integrate webcam support with real-time detection

.gitignore

@@ -7,3 +7,4 @@ postgres_data
 media/*
 !media/.gitkeep
 media-analyzer-src.tar.gz
+media-analyzer/media

GEMINI.md

@@ -0,0 +1,59 @@
+# Media Analyzer
+
+## Project Overview
+
+This project is a real-time video streaming and AI analysis platform. It ingests RTMP video streams, processes them with computer vision AI models, and provides live analysis results through a responsive web dashboard.
+
+The architecture is based on microservices and includes:
+
+*   **Backend**: A Django application that handles video stream management, AI processing, and WebSocket communication for real-time updates.
+*   **Frontend**: An Angular single-page application that provides a user interface for stream viewing and analysis visualization.
+*   **AI Processing**: A Python-based analysis engine that uses various adapters for different types of analysis, such as object detection, logo detection, and motion analysis. The engine can be configured to run locally, on a remote LAN worker, or in the cloud.
+*   **Streaming**: An NGINX server with the RTMP module ingests video streams and converts them to HLS for web playback.
+*   **Infrastructure**: The entire platform is containerized using Docker and can be deployed with Docker Compose for development or Kubernetes for production.
+
+## Building and Running
+
+### Docker Compose (Development)
+
+To run the application in a development environment, use Docker Compose:
+
+```bash
+# Start all services
+docker compose up
+
+# Run migrations (in a separate terminal)
+docker compose --profile tools up migrate
+```
+
+The application will be accessible at the following URLs:
+
+*   **Frontend**: `http://localhost:4200`
+*   **Backend API**: `http://localhost:8000`
+*   **RTMP Stream**: `rtmp://localhost:1935/live`
+*   **HLS Stream**: `http://localhost:8081/hls`
+
+### Kubernetes (Production-like)
+
+To deploy the application to a Kubernetes cluster, you can use the provided scripts:
+
+```bash
+# Build and push images to a local registry
+./k8s/build-for-ctlptl.sh
+
+# Deploy to Kubernetes
+kubectl apply -k k8s/overlays/development
+
+# Check deployment status
+kubectl get pods -n media-analyzer
+
+# Access the application via port forwarding
+kubectl port-forward service/frontend -n media-analyzer 4200:80
+```
+
+## Development Conventions
+
+*   **Backend**: The backend is a Django application. Follow Django best practices for development.
+*   **Frontend**: The frontend is an Angular application. Follow Angular best practices for development.
+*   **AI Processing**: The AI processing engine is designed to be extensible. To add a new analysis capability, create a new adapter and integrate it with the `AnalysisEngine`.
+*   **Testing**: The project includes a `test_unified_ai.py` file, which suggests that there is a testing framework in place. Run existing tests and add new ones when making changes.

def/improvements/angular_compose

@@ -0,0 +1,88 @@
+You have a few options to speed up frontend development while keeping the backend services
+  running:
+
+  Option 1: Hybrid Development (Recommended)
+
+  Run frontend locally, backend in Docker:
+
+  # Terminal 1: Start backend services only
+  docker compose up postgres redis backend celery-logo celery-default file-watcher nginx-rtmp
+
+  # Terminal 2: Run Angular dev server locally
+  cd frontend && ng serve --host 0.0.0.0 --port 4200
+
+  Pros: Hot reload, fast rebuilds, best developer experience
+  Cons: Need to manage CORS and proxy config
+
+  Option 2: Docker Bind Mount + Manual Rebuild
+
+  Keep frontend container but rebuild manually:
+
+  # Build frontend assets locally
+  cd frontend && ng build --watch
+
+  # Or for production build
+  cd frontend && ng build --configuration production
+
+  # Assets go to dist/ and can be volume mounted to nginx
+
+  Option 3: Frontend Container with Volume Mount
+
+  Modify docker-compose.yml for dev:
+
+  frontend:
+    build:
+      context: ./frontend
+      target: development
+    volumes:
+      - ./frontend/src:/app/src  # Mount source
+      - ./frontend/dist:/usr/share/nginx/html  # Mount build output
+    command: sh -c "ng build --watch & nginx -g 'daemon off;'"
+
+  Option 4: Live Reload in Container
+
+  Add this to your frontend Dockerfile development stage:
+
+  FROM node:18 AS development
+  WORKDIR /app
+  COPY package*.json ./
+  RUN npm ci
+  COPY . .
+  # Start both ng build --watch and nginx
+  CMD ["sh", "-c", "ng build --watch --output-path /usr/share/nginx/html & nginx -g 'daemon off;'"]
+
+  My Recommendation: Option 1
+
+  For your use case, I'd run:
+
+  # Keep backend running
+  docker compose up -d postgres redis backend celery-logo celery-default file-watcher nginx-rtmp
+
+  # Run frontend locally with proxy
+  cd frontend && ng serve --proxy-config proxy.conf.json
+
+  Create frontend/proxy.conf.json:
+  {
+    "/api/*": {
+      "target": "http://localhost:8000",
+      "secure": false,
+      "logLevel": "debug"
+    },
+    "/streaming/*": {
+      "target": "http://localhost:8000",
+      "secure": false
+    },
+    "/ws/*": {
+      "target": "http://localhost:8000",
+      "secure": false,
+      "ws": true
+    }
+  }
+
+  This gives you:
+  - ✅ Instant hot reload
+  - ✅ Fast TypeScript compilation
+  - ✅ Backend services running
+  - ✅ No container rebuilds
+
+  Would you like me to set up the proxy config for the hybrid approach?

media-analyzer/backend/ai_processing/analysis_engine.py

@@ -15,6 +15,8 @@ logger = logging.getLogger(__name__)
 class AnalysisEngine:
     """Main analysis engine that orchestrates capability-specific adapters with execution strategies"""
     
+    _strategy_logged = False
+    
     def __init__(self):
         self.object_detector = None
         self.logo_detector = None
@@ -66,7 +68,9 @@ class AnalysisEngine:
                 logger.warning(f"Unknown strategy type {strategy_type}, falling back to local")
                 self.execution_strategy = strategy_configs['local']()
                 
+            if not AnalysisEngine._strategy_logged:
                 logger.info(f"Configured execution strategy: {strategy_type}")
+                AnalysisEngine._strategy_logged = True
             
         except Exception as e:
             logger.error(f"Failed to configure execution strategy: {e}")
@@ -76,8 +80,17 @@ class AnalysisEngine:
     def extract_frame_from_segment(self, segment_path, timestamp=None):
         """Extract frame from video segment"""
         try:
+            import os
+            logger.debug(f"Attempting to extract frame from: {segment_path}")
+            
+            if not os.path.exists(segment_path):
+                logger.error(f"Segment file does not exist: {segment_path}")
+                return None
+            
             cap = cv2.VideoCapture(segment_path)
+            
             if not cap.isOpened():
+                logger.error(f"OpenCV failed to open: {segment_path}")
                 return None
                 
             # For TS segments, seeking is problematic, just read first frame
@@ -88,6 +101,8 @@ class AnalysisEngine:
             if ret:
                 frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                 return Image.fromarray(frame_rgb)
+            else:
+                logger.error(f"Failed to read frame from {segment_path}")
             return None
                 
         except Exception as e:

media-analyzer/backend/ai_processing/migrations/0003_rename_stream_id_to_stream_key.py

@@ -0,0 +1,23 @@
+# Generated migration for stream_id to stream_key rename
+
+from django.db import migrations
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('ai_processing', '0002_make_provider_nullable'),
+    ]
+
+    operations = [
+        migrations.RenameField(
+            model_name='videoanalysis',
+            old_name='stream_id',
+            new_name='stream_key',
+        ),
+        migrations.RenameField(
+            model_name='processingqueue',
+            old_name='stream_id',
+            new_name='stream_key',
+        ),
+    ]

media-analyzer/backend/ai_processing/migrations/0004_remove_processingqueue_ai_processi_stream__60b035_idx_and_more.py

@@ -0,0 +1,35 @@
+# Generated by Django 5.0.6 on 2025-08-22 00:19
+
+import ai_processing.models
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('ai_processing', '0003_rename_stream_id_to_stream_key'),
+    ]
+
+    operations = [
+        migrations.RemoveIndex(
+            model_name='processingqueue',
+            name='ai_processi_stream__60b035_idx',
+        ),
+        migrations.RemoveIndex(
+            model_name='videoanalysis',
+            name='ai_processi_stream__b961da_idx',
+        ),
+        migrations.AlterField(
+            model_name='videoanalysis',
+            name='confidence_threshold',
+            field=models.FloatField(default=ai_processing.models.get_default_confidence_threshold),
+        ),
+        migrations.AddIndex(
+            model_name='processingqueue',
+            index=models.Index(fields=['stream_key'], name='ai_processi_stream__aecb17_idx'),
+        ),
+        migrations.AddIndex(
+            model_name='videoanalysis',
+            index=models.Index(fields=['stream_key', 'timestamp'], name='ai_processi_stream__d99710_idx'),
+        ),
+    ]

media-analyzer/backend/ai_processing/models.py

@@ -2,6 +2,15 @@ from django.db import models
 import uuid
 
 
+def get_default_confidence_threshold():
+    """Get default confidence threshold from settings"""
+    try:
+        from django.conf import settings
+        return settings.LOGO_DETECTION_CONFIG['confidence_threshold']
+    except:
+        return 0.6  # Fallback if settings not available
+
+
 class AnalysisProvider(models.Model):
     id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)
     name = models.CharField(max_length=100, unique=True)
@@ -28,20 +37,20 @@ class Brand(models.Model):
 
 class VideoAnalysis(models.Model):
     id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)
-    stream_id = models.CharField(max_length=100)
+    stream_key = models.CharField(max_length=100)  # Use stream_key instead of stream_id
     segment_path = models.CharField(max_length=500)
     timestamp = models.DateTimeField(auto_now_add=True)
     processing_time = models.FloatField(null=True)
     provider = models.ForeignKey(AnalysisProvider, on_delete=models.CASCADE, null=True, blank=True)
     analysis_type = models.CharField(max_length=50)
-    confidence_threshold = models.FloatField(default=0.5)
+    confidence_threshold = models.FloatField(default=get_default_confidence_threshold)
     frame_timestamp = models.FloatField()
     external_request_id = models.CharField(max_length=200, null=True)
     
     def to_dict(self):
         return {
             'id': str(self.id),
-            'stream_id': self.stream_id,
+            'stream_key': self.stream_key,
             'timestamp': self.timestamp.isoformat(),
             'processing_time': self.processing_time,
             'analysis_type': self.analysis_type,
@@ -53,7 +62,7 @@ class VideoAnalysis(models.Model):
     
     class Meta:
         indexes = [
-            models.Index(fields=['stream_id', 'timestamp']),
+            models.Index(fields=['stream_key', 'timestamp']),
             models.Index(fields=['analysis_type']),
         ]
 
@@ -128,7 +137,7 @@ class Brand(models.Model):
 
 class ProcessingQueue(models.Model):
     id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)
-    stream_id = models.CharField(max_length=100)
+    stream_key = models.CharField(max_length=100)  # Use stream_key instead of stream_id
     segment_path = models.CharField(max_length=500)
     priority = models.IntegerField(default=0)
     status = models.CharField(max_length=20, choices=[
@@ -146,5 +155,5 @@ class ProcessingQueue(models.Model):
     class Meta:
         indexes = [
             models.Index(fields=['status', 'priority']),
-            models.Index(fields=['stream_id']),
+            models.Index(fields=['stream_key']),
         ]

media-analyzer/backend/ai_processing/processors/video_analyzer.py

@@ -50,12 +50,12 @@ class VideoAnalyzer:
         except Exception as e:
             logger.error(f"Error setting up providers: {e}")
     
-    def queue_segment_analysis(self, stream_id, segment_path):
+    def queue_segment_analysis(self, stream_key, segment_path):
         """Queue video segment for analysis"""
         try:
             # Check if already queued
             existing = ProcessingQueue.objects.filter(
-                stream_id=stream_id,
+                stream_key=stream_key,
                 segment_path=segment_path,
                 status__in=['pending', 'processing']
             ).exists()
@@ -66,14 +66,14 @@ class VideoAnalyzer:
             
             # Create queue item
             queue_item = ProcessingQueue.objects.create(
-                stream_id=stream_id,
+                stream_key=stream_key,
                 segment_path=segment_path,
                 analysis_types=['logo_detection'],
                 priority=1
             )
             
             # Trigger async processing
-            process_video_segment.delay(stream_id, segment_path)
+            process_video_segment.delay(stream_key, segment_path)
             
             logger.info(f"Queued segment for analysis: {segment_path}")
             return True

media-analyzer/backend/ai_processing/tasks.py

@@ -11,13 +11,13 @@ channel_layer = get_channel_layer()
 
 
 @shared_task(bind=True, queue='logo_detection')
-def analyze_logo_detection(self, stream_id, segment_path):
+def analyze_logo_detection(self, stream_key, segment_path):
     """Dedicated task for logo detection analysis"""
     queue_item = None
     try:
         # Update queue status
         queue_item = ProcessingQueue.objects.filter(
-            stream_id=stream_id, 
+            stream_key=stream_key, 
             segment_path=segment_path,
             status='pending'
         ).first()
@@ -50,20 +50,22 @@ def analyze_logo_detection(self, stream_id, segment_path):
                 queue_item.save()
             return {"error": "Failed to extract frame"}
         
-        # Analyze for logos only
+        # Analyze for logos only - use configured threshold
-        analysis_results = engine.analyze_frame(frame, ['logo_detection'], confidence_threshold=0.3)
+        from django.conf import settings
+        confidence = settings.LOGO_DETECTION_CONFIG['confidence_threshold']
+        analysis_results = engine.analyze_frame(frame, ['logo_detection'], confidence_threshold=confidence)
         
         # Store results
         provider_info = config_manager.get_provider_by_type(logo_config['provider_type'])
         provider = AnalysisProvider.objects.get(id=provider_info['id'])
         
         analysis = VideoAnalysis.objects.create(
-            stream_id=stream_id,
+            stream_key=stream_key,
             segment_path=segment_path,
             provider=provider,
             analysis_type='logo_detection',
             frame_timestamp=0.0,
-            confidence_threshold=0.3
+            confidence_threshold=confidence
         )
         
         detections = []
@@ -83,8 +85,10 @@ def analyze_logo_detection(self, stream_id, segment_path):
         
         # Send results via WebSocket if detections found
         if detections:
+            websocket_group = f"stream_{stream_key}"
+            logger.info(f"Sending websocket update to group: {websocket_group}")
             async_to_sync(channel_layer.group_send)(
-                f"stream_{stream_id}",
+                websocket_group,
                 {
                     "type": "analysis_update",
                     "analysis": analysis.to_dict()
@@ -96,11 +100,12 @@ def analyze_logo_detection(self, stream_id, segment_path):
             queue_item.status = 'completed'
             queue_item.save()
         
-        if detections:
+        result = {
-            logger.info(f"Logo detection: {len(detections)} detections found")
+            "detections": len(detections), 
-        else:
+            "analysis_id": str(analysis.id),
-            logger.debug(f"Logo detection: no detections found")
+            "brands": [d['label'] for d in detections] if detections else []
-        return {"detections": len(detections), "analysis_id": str(analysis.id)}
+        }
+        return result
         
     except Exception as e:
         logger.error(f"Logo detection failed for {segment_path}: {e}")
@@ -112,13 +117,13 @@ def analyze_logo_detection(self, stream_id, segment_path):
 
 
 @shared_task(bind=True, queue='visual_analysis') 
-def analyze_visual_properties(self, stream_id, segment_path):
+def analyze_visual_properties(self, stream_key, segment_path):
     """Dedicated task for visual property analysis"""
     queue_item = None
     try:
         # Update queue status
         queue_item = ProcessingQueue.objects.filter(
-            stream_id=stream_id, 
+            stream_key=stream_key, 
             segment_path=segment_path,
             status='pending'
         ).first()
@@ -145,7 +150,7 @@ def analyze_visual_properties(self, stream_id, segment_path):
         
         # Store results (no provider needed for local visual analysis)
         analysis = VideoAnalysis.objects.create(
-            stream_id=stream_id,
+            stream_key=stream_key,
             segment_path=segment_path,
             provider=None,  # Local analysis
             analysis_type='visual_analysis',
@@ -165,7 +170,7 @@ def analyze_visual_properties(self, stream_id, segment_path):
         
         # Send results via WebSocket
         async_to_sync(channel_layer.group_send)(
-            f"stream_{stream_id}",
+            f"stream_{stream_key}",
             {
                 "type": "analysis_update",
                 "analysis": analysis.to_dict()
@@ -190,17 +195,17 @@ def analyze_visual_properties(self, stream_id, segment_path):
 
 
 @shared_task(bind=True)
-def process_video_segment(self, stream_id, segment_path):
+def process_video_segment(self, stream_key, segment_path):
     """Main task that dispatches to specialized analysis tasks"""
     try:
         # Dispatch to specialized queues based on available capabilities
         active_capabilities = config_manager.get_active_capabilities()
         
         if 'logo_detection' in active_capabilities:
-            analyze_logo_detection.delay(stream_id, segment_path)
+            analyze_logo_detection.delay(stream_key, segment_path)
         
         # Visual analysis disabled for performance - only logo detection
-        # analyze_visual_properties.delay(stream_id, segment_path)
+        # analyze_visual_properties.delay(stream_key, segment_path)
         
         return {"dispatched": True, "capabilities": active_capabilities}
         
@@ -222,7 +227,7 @@ def reload_analysis_config():
 
 
 @shared_task
-def analyze_frame_task(stream_id, segment_path, frame_timestamp=0.0):
+def analyze_frame_task(stream_key, segment_path, frame_timestamp=0.0):
     """Analyze a single frame from video segment"""
     try:
         engine = AnalysisEngine()
@@ -251,7 +256,7 @@ def analyze_frame_task(stream_id, segment_path, frame_timestamp=0.0):
         results = engine.analyze_frame(frame, ['logo_detection', 'visual_analysis'])
         
         return {
-            "stream_id": stream_id,
+            "stream_key": stream_key,
             "results": results,
             "frame_timestamp": frame_timestamp
         }

media-analyzer/backend/media_analyzer/asgi.py

@@ -26,7 +26,7 @@ application = ProtocolTypeRouter({
     "http": django_asgi_app,
     "websocket": AuthMiddlewareStack(
         URLRouter([
-            path("ws/stream/<str:stream_id>/", StreamAnalysisConsumer.as_asgi()),
+            path("ws/stream/", StreamAnalysisConsumer.as_asgi()),
         ])
     ),
 })

media-analyzer/backend/media_analyzer/settings/base.py

@@ -244,7 +244,7 @@ else:
 
 # Logo Detection Configuration
 LOGO_DETECTION_CONFIG = {
-    'confidence_threshold': float(os.getenv('LOGO_CONFIDENCE_THRESHOLD', '0.3')),
+    'confidence_threshold': float(os.getenv('LOGO_CONFIDENCE_THRESHOLD', '0.6')),
     'enabled_brands': os.getenv('ENABLED_BRANDS', 'Apple,Google,Nike,Coca-Cola,McDonald,Amazon').split(','),
     'use_cloud_vision': USE_CLOUD_VISION,
 }

media-analyzer/backend/media_analyzer/urls.py

@@ -21,7 +21,8 @@ from django.conf.urls.static import static
 
 urlpatterns = [
     path('admin/', admin.site.urls),
-    path('api/', include('streaming.urls')),
+    path('api/streaming/', include('streaming.urls')),
+    path('api/', include('api.urls')),
     path('streaming/', include('streaming.urls')),
 ]
 

media-analyzer/backend/streaming/consumers.py

@@ -11,28 +11,16 @@ class StreamAnalysisConsumer(AsyncWebsocketConsumer):
     """WebSocket consumer for real-time analysis updates"""
     
     async def connect(self):
-        self.stream_id = self.scope['url_route']['kwargs']['stream_id']
+        # Initialize subscription set for dynamic stream groups
-        self.room_group_name = f'stream_{self.stream_id}'
+        self.subscribed_streams = set()
-        
-        # Join stream group
-        await self.channel_layer.group_add(
-            self.room_group_name,
-            self.channel_name
-        )
-        
         await self.accept()
-        logger.info(f"WebSocket connected for stream {self.stream_id}")
+        logger.info("WebSocket connected - ready to subscribe to streams")
-        
-        # Send recent analysis results
-        await self.send_recent_analysis()
     
     async def disconnect(self, close_code):
-        # Leave stream group
+        # Leave all subscribed stream groups
-        await self.channel_layer.group_discard(
+        for stream_key in getattr(self, 'subscribed_streams', []):
-            self.room_group_name,
+            await self.channel_layer.group_discard(f"stream_{stream_key}", self.channel_name)
-            self.channel_name
+        logger.info("WebSocket disconnected")
-        )
-        logger.info(f"WebSocket disconnected for stream {self.stream_id}")
     
     async def receive(self, text_data):
         """Handle incoming WebSocket messages"""
@@ -45,10 +33,20 @@ class StreamAnalysisConsumer(AsyncWebsocketConsumer):
                     'type': 'pong',
                     'timestamp': data.get('timestamp')
                 }))
+            elif message_type == 'subscribe':
+                stream_key = data.get('stream_id')  # Frontend still sends 'stream_id' but it's actually stream_key
+                if stream_key and stream_key not in self.subscribed_streams:
+                    self.subscribed_streams.add(stream_key)
+                    await self.channel_layer.group_add(f"stream_{stream_key}", self.channel_name)
+                    await self.send_recent_analysis(stream_key)
+            elif message_type == 'unsubscribe':
+                stream_key = data.get('stream_id')  # Frontend still sends 'stream_id' but it's actually stream_key
+                if stream_key and stream_key in self.subscribed_streams:
+                    self.subscribed_streams.remove(stream_key)
+                    await self.channel_layer.group_discard(f"stream_{stream_key}", self.channel_name)
             elif message_type == 'request_analysis':
                 # Trigger analysis if needed
                 pass
-                
         except json.JSONDecodeError:
             logger.error("Invalid JSON received")
     
@@ -60,21 +58,20 @@ class StreamAnalysisConsumer(AsyncWebsocketConsumer):
         }))
     
     @database_sync_to_async
-    def get_recent_analysis(self):
+    def get_recent_analysis(self, stream_key):
-        """Get recent analysis results for stream"""
+        """Get recent analysis results for a given stream"""
         try:
             analyses = VideoAnalysis.objects.filter(
-                stream_id=self.stream_id
+                stream_key=stream_key
             ).order_by('-timestamp')[:5]
-            
             return [analysis.to_dict() for analysis in analyses]
         except Exception as e:
-            logger.error(f"Error getting recent analysis: {e}")
+            logger.error(f"Error getting recent analysis for {stream_key}: {e}")
             return []
     
-    async def send_recent_analysis(self):
+    async def send_recent_analysis(self, stream_key):
-        """Send recent analysis results to client"""
+        """Send recent analysis results to client for the given stream"""
-        recent_analyses = await self.get_recent_analysis()
+        recent_analyses = await self.get_recent_analysis(stream_key)
         if recent_analyses:
             await self.send(text_data=json.dumps({
                 'type': 'recent_analysis',

media-analyzer/backend/streaming/ffmpeg_handler.py

@@ -1,6 +1,7 @@
 import ffmpeg
 import logging
 import subprocess
+import platform
 from pathlib import Path
 
 logger = logging.getLogger(__name__)
@@ -32,5 +33,46 @@ class FFmpegHandler:
         )
         return ffmpeg.run_async(output, pipe_stdout=True, pipe_stderr=True)
     
+    def webcam_to_hls(self, device_index: int, output_path: str, width: int = 640, height: int = 480, fps: int = 30) -> subprocess.Popen:
+        """Convert webcam stream to HLS (cross-platform)"""
+        system = platform.system().lower()
+        
+        if system == 'windows':
+            # Windows: DirectShow
+            stream = ffmpeg.input(
+                f'video="Integrated Camera"',
+                f='dshow',
+                video_size=f'{width}x{height}',
+                framerate=fps
+            )
+        elif system == 'darwin':  # macOS
+            # macOS: AVFoundation
+            stream = ffmpeg.input(
+                f'{device_index}',
+                f='avfoundation',
+                video_size=f'{width}x{height}',
+                framerate=fps
+            )
+        else:  # Linux and others
+            # Linux: Video4Linux2
+            stream = ffmpeg.input(
+                f'/dev/video{device_index}',
+                f='v4l2',
+                s=f'{width}x{height}',
+                framerate=fps
+            )
+        
+        output = ffmpeg.output(
+            stream, output_path,
+            vcodec='libx264',
+            preset='ultrafast',  # Fast encoding for real-time
+            tune='zerolatency',  # Low latency
+            f='hls',
+            hls_time=2,          # Short segments for responsiveness
+            hls_list_size=10,
+            hls_flags='delete_segments'
+        )
+        return ffmpeg.run_async(output, pipe_stdout=True, pipe_stderr=True)
+
 # Singleton
 ffmpeg_handler = FFmpegHandler()

media-analyzer/backend/streaming/file_watcher.py

@@ -0,0 +1,84 @@
+import os
+import time
+import logging
+from pathlib import Path
+from django.conf import settings
+from ai_processing.processors.video_analyzer import VideoAnalyzer
+from .models import VideoStream, StreamStatus
+
+logger = logging.getLogger(__name__)
+
+class HLSFileWatcher:
+    """Watch for new HLS segment files and trigger analysis"""
+    
+    def __init__(self, media_dir=None, poll_interval=1.0):
+        self.media_dir = Path(media_dir or settings.MEDIA_ROOT)
+        self.poll_interval = poll_interval
+        self.processed_files = set()
+        self.analyzer = VideoAnalyzer()
+        
+    def get_stream_key_from_filename(self, filename):
+        """Extract stream_key from filename: 'stream_key-segment_number.ts' -> 'stream_key'"""
+        if not filename.endswith('.ts'):
+            return None
+            
+        base_name = filename.rsplit('.', 1)[0]  # Remove .ts extension
+        stream_key = base_name.rsplit('-', 1)[0]  # Remove last segment: "-123"
+        return stream_key if stream_key else None
+    
+    def process_new_segment(self, file_path):
+        """Process a new HLS segment file"""
+        try:
+            # Determine the active stream from the database
+            active_stream = VideoStream.objects.filter(status=StreamStatus.ACTIVE).first()
+            if not active_stream:
+                logger.warning(f"File watcher: No active stream found, skipping segment {file_path.name}")
+                return
+            stream_key = active_stream.stream_key
+            logger.info(f"File watcher: Processing new segment {file_path.name} (stream: {stream_key})")
+            # Queue for analysis
+            self.analyzer.queue_segment_analysis(stream_key, str(file_path))
+            logger.info(f"File watcher: Queued segment for analysis: {file_path.name}")
+        except Exception as e:
+            logger.error(f"File watcher: Error processing {file_path}: {e}")
+            import traceback
+            logger.error(f"File watcher: Traceback: {traceback.format_exc()}")
+    
+    def scan_for_new_files(self):
+        """Scan for new .ts files in the media directory"""
+        try:
+            if not self.media_dir.exists():
+                return
+                
+            current_files = set()
+            for ts_file in self.media_dir.glob("*.ts"):
+                if ts_file.is_file():
+                    current_files.add(ts_file)
+            
+            # Find new files
+            new_files = current_files - self.processed_files
+            
+            for new_file in new_files:
+                self.process_new_segment(new_file)
+                self.processed_files.add(new_file)
+                
+        except Exception as e:
+            logger.error(f"File watcher: Error scanning directory: {e}")
+    
+    def start_watching(self):
+        """Start the file watching loop"""
+        logger.info(f"File watcher: Starting to watch {self.media_dir}")
+        
+        # Initial scan to catch existing files
+        self.scan_for_new_files()
+        
+        while True:
+            try:
+                self.scan_for_new_files()
+                time.sleep(self.poll_interval)
+            except KeyboardInterrupt:
+                logger.info("File watcher: Stopped by user")
+                break
+            except Exception as e:
+                logger.error(f"File watcher: Unexpected error: {e}")
+                time.sleep(self.poll_interval)

media-analyzer/backend/streaming/management/commands/watch_hls_files.py

@@ -0,0 +1,30 @@
+from django.core.management.base import BaseCommand
+from streaming.file_watcher import HLSFileWatcher
+
+
+class Command(BaseCommand):
+    help = 'Watch for new HLS segment files and trigger analysis'
+
+    def add_arguments(self, parser):
+        parser.add_argument(
+            '--poll-interval',
+            type=float,
+            default=1.0,
+            help='Polling interval in seconds (default: 1.0)'
+        )
+
+    def handle(self, *args, **options):
+        poll_interval = options['poll_interval']
+        
+        self.stdout.write(
+            self.style.SUCCESS(f'Starting HLS file watcher (poll interval: {poll_interval}s)')
+        )
+        
+        watcher = HLSFileWatcher(poll_interval=poll_interval)
+        
+        try:
+            watcher.start_watching()
+        except KeyboardInterrupt:
+            self.stdout.write(
+                self.style.SUCCESS('HLS file watcher stopped')
+            )

media-analyzer/backend/streaming/source_adapters.py

@@ -4,6 +4,9 @@ from pathlib import Path
 from django.conf import settings
 from .models import VideoStream, StreamStatus
 from .ffmpeg_handler import ffmpeg_handler
+import threading
+import os
+import signal
 
 logger = logging.getLogger(__name__)
 
@@ -43,6 +46,13 @@ class RTMPSourceAdapter(VideoSourceAdapter):
         try:
             self.update_stream_status(StreamStatus.STARTING)
             
+            # Check if any other stream is active (only one stream allowed)
+            active_streams = VideoStream.objects.filter(status=StreamStatus.ACTIVE).exclude(id=self.stream.id)
+            if active_streams.exists():
+                logger.warning(f"Cannot start RTMP - another stream is active: {active_streams.first().name}")
+                self.update_stream_status(StreamStatus.ERROR)
+                return False
+            
             # Files go directly in media directory
             media_dir = Path(settings.MEDIA_ROOT)
             
@@ -53,9 +63,14 @@ class RTMPSourceAdapter(VideoSourceAdapter):
             
             # Start FFmpeg conversion
             self.process = ffmpeg_handler.rtmp_to_hls(rtmp_url, playlist_path)
-            
+            # Persist FFmpeg PID for stop operations
+            try:
+                pid_file = media_dir / f'{self.stream.stream_key}.pid'
+                with pid_file.open('w') as f:
+                    f.write(str(self.process.pid))
+            except Exception as e:
+                logger.error(f"RTMPSourceAdapter: Failed to write PID file: {e}")
             # HLS URL is now generated dynamically from settings
-            
             self.update_stream_status(StreamStatus.ACTIVE)
             logger.info(f"Started RTMP processing for stream {self.stream.id}")
             return True
@@ -68,15 +83,31 @@ class RTMPSourceAdapter(VideoSourceAdapter):
     def stop_processing(self) -> bool:
         try:
             self.update_stream_status(StreamStatus.STOPPING)
-            
+            media_dir = Path(settings.MEDIA_ROOT)
+            pid_file = media_dir / f'{self.stream.stream_key}.pid'
+            # Attempt to terminate in-memory process
             if self.process and self.process.poll() is None:
                 self.process.terminate()
+                try:
                     self.process.wait(timeout=10)
-                
+                except Exception:
+                    pass
+            # Fallback: terminate by PID file
+            elif pid_file.exists():
+                try:
+                    pid = int(pid_file.read_text())
+                    os.kill(pid, signal.SIGTERM)
+                except Exception as kill_err:
+                    logger.error(f"RTMPSourceAdapter: Failed to kill PID {pid}: {kill_err}")
+            # Cleanup PID file
+            if pid_file.exists():
+                try:
+                    pid_file.unlink()
+                except Exception as unlink_err:
+                    logger.error(f"RTMPSourceAdapter: Failed to remove PID file: {unlink_err}")
             self.update_stream_status(StreamStatus.INACTIVE)
             logger.info(f"Stopped RTMP processing for stream {self.stream.id}")
             return True
-            
         except Exception as e:
             logger.error(f"Failed to stop RTMP processing: {e}")
             self.update_stream_status(StreamStatus.ERROR)
@@ -125,6 +156,119 @@ class FileSourceAdapter(VideoSourceAdapter):
         return str(media_dir / f'{self.stream.stream_key}.m3u8')
 
 
+class WebcamSourceAdapter(VideoSourceAdapter):
+    """Adapter for webcam streams"""
+    
+    def start_processing(self) -> bool:
+        try:
+            logger.info(f"Starting webcam processing for stream {self.stream.id} with key {self.stream.stream_key}")
+            self.update_stream_status(StreamStatus.STARTING)
+            
+            # Check if any other stream is active (only one stream allowed)
+            active_streams = VideoStream.objects.filter(status=StreamStatus.ACTIVE).exclude(id=self.stream.id)
+            if active_streams.exists():
+                logger.warning(f"Cannot start webcam - another stream is active: {active_streams.first().name}")
+                self.update_stream_status(StreamStatus.ERROR)
+                return False
+            
+            # Files go directly in media directory
+            media_dir = Path(settings.MEDIA_ROOT)
+            playlist_path = str(media_dir / f'{self.stream.stream_key}.m3u8')
+            logger.info(f"Webcam playlist path: {playlist_path}")
+            
+            # Default to webcam 0
+            device_index = 0
+            
+            # Start FFmpeg conversion
+            logger.info(f"Starting FFmpeg webcam conversion with device {device_index}")
+            self.process = ffmpeg_handler.webcam_to_hls(device_index, playlist_path)
+            
+            # Check if FFmpeg process started successfully
+            if self.process.poll() is not None:
+                # Process already exited - get error details
+                try:
+                    stdout, stderr = self.process.communicate(timeout=2)
+                    error_msg = stderr.decode('utf-8') if stderr else "Unknown FFmpeg error"
+                    logger.error(f"FFmpeg failed to start webcam: {error_msg}")
+                except Exception as comm_error:
+                    logger.error(f"FFmpeg failed and couldn't read error: {comm_error}")
+                    error_msg = "FFmpeg process failed to start"
+                
+                self.update_stream_status(StreamStatus.ERROR)
+                raise Exception(f"Webcam initialization failed: {error_msg}")
+            
+            logger.info(f"FFmpeg process started successfully with PID: {self.process.pid}")
+            # Persist FFmpeg PID for stop operations
+            try:
+                pid_file = media_dir / f'{self.stream.stream_key}.pid'
+                with pid_file.open('w') as f:
+                    f.write(str(self.process.pid))
+            except Exception as e:
+                logger.error(f"WebcamSourceAdapter: Failed to write PID file: {e}")
+            self.update_stream_status(StreamStatus.ACTIVE)
+            logger.info(f"Started webcam processing for stream {self.stream.id}")
+            # Monitor FFmpeg process and handle unexpected termination
+            threading.Thread(target=self._monitor_webcam, daemon=True).start()
+            return True
+            
+        except Exception as e:
+            logger.error(f"Failed to start webcam processing: {e}")
+            logger.exception(f"Full exception details:")
+            self.update_stream_status(StreamStatus.ERROR)
+            return False
+    
+    def stop_processing(self) -> bool:
+        try:
+            self.update_stream_status(StreamStatus.STOPPING)
+            media_dir = Path(settings.MEDIA_ROOT)
+            pid_file = media_dir / f'{self.stream.stream_key}.pid'
+            # Attempt to terminate in-memory process
+            if self.process and self.process.poll() is None:
+                self.process.terminate()
+                try:
+                    self.process.wait(timeout=10)
+                except Exception:
+                    pass
+            # Fallback: terminate by PID file
+            elif pid_file.exists():
+                try:
+                    pid = int(pid_file.read_text())
+                    os.kill(pid, signal.SIGTERM)
+                except Exception as kill_err:
+                    logger.error(f"WebcamSourceAdapter: Failed to kill PID {pid}: {kill_err}")
+            # Cleanup PID file
+            if pid_file.exists():
+                try:
+                    pid_file.unlink()
+                except Exception as unlink_err:
+                    logger.error(f"WebcamSourceAdapter: Failed to remove PID file: {unlink_err}")
+            self.update_stream_status(StreamStatus.INACTIVE)
+            logger.info(f"Stopped webcam processing for stream {self.stream.id}")
+            return True
+        except Exception as e:
+            logger.error(f"Failed to stop webcam processing: {e}")
+            self.update_stream_status(StreamStatus.ERROR)
+            return False
+    
+    def get_hls_output_path(self) -> str:
+        media_dir = Path(settings.MEDIA_ROOT)
+        return str(media_dir / f'{self.stream.stream_key}.m3u8')
+    
+    def _monitor_webcam(self):
+        """Monitor the FFmpeg webcam process and update stream status on exit"""
+        try:
+            exit_code = self.process.wait()
+            if exit_code != 0:
+                logger.error(f"FFmpeg webcam process terminated unexpectedly with code {exit_code}")
+                new_status = StreamStatus.ERROR
+            else:
+                logger.info(f"FFmpeg webcam process terminated normally with code {exit_code}")
+                new_status = StreamStatus.INACTIVE
+            self.update_stream_status(new_status)
+        except Exception as e:
+            logger.error(f"Error monitoring FFmpeg webcam process: {e}")
+
+
 class SourceAdapterFactory:
     """Factory for creating video source adapters"""
     
@@ -133,6 +277,7 @@ class SourceAdapterFactory:
         adapters = {
             'rtmp': RTMPSourceAdapter,
             'file': FileSourceAdapter,
+            'webcam': WebcamSourceAdapter,
         }
         
         adapter_class = adapters.get(stream.source_type)

media-analyzer/backend/streaming/urls.py

@@ -4,8 +4,9 @@ from . import views
 urlpatterns = [
     path('streams/', views.list_streams, name='list_streams'),
     path('streams/create/', views.create_stream, name='create_stream'),
-    path('streams/<int:stream_id>/start/', views.start_stream, name='start_stream'),
+    path('streams/webcam/start/', views.start_webcam_stream, name='start_webcam_stream'),
-    path('streams/<int:stream_id>/stop/', views.stop_stream, name='stop_stream'),
+    path('streams/<str:stream_key>/start/', views.start_stream, name='start_stream'),
-    path('streams/<str:stream_id>/analyze/', views.trigger_analysis, name='trigger_analysis'),
+    path('streams/<str:stream_key>/stop/', views.stop_stream, name='stop_stream'),
-    path('hls/<str:filename>', views.serve_hls_file, name='serve_hls_file'),
+    path('streams/<str:stream_key>/analyze/', views.trigger_analysis, name='trigger_analysis'),
+    path('<str:filename>', views.serve_hls_file, name='serve_hls_file'),
 ]

media-analyzer/backend/streaming/views.py

@@ -17,15 +17,30 @@ logger = logging.getLogger(__name__)
 @csrf_exempt
 @require_http_methods(["POST"])
 def create_stream(request):
-    """Create new stream"""
+    """Create or update RTMP stream (single stream pattern like webcam)"""
     try:
         data = json.loads(request.body)
+        source_type = data.get('source_type', 'rtmp')
+        
+        # Look for existing stream of this type first
+        existing_stream = VideoStream.objects.filter(source_type=source_type).first()
+        
+        if existing_stream:
+            # Update existing stream
+            existing_stream.name = data['name']
+            existing_stream.processing_mode = data.get('processing_mode', 'live')
+            existing_stream.save()
+            stream = existing_stream
+            logger.info(f"Updated existing {source_type} stream: {stream.id}")
+        else:
+            # Create new stream
             stream = VideoStream.objects.create(
                 name=data['name'],
-            source_type=data.get('source_type', 'rtmp'),
+                source_type=source_type,
                 processing_mode=data.get('processing_mode', 'live'),
                 stream_key=str(uuid.uuid4())
             )
+            logger.info(f"Created new {source_type} stream: {stream.id}")
         
         return JsonResponse({
             'id': stream.id,
@@ -35,7 +50,7 @@ def create_stream(request):
             'stream_key': stream.stream_key,
             'status': stream.status,
             'hls_playlist_url': f"{settings.HLS_BASE_URL}{settings.HLS_ENDPOINT_PATH}{stream.stream_key}.m3u8" if stream.status == 'active' else None,
-            'rtmp_ingest_url': f"rtmp://{request.get_host().split(':')[0]}:{settings.RTMP_PORT}/live/{stream.stream_key}",
+            'rtmp_ingest_url': f"rtmp://{request.get_host().split(':')[0]}:{settings.RTMP_PORT}/live",
             'created_at': stream.created_at.isoformat()
         })
         
@@ -52,9 +67,10 @@ def list_streams(request):
             'name': s.name,
             'source_type': s.source_type,
             'processing_mode': s.processing_mode,
+            'stream_key': s.stream_key,
             'status': s.status,
             'hls_playlist_url': f"{settings.HLS_BASE_URL}{settings.HLS_ENDPOINT_PATH}{s.stream_key}.m3u8" if s.status == 'active' else None,
-            'rtmp_ingest_url': f"rtmp://{request.get_host().split(':')[0]}:{settings.RTMP_PORT}/live/{s.stream_key}",
+            'rtmp_ingest_url': f"rtmp://{request.get_host().split(':')[0]}:{settings.RTMP_PORT}/live",
             'created_at': s.created_at.isoformat()
         } for s in streams]
     })
@@ -62,9 +78,9 @@ def list_streams(request):
 
 @csrf_exempt
 @require_http_methods(["POST"])
-def start_stream(request, stream_id):
+def start_stream(request, stream_key):
     """Start stream processing"""
-    stream = get_object_or_404(VideoStream, id=stream_id)
+    stream = get_object_or_404(VideoStream, stream_key=stream_key)
     
     try:
         adapter = SourceAdapterFactory.create_adapter(stream)
@@ -79,15 +95,15 @@ def start_stream(request, stream_id):
             return JsonResponse({'error': 'Failed to start stream'}, status=500)
             
     except Exception as e:
-        logger.error(f"Error starting stream {stream_id}: {e}")
+        logger.error(f"Error starting stream {stream_key}: {e}")
         return JsonResponse({'error': str(e)}, status=500)
 
 
 @csrf_exempt  
 @require_http_methods(["POST"])
-def stop_stream(request, stream_id):
+def stop_stream(request, stream_key):
     """Stop stream processing"""
-    stream = get_object_or_404(VideoStream, id=stream_id)
+    stream = get_object_or_404(VideoStream, stream_key=stream_key)
     
     try:
         adapter = SourceAdapterFactory.create_adapter(stream)
@@ -99,7 +115,7 @@ def stop_stream(request, stream_id):
             return JsonResponse({'error': 'Failed to stop stream'}, status=500)
             
     except Exception as e:
-        logger.error(f"Error stopping stream {stream_id}: {e}")
+        logger.error(f"Error stopping stream {stream_key}: {e}")
         return JsonResponse({'error': str(e)}, status=500)
 
 
@@ -115,21 +131,27 @@ def serve_hls_file(request, filename):
     
     # Trigger analysis for new .ts segments
     if filename.endswith('.ts'):
+        logger.info(f"Processing .ts file request: {filename}")
         try:
-            # Extract stream ID from UUID-based filename: 43606ec7-786c-4f7d-acf3-95981f9e5ebe-415.ts
+            # Extract stream_key from filename: "stream_key-segment_number.ts" -> "stream_key"
-            if '-' in filename:
+            # Example: "69f79422-5816-4cf0-9f44-0ac1421b8b8e-123.ts" -> "69f79422-5816-4cf0-9f44-0ac1421b8b8e"
-                # Split by dash and take first 5 parts (UUID format)
+            base_name = filename.rsplit('.', 1)[0]  # Remove .ts extension
-                parts = filename.split('-')
+            stream_key = base_name.rsplit('-', 1)[0]  # Remove last segment: "-123"
-                if len(parts) >= 5:
+            logger.info(f"Parsed stream_key: {stream_key} from filename: {filename}")
-                    stream_id = '-'.join(parts[:5])  # Reconstruct UUID
             
+            if stream_key:
                 # Queue for analysis
+                logger.info(f"Attempting to queue analysis for {filename}")
                 analyzer = VideoAnalyzer()
-                    analyzer.queue_segment_analysis(stream_id, file_path)
+                analyzer.queue_segment_analysis(stream_key, file_path)
-                    logger.info(f"Queued segment for analysis: {filename} (stream: {stream_id})")
+                logger.info(f"Queued segment for analysis: {filename} (stream: {stream_key})")
+            else:
+                logger.warning(f"No stream_key extracted from {filename}")
             
         except Exception as e:
             logger.error(f"Error queuing analysis for {filename}: {e}")
+            import traceback
+            logger.error(f"Traceback: {traceback.format_exc()}")
     
     # Determine content type
     if filename.endswith('.m3u8'):
@@ -148,23 +170,25 @@ def serve_hls_file(request, filename):
 
 @csrf_exempt
 @require_http_methods(["POST"])
-def trigger_analysis(request, stream_id):
+def trigger_analysis(request, stream_key):
     """Manually trigger analysis for testing"""
     try:
         data = json.loads(request.body) if request.body else {}
         segment_path = data.get('segment_path')
         
         if not segment_path:
-            # Find latest segment
+            # Find latest segment in media directory
-            media_dir = os.path.join(settings.BASE_DIR.parent.parent, 'media')
+            media_dir = settings.MEDIA_ROOT
             ts_files = [f for f in os.listdir(media_dir) if f.endswith('.ts')]
             if ts_files:
+                # Sort by filename to get the latest segment
+                ts_files.sort()
                 segment_path = os.path.join(media_dir, ts_files[-1])
             else:
                 return JsonResponse({'error': 'No segments found'}, status=404)
         
         analyzer = VideoAnalyzer()
-        success = analyzer.queue_segment_analysis(stream_id, segment_path)
+        success = analyzer.queue_segment_analysis(stream_key, segment_path)
         
         if success:
             return JsonResponse({'message': 'Analysis triggered', 'segment': segment_path})
@@ -174,3 +198,73 @@ def trigger_analysis(request, stream_id):
     except Exception as e:
         logger.error(f"Error triggering analysis: {e}")
         return JsonResponse({'error': str(e)}, status=500)
+
+
+@csrf_exempt
+@require_http_methods(["POST"])
+def start_webcam_stream(request):
+    """Start or reuse existing webcam stream"""
+    try:
+        # Look for existing webcam stream first
+        webcam_stream = VideoStream.objects.filter(source_type='webcam').first()
+        
+        if not webcam_stream:
+            # Create new webcam stream
+            webcam_stream = VideoStream.objects.create(
+                name='Webcam Stream',
+                source_type='webcam',
+                processing_mode='live',
+                stream_key=str(uuid.uuid4())
+            )
+            logger.info(f"Created new webcam stream: {webcam_stream.id}")
+        
+        # Check if another stream is active
+        active_streams = VideoStream.objects.filter(status=StreamStatus.ACTIVE).exclude(id=webcam_stream.id)
+        if active_streams.exists():
+            other = active_streams.first()
+            return JsonResponse({
+                'error': f'Another stream is active: {other.name}',
+                'active_stream_key': other.stream_key,
+                'active_stream_name': other.name
+            }, status=409)
+        
+        # Start the webcam stream if not already active
+        if webcam_stream.status != StreamStatus.ACTIVE:
+            adapter = SourceAdapterFactory.create_adapter(webcam_stream)
+            success = adapter.start_processing()
+            
+            if not success:
+                return JsonResponse({'error': 'Failed to start webcam'}, status=500)
+        
+        # Wait for HLS playlist to be ready before returning
+        import time
+        playlist_path = os.path.join(settings.MEDIA_ROOT, f"{webcam_stream.stream_key}.m3u8")
+        max_wait_time = 10  # seconds
+        wait_interval = 0.5  # seconds
+        elapsed_time = 0
+        
+        logger.info(f"Waiting for HLS playlist to be ready: {playlist_path}")
+        while elapsed_time < max_wait_time:
+            if os.path.exists(playlist_path) and os.path.getsize(playlist_path) > 0:
+                logger.info(f"HLS playlist ready after {elapsed_time:.1f}s")
+                break
+            time.sleep(wait_interval)
+            elapsed_time += wait_interval
+        
+        if not os.path.exists(playlist_path):
+            logger.warning(f"HLS playlist not ready after {max_wait_time}s, returning anyway")
+        
+        return JsonResponse({
+            'id': webcam_stream.id,
+            'name': webcam_stream.name,
+            'source_type': webcam_stream.source_type,
+            'processing_mode': webcam_stream.processing_mode,
+            'stream_key': webcam_stream.stream_key,
+            'status': webcam_stream.status,
+            'hls_playlist_url': f"{settings.HLS_BASE_URL}{settings.HLS_ENDPOINT_PATH}{webcam_stream.stream_key}.m3u8",
+            'created_at': webcam_stream.created_at.isoformat()
+        })
+        
+    except Exception as e:
+        logger.error(f"Error starting webcam stream: {e}")
+        return JsonResponse({'error': str(e)}, status=500)

media-analyzer/backend/test_ai.py

@@ -1,57 +0,0 @@
-#!/usr/bin/env python
-"""Quick test script to verify AI pipeline works"""
-
-import os
-import django
-import sys
-from pathlib import Path
-
-# Add the backend directory to Python path
-backend_dir = Path(__file__).parent
-sys.path.insert(0, str(backend_dir))
-
-# Configure Django
-os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'media_analyzer.settings.development')
-django.setup()
-
-from ai_processing.processors.video_analyzer import VideoAnalyzer
-from ai_processing.models import AnalysisProvider
-from PIL import Image
-import numpy as np
-
-def test_ai_pipeline():
-    print("🧪 Testing AI Pipeline...")
-    
-    # Check providers
-    providers = AnalysisProvider.objects.all()
-    print(f"📊 Found {providers.count()} providers:")
-    for p in providers:
-        print(f"  - {p.name} ({p.provider_type}) - Active: {p.active}")
-    
-    # Create test analyzer
-    analyzer = VideoAnalyzer()
-    
-    # Create a test image (simple colored rectangle)
-    print("\n🖼️  Creating test image...")
-    test_image = Image.new('RGB', (640, 480), color='red')
-    
-    # Test synchronous analysis
-    print("🔍 Running synchronous analysis...")
-    try:
-        result = analyzer.analyze_frame_sync(test_image)
-        print(f"✅ Analysis result: {result}")
-        
-        if 'error' in result:
-            print(f"❌ Error: {result['error']}")
-        else:
-            print(f"✅ Found {len(result.get('logos', []))} logo detections")
-            for logo in result.get('logos', []):
-                print(f"   - {logo['label']}: {logo['confidence']:.3f}")
-    
-    except Exception as e:
-        print(f"❌ Analysis failed: {e}")
-        import traceback
-        traceback.print_exc()
-
-if __name__ == "__main__":
-    test_ai_pipeline()

media-analyzer/backend/test_frontend_integration.py

@@ -1,88 +0,0 @@
-#!/usr/bin/env python
-"""Test frontend integration by creating sample analysis data"""
-
-import os
-import django
-import sys
-from pathlib import Path
-
-# Configure Django
-backend_dir = Path(__file__).parent
-sys.path.insert(0, str(backend_dir))
-os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'media_analyzer.settings.development')
-django.setup()
-
-from ai_processing.models import VideoAnalysis, DetectionResult, VisualAnalysis, AnalysisProvider
-from channels.layers import get_channel_layer
-from asgiref.sync import async_to_sync
-
-def create_sample_analysis():
-    """Create sample analysis data for testing frontend"""
-    print("🎯 Creating sample analysis data...")
-    
-    # Get CLIP provider
-    provider = AnalysisProvider.objects.filter(provider_type='local_clip').first()
-    
-    # Create analysis
-    analysis = VideoAnalysis.objects.create(
-        stream_id='test_stream',
-        segment_path='/fake/path.ts',
-        provider=provider,
-        analysis_type='logo_detection',
-        frame_timestamp=0.0,
-        confidence_threshold=0.3
-    )
-    
-    # Create sample detections
-    DetectionResult.objects.create(
-        analysis=analysis,
-        label='Apple',
-        confidence=0.85,
-        bbox_x=0.2,
-        bbox_y=0.3,
-        bbox_width=0.3,
-        bbox_height=0.2,
-        detection_type='logo'
-    )
-    
-    DetectionResult.objects.create(
-        analysis=analysis,
-        label='Google',
-        confidence=0.72,
-        bbox_x=0.5,
-        bbox_y=0.1,
-        bbox_width=0.25,
-        bbox_height=0.15,
-        detection_type='logo'
-    )
-    
-    # Create visual analysis
-    VisualAnalysis.objects.create(
-        analysis=analysis,
-        dominant_colors=[[255, 0, 0], [0, 255, 0], [0, 0, 255]],
-        brightness_level=0.7,
-        contrast_level=0.5,
-        saturation_level=0.8
-    )
-    
-    print(f"✅ Created analysis: {analysis.to_dict()}")
-    
-    # Try to send via WebSocket
-    try:
-        channel_layer = get_channel_layer()
-        if channel_layer:
-            async_to_sync(channel_layer.group_send)(
-                "stream_test_stream",
-                {
-                    "type": "analysis_update",
-                    "analysis": analysis.to_dict()
-                }
-            )
-            print("📡 Sent WebSocket update")
-        else:
-            print("⚠️  No channel layer configured")
-    except Exception as e:
-        print(f"❌ WebSocket send failed: {e}")
-
-if __name__ == "__main__":
-    create_sample_analysis()

media-analyzer/backend/test_full_pipeline.py

@@ -1,92 +0,0 @@
-#!/usr/bin/env python
-"""Test the complete pipeline: AI analysis -> WebSocket -> Frontend"""
-
-import os
-import django
-import sys
-import json
-import asyncio
-from pathlib import Path
-
-# Add the backend directory to Python path
-backend_dir = Path(__file__).parent
-sys.path.insert(0, str(backend_dir))
-
-# Configure Django
-os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'media_analyzer.settings.development')
-django.setup()
-
-from ai_processing.tasks import process_video_segment
-from ai_processing.models import VideoAnalysis, AnalysisProvider
-from PIL import Image, ImageDraw, ImageFont
-import tempfile
-
-def create_test_image_with_apple_logo():
-    """Create a test image with 'Apple' text as logo simulation"""
-    img = Image.new('RGB', (640, 480), color='white')
-    draw = ImageDraw.Draw(img)
-    
-    # Draw "Apple" text in the center
-    try:
-        font = ImageFont.truetype("/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf", 48)
-    except:
-        font = ImageFont.load_default()
-    
-    text = "Apple iPhone"
-    bbox = draw.textbbox((0, 0), text, font=font)
-    text_width = bbox[2] - bbox[0]
-    text_height = bbox[3] - bbox[1]
-    
-    x = (640 - text_width) // 2
-    y = (480 - text_height) // 2
-    
-    draw.text((x, y), text, fill='black', font=font)
-    
-    return img
-
-def test_full_pipeline():
-    print("🧪 Testing Complete Pipeline...")
-    
-    # Create test image
-    print("🖼️  Creating test image with Apple logo simulation...")
-    test_image = create_test_image_with_apple_logo()
-    
-    # Save to temporary file as a fake video segment
-    with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as tmp_file:
-        test_image.save(tmp_file.name)
-        fake_segment_path = tmp_file.name
-    
-    print(f"💾 Saved test image to: {fake_segment_path}")
-    
-    # Test the processing task directly
-    print("🚀 Triggering analysis task...")
-    try:
-        result = process_video_segment('test_stream', fake_segment_path)
-        print(f"✅ Task result: {result}")
-        
-        # Check if analysis was stored
-        analysis = VideoAnalysis.objects.filter(stream_id='test_stream').last()
-        if analysis:
-            print(f"📊 Analysis stored: {analysis.to_dict()}")
-            
-            detections = analysis.detections.all()
-            print(f"🎯 Found {detections.count()} detections:")
-            for detection in detections:
-                print(f"   - {detection.label}: {detection.confidence:.3f}")
-        else:
-            print("❌ No analysis found in database")
-            
-    except Exception as e:
-        print(f"❌ Task failed: {e}")
-        import traceback
-        traceback.print_exc()
-    
-    finally:
-        # Cleanup
-        try:
-            os.unlink(fake_segment_path)
-        except:
-            pass
-
-if __name__ == "__main__":
-    test_full_pipeline()

media-analyzer/docker-compose.yml

@@ -9,6 +9,12 @@ services:
     volumes:
       - ./backend:/app
       - ./media:/app/media
+    # Linux webcam support (comment out on macOS/Windows if needed)
+    devices:
+      - /dev/video0:/dev/video0
+    group_add:
+      - video
+    user: "${UID:-1000}:${GID:-1000}"
     environment:
       - DEBUG=1
       - DB_HOST=postgres
@@ -17,6 +23,8 @@ services:
       - DB_PASSWORD=media_pass
       - REDIS_HOST=redis
       - REDIS_PORT=6379
+      # RTMP ingestion host reachable from backend container
+      - RTMP_HOST=nginx-rtmp
       - HLS_BASE_URL=http://nginx-rtmp:8081
       - HLS_ENDPOINT_PATH=/
       - MEDIA_ROOT=/app/media
@@ -104,6 +112,30 @@ services:
         condition: service_started
     command: ./entrypoint-celery.sh --queues=default,config_management --hostname=default-worker@%h
 
+  # File Watcher - Monitor HLS segments
+  file-watcher:
+    build:
+      context: ./backend
+      target: development
+    volumes:
+      - ./backend:/app
+      - ./media:/app/media
+    environment:
+      - DEBUG=1
+      - DB_HOST=postgres
+      - DB_NAME=media_analyzer
+      - DB_USER=media_user
+      - DB_PASSWORD=media_pass
+      - REDIS_HOST=redis
+      - REDIS_PORT=6379
+      - MEDIA_ROOT=/app/media
+    depends_on:
+      postgres:
+        condition: service_healthy
+      redis:
+        condition: service_started
+    command: python manage.py watch_hls_files --poll-interval=2
+
   # Angular Frontend with NGINX (unified approach)
   frontend:
     build:
@@ -145,7 +177,9 @@ services:
       - "0.0.0.0:1935:1935"  # RTMP port - bind to all interfaces
       - "0.0.0.0:8081:80"    # HTTP port for HLS - bind to all interfaces
     volumes:
-      - ./media:/var/www/html
+      # Persist HLS segments and playlists to host media directory
+      - ./media:/var/www/media
+      # Custom nginx.conf for RTMP/HLS configuration
       - ./docker/nginx.conf:/etc/nginx/nginx.conf
     depends_on:
       - postgres

media-analyzer/docker/nginx.conf

@@ -13,7 +13,7 @@ rtmp {
             
             # Turn on HLS
             hls on;
-            hls_path /var/www/html;
+            hls_path /var/www/media;
             hls_fragment 3;
             hls_playlist_length 60;
             
@@ -34,42 +34,26 @@ http {
     server {
         listen 80;
 
-        location / {
-            # Disable cache
-            add_header 'Cache-Control' 'no-cache';
-
-            # CORS setup
-            add_header 'Access-Control-Allow-Origin' '*' always;
-            add_header 'Access-Control-Expose-Headers' 'Content-Length,Content-Range';
-            add_header 'Access-Control-Allow-Headers' 'Range';
-
-            # allow CORS preflight requests
-            if ($request_method = 'OPTIONS') {
-                add_header 'Access-Control-Allow-Origin' '*';
-                add_header 'Access-Control-Max-Age' 1728000;
-                add_header 'Content-Type' 'text/plain charset=UTF-8';
-                add_header 'Content-Length' 0;
-                return 204;
-            }
     
         # HLS playlist files
         location ~* \.m3u8$ {
             add_header Content-Type application/vnd.apple.mpegurl;
+            add_header Access-Control-Allow-Origin *;
             add_header Cache-Control no-cache;
-                root /var/www/html/;
+            root /var/www/media;
         }
         
         # HLS segment files  
         location ~* \.ts$ {
             add_header Content-Type video/mp2t;
+            add_header Access-Control-Allow-Origin *;
             add_header Cache-Control no-cache;
-                root /var/www/html/;
+            root /var/www/media;
         }
 
-            # Default location
+        # Fallback for other requests
         location / {
-                root /var/www/html/;
+            root /var/www/media;
-            }
         }
     }
 }

media-analyzer/frontend/nginx.conf

@@ -62,15 +62,16 @@ server {
         proxy_set_header X-Forwarded-Proto $scheme;
     }
 
-    # HLS streaming proxy 
+    # HLS streaming proxy: route to nginx-rtmp HLS server
     location /streaming/ {
-        resolver 127.0.0.11 valid=30s;
+        # HLS proxy: strip /streaming/ prefix and forward to nginx-rtmp
-        set $backend backend:8000;
+        rewrite ^/streaming/(.*)$ /$1 break;
-        proxy_pass http://$backend;
+        proxy_pass http://nginx-rtmp;
+        proxy_http_version 1.1;
         proxy_set_header Host $host;
-        proxy_set_header X-Real-IP $remote_addr;
+        add_header Access-Control-Allow-Origin *;
-        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        add_header Cache-Control no-cache;
-        proxy_set_header X-Forwarded-Proto $scheme;
+        proxy_buffering off;
     }
 
 

media-analyzer/frontend/src/app/app.component.html

@@ -6,7 +6,8 @@
   <main class="main-content">
     <div class="controls-section">
       <app-stream-control 
-        (streamSelected)="onStreamSelected($event)">
+        (streamSelected)="onStreamSelected($event)"
+        (streamStopped)="onStreamStopped()">
       </app-stream-control>
     </div>
     

media-analyzer/frontend/src/app/app.component.scss

@@ -14,26 +14,44 @@
   
   .main-content {
     display: grid;
-    grid-template-columns: 1fr 2fr;
+    grid-template-columns: 350px 1fr;
+    grid-template-rows: auto auto;
+    grid-template-areas: 
+      "controls viewer"
+      "controls analysis";
     gap: 2rem;
     max-width: 1400px;
     margin: 0 auto;
     
     @media (max-width: 768px) {
       grid-template-columns: 1fr;
+      grid-template-rows: auto auto auto;
+      grid-template-areas: 
+        "controls"
+        "viewer"
+        "analysis";
       gap: 1rem;
     }
   }
   
   .controls-section {
+    grid-area: controls;
     background: #f8f9fa;
     padding: 1.5rem;
     border-radius: 8px;
   }
   
   .viewer-section {
+    grid-area: viewer;
     background: #000;
     padding: 1rem;
     border-radius: 8px;
   }
+  
+  .analysis-section {
+    grid-area: analysis;
+    background: #f8f9fa;
+    padding: 1rem;
+    border-radius: 8px;
+  }
 }

media-analyzer/frontend/src/app/app.component.ts

@@ -1,12 +1,12 @@
-import { Component, OnInit, OnDestroy } from '@angular/core';
+import { Component, OnInit, OnDestroy, ViewChild } from '@angular/core';
 import { RouterOutlet } from '@angular/router';
 import { HttpClientModule } from '@angular/common/http';
 import { StreamControlComponent } from './components/stream-control/stream-control.component';
 import { StreamViewerComponent } from './components/stream-viewer/stream-viewer.component';
 import { AnalysisPanelComponent } from './components/analysis-panel/analysis-panel.component';
 import { AnalysisService } from './services/analysis.service';
+import { StreamService } from './services/stream.service';
 import { DetectionResult, VisualAnalysis, Analysis } from './models/analysis';
-import { environment } from '../environments/environment';
 
 @Component({
   selector: 'app-root',
@@ -16,6 +16,8 @@ import { environment } from '../environments/environment';
   styleUrl: './app.component.scss'
 })
 export class AppComponent implements OnInit, OnDestroy {
+  @ViewChild(StreamViewerComponent) streamViewer!: StreamViewerComponent;
+  
   title = 'Media Analyzer';
   selectedStreamUrl: string = '';
   currentStreamId: string = '';
@@ -23,7 +25,10 @@ export class AppComponent implements OnInit, OnDestroy {
   currentVisual?: VisualAnalysis;
   recentAnalyses: Analysis[] = [];
 
-  constructor(private analysisService: AnalysisService) {}
+  constructor(
+    private analysisService: AnalysisService,
+    private streamService: StreamService
+  ) {}
 
   ngOnInit() {
     // Subscribe to analysis updates
@@ -47,20 +52,63 @@ export class AppComponent implements OnInit, OnDestroy {
   onStreamSelected(streamUrl: string) {
     console.log('App received stream URL:', streamUrl);
     
-    // Convert backend URL to browser-accessible URL using environment config
+    // Extract filename from backend URL, then construct a browser-resolvable URL
-    const browserUrl = streamUrl.replace(/^http:\/\/[^\/]+/, environment.hlsBaseUrl);
+    const filename = streamUrl.split('/').pop() || '';
-    this.selectedStreamUrl = browserUrl;
+    this.selectedStreamUrl = `/streaming/${filename}`;
-    console.log('Converted to browser URL:', browserUrl);
+    console.log('Using HLS URL:', this.selectedStreamUrl);
     
-    // Extract stream ID from filename: 476c0bd7-d037-4b6c-a29d-0773c19a76c5.m3u8
+    // Retry function to get active stream (with small delays to allow DB update)
-    const streamIdMatch = streamUrl.match(/([0-9a-f-]+)\.m3u8/);
+    const getActiveStreamWithRetry = (attempt = 1, maxAttempts = 3) => {
-    if (streamIdMatch) {
+      this.streamService.getStreams().subscribe({
-      this.currentStreamId = streamIdMatch[1];
+        next: (response) => {
-      console.log('Extracted stream ID:', this.currentStreamId);
+          const activeStream = response.streams.find(stream => stream.status === 'active');
+          if (activeStream) {
+            this.currentStreamId = activeStream.stream_key;
+            console.log('Found active stream with key:', this.currentStreamId);
             // Connect to WebSocket for this stream
             this.analysisService.connectToStream(this.currentStreamId);
+          } else if (attempt < maxAttempts) {
+            console.log(`No active stream found (attempt ${attempt}/${maxAttempts}), retrying in 1s...`);
+            setTimeout(() => getActiveStreamWithRetry(attempt + 1, maxAttempts), 1000);
           } else {
-      console.error('Could not extract stream ID from URL:', streamUrl);
+            console.log('No active stream found after retries, falling back to filename parsing');
+            this.fallbackToFilenameExtraction(filename);
           }
+        },
+        error: (error) => {
+          console.error('Failed to get streams from API:', error);
+          this.fallbackToFilenameExtraction(filename);
+        }
+      });
+    };
+    
+    // Start the retry process
+    getActiveStreamWithRetry();
+  }
+  
+  private fallbackToFilenameExtraction(filename: string) {
+    const streamIdMatch = filename.match(/^([a-zA-Z0-9-]+)\.m3u8$/);
+    if (streamIdMatch) {
+      this.currentStreamId = streamIdMatch[1];
+      console.log('Fallback: Extracted stream ID from filename:', this.currentStreamId);
+      this.analysisService.connectToStream(this.currentStreamId);
+    } else {
+      console.error('Could not extract stream ID from filename:', filename);
+    }
+  }
+
+  onStreamStopped() {
+    console.log('Stream stopped - clearing player');
+    // Clear the stream from player
+    if (this.streamViewer) {
+      this.streamViewer.clearStream();
+    }
+    // Clear app state
+    this.selectedStreamUrl = '';
+    this.currentStreamId = '';
+    this.currentDetections = [];
+    this.currentVisual = undefined;
+    // Disconnect from WebSocket
+    this.analysisService.disconnect();
   }
 }

media-analyzer/frontend/src/app/app.config.ts

@@ -1,8 +1,9 @@
 import { ApplicationConfig } from '@angular/core';
 import { provideRouter } from '@angular/router';
+import { provideHttpClient } from '@angular/common/http';
 
 import { routes } from './app.routes';
 
 export const appConfig: ApplicationConfig = {
-  providers: [provideRouter(routes)]
+  providers: [provideRouter(routes), provideHttpClient()]
 };

media-analyzer/frontend/src/app/components/stream-control/stream-control.component.html

@@ -1,9 +1,63 @@
 <div class="stream-control">
   <h2>Stream Control</h2>
   
+  <!-- Tabs -->
+  <div class="tabs">
+    <button 
+      class="tab" 
+      [class.active]="activeTab === 'webcam'"
+      (click)="switchTab('webcam')">
+      📹 Webcam
+    </button>
+    <button 
+      class="tab" 
+      [class.active]="activeTab === 'rtmp'"
+      (click)="switchTab('rtmp')">
+      📡 RTMP Stream
+    </button>
+  </div>
+  
+  <!-- Webcam Tab -->
+  <div class="tab-content" *ngIf="activeTab === 'webcam'">
+    <div class="webcam-section">
+      <h3>Webcam Stream</h3>
+      <p class="description">Start your webcam for real-time logo detection</p>
+      
+      <button (click)="startWebcam()" class="btn btn-webcam">📹 Start Webcam</button>
+      
+      <!-- Webcam Stream List -->
+      <div class="streams-list" *ngIf="webcamStreams.length > 0">
+        <h4>Active Webcam Stream</h4>
+        <div class="stream-item" *ngFor="let stream of webcamStreams">
+          <div class="stream-info">
+            <h4>{{ stream.name }}</h4>
+            <p class="status">Status: <span [class]="'status-' + stream.status">{{ stream.status }}</span></p>
+            <p class="stream-key">Stream Key: <code>{{ stream.stream_key }}</code></p>
+          </div>
+          
+          <div class="stream-actions">
+            <button (click)="startStream(stream)" 
+                    [disabled]="stream.status === 'active'" 
+                    class="btn btn-success">Start</button>
+            <button (click)="stopStream(stream)" 
+                    [disabled]="stream.status === 'inactive'" 
+                    class="btn btn-danger">Stop</button>
+            <button (click)="selectStream(stream)" 
+                    *ngIf="stream.hls_playlist_url"
+                    class="btn btn-info">Load</button>
+          </div>
+        </div>
+      </div>
+    </div>
+  </div>
+  
+  <!-- RTMP Tab -->
+  <div class="tab-content" *ngIf="activeTab === 'rtmp'">
+    <div class="rtmp-section">
       <!-- Create New Stream -->
       <div class="create-stream">
-    <h3>Create Stream</h3>
+        <h3>Create RTMP Stream</h3>
+        <p class="description">Create a stream for OBS or other RTMP sources</p>
         <input 
           type="text" 
           [(ngModel)]="newStreamName" 
@@ -12,10 +66,10 @@
         <button (click)="createStream()" class="btn btn-primary">Create Stream</button>
       </div>
       
-  <!-- Stream List -->
+      <!-- RTMP Stream List -->
-  <div class="streams-list">
+      <div class="streams-list" *ngIf="rtmpStreams.length > 0">
-    <h3>Streams</h3>
+        <h4>RTMP Streams</h4>
-    <div class="stream-item" *ngFor="let stream of streams">
+        <div class="stream-item" *ngFor="let stream of rtmpStreams">
           <div class="stream-info">
             <h4>{{ stream.name }}</h4>
             <p class="status">Status: <span [class]="'status-' + stream.status">{{ stream.status }}</span></p>
@@ -36,4 +90,6 @@
           </div>
         </div>
       </div>
+    </div>
+  </div>
 </div>

media-analyzer/frontend/src/app/components/stream-control/stream-control.component.scss

@@ -0,0 +1,134 @@
+.stream-control {
+  padding: 20px;
+}
+
+.tabs {
+  display: flex;
+  margin-bottom: 20px;
+  border-bottom: 2px solid #ddd;
+}
+
+.tab {
+  padding: 12px 24px;
+  border: none;
+  background: transparent;
+  cursor: pointer;
+  font-size: 16px;
+  font-weight: 500;
+  border-bottom: 3px solid transparent;
+  margin-right: 8px;
+  transition: all 0.2s;
+}
+
+.tab:hover {
+  background: #f8f9fa;
+}
+
+.tab.active {
+  color: #007bff;
+  border-bottom-color: #007bff;
+  background: #f8f9fa;
+}
+
+.tab-content {
+  margin-top: 20px;
+}
+
+.webcam-section, .rtmp-section {
+  min-height: 300px;
+}
+
+.description {
+  color: #666;
+  margin-bottom: 15px;
+  font-style: italic;
+}
+
+.btn-webcam {
+  background: #4CAF50;
+  color: white;
+  border: none;
+  padding: 12px 24px;
+  border-radius: 8px;
+  cursor: pointer;
+  font-size: 16px;
+  font-weight: 500;
+}
+
+.btn-webcam:hover {
+  background: #45a049;
+}
+
+.create-stream {
+  margin-bottom: 20px;
+  padding: 15px;
+  border: 1px solid #ddd;
+  border-radius: 8px;
+}
+
+.input {
+  padding: 8px;
+  margin-right: 10px;
+  border: 1px solid #ddd;
+  border-radius: 4px;
+  min-width: 200px;
+}
+
+.btn {
+  padding: 8px 16px;
+  border: none;
+  border-radius: 4px;
+  cursor: pointer;
+  margin-right: 8px;
+}
+
+.btn-primary { background: #007bff; color: white; }
+.btn-success { background: #28a745; color: white; }
+.btn-danger { background: #dc3545; color: white; }
+.btn-info { background: #17a2b8; color: white; }
+
+.btn:disabled {
+  background: #6c757d !important;
+  cursor: not-allowed;
+}
+
+.streams-list {
+  border: 1px solid #ddd;
+  border-radius: 8px;
+  padding: 15px;
+}
+
+.stream-item {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  padding: 15px;
+  border-bottom: 1px solid #eee;
+}
+
+.stream-item:last-child {
+  border-bottom: none;
+}
+
+.stream-info h4 {
+  margin: 0 0 8px 0;
+}
+
+.source-type {
+  font-weight: normal;
+  font-size: 0.8em;
+  color: #666;
+}
+
+.status-active { color: #28a745; font-weight: bold; }
+.status-inactive { color: #6c757d; }
+.status-starting { color: #ffc107; }
+.status-stopping { color: #fd7e14; }
+.status-error { color: #dc3545; font-weight: bold; }
+
+code {
+  background: #f8f9fa;
+  padding: 2px 4px;
+  border-radius: 3px;
+  font-size: 0.9em;
+}

media-analyzer/frontend/src/app/components/stream-control/stream-control.component.ts

@@ -1,20 +1,8 @@
 import { Component, EventEmitter, Output } from '@angular/core';
-import { HttpClient } from '@angular/common/http';
 import { CommonModule } from '@angular/common';
 import { FormsModule } from '@angular/forms';
-import { environment } from '../../../environments/environment';
+import { StreamService } from '../../services/stream.service';
-
+import { Stream } from '../../models/stream';
-interface Stream {
-  id: number;
-  name: string;
-  source_type: string;
-  processing_mode: string;
-  status: string;
-  stream_key: string;
-  hls_playlist_url: string | null;
-  rtmp_ingest_url: string;
-  created_at: string;
-}
 
 @Component({
   selector: 'app-stream-control',
@@ -25,17 +13,19 @@ interface Stream {
 })
 export class StreamControlComponent {
   @Output() streamSelected = new EventEmitter<string>();
+  @Output() streamStopped = new EventEmitter<void>();
   
   streams: Stream[] = [];
   newStreamName = '';
   selectedStream: Stream | null = null;
+  activeTab: 'rtmp' | 'webcam' = 'webcam';
 
-  constructor(private http: HttpClient) {
+  constructor(private streamService: StreamService) {
     this.loadStreams();
   }
 
   loadStreams() {
-    this.http.get<{streams: Stream[]}>(`${environment.apiUrl}/streams/`).subscribe({
+    this.streamService.getStreams().subscribe({
       next: (response) => {
         this.streams = response.streams;
       },
@@ -46,7 +36,7 @@ export class StreamControlComponent {
   createStream() {
     if (!this.newStreamName) return;
 
-    this.http.post<Stream>(`${environment.apiUrl}/streams/create/`, {
+    this.streamService.createStream({
       name: this.newStreamName,
       source_type: 'rtmp',
       processing_mode: 'live'
@@ -60,8 +50,12 @@ export class StreamControlComponent {
   }
 
   startStream(stream: Stream) {
-    this.http.post(`${environment.apiUrl}/streams/${stream.id}/start/`, {}).subscribe({
+    this.streamService.startStream(stream.stream_key).subscribe({
-      next: () => {
+      next: (response) => {
+        console.log('Stream started, HLS URL:', response.hls_playlist_url);
+        // Emit the stream selection immediately with the HLS URL from response
+        this.streamSelected.emit(response.hls_playlist_url);
+        // Then reload streams to get updated status
         this.loadStreams();
       },
       error: (error) => console.error('Error starting stream:', error)
@@ -69,14 +63,56 @@ export class StreamControlComponent {
   }
 
   stopStream(stream: Stream) {
-    this.http.post(`${environment.apiUrl}/streams/${stream.id}/stop/`, {}).subscribe({
+    this.streamService.stopStream(stream.stream_key).subscribe({
       next: () => {
         this.loadStreams();
+        // Emit event to clear the player
+        this.streamStopped.emit();
       },
       error: (error) => console.error('Error stopping stream:', error)
     });
   }
 
+  startWebcam() {
+    this.streamService.startWebcamStream().subscribe({
+      next: (stream) => {
+        this.loadStreams();
+        // Backend now waits for HLS to be ready, so we can directly select
+        this.selectStream(stream);
+      },
+      error: (error) => {
+        console.error('Error starting webcam:', error);
+        if (error.status === 409) {
+          const activeStreamKey = error.error.active_stream_key;
+          if (activeStreamKey) {
+            console.log(`Stopping active stream ${activeStreamKey} before retrying webcam`);
+            this.streamService.stopStream(activeStreamKey).subscribe({
+              next: () => this.startWebcam(),
+              error: (stopError) => {
+                console.error('Error stopping active stream:', stopError);
+                alert(`Cannot start webcam: ${error.error.error}`);
+              }
+            });
+          } else {
+            alert(`Cannot start webcam: ${error.error.error}`);
+          }
+        }
+      }
+    });
+  }
+
+  switchTab(tab: 'rtmp' | 'webcam') {
+    this.activeTab = tab;
+  }
+
+  get rtmpStreams() {
+    return this.streams.filter(stream => stream.source_type === 'rtmp');
+  }
+
+  get webcamStreams() {
+    return this.streams.filter(stream => stream.source_type === 'webcam');
+  }
+
   selectStream(stream: Stream) {
     this.selectedStream = stream;
     if (stream.hls_playlist_url) {

media-analyzer/frontend/src/app/components/stream-viewer/stream-viewer.component.ts

@@ -188,4 +188,24 @@ export class StreamViewerComponent implements AfterViewInit, OnDestroy, OnChange
     const canvas = this.overlayElement.nativeElement;
     this.ctx.clearRect(0, 0, canvas.width, canvas.height);
   }
+
+  clearStream() {
+    const video = this.videoElement.nativeElement;
+    
+    // Stop and clear HLS
+    if (this.hls) {
+      this.hls.destroy();
+      this.hls = undefined;
+    }
+    
+    // Clear video source and stop playback
+    video.src = '';
+    video.srcObject = null;
+    video.load(); // Reset video element
+    
+    // Clear overlay
+    this.clearOverlay();
+    
+    console.log('Stream cleared');
+  }
 }

media-analyzer/frontend/src/app/models/stream.ts

@@ -1,2 +1,11 @@
 export interface Stream {
+  id: number;
+  name: string;
+  source_type: 'rtmp' | 'webcam' | 'file' | 'url';
+  processing_mode: 'live' | 'batch';
+  status: 'inactive' | 'starting' | 'active' | 'stopping' | 'error';
+  stream_key: string;
+  hls_playlist_url?: string;
+  rtmp_ingest_url?: string;
+  created_at: string;
 }

media-analyzer/frontend/src/app/services/analysis.service.ts

@@ -10,6 +10,7 @@ export class AnalysisService {
   private currentDetections = new BehaviorSubject<DetectionResult[]>([]);
   private currentVisual = new BehaviorSubject<VisualAnalysis | null>(null);
   private recentAnalyses = new BehaviorSubject<Analysis[]>([]);
+  private streamStartTime: Date | null = null;
   
   public detections$ = this.currentDetections.asObservable();
   public visual$ = this.currentVisual.asObservable();
@@ -23,16 +24,34 @@ export class AnalysisService {
   }
 
   connectToStream(streamId: string) {
-    this.websocketService.connect(streamId);
+    this.streamStartTime = new Date();
+    this.websocketService.subscribe(streamId);
   }
 
   disconnect() {
+    this.websocketService.unsubscribe();
     this.websocketService.disconnect();
     this.currentDetections.next([]);
     this.currentVisual.next(null);
+    this.streamStartTime = null;
   }
 
   private handleAnalysisUpdate(analysis: Analysis) {
+    console.log('Received analysis update:', analysis);
+    
+    // Filter out analysis from before stream started (with 30 second buffer for recent analysis)
+    if (this.streamStartTime && analysis.timestamp) {
+      const analysisTime = new Date(analysis.timestamp);
+      const bufferTime = new Date(this.streamStartTime.getTime() - 30000); // 30 seconds before stream start
+      if (analysisTime < bufferTime) {
+        console.log('Ignoring old analysis from before stream start:', {
+          analysisTime: analysisTime.toISOString(),
+          streamStart: this.streamStartTime.toISOString()
+        });
+        return;
+      }
+    }
+    
     // Update recent analyses list
     const current = this.recentAnalyses.value;
     const updated = [analysis, ...current.slice(0, 9)]; // Keep last 10

media-analyzer/frontend/src/app/services/stream.service.ts

@@ -1,9 +1,34 @@
 import { Injectable } from '@angular/core';
+import { HttpClient } from '@angular/common/http';
+import { Observable } from 'rxjs';
+import { Stream } from '../models/stream';
+import { environment } from '../../environments/environment';
 
 @Injectable({
   providedIn: 'root'
 })
 export class StreamService {
+  private apiUrl = `${environment.apiUrl}/streaming`;
 
-  constructor() { }
+  constructor(private http: HttpClient) { }
+
+  getStreams(): Observable<{streams: Stream[]}> {
+    return this.http.get<{streams: Stream[]}>(`${this.apiUrl}/streams/`);
+  }
+
+  createStream(streamData: any): Observable<Stream> {
+    return this.http.post<Stream>(`${this.apiUrl}/streams/create/`, streamData);
+  }
+
+  startWebcamStream(): Observable<Stream> {
+    return this.http.post<Stream>(`${this.apiUrl}/streams/webcam/start/`, {});
+  }
+
+  startStream(streamKey: string): Observable<{message: string, hls_playlist_url: string}> {
+    return this.http.post<{message: string, hls_playlist_url: string}>(`${this.apiUrl}/streams/${streamKey}/start/`, {});
+  }
+
+  stopStream(streamKey: string): Observable<{message: string}> {
+    return this.http.post<{message: string}>(`${this.apiUrl}/streams/${streamKey}/stop/`, {});
+  }
 }

media-analyzer/frontend/src/app/services/websocket.service.ts

@@ -7,6 +7,7 @@ import { Analysis } from '../models/analysis';
 })
 export class WebsocketService {
   private socket?: WebSocket;
+  private currentStreamId?: string;
   private analysisSubject = new Subject<Analysis>();
   private connectionStatus = new BehaviorSubject<boolean>(false);
   
@@ -15,13 +16,13 @@ export class WebsocketService {
 
   constructor() { }
 
-  connect(streamId: string) {
+  connect() {
     if (this.socket?.readyState === WebSocket.OPEN) {
       return;
     }
 
     const protocol = window.location.protocol === 'https:' ? 'wss:' : 'ws:';
-    const wsUrl = `${protocol}//${window.location.host}/ws/stream/${streamId}/`;
+    const wsUrl = `${protocol}//${window.location.host}/ws/stream/`;
     console.log('Connecting to WebSocket:', wsUrl);
     
     this.socket = new WebSocket(wsUrl);
@@ -63,6 +64,7 @@ export class WebsocketService {
     if (this.socket) {
       this.socket.close();
       this.socket = undefined;
+      this.currentStreamId = undefined;
       this.connectionStatus.next(false);
     }
   }
@@ -79,4 +81,17 @@ export class WebsocketService {
       timestamp: Date.now()
     });
   }
+  
+  subscribe(streamId: string) {
+    this.currentStreamId = streamId;
+    this.connect();
+    this.send({ type: 'subscribe', stream_id: streamId });
+  }
+  
+  unsubscribe() {
+    if (this.currentStreamId) {
+      this.send({ type: 'unsubscribe', stream_id: this.currentStreamId });
+      this.currentStreamId = undefined;
+    }
+  }
 }

thevideo.MD

@@ -18,19 +18,20 @@ video structure
 			keyboards (early days)
 			music vs coding (the gap gets wider)
 			recurrent back to basics
+		behind the scenes
+			the setup
+			deskmeter
+			timelapses/ffmpeg
 	demo
 		phase 1
 		phase 2
 		phase 3
 	extras
-		behind the scenes
+		opinions
-			the setup
-			deskmeter
-			timelapses/ffmpeg
-		make your own path
 			bootcamps
 			pimp-up-your-profile new trend
-		for seenka
+			show the current state of my use of AI tools
+		motivations
 			im not in it (just) for the money
 			video processing is my passion (?