Update app.py

app.py

@@ -12,9 +12,9 @@ import matplotlib.pyplot as plt
 import pylab # this allows you to control figure size 
 pylab.rcParams['figure.figsize'] = (10.0, 8.0) # this controls figure size in the notebook
 
-# import io
-# import streamlit as st
-# bytes_data=None
+import io
+import streamlit as st
+bytes_data=None
 
 ##################################################3
 
@@ -25,21 +25,21 @@ import gradio as gr
 
 app = Flask(__name__)
 
-# flag1 = True
+flag1 = True
 
-# @app.route('/at')
-# def testme():
-#     global flag1
-#     # return "i am in testme"
-#     while flag1 is True:
+@app.route('/at')
+def testme():
+    global flag1
+    # return "i am in testme"
+    while flag1 is True:
         
-#         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)
-#         if bytes_data is None:
-#             flag1 = False
-#             st.stop()
+        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)
+        if bytes_data is None:
+            flag1 = False
+            st.stop()
 
 # def attend():
 #     # Face recognition variables
@@ -128,114 +128,8 @@ app = Flask(__name__)
 #     run_face_recognition()
 
 #     return redirect(url_for('show_table'))
-##########################################################################
-def snap(image,video):
-    return [image,video]
-
-
-# @app.route('/at')
-# def attend():
-#     # Face recognition variables
-#     known_faces_names = ["Sarwan Sir", "Vikas","Lalit","Jasmeen","Anita Ma'am"]
-#     known_face_encodings = []
-
-#     # Load known face encodings
-#     sir_image = face_recognition.load_image_file("photos/sir.jpeg")
-#     sir_encoding = face_recognition.face_encodings(sir_image)[0]
-
-#     vikas_image = face_recognition.load_image_file("photos/vikas.jpg")
-#     vikas_encoding = face_recognition.face_encodings(vikas_image)[0]
-
-#     lalit_image = face_recognition.load_image_file("photos/lalit.jpg")
-#     lalit_encoding = face_recognition.face_encodings(lalit_image)[0]
-
-#     jasmine_image = face_recognition.load_image_file("photos/jasmine.jpg")
-#     jasmine_encoding = face_recognition.face_encodings(jasmine_image)[0]
-
-#     maam_image = face_recognition.load_image_file("photos/maam.png")
-#     maam_encoding = face_recognition.face_encodings(maam_image)[0]
 
-#     known_face_encodings = [sir_encoding, vikas_encoding,lalit_encoding,jasmine_encoding,maam_encoding]
 
-#     students = known_faces_names.copy()
-
-#     face_locations = []
-#     face_encodings = []
-#     face_names = []
-
-#     now = datetime.now()
-#     current_date = now.strftime("%Y-%m-%d")
-#     csv_file = open(f"{current_date}.csv", "a+", newline="")
-    
-#     csv_writer = csv.writer(csv_file)
-    
-
-#     # 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'))
-
-def gradio_interface():
-    demo = gr.Interface(
-        snap,
-        [gr.Image(source="webcam", tool=None), gr.Video(source="webcam")],
-        ["image", "video"],
-    )
-    return demo
-
-
-@app.route('/at')
-def gradio():
-    interface = gradio_interface()
-    return interface.launch()
     
 ###########################################################################
 @app.route('/table')