Update app.py

app.py

@@ -13,65 +13,57 @@ vikas_image = face_recognition.load_image_file("photos/vikas.jpg")
 vikas_encoding = face_recognition.face_encodings(vikas_image)[0]
 
 known_face_encoding = [sir_encoding, vikas_encoding]
-
 known_faces_names = ["Sarwan Sir", "Vikas"]
 
 students = known_faces_names.copy()
 
-face_locations = []
-face_encodings = []
-face_names = []
-s = True
-
 now = datetime.now()
 current_date = now.strftime("%Y-%m-%d")
 
+# Create and open the CSV file
 f = open(current_date + '.csv', 'w+', newline='')
 lnwriter = csv.writer(f)
 
-############################
-import io
-import streamlit as st
-bytes_data = None
-
-img_file_buffer = st.camera_input("Take a picture")
-if img_file_buffer is not None:
-    while True:
-        _, frame = video_capture.read()
-
-        if frame is not None and frame.size > 0:
-            small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
-            rgb_small_frame = small_frame[:, :, ::-1]
-            if s:
-                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_encoding, face_encoding)
-                    name = ""
-                    face_distance = face_recognition.face_distance(known_face_encoding, 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)
-                    if name in known_faces_names:
-                        if name in students:
-                            students.remove(name)
-                            print(students)
-                            current_time = now.strftime("%H-%M-%S")
-                            lnwriter.writerow([name, current_time, "Present"])
-            cv2.imshow("attendance system", frame)
-        else:
-            print("Failed to retrieve frame from video capture.")
-            break
-
-        if cv2.waitKey(1) & 0xFF == ord('q'):
-            break
-
-    video_capture.release()
-    cv2.destroyAllWindows()
-    f.close()
+# Initialize variables
+face_locations = []
+face_encodings = []
+face_names = []
+s = True
 
-if bytes_data is None:
-    st.stop()
+while True:
+    _, frame = video_capture.read()
+    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
+    rgb_small_frame = small_frame[:, :, ::-1]
+    
+    if s:
+        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_encoding, face_encoding)
+            name = ""
+            face_distance = face_recognition.face_distance(known_face_encoding, 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)
+            
+            if name in known_faces_names:
+                if name in students:
+                    students.remove(name)
+                    print(students)
+                    current_time = now.strftime("%H-%M-%S")
+                    lnwriter.writerow([name, current_time, "Present"])
+    
+    cv2.imshow("attendance system", frame)
+    
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# Release the video capture and close the CSV file
+video_capture.release()
+cv2.destroyAllWindows()
+f.close()