add models

.gitattributes

@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+fastfood.pth.wv.vectors_ngrams.npy filter=lfs diff=lfs merge=lfs -text
+fastfood.pth filter=lfs diff=lfs merge=lfs -text

fastfood.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e170fef96d7d064f558fc27cd74d08c4ead05d2a31456bd3808f7d6f20df66f9
+size 1025251

fastfood.pth.wv.vectors_ngrams.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f880edd2056aec8873f205736f308eebd4a3c33df7225d021c753e1d0f723cf
+size 256000128

foodnet.py

@@ -36,7 +36,7 @@ def recommend_ingredients(yum, leftovers, n=10):
   :returns
   output -> top_n recommendations
   '''
-  leftovers_embedding_sum = np.zeros([100,])
+  leftovers_embedding_sum = np.zeros([32,])
   for ingredient in leftovers:
     # pdb.set_trace()
     ingredient_embedding = yum.get_vector(ingredient, norm=True)
@@ -88,7 +88,7 @@ def recommend_ingredients_subsets(model, yum,leftovers, subset_size):
   '''
   all_outputs = {}
   for leftovers_subset in itertools.combinations(leftovers, subset_size):
-    leftovers_embedding_sum = np.empty([100,])
+    leftovers_embedding_sum = np.zeros([32,])
     for ingredient in leftovers_subset:
       ingredient_embedding = yum.word_vec(ingredient, use_norm=True)
       leftovers_embedding_sum += ingredient_embedding
@@ -252,8 +252,8 @@ if __name__ == "__main__":
     #   # model_path = input("Model filename and directory [eg. models/new_model.model]:   ")
     #   # model.save(model_path)
     # else:
-    gdown.download('https://drive.google.com/uc?id=1fXGsWEbr-1BftKtOsnxc61cM3akMAIC0', 'fastfood.pth')
-    gdown.download('https://drive.google.com/uc?id=1h_TijdSw1K9RT3dnlfIg4xtl8WPNNQmn', 'fastfood.pth.wv.vectors_ngrams.npy')
+    # gdown.download('https://drive.google.com/uc?id=1fXGsWEbr-1BftKtOsnxc61cM3akMAIC0', 'fastfood.pth')
+    # gdown.download('https://drive.google.com/uc?id=1h_TijdSw1K9RT3dnlfIg4xtl8WPNNQmn', 'fastfood.pth.wv.vectors_ngrams.npy')
     model, yum = load_model('fastfood.pth')