Update app.py

app.py

@@ -80,47 +80,50 @@ def attend():
     def run_face_recognition():
         img_file_buffer=st.camera_input("Take a picture")
         if img_file_buffer is not None:
+
+            test_image = Image.open(img_file_buffer)
+            st.image(test_image, use_column_width=True)
             
-            s = True
+            # s = True
     
-            existing_names = set(row[0] for row in csv.reader(csv_file))  # Collect existing names from the CSV file   
+            # existing_names = set(row[0] for row in csv.reader(csv_file))  # Collect existing names from the CSV file   
             
     
-            while s:
-                _, frame = img_file_buffer.read()
-                small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
-                rgb_small_frame = small_frame[:, :, ::-1]
+            # while s:
+            #     _, frame = img_file_buffer.read()
+            #     small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
+            #     rgb_small_frame = small_frame[:, :, ::-1]
                 
-                face_locations = face_recognition.face_locations(rgb_small_frame)
-                face_encodings = face_recognition.face_encodings(small_frame, face_locations)
-                face_names = []
+            #     face_locations = face_recognition.face_locations(rgb_small_frame)
+            #     face_encodings = face_recognition.face_encodings(small_frame, face_locations)
+            #     face_names = []
     
-                for face_encoding in face_encodings:
-                    matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
-                    name = ""
-                    face_distance = face_recognition.face_distance(known_face_encodings, face_encoding)
-                    best_match_index = np.argmin(face_distance)
-                    if matches[best_match_index]:
-                        name = known_faces_names[best_match_index]
+            #     for face_encoding in face_encodings:
+            #         matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
+            #         name = ""
+            #         face_distance = face_recognition.face_distance(known_face_encodings, face_encoding)
+            #         best_match_index = np.argmin(face_distance)
+            #         if matches[best_match_index]:
+            #             name = known_faces_names[best_match_index]
     
-                    face_names.append(name)
+            #         face_names.append(name)
     
                    
-                    for name in face_names:
-                        if name in known_faces_names and name in students and name not in existing_names:
-                            students.remove(name)
-                            print(students)
-                            print(f"Attendance recorded for {name}")
-                            current_time = now.strftime("%H-%M-%S")
-                            csv_writer.writerow([name, current_time, "Present"])
-                            existing_names.add(name)  # Add the name to the set of existing names
+            #         for name in face_names:
+            #             if name in known_faces_names and name in students and name not in existing_names:
+            #                 students.remove(name)
+            #                 print(students)
+            #                 print(f"Attendance recorded for {name}")
+            #                 current_time = now.strftime("%H-%M-%S")
+            #                 csv_writer.writerow([name, current_time, "Present"])
+            #                 existing_names.add(name)  # Add the name to the set of existing names
                             
-                            s = False  # Set s to False to exit the loop after recording attendance
-                            break  # Break the loop once attendance has been recorded for a name
+            #                 s = False  # Set s to False to exit the loop after recording attendance
+            #                 break  # Break the loop once attendance has been recorded for a name
     
-                cv2.imshow("Attendance System", frame)
-                if cv2.waitKey(1) & 0xFF == ord('q'):
-                    break
+            #     cv2.imshow("Attendance System", frame)
+            #     if cv2.waitKey(1) & 0xFF == ord('q'):
+            #         break
         if bytes_data is None:
             st.stop()
 #         video_capture.release()