Update app.py

app.py

@@ -105,20 +105,20 @@ with app_tab:
                 match_idx = torch.argmax(prediction)
                 st.subheader("Face Recognition Results")
                 # st.write("Similarity Scores:", prediction.tolist())
-              if prediction[match_idx] >= 0.6:
-                 name = image_paths[match_idx].split('/')[-1].split('.')[0].upper()
+    if prediction[match_idx] >= 0.6:
+        name = image_paths[match_idx].split('/')[-1].split('.')[0].upper()
 
     # Extract face location from InsightFace result
-                face_info = app.get(cv2_webcam, max_num=1)[0]
-                faceLoc = face_info.bbox.astype(int)  # returns [x1, y1, x2, y2]
+    face_info = app.get(cv2_webcam, max_num=1)[0]
+    faceLoc = face_info.bbox.astype(int)  # returns [x1, y1, x2, y2]
     
-                x1, y1, x2, y2 = faceLoc
-                cv2.rectangle(cv2_webcam, (x1, y1), (x2, y2), (0, 255, 0), 2)
-                cv2.rectangle(cv2_webcam, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
-                cv2.putText(cv2_webcam, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
+    x1, y1, x2, y2 = faceLoc
+    cv2.rectangle(cv2_webcam, (x1, y1), (x2, y2), (0, 255, 0), 2)
+    cv2.rectangle(cv2_webcam, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
+    cv2.putText(cv2_webcam, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
 
     # Convert BGR to RGB before displaying in Streamlit
-                st.image(cv2.cvtColor(cv2_webcam, cv2.COLOR_BGR2RGB), caption=f"Matched: {name}", use_column_width=True)
+    st.image(cv2.cvtColor(cv2_webcam, cv2.COLOR_BGR2RGB), caption=f"Matched: {name}", use_column_width=True)
 
 
                 if prediction[match_idx] >= 0.6: