app.py

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

from keras.models import model_from_json
from keras.preprocessing.image import img_to_array
from PIL import Image

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)

# load model
emotion_dict = {0:'angry', 1 :'happy', 2: 'neutral', 3:'sad', 4: 'surprise'}
# load json and create model
json_file = open('emotion_model1.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
classifier = model_from_json(loaded_model_json)

# load weights into new model
classifier.load_weights("emotion_model1.h5")

#load face
try:
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
except Exception:
    st.write("Error loading cascade classifiers")

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)
    image1 = Image.open(img_file_buffer)
    st.image(test_image, use_column_width=True)
    image = np.asarray(test_image)
    
    img = np.asarray(image1)
    img = cv2.resize(img,(0,0),None,0.25,0.25)

    #image gray
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(
        image=img_gray, scaleFactor=1.3, minNeighbors=5)
    try:
        for (x, y, w, h) in faces:
            cv2.rectangle(img=img, pt1=(x, y), pt2=(
                x + w, y + h), color=(255, 0, 0), thickness=2)
            roi_gray = img_gray[y:y + h, x:x + w]
            roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)
            if np.sum([roi_gray]) != 0:
                roi = roi_gray.astype('float') / 255.0
                roi = img_to_array(roi)
                roi = np.expand_dims(roi, axis=0)
                prediction = classifier.predict(roi)[0]
                maxindex = int(np.argmax(prediction))
                finalout = emotion_dict[maxindex]
                output = str(finalout)
                st.write(output)
            label_position = (x, y)
            img = cv2.putText(img, output, label_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
            st.image(img, use_column_width=True)
    except:
        st.write("face is not clear")

    #########################
    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)
        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
            image = cv2.UMat(image)
            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)
            image = cv2.UMat.get(image)
            ##############
            if name:
                if output=='happy':
                    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" )
                        st.image(image)
                    else :
                        st.write("data not updated ")
                 
                    ##############################
                       
    
                else:
                    st.write("Please smile")
    
    
            else:
                st.write("Failed")