Update app.py

app.py

@@ -1,37 +1,18 @@
-from flask import Flask, jsonify, render_template, request, send_file
-
-app = Flask(__name__)
-
-
-@app.route("/")
-def index():
-    #return 'hello'
-    return render_template("index.html")
-
-
-
-
-
-
-
-
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
-
-
-
-
-
-
-
-
-
-
-
-
+from flask import *
+from PIL import Image
 
+import face_recognition
+import cv2
+import numpy as np
+import csv
+from datetime import datetime
 
+from matplotlib import pyplot as plt # this lets you draw inline pictures in the notebooks
+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
 
 
 
@@ -39,24 +20,15 @@ if __name__ == "__main__":
 
 
 
+app = Flask(__name__)
 
-# # from PIL import Image
-# from flask import *
-# from flask_socketio import SocketIO,emit
 
-# import face_recognition
-# import cv2
-# import numpy as np
-# import csv
-# from datetime import datetime
+# @app.route("/")
+# def index():
+#     #return 'hello'
+#     return render_template("index.html")
 
-# from matplotlib import pyplot as plt # this lets you draw inline pictures in the notebooks
-# 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
 ####################################################
 # app = Flask(__name__)
 
@@ -67,118 +39,122 @@ if __name__ == "__main__":
 # def test_connect():
 #     print("Connected")
 #     emit("my response", {"data": "Connected"})
-#########################################################    
-# @app.route('/at')
-# def attend():
-#     # Face recognition variables
-#     known_faces_names = ["Sarwan Sir", "Vikas","Lalit","Jasmeen","Anita Ma'am"]
-#     known_face_encodings = []
+########################################################    
+@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]
+    # 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]
+    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]
+    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]
+    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]
+    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]
+    known_face_encodings = [sir_encoding, vikas_encoding,lalit_encoding,jasmine_encoding,maam_encoding]
 
-#     students = known_faces_names.copy()
+    students = known_faces_names.copy()
 
-#     face_locations = []
-#     face_encodings = []
-#     face_names = []
+    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="")
+    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)
-#     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
+    csv_writer = csv.writer(csv_file)
 
+    bytes_data=None
+    def run_face_recognition():
+        img_file_buffer=st.camera_input("Take a picture")
+        if img_file_buffer is not None:
+            
+            s = True
+    
+            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]
+                
+                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
+        if bytes_data is None:
+            st.stop()
 #         video_capture.release()
 #         cv2.destroyAllWindows()
-#         csv_file.close()
-
-#     # Call the function to run face recognition
-#     run_face_recognition()
-
-#     return redirect(url_for('show_table'))
-
-# @app.route('/table')
-# def show_table():
-#     # Get the current date
-#     current_date = datetime.now().strftime("%Y-%m-%d")
-#     # Read the CSV file to get attendance data
-#     attendance=[]
-#     try:
-#         with open(f"{current_date}.csv", newline="") as csv_file:
-#             csv_reader = csv.reader(csv_file)
-#             attendance = list(csv_reader)
-#     except FileNotFoundError:
-#         pass
-#     # Render the table.html template and pass the attendance data
-#     return render_template('attendance.html', attendance=attendance)
+        csv_file.close()
+
+    # Call the function to run face recognition
+    run_face_recognition()
+
+    return redirect(url_for('show_table'))
+
+@app.route('/table')
+def show_table():
+    # Get the current date
+    current_date = datetime.now().strftime("%Y-%m-%d")
+    # Read the CSV file to get attendance data
+    attendance=[]
+    try:
+        with open(f"{current_date}.csv", newline="") as csv_file:
+            csv_reader = csv.reader(csv_file)
+            attendance = list(csv_reader)
+    except FileNotFoundError:
+        pass
+    # Render the table.html template and pass the attendance data
+    return render_template('attendance.html', attendance=attendance)
 
-# @app.route("/")
-# def home():
-#     return render_template('index.html')
+@app.route("/")
+def home():
+    return render_template('index.html')
 
    
 
 
-# if __name__ == '__main__':
-#     # Start Flask application
-#     socket.run(app,host="0.0.0.0", port=5000)
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)