Go!

app.py

@@ -55,7 +55,7 @@ def greet(name, str2):
     user_df.drop("bio", axis=1, inplace=True)
     user_df = pd.concat([user_df, tfidf_df], axis=1)
 
-    suggested_arr = recommend(user_df)
+    suggested_arr = recommendOne(user_df)
 
     return "Hello " + name + "!!" + " str2=" + str2
 
@@ -271,6 +271,27 @@ def recommend(user_df, num_recommendations=5):
 	# Return the user_ids of the recommended users
 	return tinder_df['username'].iloc[sim_indices]
 
+def recommendOne(user_df, num_recommendations=1):
+
+	# Apply SVD to the feature
+	# matrix of the user_df dataframe
+	svd_matrixs = svd.transform(user_df)
+
+	# Calculate the cosine similarity
+	# between the user_df and training set users
+	cosine_sim_new = cosine_similarity(svd_matrixs, svd_matrix)
+
+	# Get the indices of the top
+	# num_recommendations similar users
+	sim_scores = list(enumerate(cosine_sim_new[0]))
+	sim_scores = sorted(sim_scores,
+						key=lambda x: x[1], reverse=True)
+	sim_indices = [i[0] for i in
+				sim_scores[1:num_recommendations+1]]
+	ser = tinder_df['username'].iloc[sim_indices]
+
+	return pd.Series(ser[sim_indices[0]])[0]
+
 # Setup complete!
 
 iface = gr.Interface(fn=greet, inputs=["text", "text"], outputs="text")