import gradio as gr
import pandas as pd
import numpy as np 
import os
#import cv2
from tqdm import tqdm
import tensorflow as tf
from tensorflow import keras
from keras.utils import np_utils
#from tensorflow.python.keras.preprocessing import image
#from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
#from skimage import io
import matplotlib.pyplot as plt
#from tensorflow.python.keras.utils import np_utils
import pickle
#with Path("modelo_entrenado.pkl").open("br")as f:
 #   new_model=pickle.load(f)
#new_model = pickle.load(open("modelo_entrenado.pkl", 'rb'))

new_model = tf.keras.models.load_model('modelo_entrenado.h5')
objects = ('angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral')
y_pos = np.arange(len(objects))
print(y_pos)



def emotion_analysis(emotions):
    objects = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
    y_pos = np.arange(len(objects))
    plt.bar(y_pos, emotions, align='center', alpha=0.9)
    plt.tick_params(axis='x', which='both', pad=10,width=4,length=10)
    plt.xticks(y_pos, objects)
    plt.ylabel('percentage')
    plt.title('emotion')
    plt.show()


def predict_image(pic):
    img = image.load_img(pic, grayscale=True, target_size=(48, 48))
    #show_img=image.load_img(pic, grayscale=False, target_size=(200, 200))
    #pic = pic.reshape(-1,48, 48,1])
    x = image.img_to_array(img)
    
    x = np.expand_dims(x, axis = 0)
    
    x /= 255
    #x = x.reshape(1,48,48,1)
    custom = new_model.predict(x)
    
    #emotion_analysis(custom[0])

    #x = np.array(x, 'float32')
    #x = x.reshape([48, 48]);

    #plt.gray()
    #plt.imshow(show_img)
    #plt.show()

    m=0.000000000000000000001
    a=custom[0]
    for i in range(0,len(a)):
        if a[i]>m:
            m=a[i]
            ind=i
        
    return ('Expression Prediction:',objects[ind])
    
iface = gr.Interface(
    predict_image,
    [
        #gr.inputs.Image(shape=None, image_mode="RGB", invert_colors=False, source="upload", tool="editor", type="numpy", label=None, optional=False)
        #gr.inputs.Image(source="upload",shape=(48,48))
        gr.inputs.Image(source="upload",type="filepath")
    ],

    "text",
    
    
    interpretation="default",
    title = 'FER',
    description = 'El ',
    examples=[["28860.png"], ["28790.png"]],
    theme = 'grass'
 )


   
iface.launch()