diff --git a/midterm/code/main.py b/midterm/code/main.py
index 7d9f67a7..dc2d75b4 100644
--- a/midterm/code/main.py
+++ b/midterm/code/main.py
@@ -159,18 +159,25 @@ def preprocessing(rating_data, anime_contact_data, debug=False):
     return pivot_table
 
 
-def predict(prediction_model, pivot_table, seed=42):
+def predict(prediction_model, pivot_table, seed=42, anime="RANDOM"):
     """
     This will choose a random anime name and our prediction_model will predict similar anime.
     """
     np.random.seed(seed)
-    random_anime = np.random.choice(pivot_table.shape[0])
-    query = pivot_table.iloc[random_anime, :].values.reshape(1, -1)
-    distance, suggestions = prediction_model.kneighbors(query, n_neighbors=6)
-    random_anime_name = pivot_table.index[random_anime]
+    print(pivot_table)
+    if anime == "RANDOM":
+        chosen_anime = np.random.choice(pivot_table.shape[0])
+        query = pivot_table.iloc[chosen_anime, :].values.reshape(1, -1)
+        chosen_anime_name = pivot_table.index[chosen_anime]
+    else:
+        query = pivot_table.loc[anime].values.reshape(1, -1)
+        chosen_anime_name = anime
+
+    distance, suggestions = prediction_model.kneighbors(
+        query, n_neighbors=6)
     for i in range(0, len(distance.flatten())):
         if i == 0:
-            print(f"Recommendations for {random_anime_name}:\n")
+            print(f"Recommendations for {chosen_anime_name}:\n")
         else:
             print(
                 f"{i}: {pivot_table.index[suggestions.flatten()[i]]}, with distance of {distance.flatten()[i]}:"
@@ -202,19 +209,21 @@ def handle_arguments():
     parser.add_argument('--metric', '-m', help='Specify metric for NearestNeighbor learner',
                         required=False, default="cosine", choices=allowed_metric)
     allowed_algorithms = ['auto', 'ball_tree', 'kd_tree', 'brute']
-    parser.add_argument('--algorithm', '-a', help='Specify alrgorithm for Nearest Neighbor learner',
+    parser.add_argument('--algorithm', '-a', help='Specify algorithm for Nearest Neighbor learner',
                         required=False, default="brute", choices=allowed_algorithms)
+    parser.add_argument('--anime', '-an', help='Specify anime to choose',
+                        required=False, default="RANDOM")
     # Parse the command-line arguments
     args = parser.parse_args()
 
     # Access the values of the arguments
-    return args.seed, args.debug, args.data_limit, args.database, args.metric, args.algorithm
+    return args.seed, args.debug, args.data_limit, args.database, args.metric, args.algorithm, args.anime
 
 
 if __name__ == "__main__":
-    seed, debug, data_limit, db, metric, algorithm = handle_arguments()
+    seed, debug, data_limit, db, metric, algorithm, anime = handle_arguments()
 
     RATING_DATA, ANIME_CONTACT_DATA = get_data(data_limit, db)
     PIVOT_TABLE = preprocessing(RATING_DATA, ANIME_CONTACT_DATA, debug)
     MODEL = create_model(PIVOT_TABLE, metric, algorithm)
-    predict(MODEL, PIVOT_TABLE, seed)
+    predict(MODEL, PIVOT_TABLE, seed, anime)