Update app.py

app.py

@@ -42,6 +42,7 @@ emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']
 # Initialize MTCNN for face detection
 mtcnn = MTCNN()
 
+# Load known faces and names
 # Load known faces and names
 known_faces = []
 known_names = []
@@ -59,13 +60,15 @@ def load_known_faces():
 
             if faces is not None:
                 for face in faces:
-                    x, y, w, h = face[0], face[1], face[2], face[3]
+                    # Convert bounding box coordinates to integers
+                    x, y, w, h = map(int, face)  # Ensure the coordinates are integers
                     roi_gray = gray[y:y+h, x:x+w]
                     known_faces.append(roi_gray)
                     known_names.append(image_name.split('.')[0])  # Assuming file name is the person's name
 
     # Train the recognizer with the known faces
-    face_recognizer.train(known_faces, np.array([i for i in range(len(known_faces))]))
+    if known_faces:  # Only train if we have faces
+        face_recognizer.train(known_faces, np.array([i for i in range(len(known_faces))]))
 
 load_known_faces()
 
@@ -78,7 +81,8 @@ def process_frame(frame):
 
     if faces is not None and len(faces) > 0:
         for face in faces:
-            x, y, w, h = face[0], face[1], face[2], face[3]
+            # Convert bounding box coordinates to integers
+            x, y, w, h = map(int, face)  # Ensure the coordinates are integers
             roi_color = frame[y:y+h, x:x+w]
             roi_gray = gray[y:y+h, x:x+w]
 
@@ -120,6 +124,7 @@ def process_frame(frame):
 
     return frame, result_text
 
+
 # Video feed display
 def video_feed(video_source):
     frame_placeholder = st.empty()  # Placeholder for displaying video frames