import tensorflow as tf
import gradio as gr
import cv2

json_file=open(r"dark_s.json","r")
loaded_model_json=json_file.read()
json_file.close()
loaded_model= tf.keras.models.model_from_json(loaded_model_json)
loaded_model.load_weights("dark_s.h5")

json_file=open(r"eyes_d.json","r")
loaded_model_json=json_file.read()
json_file.close()
loaded_model1 = tf.keras.models.model_from_json(loaded_model_json)
loaded_model1.load_weights("eyes_d.h5")

json_file=open(r"wrinkl.json","r")
loaded_model_json=json_file.read()
json_file.close()
loaded_model2 = tf.keras.models.model_from_json(loaded_model_json)
loaded_model2.load_weights("wrinkl.h5")


def image_bounder(img1):
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +'haarcascade_frontalface_default.xml')
    eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +'haarcascade_eye.xml')
    gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x,y,w,h) in faces:
        cv2.rectangle(img1,(x,y),(x+w,y+h),(255,255,0),2)
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = img1[y:y+h, x:x+w]

        eyes = eye_cascade.detectMultiScale(roi_gray)

        for (ex,ey,ew,eh) in eyes:
            cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,127,255),2)

    return img1

def classifier(Imgarr):
    l = []
    img = Imgarr.reshape(-1, 50, 50, 3)
    result = loaded_model.predict(img)
    result = result[0]
    if result[0] >= result[1]:
        l.append("dark spots")
    else:
        l.append("no dark spots")

    result = loaded_model1.predict(img)
    result = result[0]
    if result[0] >= result[1]:
        l.append("no puffy eyes")
    else:
        l.append("puffy eyes")

    result = loaded_model2.predict(img)
    result = result[0]
    if result[0] >= result[1]:
        l.append("no wrinkles on face")
    else:
        l.append("wrinkles on face")

    return "Predictions are : "+str(l)

def out(Imgarr):
    return [image_bounder(Imgarr),classifier(Imgarr)]

interface = gr.Interface(out,gr.inputs.Image(shape=(300,300)),
                          outputs = ["image","text"],
                          description="Classifier of Aging Signs of Images",
                          title="Aging Signs Classifier",
                          examples=[['42.jpg'],['71.jpg'],['11.jfif'],['56.jpg'],['1836.png']])
interface.launch()