Update app.py

app.py

@@ -675,128 +675,6 @@ from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, No
 import re
 from collections import Counter
 from googleapiclient.discovery import build
-# def process_youtube_video(url="", keywords=""):
-#     try:
-#         # Initialize variables
-#         thumbnail = None
-#         summary = "No transcript available"
-#         sentiment_label = "N/A"
-#         recommendations = ""
-#         subtitle_info = "No additional information available"
-
-#         if not url.strip():
-#             return None, "Please enter a YouTube URL", "N/A", "", ""
-
-#         video_id = extract_video_id(url)
-#         if not video_id:
-#             return None, "Invalid YouTube URL", "N/A", "", ""
-
-#         thumbnail = f"https://img.youtube.com/vi/{video_id}/maxresdefault.jpg"
-
-#         try:
-#             # Fetch transcript
-#             transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
-#             transcript = None
-#             try:
-#                 transcript = transcript_list.find_transcript(['en'])
-#             except:
-#                 transcript = transcript_list.find_generated_transcript(['en'])
-
-#             text = " ".join([t['text'] for t in transcript.fetch()])
-#             if not text.strip():
-#                 raise ValueError("Transcript is empty")
-
-#             # Generate summary
-#             model = genai.GenerativeModel("gemini-pro")
-#             summary = model.generate_content(f"Summarize this: {text[:4000]}").text
-
-#             # Extract subtitle information
-#             subtitle_info = extract_subtitle_info(text)
-
-#             # Sentiment analysis
-#             sentiment = TextBlob(text[:1000]).sentiment
-#             sentiment_label = f"{'Positive' if sentiment.polarity > 0 else 'Negative' if sentiment.polarity < 0 else 'Neutral'} ({sentiment.polarity:.2f})"
-
-#         except TranscriptsDisabled:
-#             metadata = get_video_metadata(video_id)
-#             summary = metadata.get("description", "⚠️ This video has disabled subtitles.")
-#             sentiment_label = "N/A"
-#             subtitle_info = "No subtitles available for analysis."
-#         except NoTranscriptFound:
-#             metadata = get_video_metadata(video_id)
-#             summary = metadata.get("description", "⚠️ No English transcript available.")
-#             sentiment_label = "N/A"
-#             subtitle_info = "No subtitles available for analysis."
-#         except Exception as e:
-#             return thumbnail, f"⚠️ Error processing transcript: {str(e)}", "N/A", "", ""
-
-#         # Get recommendations
-#         if keywords.strip():
-#             recommendations = get_recommendations(keywords)
-
-#         return thumbnail, summary, sentiment_label, subtitle_info, recommendations
-
-#     except Exception as e:
-#         return None, f"Error: {str(e)}", "N/A", "", ""
-
-
-# def extract_video_id(url):
-#     """
-#     Extracts the video ID from a YouTube URL.
-#     """
-#     import re
-#     match = re.search(r"(?:v=|\/)([0-9A-Za-z_-]{11})", url)
-#     return match.group(1) if match else None
-
-
-# def get_video_metadata(video_id):
-#     """
-#     Fetches video metadata such as title and description using the YouTube Data API.
-#     """
-#     try:
-#         from googleapiclient.discovery import build
-
-#         # Replace with your YouTube Data API key
-#         YOUTUBE_API_KEY = "AIzaSyD_SDF4lC3vpHVAMnBOcN2ZCTz7dRjUc98"
-#         youtube = build("youtube", "v3", developerKey=YOUTUBE_API_KEY)
-#         request = youtube.videos().list(part="snippet", id=video_id)
-#         response = request.execute()
-
-#         if "items" in response and len(response["items"]) > 0:
-#             snippet = response["items"][0]["snippet"]
-#             return {
-#                 "title": snippet.get("title", "No title available"),
-#                 "description": snippet.get("description", "No description available"),
-#             }
-#         return {}
-
-#     except Exception as e:
-#         return {"title": "Error fetching metadata", "description": str(e)}
-
-
-# def extract_subtitle_info(text):
-#     """
-#     Extracts meaningful information from the subtitles.
-#     This could include topics, key insights, or a breakdown of the content.
-#     """
-#     try:
-#         # Split text into sentences for better analysis
-#         sentences = text.split(". ")
-        
-#         # Example: Extract key topics or keywords
-#         from collections import Counter
-#         words = text.split()
-#         common_words = Counter(words).most_common(10)
-#         key_topics = ", ".join([word for word, count in common_words])
-
-#         # Example: Provide a breakdown of the content
-#         info = f"Key topics discussed: {key_topics}. \nNumber of sentences: {len(sentences)}. \nTotal words: {len(words)}."
-
-#         return info
-#     except Exception as e:
-#         return f"Error extracting subtitle information: {str(e)}"
-
-
 def process_youtube_video(url="", keywords=""):
     try:
         # Initialize variables
@@ -828,19 +706,16 @@ def process_youtube_video(url="", keywords=""):
             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
+            summary = model.generate_content(f"Summarize this: {text[:4000]}").text
 
             # Extract subtitle information
-            subtitle_info = extract_subtitle_info(cleaned_text)
+            subtitle_info = extract_subtitle_info(text)
+
+            # Sentiment analysis
+            sentiment = TextBlob(text[:1000]).sentiment
+            sentiment_label = f"{'Positive' if sentiment.polarity > 0 else 'Negative' if sentiment.polarity < 0 else 'Neutral'} ({sentiment.polarity:.2f})"
 
         except TranscriptsDisabled:
             metadata = get_video_metadata(video_id)
@@ -869,6 +744,7 @@ def extract_video_id(url):
     """
     Extracts the video ID from a YouTube URL.
     """
+    import re
     match = re.search(r"(?:v=|\/)([0-9A-Za-z_-]{11})", url)
     return match.group(1) if match else None
 
@@ -878,7 +754,10 @@ 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
+        from googleapiclient.discovery import build
+
+        # Replace with your YouTube Data API key
+        YOUTUBE_API_KEY = "AIzaSyD_SDF4lC3vpHVAMnBOcN2ZCTz7dRjUc98"
         youtube = build("youtube", "v3", developerKey=YOUTUBE_API_KEY)
         request = youtube.videos().list(part="snippet", id=video_id)
         response = request.execute()
@@ -905,6 +784,7 @@ def extract_subtitle_info(text):
         sentences = text.split(". ")
         
         # Example: Extract key topics or keywords
+        from collections import Counter
         words = text.split()
         common_words = Counter(words).most_common(10)
         key_topics = ", ".join([word for word, count in common_words])
@@ -916,23 +796,143 @@ def extract_subtitle_info(text):
     except Exception as e:
         return f"Error extracting subtitle information: {str(e)}"
 
+##########
+# def process_youtube_video(url="", keywords=""):
+#     try:
+#         # Initialize variables
+#         thumbnail = None
+#         summary = "No transcript available"
+#         sentiment_label = "N/A"
+#         recommendations = ""
+#         subtitle_info = "No additional information available"
 
-def 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
+#         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", "", ""
 
-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
+#         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