Create app.py

app.py

@@ -0,0 +1,79 @@
+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 skimage import io
+import matplotlib.pyplot as plt
+from tensorflow.python.keras.utils import np_utils
+
+new_model = tf.keras.models.load_model('my_model')
+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))
+    x = image.img_to_array(img)
+    x = np.expand_dims(x, axis = 0)
+
+    x /= 255
+
+    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,
+    [
+        gradio.inputs.Image(self, shape=None, image_mode="RGB", invert_colors=False, source="upload", tool="editor", type="numpy", label=None, optional=False)
+    ],
+
+    "text",
+    
+    ],
+    interpretation="default",
+    title = 'FER',
+    description = 'El ',
+    theme = 'grass'
+)
+
+
+   
+iface.launch()