(visible=False)
+#         }
+#         updates[page_name] = gr.update(visible=True)
+#         return updates
+    
+#     # Event Handlers
+#     login_btn.click(
+#         login_check,
+#         inputs=[username, password],
+#         outputs=[login_page, main_page, login_msg]
+#     )
+    
+#     nav_dashboard.click(lambda: show_page(dashboard_page), outputs=list(show_page(dashboard_page).keys()))
+#     nav_students.click(lambda: show_page(students_page), outputs=list(show_page(students_page).keys()))
+#     nav_teachers.click(lambda: show_page(teachers_page), outputs=list(show_page(teachers_page).keys()))
+#     nav_courses.click(lambda: show_page(courses_page), outputs=list(show_page(courses_page).keys()))
+#     nav_youtube.click(lambda: show_page(youtube_page), outputs=list(show_page(youtube_page).keys()))
+    
+#     analyze_btn.click(
+#         process_youtube_video,
+#         inputs=[video_url, keywords],
+#         outputs=[video_thumbnail, summary, sentiment, recommendations]
+#     )
+    
+#     logout_btn.click(
+#         lambda: {
+#             login_page: gr.update(visible=True),
+#             main_page: gr.update(visible=False)
+#         },
+#         outputs=[login_page, main_page]
+#     )
+
+# if __name__ == "__main__":
+#     app.launch()
 def process_youtube_video(url="", keywords=""):
     try:
-        #Initialize variables
+        # 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", "", ""
+        # If URL is provided, process the YouTube video
+        if url.strip():
+            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'])
-
-            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
+                # 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", "", ""
+
+        # If keywords are provided, get recommendations
         if keywords.strip():
             recommendations = get_recommendations(keywords)
 
@@ -197,104 +506,6 @@ def process_youtube_video(url="", keywords=""):
     except Exception as e:
         return None, f"Error: {str(e)}", "N/A", "", ""
 
-
-def extract_video_id(url):
-    """
-    Extracts the video ID from a YouTube URL.
-    """
-    match = re.search(r"(?:v=|\/)([0-9A-Za-z_-]{11})", url)
-    return match.group(1) if match else None
-
-
-def get_video_metadata(video_id):
-    """
-    Fetches video metadata such as title and description using the YouTube Data API.
-    """
-    try:
-        YOUTUBE_API_KEY = "AIzaSyD_SDF4lC3vpHVAMnBOcN2ZCTz7dRjUc98"  # Replace with your YouTube Data API key
-        youtube = build("youtube", "v3", developerKey=YOUTUBE_API_KEY)
-        request = youtube.videos().list(part="snippet", id=video_id)
-        response = request.execute()
-
-        if "items" in response and len(response["items"]) > 0:
-            snippet = response["items"][0]["snippet"]
-            return {
-                "title": snippet.get("title", "No title available"),
-                "description": snippet.get("description", "No description available"),
-            }
-        return {}
-
-    except Exception as e:
-        return {"title": "Error fetching metadata", "description": str(e)}
-
-
-def extract_subtitle_info(text):
-    """
-    Extracts meaningful information from the subtitles.
-    This could include topics, key insights, or a breakdown of the content.
-    """
-    try:
-        # Split text into sentences for better analysis
-        sentences = text.split(". ")
-        
-        # Example: Extract key topics or keywords
-        words = text.split()
-        common_words = Counter(words).most_common(10)
-        key_topics = ", ".join([word for word, count in common_words])
-
-        # Example: Provide a breakdown of the content
-        info = f"Key topics discussed: {key_topics}. \nNumber of sentences: {len(sentences)}. \nTotal words: {len(words)}."
-
-        return info
-    except Exception as e:
-        return f"Error extracting subtitle information: {str(e)}"
-
-
-def clean_text_for_analysis(text):
-    """
-    Cleans the transcript text by removing extra spaces, line breaks, and non-text elements.
-    """
-    # Remove extra spaces and line breaks
-    cleaned_text = " ".join(text.split())
-    return cleaned_text
-
-
-def get_recommendations(keywords):
-    """
-    Fetches related video recommendations based on the provided keywords.
-    This function can be expanded with a proper API or custom logic.
-    """
-    # Placeholder for fetching recommendations based on keywords
-    return f"Recommendations for: {keywords}"  # Dummy return for now
-######################################
-
-def get_recommendations(keywords, max_results=5):
-    if not keywords:
-        return "Please provide search keywords"
-    try:
-        response = requests.get(
-            "https://www.googleapis.com/youtube/v3/search",
-            params={
-                "part": "snippet",
-                "q": f"educational {keywords}",
-                "type": "video",
-                "maxResults": max_results,
-                "relevanceLanguage": "en",
-                "key": YOUTUBE_API_KEY
-            }
-        ).json()
-        
-        results = []
-        for item in response.get("items", []):
-            title = item["snippet"]["title"]
-            channel = item["snippet"]["channelTitle"]
-            video_id = item["id"]["videoId"]
-            results.append(f"📺 {title}\n👤 {channel}\n🔗 https://youtube.com/watch?v={video_id}\n")
-        
-        return "\n".join(results) if results else "No recommendations found"
-    except Exception as e:
-        return f"Error: {str(e)}"
-
 # Gradio Interface
 with gr.Blocks(theme=gr.themes.Soft()) as app:
     # Login Page
@@ -304,7 +515,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
         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():
@@ -316,7 +527,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
                 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()
@@ -327,13 +538,13 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
                     - 👥 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:
@@ -342,7 +553,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
                         value=students_data,
                         headers=["ID", "Name", "Grade", "Program"]
                     )
-                
+
                 # Teachers Content
                 teachers_page = gr.Group(visible=False)
                 with teachers_page:
@@ -351,7 +562,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
                         value=teachers_data,
                         headers=["ID", "Name", "Subject", "Qualification"]
                     )
-                
+
                 # Courses Content
                 courses_page = gr.Group(visible=False)
                 with courses_page:
@@ -360,7 +571,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
                         value=courses_data,
                         headers=["ID", "Name", "Instructor", "Level"]
                     )
-                
+
                 # YouTube Tool Content
                 youtube_page = gr.Group(visible=False)
                 with youtube_page:
@@ -376,10 +587,10 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
                                 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)
@@ -399,7 +610,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
             main_page: gr.update(visible=False),
             login_msg: "❌ Invalid credentials"
         }
-    
+
     def show_page(page_name):
         updates = {
             dashboard_page: gr.update(visible=False),
@@ -410,26 +621,26 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
         }
         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),