Update app.py

app.py

@@ -115,7 +115,7 @@ def gen_grad_img_single(grad_model, img, class_index, alpha = 0.4):
     return np.array(img).astype('uint8'), y_pred
 
 
-def gen_grad_both(grad_model, imgs, y_true, size, cols, font_size):
+def gen_grad_both(grad_model, img):
     img_c, y_pred_c = gen_grad_img_single(grad_model, imgs, 0)
     img_d, y_pred_d = gen_grad_img_single(grad_model, imgs, 1)
     y_pred_c = np.around(y_pred_c,3)
@@ -126,13 +126,14 @@ def gen_grad_both(grad_model, imgs, y_true, size, cols, font_size):
     if y_pred_c[0] > y_pred_c[1]: infer = "cat"
     else: infer = "dog"
         
-    return [img_c, img_d], y_pred_c, infer
+    return img_c, img_d, y_pred_c, infer
 
 weights = "weights.h5"
 
 def get_grad(img):
     img = img_pros(img)
-    grad_imgs, y_pred, infer = gen_grad_img_single(weights, img)
+    grad_model = create_grad_model(weights, 2, "softmax")
+    grad_imgs, y_pred, infer = gen_grad_both(grad_model, img)
     # pred_class = ""
     # if y_pred[0] > 0.5: pred_class = "cat"
     # else: pred_class = "dog"