Create prediction.py

prediction.py

@@ -0,0 +1,41 @@
+import cv2
+import numpy as np
+from keras.models import load_model
+
+
+def getAge(distr):
+    distr = distr*4
+    if distr >= 0.65 and distr <= 1.4:
+        return "0-18"
+    if distr >= 1.65 and distr <= 2.4:
+        return "19-30"
+    if distr >= 2.65 and distr <= 3.4:
+        return "31-80"
+    if distr >= 3.65 and distr <= 4.4:
+        return "80 +"
+    return "Unknown"
+
+
+def getGender(prob):
+    if prob < 0.5:
+        return "Male"
+    else:
+        return "Female"
+
+
+def getAgeGender(image_path):
+    # Loading the uploaded Image:
+    image = cv2.imread(image_path,0)
+    image = cv2.resize(image,dsize=(64,64))
+    image = image.reshape((image.shape[0],image.shape[1],1))
+
+    # Loading the trained model:
+    model = load_model('data.h5')
+    # model.summary()
+
+    # Getting the predictions:
+    image = image/255
+    val = model.predict(np.array([image]))
+    age = getAge(val[0])
+    gender = getGender(val[1])
+    return age, gender