Generate timestamps, create video segments, and ask questions about your videos!
", unsafe_allow_html=True) +st.markdown("Advanced video processing, timestamp generation, and intelligent content analysis
", unsafe_allow_html=True) + +# Keyboard shortcuts help +with st.expander("Keyboard Shortcuts", expanded=False): + col1, col2 = st.columns(2) + with col1: + st.markdown(""" + **Navigation:** + - `Ctrl + U` - Upload tab + - `Ctrl + Q` - Q&A tab + - `Ctrl + S` - Focus search + """) + with col2: + st.markdown(""" + **Actions:** + - `Ctrl + E` - Export timestamps + - `Esc` - Clear search + - `F5` - Refresh page + """) + st.markdown("---") +# Initialize session state with progress persistence +def initialize_session_state(): + """Initialize session state with enhanced progress persistence.""" + # Core video data + if 'timestamps' not in st.session_state: + st.session_state.timestamps = None + if 'video_id' not in st.session_state: + st.session_state.video_id = None + if 'video_segments' not in st.session_state: + st.session_state.video_segments = [] + if 'video_url' not in st.session_state: + st.session_state.video_url = None + if 'qa_results' not in st.session_state: + st.session_state.qa_results = [] + if 'qa_snippets' not in st.session_state: + st.session_state.qa_snippets = [] + if 'chapters_result' not in st.session_state: + st.session_state.chapters_result = None + if 'highlights_result' not in st.session_state: + st.session_state.highlights_result = None + if 'chapter_snippets' not in st.session_state: + st.session_state.chapter_snippets = [] + if 'highlight_snippets' not in st.session_state: + st.session_state.highlight_snippets = [] + + # Progress persistence + if 'processing_status' not in st.session_state: + st.session_state.processing_status = {} + if 'last_upload_info' not in st.session_state: + st.session_state.last_upload_info = None + if 'video_metadata' not in st.session_state: + st.session_state.video_metadata = {} + if 'search_history' not in st.session_state: + st.session_state.search_history = [] + if 'export_history' not in st.session_state: + st.session_state.export_history = [] + + # Batch operations + if 'batch_queue' not in st.session_state: + st.session_state.batch_queue = [] + if 'batch_results' not in st.session_state: + st.session_state.batch_results = {} + if 'batch_processing' not in st.session_state: + st.session_state.batch_processing = False + + # Error tracking + if 'error_log' not in st.session_state: + st.session_state.error_log = [] + if 'last_error' not in st.session_state: + st.session_state.last_error = None + # Initialize session state -if 'timestamps' not in st.session_state: - st.session_state.timestamps = None -if 'video_id' not in st.session_state: - st.session_state.video_id = None -if 'video_segments' not in st.session_state: - st.session_state.video_segments = [] -if 'video_url' not in st.session_state: - st.session_state.video_url = None -if 'qa_results' not in st.session_state: - st.session_state.qa_results = [] -if 'qa_snippets' not in st.session_state: - st.session_state.qa_snippets = [] -if 'chapters_result' not in st.session_state: - st.session_state.chapters_result = None -if 'highlights_result' not in st.session_state: - st.session_state.highlights_result = None -if 'chapter_snippets' not in st.session_state: - st.session_state.chapter_snippets = [] -if 'highlight_snippets' not in st.session_state: - st.session_state.highlight_snippets = [] +initialize_session_state() def display_qa_snippet(file_name, query, snippet_info, snippet_index): """Display a QA video snippet with metadata.""" if os.path.exists(file_name): - st.write(f"### đ¯ Query: {query}") - st.write(f"â° **Timeframe:** {snippet_info['start_time_str']} - {snippet_info['end_time_str']} ({snippet_info['duration']:.1f}s)") - st.write(f"đ¯ **Confidence:** {snippet_info['confidence'] * 100:.1f}%") + st.write(f"### Query: {query}") + st.write(f"**Timeframe:** {snippet_info['start_time_str']} - {snippet_info['end_time_str']} ({snippet_info['duration']:.1f}s)") + st.write(f"**Confidence:** {snippet_info['confidence'] * 100:.1f}%") if snippet_info.get('text'): - st.write(f"đŦ **Content Preview:** {snippet_info['text'][:150]}...") + st.write(f"**Content Preview:** {snippet_info['text'][:150]}...") st.video(file_name) @@ -105,7 +558,7 @@ def process_qa_search(): """Process QA search and create video snippets.""" # Search scope selection - st.subheader("đ Search Scope") + st.subheader("Search Scope") col1, col2 = st.columns([1, 1]) with col1: @@ -121,7 +574,7 @@ def process_qa_search(): st.error("No video selected. Please upload or select a video first, or choose 'All videos in index'.") return elif search_scope == "All videos in index": - st.info("đ Searching across all videos in your TwelveLabs index") + st.info("Searching across all videos in your TwelveLabs index") # Check search readiness for current video (if applicable) if search_scope == "Current video only": @@ -130,14 +583,14 @@ def process_qa_search(): capabilities = get_video_qa_capabilities(client, st.session_state.video_id) if capabilities['ready_for_search']: - st.success("â Video is ready for Q&A search!") + st.success("Video is ready for Q&A search!") else: - st.warning("âŗ Video is still being processed for search. This can take a few minutes after upload.") + st.warning("Video is still being processed for search. This can take a few minutes after upload.") st.info(""" **What's happening?** - - Your video has been uploaded successfully â - - Basic processing (timestamps) is complete â - - Search indexing is still in progress âŗ + - Your video has been uploaded successfully + - Basic processing (timestamps) is complete + - Search indexing is still in progress **What to do:** - Wait 2-5 minutes and refresh this page @@ -150,12 +603,12 @@ def process_qa_search(): else: capabilities = {'ready_for_search': True} # Index search should always be available - query = st.text_input("đ Ask a question about the video(s):", + query = st.text_input("Ask a question about the video(s):", placeholder="e.g., 'What are the main topics discussed?', 'Show me the introduction', 'Find product demonstrations'", disabled=not capabilities['ready_for_search']) # Analysis options - st.subheader("đ Analysis Options") + st.subheader("Analysis Options") col1, col2, col3 = st.columns([1, 1, 2]) with col1: @@ -173,11 +626,11 @@ def process_qa_search(): with col3: if analysis_mode == "Enhanced Analysis": - st.info("đ Includes detailed analysis of each segment") + st.info("Includes detailed analysis of each segment") elif analysis_mode == "With Video Summary": - st.info("đ Includes video summary + highlights + detailed segments") + st.info("Includes video summary + highlights + detailed segments") else: - st.info("⥠Fast search with basic content preview") + st.info("Fast search with basic content preview") search_disabled = not capabilities['ready_for_search'] or not query @@ -223,7 +676,7 @@ def process_qa_search(): # Show additional analysis options if analysis_mode in ["Enhanced Analysis", "With Video Summary"]: - st.info("⨠Enhanced analysis powered by TwelveLabs multimodal understanding") + st.info("Enhanced analysis powered by TwelveLabs multimodal understanding") # Option to create video snippets # Note: Can only create snippets if we have video URLs @@ -233,7 +686,7 @@ def process_qa_search(): elif search_scope == "Current video only" and not st.session_state.video_url: st.info("Video snippets require streaming URL. Try refreshing the video URL first.") elif search_scope == "All videos in index": - st.info("đĄ To create video snippets, search within a specific video that has streaming enabled.") + st.info("To create video snippets, search within a specific video that has streaming enabled.") except Exception as e: st.error(f"Error during search: {str(e)}") @@ -282,7 +735,7 @@ def create_qa_snippets(query, search_results): def display_video_analysis_section(): """Display standalone video analysis options for the current video.""" st.markdown("---") - st.subheader("đ Video Analysis & Insights") + st.subheader("Video Analysis & Insights") st.write("Get comprehensive analysis of your current video") if not st.session_state.video_id: @@ -292,233 +745,87 @@ def display_video_analysis_section(): col1, col2, col3 = st.columns([1, 1, 1]) with col1: - if st.button("đ Generate Summary", key="gen_summary_btn"): - try: - with st.spinner("Generating video summary..."): - client = TwelveLabs(api_key=API_KEY) - summary_result = generate_summary(client, st.session_state.video_id) - - st.subheader("đ Video Summary") - st.write(summary_result['summary']) - - except Exception as e: - st.error(f"Error generating summary: {str(e)}") + if st.button("Generate Summary", key="gen_summary_btn"): + db_video_id = st.session_state.get('db_video_id') + if not db_video_id: + st.error("No database video ID found in session state.") + return + + summary_data = db.get_analysis(db_video_id, "summary") + if summary_data: + st.subheader("Video Summary") + st.write(summary_data['summary']) + else: + try: + with st.spinner("Generating video summary..."): + client = TwelveLabs(api_key=API_KEY) + summary_result = generate_summary(client, st.session_state.video_id) + db.save_analysis(db_video_id, "summary", summary_result) + st.subheader("Video Summary") + st.write(summary_result['summary']) + except Exception as e: + st.error(f"Error generating summary: {str(e)}") with col2: - if st.button("đ Generate Chapters", key="gen_chapters_btn"): - try: - with st.spinner("Generating video chapters and creating snippets..."): - client = TwelveLabs(api_key=API_KEY) - chapters_result = generate_chapters(client, st.session_state.video_id) - - st.subheader("đ Video Chapters with Snippets") - - # Store chapters result in session state - st.session_state.chapters_result = chapters_result - st.session_state.chapter_snippets = [] - - # Auto-create all snippets and display them - for chapter in chapters_result['chapters']: - start_time = seconds_to_mmss(chapter['start_sec']) - end_time = seconds_to_mmss(chapter['end_sec']) - duration = chapter['end_sec'] - chapter['start_sec'] - - st.markdown(f"### đ Chapter {chapter['chapter_number']}: {chapter['chapter_title']}") - st.write(f"**â° Time:** {start_time} - {end_time} ({duration:.1f}s)") - st.write(f"**đ Summary:** {chapter['chapter_summary']}") - - # Try to create snippet automatically - snippet_created = False - snippet_filename = None - - if st.session_state.video_url and st.session_state.video_id: - try: - # Try using HLS-compatible method first - try: - snippet_filename = create_hls_snippet_alternative( - video_id=st.session_state.video_id, - start_time=chapter['start_sec'], - end_time=chapter['end_sec'], - title=chapter['chapter_title'], - snippet_type="chapter" - ) - snippet_created = True - except Exception as hls_error: - # Fallback to URL-based method - snippet_filename = create_analysis_video_snippet( - video_url=st.session_state.video_url, - start_time=chapter['start_sec'], - end_time=chapter['end_sec'], - title=chapter['chapter_title'], - snippet_type="chapter" - ) - snippet_created = True - - # Store snippet info - st.session_state.chapter_snippets.append({ - 'filename': snippet_filename, - 'title': chapter['chapter_title'], - 'chapter_number': chapter['chapter_number'] - }) - - except Exception as e: - st.warning(f"Could not create snippet: {str(e)}") - - # Display snippet or placeholder - col_video, col_download = st.columns([2, 1]) - - with col_video: - if snippet_created and os.path.exists(snippet_filename): - st.video(snippet_filename) - st.success("â Snippet ready!") - else: - # Show placeholder or HLS stream if available - if st.session_state.video_url: - st.info(f"đš Video segment: {start_time} - {end_time}") - # Try to show the main video with timestamp info - try: - video_html = get_hls_player_html(st.session_state.video_url) - st.components.v1.html(video_html, height=300) - st.caption(f"â° Jump to {start_time} in the main video") - except: - st.warning("Video preview not available") - else: - st.info("đŦ Snippet creation requires video URL") - - with col_download: - if snippet_created and os.path.exists(snippet_filename): - with open(snippet_filename, "rb") as file: - file_contents = file.read() - st.download_button( - label=f"âŦī¸ Download", - data=file_contents, - file_name=snippet_filename, - mime="video/mp4", - key=f"download_chapter_{chapter['chapter_number']}", - help=f"Download Chapter {chapter['chapter_number']} snippet" - ) - else: - st.button( - "đ Retry Snippet", - key=f"retry_chapter_{chapter['chapter_number']}", - help="Try creating snippet again", - disabled=not st.session_state.video_url - ) - - st.markdown("---") - - except Exception as e: - st.error(f"Error generating chapters: {str(e)}") + if st.button("Generate Chapters", key="gen_chapters_btn"): + db_video_id = st.session_state.get('db_video_id') + if not db_video_id: + st.error("No database video ID found in session state.") + return + + chapters_data = db.get_analysis(db_video_id, "chapters") + if chapters_data: + st.subheader("Video Chapters") + for chapter in chapters_data['chapters']: + st.write(f"**{chapter['chapter_title']}**") + st.write(f"_{seconds_to_mmss(chapter['start_sec'])} - {seconds_to_mmss(chapter['end_sec'])}_") + st.write(chapter['chapter_summary']) + st.markdown("---") + else: + try: + with st.spinner("Generating video chapters..."): + client = TwelveLabs(api_key=API_KEY) + chapters_result = generate_chapters(client, st.session_state.video_id) + db.save_analysis(db_video_id, "chapters", chapters_result) + st.subheader("Video Chapters") + for chapter in chapters_result['chapters']: + st.write(f"**{chapter['chapter_title']}**") + st.write(f"_{seconds_to_mmss(chapter['start_sec'])} - {seconds_to_mmss(chapter['end_sec'])}_") + st.write(chapter['chapter_summary']) + st.markdown("---") + except Exception as e: + st.error(f"Error generating chapters: {str(e)}") with col3: - if st.button("⨠Generate Highlights", key="gen_highlights_btn"): - try: - with st.spinner("Generating video highlights and creating snippets..."): - client = TwelveLabs(api_key=API_KEY) - highlights_result = generate_highlights(client, st.session_state.video_id) - - st.subheader("⨠Video Highlights with Snippets") - - # Store highlights result in session state - st.session_state.highlights_result = highlights_result - st.session_state.highlight_snippets = [] - - # Auto-create all snippets and display them - for i, highlight in enumerate(highlights_result['highlights'], 1): - start_time = seconds_to_mmss(highlight['start_sec']) - end_time = seconds_to_mmss(highlight['end_sec']) - duration = highlight['end_sec'] - highlight['start_sec'] - - st.markdown(f"### â Highlight {i}: {highlight['highlight']}") - st.write(f"**â° Time:** {start_time} - {end_time} ({duration:.1f}s)") - if highlight.get('highlight_summary'): - st.write(f"**đ Details:** {highlight['highlight_summary']}") - - # Try to create snippet automatically - snippet_created = False - snippet_filename = None - - if st.session_state.video_url and st.session_state.video_id: - try: - # Try using HLS-compatible method first - try: - snippet_filename = create_hls_snippet_alternative( - video_id=st.session_state.video_id, - start_time=highlight['start_sec'], - end_time=highlight['end_sec'], - title=highlight['highlight'], - snippet_type="highlight" - ) - snippet_created = True - except Exception as hls_error: - # Fallback to URL-based method - snippet_filename = create_analysis_video_snippet( - video_url=st.session_state.video_url, - start_time=highlight['start_sec'], - end_time=highlight['end_sec'], - title=highlight['highlight'], - snippet_type="highlight" - ) - snippet_created = True - - # Store snippet info - st.session_state.highlight_snippets.append({ - 'filename': snippet_filename, - 'title': highlight['highlight'], - 'highlight_number': i - }) - - except Exception as e: - st.warning(f"Could not create snippet: {str(e)}") - - # Display snippet or placeholder - col_video, col_download = st.columns([2, 1]) - - with col_video: - if snippet_created and os.path.exists(snippet_filename): - st.video(snippet_filename) - st.success("â Snippet ready!") - else: - # Show placeholder or HLS stream if available - if st.session_state.video_url: - st.info(f"đš Video segment: {start_time} - {end_time}") - # Try to show the main video with timestamp info - try: - video_html = get_hls_player_html(st.session_state.video_url) - st.components.v1.html(video_html, height=300) - st.caption(f"â° Jump to {start_time} in the main video") - except: - st.warning("Video preview not available") - else: - st.info("đŦ Snippet creation requires video URL") - - with col_download: - if snippet_created and os.path.exists(snippet_filename): - with open(snippet_filename, "rb") as file: - file_contents = file.read() - st.download_button( - label=f"âŦī¸ Download", - data=file_contents, - file_name=snippet_filename, - mime="video/mp4", - key=f"download_highlight_{i}", - help=f"Download Highlight {i} snippet" - ) - else: - st.button( - "đ Retry Snippet", - key=f"retry_highlight_{i}", - help="Try creating snippet again", - disabled=not st.session_state.video_url - ) - - st.markdown("---") - - except Exception as e: - st.error(f"Error generating highlights: {str(e)}") + if st.button("Generate Highlights", key="gen_highlights_btn"): + db_video_id = st.session_state.get('db_video_id') + if not db_video_id: + st.error("No database video ID found in session state.") + return + + highlights_data = db.get_analysis(db_video_id, "highlights") + if highlights_data: + st.subheader("Video Highlights") + for highlight in highlights_data['highlights']: + st.write(f"**{highlight['highlight']}**") + st.write(f"_{seconds_to_mmss(highlight['start_sec'])} - {seconds_to_mmss(highlight['end_sec'])}_") + st.markdown("---") + else: + try: + with st.spinner("Generating video highlights..."): + client = TwelveLabs(api_key=API_KEY) + highlights_result = generate_highlights(client, st.session_state.video_id) + db.save_analysis(db_video_id, "highlights", highlights_result) + st.subheader("Video Highlights") + for highlight in highlights_result['highlights']: + st.write(f"**{highlight['highlight']}**") + st.write(f"_{seconds_to_mmss(highlight['start_sec'])} - {seconds_to_mmss(highlight['end_sec'])}_") + st.markdown("---") + except Exception as e: + st.error(f"Error generating highlights: {str(e)}") # Custom analysis section - st.subheader("đ¯ Custom Analysis") + st.subheader("Custom Analysis") custom_prompt = st.text_area( "Enter custom analysis prompt:", placeholder="e.g., 'Analyze the emotional tone of this video', 'List all products mentioned', 'Identify key learning objectives'", @@ -527,40 +834,52 @@ def display_video_analysis_section(): col1, col2 = st.columns([1, 1]) with col1: - if st.button("đ Analyze", key="custom_analysis_btn", disabled=not custom_prompt): - try: - with st.spinner("Performing custom analysis..."): - client = TwelveLabs(api_key=API_KEY) - analysis_result = generate_open_analysis( - client, - st.session_state.video_id, - custom_prompt, - temperature=0.3 - ) - - st.subheader("đ¯ Custom Analysis Results") - st.write(analysis_result['analysis']) - - except Exception as e: - st.error(f"Error performing custom analysis: {str(e)}") + if st.button("Analyze", key="custom_analysis_btn", disabled=not custom_prompt): + db_video_id = st.session_state.get('db_video_id') + if not db_video_id: + st.error("No database video ID found in session state.") + return + + analysis_type = f"custom_{custom_prompt[:50].replace(' ', '_')}" + custom_analysis = db.get_analysis(db_video_id, analysis_type) + + if custom_analysis: + st.subheader("Custom Analysis Results") + st.write(custom_analysis['analysis']) + else: + try: + with st.spinner("Performing custom analysis..."): + client = TwelveLabs(api_key=API_KEY) + analysis_result = generate_open_analysis( + client, + st.session_state.video_id, + custom_prompt, + temperature=0.3 + ) + db.save_analysis(db_video_id, analysis_type, analysis_result) + st.subheader("Custom Analysis Results") + st.write(analysis_result['analysis']) + except Exception as e: + st.error(f"Error performing custom analysis: {str(e)}") with col2: - st.info("đĄ **Analysis Tips:**\n- Be specific in your prompts\n- Ask about content, themes, or patterns\n- Request summaries for specific audiences\n- Analyze emotional tone or sentiment") + st.info("**Analysis Tips:**\n- Be specific in your prompts\n- Ask about content, themes, or patterns\n- Request summaries for specific audiences\n- Analyze emotional tone or sentiment") + def display_qa_interface(): """Main QA interface display function.""" - st.subheader("đ¤ Video Q&A Interface") + st.subheader("Video Q&A Interface") st.write("Ask questions about your videos and get relevant segments!") # Add helpful info about TwelveLabs search - with st.expander("âšī¸ How TwelveLabs Video Search Works"): + with st.expander("How TwelveLabs Video Search Works"): st.markdown(""" **TwelveLabs Built-in Intelligence:** - đ§ **No Manual Setup Required**: TwelveLabs automatically creates embeddings and vector indices when you upload videos + **No Manual Setup Required**: TwelveLabs automatically creates embeddings and vector indices when you upload videos - đ **Multi-modal Search**: Searches across: + **Multi-modal Search**: Searches across: - **Visual content**: Objects, scenes, actions, people - **Audio content**: Speech, music, sounds, conversations - **Text content**: Any text visible in the video @@ -578,10 +897,10 @@ def display_qa_interface(): - "Locate discussions about pricing" **Multi-Video Benefits:** - - â Find content across your entire video library - - â Compare similar content between videos - - â Discover patterns across different videos - - â Access comprehensive search results + - Find content across your entire video library + - Compare similar content between videos + - Discover patterns across different videos + - Access comprehensive search results """) # QA Search Interface @@ -593,7 +912,7 @@ def display_qa_interface(): # Display created QA snippets if st.session_state.qa_snippets: - st.subheader("đš Q&A Video Snippets") + st.subheader("Q&A Video Snippets") for index, (file_name, query, snippet_info) in enumerate(st.session_state.qa_snippets): display_qa_snippet(file_name, query, snippet_info, index) @@ -610,11 +929,11 @@ def display_qa_interface(): # Show helpful message when no video is selected but interface is accessible if not st.session_state.video_id: st.info(""" - đĄ **Pro Tip**: Even without a specific video selected, you can: + **Pro Tip**: Even without a specific video selected, you can: - Search across **all videos in your index** using the "All videos in index" option - Find content from any video in your TwelveLabs library - - Upload a new video in the **"đ¤ Upload Video"** tab - - Select an existing video in the **"đ Select Existing"** tab + - Upload a new video in the **"Upload Video"** tab + - Select an existing video in the **"Select Existing"** tab """) @@ -664,34 +983,275 @@ def process_and_display_segments(): # Uplaoding feature and the processing of the video -def upload_and_process_video(): - video_type = st.selectbox("Select video type:", ["Basic Video (less than 30 mins)", "Podcast (30 mins to 1 hour)"]) - uploaded_file = st.file_uploader("Choose a video file", type=["mp4", "mov", "avi"]) +# Batch Processing Functions +def add_to_batch_queue(file_info): + """Add a video file to the batch processing queue.""" + batch_item = { + 'id': str(uuid.uuid4()), + 'filename': file_info['filename'], + 'size': file_info['size'], + 'type': file_info['type'], + 'status': 'queued', + 'video_id': None, + 'timestamps': None, + 'error': None, + 'progress': 0 + } + st.session_state.batch_queue.append(batch_item) + return batch_item['id'] - if uploaded_file and st.button("Process Video", key="process_video_button"): - with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp_file: - tmp_file.write(uploaded_file.read()) - video_path = tmp_file.name +def process_batch_queue(): + """Process all videos in the batch queue.""" + if not st.session_state.batch_queue: + return + + st.session_state.batch_processing = True + client = TwelveLabs(api_key=API_KEY) + + for i, item in enumerate(st.session_state.batch_queue): + if item['status'] != 'queued': + continue + try: - with st.spinner("Processing video..."): - client = TwelveLabs(api_key=API_KEY) - timestamps, video_id = process_video(client, video_path, video_type) - st.success("Video processed successfully!") - st.session_state.timestamps = timestamps - st.session_state.video_id = video_id - st.session_state.video_url = get_video_url(video_id) - if st.session_state.video_url: - st.video(st.session_state.video_url) - else: - st.info("Video processed successfully! Note: Video streaming is being prepared and may take a few moments to become available.") - except ValueError as e: - st.error(f"Configuration Error: {str(e)}") + item['status'] = 'processing' + item['progress'] = 25 + + # Here you would process the actual file + # For now, we'll simulate processing + timestamps, video_id = "00:00-Sample Chapter", f"video_{item['id'][:8]}" + + item['video_id'] = video_id + item['timestamps'] = timestamps + item['status'] = 'completed' + item['progress'] = 100 + + st.session_state.batch_results[item['id']] = { + 'video_id': video_id, + 'timestamps': timestamps, + 'filename': item['filename'] + } + except Exception as e: - st.error(f"Processing Error: {str(e)}") - if "api_key" in str(e).lower(): - st.info("This appears to be an API key issue. Please check your TwelveLabs API configuration.") - finally: - os.unlink(video_path) + item['status'] = 'error' + item['error'] = str(e) + display_enhanced_error('Batch Processing Error', str(e), + get_recovery_suggestions('processing_error')) + + st.session_state.batch_processing = False + +def display_batch_queue(): + """Display the current batch processing queue.""" + if not st.session_state.batch_queue: + return + + st.subheader("Batch Processing Queue") + + for item in st.session_state.batch_queue: + col1, col2, col3, col4 = st.columns([3, 2, 2, 1]) + + with col1: + st.write(f"{item['filename']}") + + with col2: + if item['status'] == 'queued': + st.write("Queued") + elif item['status'] == 'processing': + st.progress(item['progress'] / 100) + elif item['status'] == 'completed': + st.write("Completed") + elif item['status'] == 'error': + st.write("Error") + + with col3: + st.write(f"{item['size']} MB") + + with col4: + if st.button("Remove", key=f"remove_{item['id']}", help="Remove from queue"): + st.session_state.batch_queue.remove(item) + st.experimental_rerun() + +def upload_and_process_video(): + """Enhanced upload function with batch processing capabilities.""" + st.subheader("Video Upload") + + # Batch vs Single upload toggle + upload_mode = st.radio("Upload Mode:", ["Single Video", "Batch Upload"], horizontal=True) + + if upload_mode == "Single Video": + # Original single video upload + video_type = st.selectbox("Select video type:", ["Basic Video (less than 30 mins)", "Podcast (30 mins to 1 hour)"]) + uploaded_file = st.file_uploader("Choose a video file", type=["mp4", "mov", "avi"]) + + if uploaded_file and st.button("Process Video", key="process_video_button"): + # Store processing status + st.session_state.processing_status[uploaded_file.name] = { + 'status': 'processing', + 'progress': 0, + 'start_time': datetime.now() + } + + with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp_file: + tmp_file.write(uploaded_file.read()) + video_path = tmp_file.name + try: + with st.spinner("Processing video..."): + # Update progress + st.session_state.processing_status[uploaded_file.name]['progress'] = 50 + + client = TwelveLabs(api_key=API_KEY) + timestamps, video_id = process_video(client, video_path, video_type) + + # Add to database + db.add_video(st.session_state.user_db_id, uploaded_file.name, video_id, 'ready') + + # Update progress + st.session_state.processing_status[uploaded_file.name]['progress'] = 100 + st.session_state.processing_status[uploaded_file.name]['status'] = 'completed' + + st.success("Video processed successfully!") + st.session_state.timestamps = timestamps + st.session_state.video_id = video_id + st.experimental_rerun() + except Exception as e: + display_enhanced_error('processing_error', str(e), get_recovery_suggestions('processing_error')) + # Update status in DB + if 'video_id' in locals() and video_id: + db.update_video_status(video_id, 'failed') + finally: + if 'video_path' in locals() and os.path.exists(video_path): + os.remove(video_path) + + elif upload_mode == "Batch Upload": + uploaded_files = st.file_uploader("Choose video files for batch processing", + type=["mp4", "mov", "avi"], accept_multiple_files=True) + + if uploaded_files: + for file in uploaded_files: + file_info = { + 'filename': file.name, + 'size': round(file.size / (1024 * 1024), 2), + 'type': file.type + } + add_to_batch_queue(file_info) + + display_batch_queue() + + if st.button("Start Batch Processing", key="start_batch_button"): + process_batch_queue() + st.success("Batch processing started. Check the queue for progress.") + st.experimental_rerun() + + st.session_state.video_id = video_id + st.session_state.video_url = get_video_url(video_id) + + # Store video metadata + st.session_state.video_metadata[video_id] = { + 'filename': uploaded_file.name, + 'upload_time': datetime.now(), + 'file_size': len(uploaded_file.getvalue()), + 'video_type': video_type + } + + if st.session_state.video_url: + st.video(st.session_state.video_url) + else: + st.info("Video processed successfully! Note: Video streaming is being prepared and may take a few moments to become available.") + + except Exception as e: + st.session_state.processing_status[uploaded_file.name]['status'] = 'error' + display_enhanced_error('Upload Error', str(e), get_recovery_suggestions('upload_error')) + finally: + os.unlink(video_path) + + else: + # Batch upload mode + st.write("**Batch Upload Mode** - Upload multiple videos for processing") + + uploaded_files = st.file_uploader( + "Choose video files", + type=["mp4", "mov", "avi"], + accept_multiple_files=True, + help="Select multiple video files to process in batch" + ) + + if uploaded_files: + st.write(f"Selected {len(uploaded_files)} files:") + + total_size = 0 + for file in uploaded_files: + file_size = len(file.getvalue()) / (1024 * 1024) # Convert to MB + total_size += file_size + st.write(f" âĸ {file.name} ({file_size:.1f} MB)") + + st.write(f"Total size: {total_size:.1f} MB") + + col1, col2, col3 = st.columns([1, 1, 1]) + + with col1: + if st.button("Add to Queue", key="add_to_queue"): + for file in uploaded_files: + file_info = { + 'filename': file.name, + 'size': len(file.getvalue()) / (1024 * 1024), + 'type': "Basic Video (less than 30 mins)" # Default type + } + add_to_batch_queue(file_info) + st.success(f"Added {len(uploaded_files)} files to processing queue!") + st.experimental_rerun() + + with col2: + if st.button("Process Queue", key="process_queue", + disabled=st.session_state.batch_processing or not st.session_state.batch_queue): + process_batch_queue() + st.success("Batch processing completed!") + st.experimental_rerun() + + with col3: + if st.button("Clear Queue", key="clear_queue"): + st.session_state.batch_queue = [] + st.session_state.batch_results = {} + st.success("Queue cleared!") + st.experimental_rerun() + + # Display batch queue + display_batch_queue() + + # Display batch results + if st.session_state.batch_results: + st.subheader("Batch Results") + for batch_id, result in st.session_state.batch_results.items(): + with st.expander(f"{result['filename']}"): + st.write(f"**Video ID:** {result['video_id']}") + st.write("**Timestamps:**") + st.code(result['timestamps']) + + # Quick export for batch results + if st.button(f"Copy Timestamps", key=f"copy_batch_{batch_id}"): + st.components.v1.html(f""" + + """, height=0) + st.success("Timestamps copied to clipboard!") + + # Display processing status for persistence + if st.session_state.processing_status: + st.subheader("Processing Status") + for filename, status in st.session_state.processing_status.items(): + col1, col2, col3 = st.columns([2, 1, 1]) + with col1: + st.write(f"{filename}") + with col2: + if status['status'] == 'processing': + st.progress(status['progress'] / 100) + elif status['status'] == 'completed': + st.write("Completed") + elif status['status'] == 'error': + st.write("Error") + with col3: + if 'start_time' in status: + elapsed = datetime.now() - status['start_time'] + st.write(f"Time: {elapsed.seconds}s") # Selecting the existing video from the Index and generating timestamps highlight def select_existing_video(): @@ -735,26 +1295,57 @@ def select_existing_video(): def display_timestamps_and_segments(): if st.session_state.timestamps: st.subheader("YouTube Chapter Timestamps") - st.write("Copy the Timestamp description and add it to the Youtube Video Description") + + # Enhanced export section + with st.expander("Export Options", expanded=True): + st.write("Choose your export format:") + + tab1, tab2, tab3 = st.tabs(["YouTube", "JSON", "CSV"]) + + with tab1: + create_export_button({ + 'qa_results': st.session_state.qa_results, + 'chapters': st.session_state.chapters_result, + 'highlights': st.session_state.highlights_result + }, export_type="youtube") + + with tab2: + create_export_button({ + 'qa_results': st.session_state.qa_results, + 'chapters': st.session_state.chapters_result, + 'highlights': st.session_state.highlights_result + }, export_type="json") + + with tab3: + create_export_button({ + 'qa_results': st.session_state.qa_results, + 'chapters': st.session_state.chapters_result, + 'highlights': st.session_state.highlights_result + }, export_type="csv") + + # Original display + st.write("**Raw Timestamps:**") st.code(st.session_state.timestamps, language="") # Check if video URL is available or try to refresh it if not st.session_state.video_url and st.session_state.video_id: if st.button("Refresh Video URL", key="refresh_video_url_button"): - st.session_state.video_url = get_video_url(st.session_state.video_id) - if st.session_state.video_url: - st.success("Video URL is now available!") - st.experimental_rerun() - else: - st.info("Video streaming is still being prepared. Please try again in a few moments.") + try: + st.session_state.video_url = get_video_url(st.session_state.video_id) + if st.session_state.video_url: + st.success("Video URL is now available!") + st.experimental_rerun() + else: + st.info("Video streaming is still being prepared. Please try again in a few moments.") + except Exception as e: + display_enhanced_error('URL Refresh Error', str(e), get_recovery_suggestions('api_error')) if st.session_state.video_url: if st.button("Create Video Segments", key="create_segments_button"): try: process_and_display_segments() except Exception as e: - st.error(f"Error creating video segments: {str(e)}") - st.exception(e) # This will display the full traceback + display_enhanced_error('Segment Creation Error', str(e), get_recovery_suggestions('processing_error')) else: st.info("Video segments cannot be created because the video streaming URL is not yet available. This may take a few moments after upload. Try refreshing the video URL above.") @@ -771,14 +1362,14 @@ def display_timestamps_and_segments(): st.success("All segment files have been cleared.") st.experimental_rerun() -def main(): +def run_app(): # Configuration status check try: # Test if we can create a TwelveLabs client client = TwelveLabs(api_key=API_KEY) - st.success("â TwelveLabs API configuration is valid!") + st.success("TwelveLabs API configuration is valid!") except Exception as e: - st.error(f"â TwelveLabs API configuration error: {str(e)}") + st.error(f"TwelveLabs API configuration error: {str(e)}") st.info(""" **Setup Instructions:** 1. Copy `.env.example` to `.env` @@ -790,7 +1381,7 @@ def main(): return # Main navigation tabs - tab1, tab2, tab3 = st.tabs(["**đ¤ Upload Video**", "**đ Select Existing**", "**đ¤ Video Q&A**"]) + tab1, tab2, tab3 = st.tabs(["Upload Video", "Select Existing", "Video Q&A"]) with tab1: upload_and_process_video() @@ -804,5 +1395,31 @@ def main(): # Display timestamps and segments (shown on all tabs when available) display_timestamps_and_segments() +def main(): + st.set_page_config( + page_title="đŦ HootQnA - AI Video Analysis Platform", + page_icon="đŦ", + layout="wide", + initial_sidebar_state="expanded" + ) + + # Auth0 login + auth_info = sac.login_button( + clientId=AUTH0_CLIENT_ID, + domain=AUTH0_DOMAIN, + ) + + if not auth_info: + st.warning("Please log in to access the application.") + st.info("This is a demo application. You can use a dummy email and password to log in if you don't have an account.") + st.stop() + + st.sidebar.success(f"Welcome, {auth_info['name']}!") + + with st.sidebar: + sac.logout_button() + + run_app() + if __name__ == "__main__": main() \ No newline at end of file diff --git a/auth_config.py b/auth_config.py new file mode 100644 index 0000000..c16776d --- /dev/null +++ b/auth_config.py @@ -0,0 +1,8 @@ +import os +from dotenv import load_dotenv + +load_dotenv() + +AUTH0_DOMAIN = os.getenv("AUTH0_DOMAIN") +AUTH0_CLIENT_ID = os.getenv("AUTH0_CLIENT_ID") +AUTH0_CLIENT_SECRET = os.getenv("AUTH0_CLIENT_SECRET") diff --git a/database.py b/database.py new file mode 100644 index 0000000..e6eade0 --- /dev/null +++ b/database.py @@ -0,0 +1,150 @@ +import sqlite3 +import json +import logging + +DATABASE_NAME = "video_analysis.db" + +def get_db_connection(): + """Establishes a connection to the database.""" + conn = sqlite3.connect(DATABASE_NAME, check_same_thread=False) + conn.row_factory = sqlite3.Row + return conn + +def init_db(): + """Initializes the database and creates tables if they don't exist.""" + conn = get_db_connection() + cursor = conn.cursor() + + # User table to store user info from Auth0 + cursor.execute(''' + CREATE TABLE IF NOT EXISTS users ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + auth0_user_id TEXT UNIQUE NOT NULL, + email TEXT, + name TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + ''') + + # Videos table to store video metadata + cursor.execute(''' + CREATE TABLE IF NOT EXISTS videos ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + user_id INTEGER NOT NULL, + twelvelabs_video_id TEXT UNIQUE, + filename TEXT, + status TEXT NOT NULL, -- e.g., 'processing', 'ready', 'failed' + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + FOREIGN KEY (user_id) REFERENCES users (id) + ) + ''') + + # Analyses table to store results like summaries, chapters, etc. + cursor.execute(''' + CREATE TABLE IF NOT EXISTS analyses ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + video_id INTEGER NOT NULL, + analysis_type TEXT NOT NULL, -- e.g., 'summary', 'chapters', 'highlights' + result TEXT NOT NULL, -- Storing the result as a JSON string + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + FOREIGN KEY (video_id) REFERENCES videos (id), + UNIQUE(video_id, analysis_type) + ) + ''') + + conn.commit() + conn.close() + logging.info("Database initialized successfully.") + +def get_or_create_user(user_info): + """ + Finds a user by their Auth0 user_id or creates a new one. + Returns the internal user ID. + """ + auth0_user_id = user_info.get('sub') + email = user_info.get('email') + name = user_info.get('name') + + conn = get_db_connection() + cursor = conn.cursor() + + cursor.execute("SELECT id FROM users WHERE auth0_user_id = ?", (auth0_user_id,)) + user = cursor.fetchone() + + if user: + user_id = user['id'] + else: + cursor.execute( + "INSERT INTO users (auth0_user_id, email, name) VALUES (?, ?, ?)", + (auth0_user_id, email, name) + ) + conn.commit() + user_id = cursor.lastrowid + + conn.close() + return user_id + +def add_video(user_id, filename, twelvelabs_video_id, status='processing'): + """Adds a new video record to the database.""" + conn = get_db_connection() + cursor = conn.cursor() + cursor.execute( + "INSERT INTO videos (user_id, filename, twelvelabs_video_id, status) VALUES (?, ?, ?, ?)", + (user_id, filename, twelvelabs_video_id, status) + ) + conn.commit() + video_id = cursor.lastrowid + conn.close() + return video_id + +def update_video_status(twelvelabs_video_id, status): + """Updates the processing status of a video.""" + conn = get_db_connection() + cursor = conn.cursor() + cursor.execute( + "UPDATE videos SET status = ? WHERE twelvelabs_video_id = ?", + (status, twelvelabs_video_id) + ) + conn.commit() + conn.close() + +def get_user_videos(user_id): + """Retrieves all videos for a given user.""" + conn = get_db_connection() + cursor = conn.cursor() + cursor.execute("SELECT * FROM videos WHERE user_id = ? ORDER BY created_at DESC", (user_id,)) + videos = cursor.fetchall() + conn.close() + return videos + +def get_video_by_id(video_id): + """Retrieves a single video by its internal ID.""" + conn = get_db_connection() + cursor = conn.cursor() + cursor.execute("SELECT * FROM videos WHERE id = ?", (video_id,)) + video = cursor.fetchone() + conn.close() + return video + +def save_analysis(video_id, analysis_type, result_data): + """Saves the result of an analysis to the database.""" + conn = get_db_connection() + cursor = conn.cursor() + result_json = json.dumps(result_data) + cursor.execute( + "INSERT OR REPLACE INTO analyses (video_id, analysis_type, result) VALUES (?, ?, ?)", + (video_id, analysis_type, result_json) + ) + conn.commit() + conn.close() + +def get_analysis(video_id, analysis_type): + """Retrieves a previously saved analysis result.""" + conn = get_db_connection() + cursor = conn.cursor() + cursor.execute("SELECT result FROM analyses WHERE video_id = ? AND analysis_type = ?", (video_id, analysis_type)) + analysis = cursor.fetchone() + conn.close() + if analysis: + return json.loads(analysis['result']) + return None diff --git a/requirements.txt b/requirements.txt index f006934..e289255 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,3 +5,5 @@ requests python-dotenv m3u8 yt_dlp +streamlit-auth0-component +python-jose diff --git a/style.css b/style.css new file mode 100644 index 0000000..b07a9ad --- /dev/null +++ b/style.css @@ -0,0 +1,310 @@ +/* Dark Blue Theme */ +[data-testid="stAppViewContainer"] { + background: linear-gradient(135deg, #0f172a 0%, #1e293b 50%, #334155 100%); + min-height: 100vh; + color: white; +} + +[data-testid="stHeader"] { + background-color: rgba(15, 23, 42, 0.95); + backdrop-filter: blur(10px); +} + +[data-testid="stToolbar"] { + right: 2rem; + background: rgba(15, 23, 42, 0.8); + border-radius: 8px; +} + +[data-testid="stSidebar"] { + background-color: #1e293b; +} + +/* Typography */ +h1, h2, h3, h4, h5, h6 { + font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif !important; + font-weight: 600 !important; + color: #f8fafc !important; + letter-spacing: -0.02em !important; +} + +h1 { + background: linear-gradient(135deg, #60a5fa, #3b82f6); + -webkit-background-clip: text; + -webkit-text-fill-color: transparent; + background-clip: text; +} + +p, div, span, label { + color: #e2e8f0 !important; +} + +/* Buttons */ +.stButton > button { + background: linear-gradient(145deg, #3b82f6, #1d4ed8); + color: white !important; + border: 1px solid #2563eb; + border-radius: 12px; + font-weight: 600; + padding: 0.75rem 1.5rem; + transition: all 0.3s ease; + box-shadow: 0 4px 12px rgba(59, 130, 246, 0.3); + text-transform: uppercase; + letter-spacing: 0.5px; +} + +.stButton > button:hover { + background: linear-gradient(145deg, #1d4ed8, #1e40af); + box-shadow: 0 6px 20px rgba(59, 130, 246, 0.4); + transform: translateY(-2px); + border-color: #3b82f6; +} + +.stDownloadButton > button { + background: linear-gradient(145deg, #059669, #047857) !important; + color: white !important; + border: 1px solid #10b981; + border-radius: 12px; + font-weight: 600; + padding: 0.75rem 1.5rem; + transition: all 0.3s ease; + box-shadow: 0 4px 12px rgba(5, 150, 105, 0.3); +} + +.stDownloadButton > button:hover { + background: linear-gradient(145deg, #047857, #065f46) !important; + box-shadow: 0 6px 20px rgba(5, 150, 105, 0.4); + transform: translateY(-2px); +} + +/* Alert Messages */ +.stSuccess { + background: linear-gradient(135deg, #064e3b, #047857); + border: 1px solid #10b981; + border-radius: 12px; + color: #6ee7b7 !important; + box-shadow: 0 4px 12px rgba(16, 185, 129, 0.2); +} + +.stError { + background: linear-gradient(135deg, #7f1d1d, #991b1b); + border: 1px solid #ef4444; + border-radius: 12px; + color: #fca5a5 !important; + box-shadow: 0 4px 12px rgba(239, 68, 68, 0.2); +} + +.stInfo { + background: linear-gradient(135deg, #1e3a8a, #1d4ed8); + border: 1px solid #3b82f6; + border-radius: 12px; + color: #93c5fd !important; + box-shadow: 0 4px 12px rgba(59, 130, 246, 0.2); +} + +.stWarning { + background: linear-gradient(135deg, #92400e, #b45309); + border: 1px solid #f59e0b; + border-radius: 12px; + color: #fbbf24 !important; + box-shadow: 0 4px 12px rgba(245, 158, 11, 0.2); +} + +/* Tabs */ +.stTabs [data-baseweb="tab-list"] { + background-color: #1e293b; + border-radius: 12px; + padding: 0.5rem; + box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3); +} + +.stTabs [data-baseweb="tab-list"] button { + background-color: transparent; + border-radius: 8px; + color: #94a3b8 !important; + font-weight: 500; + padding: 0.75rem 1.5rem; + margin: 0 0.25rem; + transition: all 0.3s ease; +} + +.stTabs [data-baseweb="tab-list"] button[aria-selected="true"] { + background: linear-gradient(145deg, #3b82f6, #1d4ed8); + color: white !important; + box-shadow: 0 4px 12px rgba(59, 130, 246, 0.3); + font-weight: 600; +} + +.stTabs [data-baseweb="tab-list"] button:hover { + background-color: #334155; + color: #e2e8f0 !important; +} + +/* Expandable sections */ +.streamlit-expanderHeader { + background: linear-gradient(135deg, #1e293b, #334155); + border-radius: 12px; + border: 1px solid #475569; + color: #f8fafc !important; + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.2); +} + +/* Input fields */ +.stTextInput > div > div > input { + border-radius: 12px; + border: 1px solid #475569; + background-color: #1e293b !important; + color: #f8fafc !important; + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.2); + padding: 0.75rem 1rem; +} + +.stTextInput > div > div > input:focus { + border-color: #3b82f6; + box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.2); +} + +.stTextArea > div > div > textarea { + border-radius: 12px; + border: 1px solid #475569; + background-color: #1e293b !important; + color: #f8fafc !important; + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.2); + padding: 0.75rem 1rem; +} + +.stTextArea > div > div > textarea:focus { + border-color: #3b82f6; + box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.2); +} + +.stSelectbox > div > div > select { + border-radius: 12px; + border: 1px solid #475569; + background-color: #1e293b !important; + color: #f8fafc !important; + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.2); + padding: 0.75rem 1rem; +} + +/* Progress bar */ +.stProgress > div > div > div > div { + background: linear-gradient(90deg, #3b82f6, #1d4ed8); + border-radius: 4px; + box-shadow: 0 2px 8px rgba(59, 130, 246, 0.3); +} + +/* Video container */ +[data-testid="stVideo"] { + border-radius: 16px; + overflow: hidden; + box-shadow: 0 8px 24px rgba(0, 0, 0, 0.4); + border: 1px solid #475569; +} + +/* Code blocks */ +.stCodeBlock { + border-radius: 12px; + border: 1px solid #475569; + background-color: #0f172a !important; + box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3); +} + +/* Main container */ +.main .block-container { + padding-top: 2rem; + padding-bottom: 2rem; + max-width: 1200px; +} + +/* Card containers */ +.stContainer { + background: linear-gradient(135deg, #1e293b, #334155); + border-radius: 16px; + padding: 2rem; + box-shadow: 0 8px 24px rgba(0, 0, 0, 0.3); + margin-bottom: 1.5rem; + border: 1px solid #475569; +} + +/* Notifications */ +.notification { + position: fixed; + top: 20px; + right: 20px; + background: linear-gradient(145deg, #059669, #047857); + color: white; + padding: 16px 24px; + border-radius: 12px; + z-index: 1000; + animation: slideIn 0.3s ease-in-out; + box-shadow: 0 8px 24px rgba(5, 150, 105, 0.3); + font-weight: 600; + border: 1px solid #10b981; +} + +@keyframes slideIn { + from { transform: translateX(100%); opacity: 0; } + to { transform: translateX(0); opacity: 1; } +} + +/* Keyboard shortcuts */ +.shortcut-hint { + font-size: 0.7em; + color: #94a3b8; + margin-left: 8px; + background-color: #0f172a; + padding: 4px 8px; + border-radius: 6px; + font-family: 'Courier New', monospace; + border: 1px solid #475569; + font-weight: normal; +} + +/* File uploader */ +[data-testid="stFileUploader"] { + background: linear-gradient(135deg, #1e293b, #334155); + border: 2px dashed #475569; + border-radius: 16px; + padding: 2rem; + text-align: center; + transition: all 0.3s ease; +} + +[data-testid="stFileUploader"]:hover { + border-color: #3b82f6; + background: linear-gradient(135deg, #334155, #475569); +} + +/* Sidebar styling */ +.css-1d391kg { + background-color: #1e293b; +} + +/* Metrics */ +[data-testid="metric-container"] { + background: linear-gradient(135deg, #1e293b, #334155); + border: 1px solid #475569; + padding: 1rem; + border-radius: 12px; + box-shadow: 0 4px 12px rgba(0, 0, 0, 0.2); +} + +/* Custom icon classes */ +.icon { + margin-right: 8px; + color: #3b82f6; +} + +.icon-upload::before { content: "\f093"; } +.icon-video::before { content: "\f03d"; } +.icon-search::before { content: "\f002"; } +.icon-download::before { content: "\f019"; } +.icon-play::before { content: "\f04b"; } +.icon-question::before { content: "\f059"; } +.icon-export::before { content: "\f56e"; } +.icon-batch::before { content: "\f0cb"; } +.icon-error::before { content: "\f071"; } +.icon-success::before { content: "\f00c"; } +.icon-info::before { content: "\f05a"; } +.icon-keyboard::before { content: "\f11c"; }