analysis on all possible files in a provided directory, writes to JSON

predict.py

@@ -3,8 +3,12 @@ from keras.utils import load_img, img_to_array
 from keras.applications.vgg16 import preprocess_input, decode_predictions
 
 def predict(model, path):
+    # only allow valid file types
+    if not (".jpg" in path or ".jpeg" in path):
+        return None
+
     # receive image path as CLI argument
-    img = load_img(path ,color_mode='rgb', target_size=(224, 224))
+    img = load_img(path, color_mode='rgb', target_size=(224, 224))
 
     # loaded image to np array for model to read
     x = img_to_array(img)
@@ -15,5 +19,11 @@ def predict(model, path):
     x = preprocess_input(x)
     features = model.predict(x)
     p = decode_predictions(features)
+    
+    for predict in p:
+        i = 0
+        while i < len(predict):
+            predict[i] = (predict[i][0], predict[i][1], str(predict[i][2]))
+            i = i + 1
 
     return p