from keras.models import load_model
from PIL import Image
import numpy as np
import cv2 
import requests
import face_recognition
import os
from datetime import datetime

#the following are to do with this interactive notebook code
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

Images = []
classnames = []
myList = os.listdir()
#st.write(myList)
for cls in myList:
    if os.path.splitext(cls)[1] == ".jpg"  :
        curImg = cv2.imread(f'{cls}')
        Images.append(curImg)
        classnames.append(os.path.splitext(cls)[0])
st.write(classnames)

def findEncodings(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

encodeListknown = findEncodings(Images)
st.write('Encoding Complete')

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)
     
     image = np.asarray(test_image)
    
     #########################
     imgS = cv2.resize(image,(0,0),None,0.25,0.25)
     imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
     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)
         #print(faceDis)
         matchIndex = np.argmin(faceDis)

         if matches[matchIndex]:
             name = classnames[matchIndex]
             st.write(name)
             y1, x2, y2, x1 = faceLoc
             y1, x2, y2, x1 = y1*4,x2*4,y2*4,x1*4
             cv2.rectangle(image,(x1,y1),(x2,y2),(0,255,0),2)
             cv2.rectangle(image,(x1,y2-35),(x2,y2),(0,255,0),cv2.FILLED)
             cv2.putText(image,name,(x1+6,y2-6),cv2.FONT_HERSHEY_COMPLEX,1,(255, 255, 255),2)

             ##############
             url = "https://kiwi-whispering-plier.glitch.me/update" 
             
             data = {
                     'name': name,
             }
             response = requests.get(url, params=data)
             
             if response.status_code == 200 : 
                 st.write(" data updated on : https://kiwi-whispering-plier.glitch.me" )
             else : st.write("data not updated ")
                 
            ##############################
                 
     st.image(image)
if bytes_data is None:
     st.stop()