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import gradio as gr
#import sys
#import os
import pandas as pd
import numpy as np
#import cv2
#import matplotlib.pyplot as plt
#from PIL import Image
#import keras
#import tensorflow as tf
#from keras.models import Model
#from keras.optimizers import Adam
#from keras.applications.vgg16 import VGG16, preprocess_input
#from keras.applications.vgg19 import VGG19, preprocess_input
#from keras.preprocessing.image import ImageDataGenerator
#from keras.callbacks import ModelCheckpoint, EarlyStopping
#from keras.layers import Dense, Dropout, Flatten, MaxPooling2D, Conv2D
#from pathlib import Path
#from sklearn.metrics import accuracy_score
#from keras.models import model_from_json
#from keras.preprocessing import image
#from keras.applications.vgg16 import VGG16, preprocess_input
#import heapq
#file = open("focusondriving.json", 'r')
#model_json2 = file.read()
#file.close()
#loaded_model = model_from_json(model_json2)
#loaded_model.load_weights("focusondriving.h5")
class_dict = {
'c0': 'hands on the wheel',
'c1': 'mobile in right hand',
'c2': 'talking on the phone with right hand',
'c3': "mobile in left hand",
'c4': 'talking on the phone with left hand',
'c5': 'touching at the dash',
'c6': 'drinking',
'c7': 'reaching behind',
'c8': 'touching the head',
'c9': 'looking to the side'
}
def predict_image(pic):
# img = image.load_img(pic, target_size=(224, 224))
# x = image.img_to_array(img)
# x = np.expand_dims(x, axis=0)
# x = preprocess_input(x)
# preds = loaded_model.predict(x)
# preds = list(preds[0])
#list_desc_order = heapq.nlargest(2, range(len(preds)), key=preds.__getitem__)
#result1 = f'c{list_desc_order[0]}'
#result2 = '-'
#result2_ = 0
#if preds[list_desc_order[1]] > 0.3:
# result2 = f'c{list_desc_order[1]}'
# result2_ = round(preds[list_desc_order[1]], 2)
#txt = f"category {directory} result 1 {result1} {round(preds[list_desc_order[0]],2)} | result2 {result2} {result2_}"
#txt = f"categoria {directory}"
#score = round(preds[list_desc_order[0]], 2)*100
#score = int(score)
#txt2 = f"resultado: {class_dict.get(result1)} probabilidad {score}%"
txt3="pepe"
return txt3
iface = gr.Interface(
predict_image,
[
gr.inputs.Image(source="upload",type="filepath", label="Imagen")
],
"text",
interpretation="default",
title = 'FER - Facial Expression Recognition',
description = 'Probablemente nos daremos cuenta de que muchas veces se miente cuando se tratan las emociones, ¿pero nuestra cara también miente? https://saturdays.ai/2022/03/16/detectando-emociones-mediante-imagenes-con-inteligencia-artificial/ ',
theme = 'grass'
)
iface.launch() |