More comments!

app.py

@@ -13,12 +13,13 @@ from sklearn.metrics.pairwise import cosine_similarity
 from sklearn.preprocessing import LabelEncoder
 from sklearn.preprocessing import OneHotEncoder
 
-# TODO: Implement MBTI features
+# BiteBuddies AI/ML Stack
+# Tinder-style Recommender System/Collaborative Filtering
 # Testing: curl -X POST https://debisoft-junction.hf.space/api/predict -H 'Content-Type: application/json' -d '{"data": [<name>,<body_profile_type>]}
-#  body_profile_type => [0-4] 
+#  body_profile_type => [0-4]
 # Eg. curl -X POST https://debisoft-junction.hf.space/api/predict -H 'Content-Type: application/json' -d '{"data": ["David",4]}
-
 # Adapted from https://www.geeksforgeeks.org/predict-tinder-matches-with-machine-learning/
+# TODO: Implement MBTI features
 
 def greet(name, body_profile_type):
     user_df = {}