Update app.py

app.py

@@ -2,61 +2,28 @@ 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()
+    custom = new_model.predict(x)
 
     m=0.000000000000000000001
     a=custom[0]
@@ -70,17 +37,16 @@ def predict_image(pic):
 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")
+        
+        gr.inputs.Image(source="upload",type="filepath", label="Imagen")
     ],
 
     "text",
     
     
     interpretation="default",
-    title = 'FER',
-    description = 'El ',
+    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/ ',
     examples=[["28860.png"], ["28790.png"], ["28953.png"], ["30369.png"], ["28722.png"], ["29026.png"], ["28857.png"], ["28795.png"], ["28880.png"], ["28735.png"], ["28757.png"], ["28727.png"], ["28874.png"], ["28723.png"]],
     theme = 'grass'
  )