# import tensorflow
# from keras.models import model_from_json

import gradio as gr
import tensorflow
import pickle
import os
import tensorflow as tf
from tensorflow import keras

#Agregar red neuronal, función q la importe desde la carpeta creada
model = tf.keras.models.load_model('model')

image_size = 250

def predict_image(img):
    img_4d=img.reshape(-1,image_size,image_size,3)
    prediction=model.predict(img_4d, verbose=0)[0]
    print(prediction)
    return {"Victoria": float(prediction[i]) for i in range(1)}    

image = gr.inputs.Image(shape=(image_size,image_size))
label = gr.outputs.Label(num_top_classes=1)

sample_images = []
base_dir = './examples/'
for file in os.listdir(base_dir):
    if file == ".git" or file == ".ipynb_checkpoints":
      continue
    sample_images.append(base_dir + '/' + file)

iface = gr.Interface(fn=predict_image,
             inputs=image,
             outputs=label,
             title="Self Facial Recognition",
             examples=sample_images)

iface.launch()