Update app.py

app.py

@@ -27,6 +27,8 @@ class AttendanceSystem:
         self.recognition_thread = None
         self.last_recognition_time = {}
         self.recognition_cooldown = 5  # seconds between recognitions for same person
+        self.video_file_path = None
+        self.video_processing = False
         
         # Create directories for data storage
         os.makedirs("data", exist_ok=True)
@@ -274,6 +276,9 @@ class AttendanceSystem:
             if self.is_streaming:
                 return "⚠️ Video stream is already running!"
             
+            # Clear previous video file if switching from file to camera
+            self.video_file_path = None
+            
             self.video_capture = cv2.VideoCapture(camera_source)
             if not self.video_capture.isOpened():
                 return "❌ Could not open camera/video source!"
@@ -302,15 +307,62 @@ class AttendanceSystem:
             self.recognition_thread.daemon = True
             self.recognition_thread.start()
             
-            return "✅ Video stream started successfully!"
+            return "✅ Live camera stream started successfully!"
             
         except Exception as e:
             return f"❌ Error starting video stream: {e}"
     
+    def process_uploaded_video(self, video_path):
+        """Process an uploaded video file for face recognition"""
+        try:
+            if self.is_streaming:
+                return "⚠️ Please stop current stream before processing a video file!"
+            
+            if not os.path.exists(video_path):
+                return "❌ Video file not found!"
+            
+            self.video_file_path = video_path
+            self.video_processing = True
+            
+            def video_processing_loop():
+                cap = cv2.VideoCapture(video_path)
+                fps = cap.get(cv2.CAP_PROP_FPS)
+                frame_delay = 1.0 / fps if fps > 0 else 0.03
+                
+                while self.video_processing and cap.isOpened():
+                    ret, frame = cap.read()
+                    if not ret:
+                        break
+                    
+                    # Process frame for face recognition
+                    processed_frame = self.process_video_frame(frame)
+                    
+                    # Add to queue for display
+                    if not self.frame_queue.full():
+                        try:
+                            self.frame_queue.put_nowait(processed_frame)
+                        except queue.Full:
+                            pass
+                    
+                    time.sleep(frame_delay)
+                
+                cap.release()
+                self.video_processing = False
+            
+            self.recognition_thread = threading.Thread(target=video_processing_loop)
+            self.recognition_thread.daemon = True
+            self.recognition_thread.start()
+            
+            return f"✅ Video processing started successfully! ({os.path.basename(video_path)})"
+            
+        except Exception as e:
+            return f"❌ Error processing video: {e}"
+    
     def stop_video_stream(self):
-        """Stop video streaming"""
+        """Stop video streaming or processing"""
         try:
             self.is_streaming = False
+            self.video_processing = False
             
             if self.video_capture:
                 self.video_capture.release()
@@ -326,10 +378,10 @@ class AttendanceSystem:
                 except queue.Empty:
                     break
             
-            return "✅ Video stream stopped successfully!"
+            return "✅ Video stream/processing stopped successfully!"
             
         except Exception as e:
-            return f"❌ Error stopping video stream: {e}"
+            return f"❌ Error stopping video: {e}"
     
     def get_current_frame(self):
         """Get current frame for display"""
@@ -434,7 +486,7 @@ attendance_system = AttendanceSystem()
 
 def create_interface():
     with gr.Blocks(
-        title="🎯 Advanced Attendance System with Live Recognition",
+        title="🎯 Advanced Attendance System with Video Recognition",
         theme=gr.themes.Soft(),
         css="""
         .gradio-container {
@@ -448,57 +500,72 @@ def create_interface():
             border-radius: 5px;
             margin: 5px 0;
         }
+        .video-option-tabs {
+            margin-bottom: 15px;
+        }
         """
     ) as demo:
         
         gr.Markdown(
             """
-            # 🎯 Advanced Attendance System with Live Face Recognition
+            # 🎯 Advanced Attendance System with Face Recognition
             
-            **Comprehensive facial recognition system with automatic worker registration and attendance tracking**
+            **Comprehensive facial recognition system with live camera and video file processing**
             
             ## 🚀 **Key Features:**
-            - **🎥 Live Video Stream Recognition** - Real-time face detection from camera/CCTV
+            - **🎥 Live Camera Recognition** - Real-time face detection from camera/CCTV
+            - **📹 Video File Processing** - Process pre-recorded videos for attendance
             - **🤖 Automatic Worker Registration** - Auto-register unknown faces with unique IDs
             - **👤 Manual Registration** - Register workers manually with photos
             - **📅 24-Hour Attendance Rule** - One attendance mark per worker per day
             - **📊 Advanced Analytics** - Detailed reports and data export
-            - **💾 Persistent Data Storage** - All data saved locally in `/data` folder
-            
-            ## 📁 **Data Storage Location:**
-            - **Worker Database:** `/data/workers.pkl`
-            - **Attendance Records:** `/data/attendance.json` 
-            - **Face Images:** `/data/faces/` folder
             """
         )
         
         with gr.Tabs():
-            # Live Recognition Tab
-            with gr.Tab("🎥 Live Recognition", elem_classes="tab-nav"):
-                gr.Markdown("### Real-time Face Recognition and Attendance")
+            # Video Recognition Tab
+            with gr.Tab("🎥 Video Recognition", elem_classes="tab-nav"):
+                gr.Markdown("### Face Recognition from Live Camera or Video File")
                 
                 with gr.Row():
                     with gr.Column(scale=1):
-                        camera_source = gr.Number(
-                            label="Camera Source (0 for default camera, or RTSP URL)",
-                            value=0,
-                            precision=0
-                        )
+                        with gr.Tabs(selected="live", elem_classes="video-option-tabs") as video_tabs:
+                            with gr.Tab("Live Camera", id="live"):
+                                camera_source = gr.Number(
+                                    label="Camera Source (0 for default camera, or RTSP URL)",
+                                    value=0,
+                                    precision=0
+                                )
+                                
+                                with gr.Row():
+                                    start_stream_btn = gr.Button(
+                                        "🎥 Start Live Recognition",
+                                        variant="primary",
+                                        size="lg"
+                                    )
+                            
+                            with gr.Tab("Upload Video", id="upload"):
+                                video_file = gr.Video(
+                                    label="Upload Video File",
+                                    sources=["upload"],
+                                    type="filepath"
+                                )
+                                
+                                with gr.Row():
+                                    process_video_btn = gr.Button(
+                                        "📹 Process Video File",
+                                        variant="primary",
+                                        size="lg"
+                                    )
                         
-                        with gr.Row():
-                            start_stream_btn = gr.Button(
-                                "🎥 Start Live Recognition",
-                                variant="primary",
-                                size="lg"
-                            )
-                            stop_stream_btn = gr.Button(
-                                "⏹️ Stop Stream",
-                                variant="secondary",
-                                size="lg"
-                            )
+                        stop_stream_btn = gr.Button(
+                            "⏹️ Stop Processing",
+                            variant="stop",
+                            size="lg"
+                        )
                         
                         stream_status = gr.Textbox(
-                            label="Stream Status",
+                            label="Processing Status",
                             value="Ready to start...",
                             interactive=False,
                             lines=2
@@ -507,10 +574,9 @@ def create_interface():
                         gr.Markdown(
                             """
                             **📋 Instructions:**
-                            1. Click "Start Live Recognition" to begin
-                            2. System will automatically detect and register new faces
-                            3. Known workers will be marked present (once per day)
-                            4. New workers get auto-assigned IDs (W0001, W0002, etc.)
+                            - **Live Camera:** Select camera source and click "Start Live Recognition"
+                            - **Video File:** Upload a video file and click "Process Video File"
+                            - Click "Stop Processing" to stop current session
                             
                             **🎨 Color Coding:**
                             - 🟢 **Green:** Known worker (attendance marked)
@@ -520,6 +586,12 @@ def create_interface():
                         )
                     
                     with gr.Column(scale=1):
+                        video_output = gr.Image(
+                            label="Recognition Output",
+                            streaming=True,
+                            interactive=False
+                        )
+                        
                         live_attendance_display = gr.Markdown(
                             value=attendance_system.get_today_attendance(),
                             label="Live Attendance Updates"
@@ -603,55 +675,6 @@ def create_interface():
                             value="Select date range and click 'Generate Report' to view attendance analytics.",
                             label="Attendance Report"
                         )
-            
-            # System Info Tab
-            with gr.Tab("ℹ️ System Information", elem_classes="tab-nav"):
-                gr.Markdown(
-                    """
-                    ## 📖 System Guide
-                    
-                    ### 🎥 Live Recognition System
-                    - **Camera Setup:** Use camera index (0, 1, 2...) or RTSP URL for IP cameras
-                    - **Auto Registration:** Unknown faces automatically get worker IDs (W0001, W0002...)
-                    - **24-Hour Rule:** Each worker can only be marked present once per day
-                    - **Real-time Processing:** Continuous face detection and recognition
-                    
-                    ### 👤 Manual Registration
-                    - Upload clear, front-facing photos for best results
-                    - One face per image for registration
-                    - Workers get unique IDs automatically assigned
-                    
-                    ### 📁 Data Storage Structure
-                    ```
-                    /data/
-                    ├── workers.pkl          # Worker database (embeddings, names, IDs)
-                    ├── attendance.json      # All attendance records
-                    └── faces/               # Saved face images
-                        ├── W0001_John_Doe.jpg
-                        ├── W0002_Jane_Smith.jpg
-                        └── ...
-                    ```
-                    
-                    ### 🔧 Technical Features
-                    - **Face Recognition:** Uses DeepFace with Facenet embeddings
-                    - **Distance Threshold:** 10 for face matching accuracy
-                    - **Threading:** Separate threads for video processing and UI
-                    - **Queue Management:** Efficient frame processing with queue system
-                    - **Error Handling:** Robust error handling and recovery
-                    
-                    ### 🚨 Troubleshooting
-                    - **Camera Issues:** Check camera permissions and connections
-                    - **Poor Recognition:** Ensure good lighting and clear face visibility
-                    - **Performance:** Reduce video resolution for better performance
-                    - **Storage:** Check disk space for face image storage
-                    
-                    ### 🔒 Privacy & Security
-                    - All data stored locally in `/data` folder
-                    - No external API calls or data transmission
-                    - Face images saved securely with worker IDs
-                    - Attendance records in JSON format for easy backup
-                    """
-                )
         
         # Event handlers
         start_stream_btn.click(
@@ -660,6 +683,12 @@ def create_interface():
             outputs=[stream_status]
         )
         
+        process_video_btn.click(
+            fn=attendance_system.process_uploaded_video,
+            inputs=[video_file],
+            outputs=[stream_status]
+        )
+        
         stop_stream_btn.click(
             fn=attendance_system.stop_video_stream,
             outputs=[stream_status]
@@ -694,8 +723,17 @@ def create_interface():
             outputs=[export_status, export_file]
         )
         
-        # Remove the problematic auto-refresh implementation
-        # Users will need to manually click the refresh button
+        # Video frame update
+        def update_video_frame():
+            while True:
+                frame = attendance_system.get_current_frame()
+                if frame is not None:
+                    # Convert BGR to RGB
+                    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                    yield frame
+                time.sleep(0.03)
+        
+        demo.load(update_video_frame, None, video_output, every=0.03)
     
     return demo