import cv2
import numpy as np
import face_recognition
import os
from datetime import datetime
import gradio as gr




def faceEncodings(images):
    encodeList = []
    for img in images:
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        encode = face_recognition.face_encodings(img)[0]
        encodeList.append(encode)
    return encodeList

def Attandance(text,video,image):
  int framecount=0
  names=[]
  path = text
  images = []
  personNames = []
  myList = os.listdir(path)
  unkownEncodings=[]
    
  print(myList)
  for cu_img in myList:
        current_Img = cv2.imread(f'{path}/{cu_img}')
        images.append(current_Img)
        personNames.append(os.path.splitext(cu_img)[0])
  print(personNames)
  encodeListKnown = faceEncodings(images)
  print('All Encodings Complete!!!')
  if video is not None:
      cap = cv2.VideoCapture(video)
      index=1
      while True:
          try:
            if framecount>4:
                break
            else:
                framecount=framecount+1
            ret, frame = cap.read()
            #faces = cv2.resize(frame, (0, 0), None, 0.25, 0.25)
            faces = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    
            facesCurrentFrame = face_recognition.face_locations(faces)
            encodesCurrentFrame = face_recognition.face_encodings(faces, facesCurrentFrame)
    
            for encodeFace, faceLoc in zip(encodesCurrentFrame, facesCurrentFrame):
                matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
                faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
                # print(faceDis)
                matchIndex = np.argmin(faceDis)
    
                if matches[matchIndex]:
                    name = personNames[matchIndex].upper()    
                    if names.count(name) == 0:
                        names.append(name)
        
            cv2.waitKey(1)
          except:
              break
      return ' '.join(names)
  else:
        try:
            #faces = cv2.resize(frame, (0, 0), None, 0.25, 0.25)
            faces = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    
            facesCurrentFrame = face_recognition.face_locations(faces)
            encodesCurrentFrame = face_recognition.face_encodings(faces, facesCurrentFrame)
    
            for encodeFace, faceLoc in zip(encodesCurrentFrame, facesCurrentFrame):
                matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
                faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
                # print(faceDis)
                matchIndex = np.argmin(faceDis)
    
                if matches[matchIndex]:
                    name = personNames[matchIndex].upper()    
                    if names.count(name) == 0:
                        names.append(name)
        
            cv2.waitKey(1)
        except:
             pass
        return ' '.join(names)

demo=gr.Interface(fn=Attandance,
                  inputs=["text","video","image"],
                  outputs="text",
                  title="Face Attendance",
                  
)
demo.launch(debug=True)