Update app.py

app.py

@@ -8,25 +8,35 @@ import csv
 from datetime import datetime
 
 ############################################
-# 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
+from flask import Flask,render_template
+import face_recognition
+import sqlite3
 
-# import io
-# import streamlit as st
-# bytes_data=None
+#################
 from flask_socketio import SocketIO,emit
 import base64
-##################################################3
-
-# import gradio as gr
+import numpy as np
+import cv2 
+import numpy as np
+from keras.models import load_model
+##################
 
 
 
+app = Flask (__name__ )
 
-app = Flask(__name__)
+#################
 app.config['SECRET_KEY'] = 'secret!'
 socket = SocketIO(app,async_mode="eventlet")
+#######################
+
+
+######################
+
+# load model and labels
+np.set_printoptions(suppress=True)
+model = load_model(r"keras_model.h5", compile=False)
+class_names = open(r"labels.txt", "r").readlines()
 
 def base64_to_image(base64_string):
     # Extract the base64 encoded binary data from the input string
@@ -59,136 +69,74 @@ def receive_image(image):
     class_name = class_names[index]
     confidence_score = prediction[0][index]
     emit("result",{"name":str(class_name),"score":str(confidence_score)})
-# flag1 = 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()
-
-# 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'))
+@app.route ("/")
+def home():
+  return render_template("index.html")
+
+@app.route ("/storedata" , methods =[ 'GET' ] )
+def storedata():
+  # uname    =request.form.get("uname")
+  # matching =request.form.get("matching")
+  # rtime    =request.form.get("rtime")
+  name    =request.args.get("class_name")
+  score =request.args.get("confidence_score")
+  # rtime    =request.args.get("rtime")
+  
+  con  = sqlite3.connect("facedb")    # connect  database
+  con.row_factory = sqlite3.Row   # create object of Row
+  cur = con.cursor()              # create cursor object, which will hold records 
+                                  # being fetched from database. 
+  cur.execute( "insert into userdata (username ,  matching , recordingtime) values ('%s',%s,'%s')"%(uname,matching,rtime))
+  #cur.execute("select * from students where email=='%s' and pswd=='%s'"%(useremail,userpswd))
+  con.commit()
+  con.close()
+   
+  return  redirect(url_for( 'datafetch'))
+
+@app.route ("/data" )
+def datafetch():
+  # connect to Sqlite database and fetch data
+  con  = sqlite3.connect("facedb")  # connect sms database
+  con.row_factory = sqlite3.Row  # create object of Row
+  cur = con.cursor()             # create cursor object, which will hold records 
+                        # being fetched from database. 
+  cur.execute( "select id, username, matching, recordingtime from userdata order by id desc limit 10")  # execute a SQL query to get the data
+  rows = cur.fetchall()          # all the data pulled from database is stored in rows object 
+  con.close ()
+  return render_template ('data.html', data = rows)
 
 
+
+
+if __name__ == '__main__': 
+  socket.run(app,host="0.0.0.0", port=7860)
     
 ###########################################################################
-@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('/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')
 
    
 
 
-if __name__ == "__main__":
-    socket.run(app,host="0.0.0.0", port=7860)
+# if __name__ == "__main__":
+#     socket.run(app,host="0.0.0.0", port=7860)