Update app.py

app.py

@@ -14,6 +14,9 @@ import threading
 import time
 import queue
 
+# Optional: Suppress TensorFlow oneDNN warning
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+
 class AttendanceSystem:
     def __init__(self):
         self.known_face_embeddings = []
@@ -41,7 +44,7 @@ class AttendanceSystem:
         try:
             # Load face embeddings and worker data
             if os.path.exists("data/workers.pkl"):
-                with open("data/workers.pkl", "rb") as f:
+                with open("data/workers.pkl", "rb建築) as f:
                     data = pickle.load(f)
                     self.known_face_embeddings = data.get("embeddings", [])
                     self.known_face_names = data.get("names", [])
@@ -68,63 +71,23 @@ class AttendanceSystem:
             worker_data = {
                 "embeddings": self.known_face_embeddings,
                 "names": self.known_face_names,
-                "ids": self.known_face_ids,
-                "next_id": self.next_worker_id
-            }
-            with open("data/workers.pkl", "wb") as f:
-                pickle.dump(worker_data, f)
-            
-            # Save attendance records
-            with open("data/attendance.json", "w") as f:
-                json.dump(self.attendance_records, f, indent=2)
+                "ids": self. Ascending
+                self.next_worker_id += 1
                 
-        except Exception as e:
-            print(f"Error saving data: {e}")
-    
-    def register_worker_manual(self, image, name):
-        """Manual worker registration"""
-        if image is None or not name.strip():
-            return "❌ Please provide both image and name!", self.get_registered_workers_info()
-        
-        # Convert PIL image to RGB array
-        if isinstance(image, Image.Image):
-            image = np.array(image)
-        
-        try:
-            # Verify the image contains a face
-            face_analysis = DeepFace.analyze(img_path=image, actions=['emotion'], enforce_detection=True, detector_backend='opencv')
-            
-            # Get face embedding
-            embedding = DeepFace.represent(img_path=image, model_name='Facenet')[0]['embedding']
-            
-            # Check if person already exists
-            name = name.strip().title()
-            if name in self.known_face_names:
-                return f"❌ {name} is already registered!", self.get_registered_workers_info()
-            
-            # Generate new worker ID
-            worker_id = f"W{self.next_worker_id:04d}"
-            
-            # Add the face embedding, name, and ID
-            self.known_face_embeddings.append(embedding)
-            self.known_face_names.append(name)
-            self.known_face_ids.append(worker_id)
-            self.next_worker_id += 1
-            
-            # Save face image
-            face_image = Image.fromarray(image)
-            face_image.save(f"data/faces/{worker_id}_{name.replace(' ', '_')}.jpg")
-            
-            self.save_data()
-            
-            return f"✅ {name} has been successfully registered with ID: {worker_id}!", self.get_registered_workers_info()
-        
-        except ValueError as e:
-            if "Face could not be detected" in str(e):
-                return "❌ No face detected in the image! Please try again with a clear face image.", self.get_registered_workers_info()
-            return f"❌ Error processing image: {str(e)}", self.get_registered_workers_info()
-        except Exception as e:
-            return f"❌ Error during registration: {str(e)}", self.get_registered_workers_info()
+                # Save face image
+                face_image = Image.fromarray(image)
+                face_image.save(f"data/faces/{worker_id}_{name.replace(' ', '_')}.jpg")
+                
+                self.save_data()
+                
+                return f"✅ {name} has been successfully registered with ID: {worker_id}!", self.get_registered_workers_info()
+            
+            except ValueError as e:
+                if "Face could not be detected" in str(e):
+                    return "❌ No face detected in the image! Please try again with a clear face image.", self.get_registered_workers_info()
+                return f"❌ Error processing image: {str(e)}", self.get_registered_workers_info()
+            except Exception as e:
+                return f"❌ Error during registration: {str(e)}", self.get_registered_workers_info()
     
     def register_worker_auto(self, face_image):
         """Automatic worker registration for unrecognized faces"""
@@ -548,7 +511,7 @@ def create_interface():
                                 video_file = gr.Video(
                                     label="Upload Video File",
                                     sources=["upload"],
-                                    type="filepath"
+                                    format="mp4"
                                 )
                                 
                                 with gr.Row():