Update app.py

app.py

@@ -152,12 +152,36 @@ else:  # Emotion Detection
             cv2.putText(frame, result_text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
         return frame, result_text
 
-    if mode == "Image":
-        uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
-        if uploaded_image:
-            image = np.array(Image.open(uploaded_image))
-            frame, result_text = process_frame(image)
-            st.image(frame, caption=result_text)
+ if mode == "Image":
+    uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
+    name_for_image = st.text_input("Enter Name (optional, for registration):")
+    register_image_button = st.button("Register Image")
+
+    if uploaded_image:
+        image = np.array(Image.open(uploaded_image))
+        gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(100, 100))
+
+        if len(faces) > 0:
+            for (x, y, w, h) in faces:
+                face_roi = gray_image[y:y + h, x:x + w]
+                cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)  # Annotate detected face
+
+                # Save face if user provides a name
+                if register_image_button and name_for_image:
+                    person_dir = os.path.join(KNOWN_FACES_DIR, name_for_image)
+                    if not os.path.exists(person_dir):
+                        os.makedirs(person_dir)
+                    face_filename = os.path.join(person_dir, f"{name_for_image}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jpg")
+                    cv2.imwrite(face_filename, face_roi)
+                    st.success(f"Face for {name_for_image} has been saved successfully!")
+                    label_ids_rev = train_recognizer()  # Retrain recognizer after adding new face
+
+        else:
+            st.warning("No face detected in the uploaded image. Please try another image.")
+
+        st.image(image, caption="Processed Image with Face Annotations")
+
 
     elif mode == "Camera":
         cap = cv2.VideoCapture(0)