Update app.py

app.py

@@ -17,10 +17,6 @@ import streamlit as st
 import streamlit_webrtc as webrtc
 
 
-
-
-
-
 app = Flask(__name__)
 
 
@@ -77,28 +73,58 @@ def attend():
     
     csv_writer = csv.writer(csv_file)
 
-@app.route('/at')
-def cam():
-    return render_template('attendance.html')
-
-def main():
-    webrtc_ctx = webrtc.client(ctx=st)
-    webrtc_ctx.create_stream()
-    
-    while True:
-        if webrtc_ctx.video_receiver:
-            frame = webrtc_ctx.video_receiver.get_frame()
-            if frame is not None:
-                image = frame.to_ndarray(format="bgr24")
-                # Process the image with OpenCV as needed
-                
-                # Display the processed image
-                st.image(image, channels="BGR")
-        else:
-            st.write("Waiting for video...")
-        st.sleep(0.1)
-
-    return render_template('attendance.html')
+    # Function to run face recognition
+    def run_face_recognition():
+        video_capture = cv2.VideoCapture(0)
+        s = True
+
+        existing_names = set(row[0] for row in csv.reader(csv_file))  # Collect existing names from the CSV file   
+        
+
+        while s:
+            _, frame = video_capture.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 = []
+
+            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)
+
+               
+                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
+
+            cv2.imshow("Attendance System", frame)
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+
+        video_capture.release()
+        cv2.destroyAllWindows()
+        csv_file.close()
+
+    # Call the function to run face recognition
+    run_face_recognition()
+
+    return redirect(url_for('show_table'))