Update app.py

app.py

@@ -7,6 +7,7 @@ from PIL import Image
 import sqlite3
 from huggingface_hub import HfApi
 from datetime import datetime
+import face_recognition
 
 # Constants
 KNOWN_FACES_DIR = "known_faces"  # Directory to save user images
@@ -137,14 +138,18 @@ if st.checkbox("Show registered students"):
 
 # Face and Emotion Detection Function
 def detect_faces_and_emotions(image):
-    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    # Convert the image to RGB (as OpenCV works with BGR by default)
+    rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Detect faces using OpenCV's Haar Cascade
     face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.3, minNeighbors=5)
-    
+    faces = face_cascade.detectMultiScale(rgb_image, scaleFactor=1.3, minNeighbors=5)
+
+    # If faces are detected, predict emotions
     for (x, y, w, h) in faces:
-        face = gray_image[y:y+h, x:x+w]
+        face = rgb_image[y:y+h, x:x+w]
         resized_face = cv2.resize(face, (48, 48))  # Resize face to 48x48
-        rgb_face = cv2.cvtColor(resized_face, cv2.COLOR_GRAY2RGB)
+        rgb_face = cv2.cvtColor(resized_face, cv2.COLOR_BGR2RGB)
         normalized_face = rgb_face / 255.0
         reshaped_face = np.reshape(normalized_face, (1, 48, 48, 3))
         
@@ -152,6 +157,7 @@ def detect_faces_and_emotions(image):
         emotion_prediction = emotion_model.predict(reshaped_face)
         emotion_label = np.argmax(emotion_prediction)
         return EMOTION_LABELS[emotion_label]
+    
     return None
 
 # UI for Emotion Detection (Only using webcam now)
@@ -163,6 +169,8 @@ if st.sidebar.selectbox("Menu", ["Register Student", "Face Recognition and Emoti
     if camera_image:
         img = Image.open(camera_image)
         img_array = np.array(img)
+
+        # Detect emotion in the captured image
         emotion_label = detect_faces_and_emotions(img_array)
         if emotion_label:
             st.success(f"Emotion Detected: {emotion_label}")