Update app.py

app.py

@@ -5,19 +5,20 @@ import face_recognition
 import os
 from datetime import datetime
 
+
 def greet(video):
     path = "ImagesAttendance"
     images = []
     classNames = []
     myList = os.listdir(path)
     print(myList)
-    for cl in myList
+    for cl in myList:
         curImg = cv2.imread(f'{path}/{cl}')
         images.append(curImg)
         classNames.append(os.path.splitext(cl)[0])
-    print(classNames)  
-    
-    def findEncoding(images):
+    print(classNames)
+
+    def findEncodings(images):
         encodeList = []
         for img in images:
             img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
@@ -26,7 +27,7 @@ def greet(video):
         return encodeList
 
     def markAttendance(name):
-        with open('Attendance.csv','r+') as f:
+        with open('Attendance.csv', 'r+') as f:
             myDataList = f.readlines()
             nameList = []
             for line in myDataList:
@@ -38,40 +39,41 @@ def greet(video):
                 f.writelinbes(f'\n{name},{dtString}')
     encodeListKnown = findEncodings(images)
     print('Encoding Complete')
-                                                 
+
     cap = cv2.VideoCapture(video)
-    
+
     while True:
-    succes, img = cap.read()
-    imgS = cv2.resize(img,(0,0),None,0.25,0.25)
-    imgS = cv2.cvtColor(imgs, cv2.COLOR_BGR2GRAY)
-    
-    facesCurFrame = face:recognition.face_locations(imgS)
-    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
-    
-    for encodeFace_faceLoc in zip(encodesCurFrame, facesCurFrame):
-        matches = face_recognition.compare_faces(encodeListKnown,encodeFace)
-        faceDis = face_recognition.face_distance(encodeListKnown,encodeFace) 
-        matchIndex = np.argmin(faceDis)
-     
-    if matches[matchIndex]:
-        name = classNames[matchIndex].upper()
-        y1,x2,y2,x1 = faceLoc
-        y1,x2,y2,x1 = y1*4,x2*4,y2*4,x1*4
-        cv2.rectangle(img,(x1,y1),(x2,y2),(0,255,0),2)
-        cv2.rectangle(img,(x1,y2-35),(x2,y2),(0,255,0),cv2.FILED)
-        cv2.putText(img,name,(x1+6,y2-6),cv2.FONT_HERSHEY_COMPLEX,1,(255,255,255),2)
-        markAttendance(name)        
-    cv2.imshow('Webcam',img)
-    cv2.waitKey(1)
-        
-        
-    return gray
-    
+        succes, img = cap.read()
+        imgS = cv2.resize(img, (0, 0), None, 0.25, 0.25)
+        imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2GRAY)
+
+        facesCurFrame = face_recognition.face_locations(imgS)
+        encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
+
+        for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
+            matches = face_recognition.compare_faces(
+                encodeListKnown, encodeFace)
+            faceDis = face_recognition.face_distance(
+                encodeListKnown, encodeFace)
+            matchIndex = np.argmin(faceDis)
+
+        if matches[matchIndex]:
+            name = classNames[matchIndex].upper()
+            y1, x2, y2, x1 = faceLoc
+            y1, x2, y2, x1 = y1*4, x2*4, y2*4, x1*4
+            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.rectangle(img, (x1, y2-35), (x2, y2), (0, 255, 0), cv2.FILED)
+            cv2.putText(img, name, (x1+6, y2-6),
+                        cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
+            markAttendance(name)
+        cv2.imshow('Webcam', img)
+        cv2.waitKey(1)
+
+
 iface = gr.Interface(
-fn=greet, 
-inputs=gr.Video(source = "webcam", format = "mp4", streaming = "True"),  
-outputs="image"
+    fn=greet,
+    inputs=gr.Video(source="webcam", format="mp4", streaming="True"),
+    outputs="image"
 )
 
 iface.launch()