removed the cv windows

Attendance.txt

@@ -0,0 +1,6 @@
+ross
+phoebe
+chandler
+rachel
+joe
+monica

app.py

@@ -2,15 +2,12 @@ import gradio as gr
 from PIL import Image
 from processing import process_image, generate_embeddings , recognize_faces
 
-def driver(image,zip_file,date):
+def driver(image,zip_file):
     image.save('class_attendance.jpg')
     fig = process_image('class_attendance.jpg')
     generate_embeddings(zip_file)
-    recognize_faces("embeddings.pkl",date)
-    file_name = f"{date}.txt"
-
-    with open(file_name, 'r') as file:
-        content = file.read()
+    recognize_faces("embeddings.pkl")
+    file_name = "Attendance.txt"
     image_detected = Image.open('image_detected.jpg')
     image_grid = Image.open('image_grid.jpg')
     return file_name,image_detected,image_grid
@@ -22,11 +19,11 @@ with open("description.md", "r") as file:
 
 demo = gr.Interface(
     fn=driver,
-    inputs=[gr.Image(label="Upload the image of group/class",type="pil"),gr.File(label="Upload ZIP file containing images of students/employees"),gr.Textbox(label="enter date")],
+    inputs=[gr.Image(label="Upload the image of group/class",type="pil"),gr.File(label="Upload ZIP file containing images of students/employees")],
     outputs=[gr.File(label="Download Attendance File"),gr.Image(label="Image with face detections"),"image"],
     title="Automated Attendance System",
     description=description_text,
-    examples=[["abc1.jpg","friends.zip","01-03-2005"],["abc2.jpg","friends.zip","10-04-2006"]],
+    examples=[["abc1.jpg","friends.zip"],["abc2.jpg","friends.zip"]],
     article="<b>if you find any unexpected or wrong results please flag them so that we can improve our model for those type of inputs.<b>"
 )
-demo.launch(share=False,inline=False)
+demo.launch(inline=False)

embeddings.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9d0cd7d29209df0875b951bd8061cf5a6d00a282caac52e96e0fbf434ee6cee9
-size 50353
+oid sha256:6764c888ef76af345be6cc478a3a95ec8ea98e339e51ae506a3a22fee8302e86
+size 12676

processing.py

@@ -36,13 +36,13 @@ def process_image(image_path):
     gray_image = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
     faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5)
 
-    cv2.namedWindow("output", cv2.WINDOW_NORMAL)       
-    cv2.resizeWindow("output", resized.shape[0],resized.shape[1])    
+    # cv2.namedWindow("output", cv2.WINDOW_NORMAL)       
+    # cv2.resizeWindow("output", resized.shape[0],resized.shape[1])    
     for (x, y, w, h) in faces:
         cv2.rectangle(image_rgb, (x, y), (x+w, y+h), (0, 255, 0), 2)
-        cv2.imshow("output", image_rgb)
+        # cv2.imshow("output", image_rgb)
     #     cv2.waitKey(0)
-    cv2.destroyAllWindows()
+    # cv2.destroyAllWindows()
 
     # convert the image back to RGB format and adjust the brighness and contrast after processing
 
@@ -177,13 +177,13 @@ def generate_embeddings(zip_path):
         # store the array in the database
         database[os.path.splitext(filename)[0]] = signature
 
-    cv2.destroyAllWindows()
+    # cv2.destroyAllWindows()
         # make a file named data_processed.pkl and store the database in it
     myfile = open("embeddings.pkl", "wb")
     pickle.dump(database, myfile)
     myfile.close()
 
-def recognize_faces(embeddigns_path,date):
+def recognize_faces(embeddigns_path):
     myfile = open(embeddigns_path, "rb")
     database = pickle.load(myfile)
     myfile.close()
@@ -236,11 +236,11 @@ def recognize_faces(embeddigns_path,date):
     plt.tight_layout()
     fig.savefig('image_grid.jpg')
             
-    cv2.destroyAllWindows()
+    # cv2.destroyAllWindows()
     # store the name of people present in a text file 
     attendance = [name for name in predicted if name != 'unknown']
 
-    file_name = f"{date}.txt"
+    file_name = "Attendance.txt"
 
     with open(file_name, 'w') as file:
         for item in attendance: