import cv2
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from keras_vggface.vggface import VGGFace
from scipy.spatial.distance import cosine
from mtcnn.mtcnn import MTCNN
import gradio as gr
from tensorflow.keras.models import Model
import pathlib
plt = platform.system()
if plt == 'Linux':
    pathlib.WindowsPath = pathlib.PosixPath
base_model = VGGFace(model="senet50")
# same as the following
x = base_model.layers[-2].output
model = Model(inputs=base_model.inputs, outputs=x, name="EmbeddingModel")


def img_to_encoding(image_path, model):
    img = Image.open(image_path)
    if img is not None:
        img = np.array(img)
        img = cv2.resize(img, (224, 224), interpolation=cv2.INTER_AREA)
        plt.imshow(img)
        plt.show()
        x = np.expand_dims(img, axis=0)
        embedding = model.predict(x)[0, :]
        print(embedding)
        return embedding


database = {}
database["dr adnan"] = img_to_encoding(
    "86055fdb-7441-422e-b501-ffac2221dae0.jpg", model)
database["wasay"] = img_to_encoding("Wasay (1).jpg", model)
database["fatima"] = img_to_encoding("IMG_20220826_095746.jpg", model)
database["omer"] = img_to_encoding("Omer_1.jpg", model)
database["saad"] = img_to_encoding("IMG20220825113812.jpg", model)
database["waleed"] = img_to_encoding("IMG_20220825_113352.jpg", model)
database["talha"] = img_to_encoding("IMG20220825113526.jpg", model)
database["asfand"] = img_to_encoding("imgAsfand.jpg", model)
database["afrasiyab"] = img_to_encoding("imgAfra.jpg", model)


def who_is_it(image):

    # START CODE HERE

    # Step 1: Compute the target "encoding" for the image. Use img_to_encoding() see example above. ## (≈ 1 line)
    if image is not None:
        img = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
        x = np.expand_dims(img, axis=0)
        encoding = model.predict(x)[0, :]
        ## Step 2: Find the closest encoding ##

        # Initialize "min_dist" to a large value, say 100 (≈1 line)
        min_dist = 10000000
        identity = "Not in database"
        # Loop over the database dictionary's names and encodings.
        for (name, db_enc) in database.items():
            # Compute L2 distance between the target "encoding" and the current db_enc from the database. (≈ 1 line)
            dist = cosine(db_enc, encoding)
            print(dist)
            # If this distance is less than the min_dist, then set min_dist to dist, and identity to name. (≈ 3 lines)
            if dist < min_dist:
                min_dist = dist
                identity = name
        # END CODE HERE
    if min_dist < 0.3:
        return identity
    else:
        return ("Not in database")


image = gr.inputs.Image(shape=(250, 250))
label = gr.outputs.Label()
faceModel = MTCNN()
faces = faceModel.detect_faces(image)
for face in faces:
     x, y, w, h = face["box"]
      img = image[y:y+h, x:x+w]
       if img is not None:
            img = cv2.resize(img, (224, 224))
            # prepare the image
            img = np.expand_dims(img, axis=0)
        else:
            img = "No face found"
intf = gr.Interface(fn=who_is_it, inputs=img, outputs=label)
intf.launch(inline=False)