Update app.py

app.py

@@ -4,29 +4,40 @@ from tensorflow.keras.preprocessing import image
 import numpy as np
 
 
-
-
-
-
 def predict_age_gender(image):
+    
+    json_file_2 = open('model_gender_2.json', 'r')
+    loaded_file_json_2 = json_file_2.read()
+    json_file_2.close()
+
+    model_2 = model_from_json(loaded_file_json_2)
+    model_2.load_weights('model_gender_2.h5')
+    
+    
     json_file = open('xception.json', 'r')
     loaded_file_json = json_file.read()
     json_file.close()
-    
+
     model = model_from_json(loaded_file_json)
     model.load_weights('xception_modelv2.h5')
+    
+    
 
     images = np.resize(image, new_shape=(1, 128, 128, 3))
     img_pixels = np.expand_dims(images, axis=0)
-    img_pixels = images.astype('float')
+    img_pixels = img_pixels.astype('float64')
     img_pixels = img_pixels.reshape((1, 128, 128, 3))
     img_pixels /= 255
 
+    class_names_gender = ['Fmale', 'Male']
+    gender_predict = model_2.predict(img_pixels)
     predict = model.predict(img_pixels)
-    gender_predict = predict[0]
-    age_predict = predict[1]
-    return {'Gender': ['Fmale' if gender_predict > 0.5 else 'Male'], 'Age': age_predict[0][0]}
+    age_result = predict[1][0].round()
+    
 
+    return 'Female' if gender_predict<0.5 else 'Male', age_result
 
-iface = gr.Interface(predict_age_gender, gr.Image(), gr.Text())
-iface.launch()
+    
+    
+iface = gr.Interface(predict_age_gender, gr.Image(), [gr.Label(label='Gender'), gr.Number(label='Age')],)
+iface.launch()