diff --git a/data-computer/v1/lda.py b/data-computer/v1/lda.py
index 84ca117..b7206a1 100755
--- a/data-computer/v1/lda.py
+++ b/data-computer/v1/lda.py
@@ -22,7 +22,7 @@
     return text_with_no_accent
 
 def uniformize(text):
-    # del accents
+    # del accents, using remove_accents function
     text = remove_accents(text)
 
     # remove punctuation except " ' "
@@ -46,6 +46,7 @@
 
 # Max topic
 def max_topic(dico):
+    # for a dictionary of topics, return a json with a single key "best topic" and his value is the value of the dictionary.
     best_topic = {}
     best_proba = 0
     for topic in dico:
@@ -58,14 +59,14 @@
 
 
 # WS
-# Datas
+# load all datas
 all_data = []
 for line in sys.stdin:
     data=json.loads(line)
     all_data.append(data)
 
 
-# params
+# following parameters depends of the size of the corpus : num_topics and num_iterations
 n = len(all_data)
 if n< 1001:
     num_topics = 10
@@ -91,12 +92,15 @@
 
 lda_model = models.LdaModel(corpus, num_topics=num_topics, id2word=dictionary,iterations=num_iterations,alpha="symmetric", eta = "auto",minimum_probability=0.1)
 
-# #To see topics (just for me)
+
+# #To see topics (to test it with a jsonl file)
 # sys.stdout.write(json.dumps(lda_model.print_topics()))
 
 # #Get coherence
 # cm = models.coherencemodel.CoherenceModel(model=lda_model, texts=texts, coherence='c_v')
 # cm.get_coherence()
+# exit()
+
 
 # extract infos
 for line in all_data:
@@ -114,7 +118,7 @@
     line["value"]["topics"]=topic_info
     line["value"]["best_topic"]=max_topic(topic_info)
 
-# Write all corpus in once
+# Write output
 for line in all_data:
     sys.stdout.write(json.dumps(line))
     sys.stdout.write("\n")