import gradio as gr
import numpy as np
import cv2
import os
from imutils import resize
import pickle
from sklearn.preprocessing import LabelEncoder
from sklearn.svm import SVC
import numpy as np
import cv2
from imutils import resize


def calc_embeddings(all_files, names):
    detector = cv2.dnn.readNetFromCaffe(
        "../deploy.prototxt.txt", "../res10_300x300_ssd_iter_140000.caffemodel"
    )
    embedder = cv2.dnn.readNetFromTorch("../openface.nn4.small2.v1.t7")
    knownNames = []
    knownEmbeddings = []
    total = 0
    for file in all_files:
        name = names[total]
        path = os.path.join(os.getcwd(), name, file)
        # f = open(f"/celeb_dataset/'{name}'/{file}", "rb")
        f = open(path, "rb")
        file_bytes = np.asarray(bytearray(f.read()), dtype=np.uint8)
        image = cv2.imdecode(file_bytes, 1)
        image = resize(image, width=600)
        (h, w) = image.shape[:2]

        imageBlob = cv2.dnn.blobFromImage(
            cv2.resize(image, (300, 300)),
            1.0,
            (300, 300),
            (104.0, 177.0, 123.0),
            swapRB=False,
            crop=False,
        )
        detector.setInput(imageBlob)
        detections = detector.forward()

        if len(detections) > 0:
            i = np.argmax(detections[0, 0, :, 2])
            confidence = detections[0, 0, i, 2]

        if confidence > 0.5:
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")
            face = image[startY:endY, startX:endX]
            (fH, fW) = face.shape[:2]
            if fW < 20 or fH < 20:
                continue

            faceBlob = cv2.dnn.blobFromImage(
                face, 1.0 / 255, (96, 96), (0, 0, 0), swapRB=True, crop=False
            )
            embedder.setInput(faceBlob)
            vec = embedder.forward()
            knownNames.append(name)
            knownEmbeddings.append(vec.flatten())
        total += 1
    with open("../unknownEmbeddings.pkl", "rb") as fp:
        l = pickle.load(fp)
    with open("../unknownNames.pkl", "rb") as fp:
        n = pickle.load(fp)
    for i in l:
        knownEmbeddings.append(i)
    knownNames = knownNames + n
    return knownEmbeddings, knownNames


def recognize(embeddings, names):

    le = LabelEncoder()
    labels = le.fit_transform(names)
    recognizer = SVC(C=1.0, kernel="linear", probability=True)
    recognizer.fit(embeddings, names)

    return le, recognizer


def run_inference(myImage):

    os.chdir("celeb_dataset")
    celebs = []
    scores = dict()

    for celeb in os.listdir():
        files = []
        names = []
        if celeb in celebs:
            continue
        name = celeb
        celebs.append(name)
        for file in os.listdir(celeb):
            files.append(file)
            names.append(name)
        embeddings, names = calc_embeddings(files, names)
        le, model = recognize(embeddings, names)
        detector = cv2.dnn.readNetFromCaffe(
            "../deploy.prototxt.txt",
            "../res10_300x300_ssd_iter_140000.caffemodel",
        )
        embedder = cv2.dnn.readNetFromTorch("../openface.nn4.small2.v1.t7")
        (h, w) = myImage.shape[:2]
        imageBlob = cv2.dnn.blobFromImage(
            cv2.resize(myImage, (300, 300)),
            1.0,
            (300, 300),
            (104.0, 177.0, 123.0),
            swapRB=False,
            crop=False,
        )
        detector.setInput(imageBlob)
        detections = detector.forward()
        for i in range(0, detections.shape[2]):
            confidence = detections[0, 0, i, 2]
            if confidence > 0.5:
                box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
                (startX, startY, endX, endY) = box.astype("int")
                face = myImage[startY:endY, startX:endX]
                (fH, fW) = face.shape[:2]
                if fW < 20 or fH < 20:
                    continue

                faceBlob = cv2.dnn.blobFromImage(
                    face, 1.0 / 255, (96, 96), (0, 0, 0), swapRB=True, crop=False
                )
                embedder.setInput(faceBlob)
                vec = embedder.forward()
                preds = model.predict_proba(vec)[0]
                j = np.argmax(preds)
                proba = preds[j]
                name = le.classes_[j]
                # text = "{}: {:.2f}%".format(name, proba * 100)
                scores[name] = proba * 100
    result = sorted(scores.items(), key=lambda x: x[1], reverse=True)
    return result


iface = gr.Interface(
    fn=run_inference,
    inputs="image",
    outputs="label",
    live=True,
    interpretation="default",
    title="Who do you look Like?!",
)
iface.launch()