diff --git a/data-computer/v1/lda.py b/data-computer/v1/lda.py
index 3728339..916b5e3 100755
--- a/data-computer/v1/lda.py
+++ b/data-computer/v1/lda.py
@@ -2,10 +2,92 @@
 # -*- coding: utf-8 -*-
 import json
 import sys
+from gensim import corpora, models
+import unicodedata
+import string
+import math
 
+# params
+num_topics = 5  # Number of topics
+num_iterations=100 # "epochs" ====> shall it depends of number of docs ? number of topics ?
+
+stopwords_lists = ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd",
+                    'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers',
+                    'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which',
+                    'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been',
+                    'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if',
+                    'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into',
+                    'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off',
+                    'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any',
+                    'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so',
+                    'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll',
+                    'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't",
+                    'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn',
+                    "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't",
+                    'won', "won't", 'wouldn', "wouldn't", 'au', 'aux', 'avec', 'ce', 'ces', 'dans', 'de', 'des', 'du', 'elle', 'en',
+                    'et', 'eux', 'il', 'ils', 'je', 'la', 'le', 'les', 'leur', 'lui', 'ma', 'mais', 'me', 'meme', 'mes', 'moi', 'mon',
+                    'ne', 'nos', 'notre', 'nous', 'on', 'ou', 'par', 'pas', 'pour', 'qu', 'que', 'qui', 'sa', 'se', 'ses', 'son', 'sur',
+                    'ta', 'te', 'tes', 'toi', 'ton', 'tu', 'un', 'une', 'vos', 'votre', 'vous', 'c', 'd', 'j', 'l', 'm', 'n', 's', 't',
+                    'y', 'ete', 'etee', 'etees', 'etes', 'etant', 'etante', 'etants', 'etantes', 'suis', 'es', 'est', 'sommes', 'etes', 'sont',
+                    'serai', 'seras', 'sera', 'serons', 'serez', 'seront', 'serais', 'serait', 'serions', 'seriez', 'seraient', 'etais', 'etait',
+                    'etions', 'etiez', 'etaient', 'fus', 'fut', 'fumes', 'futes', 'furent', 'sois', 'soit', 'soyons', 'soyez', 'soient', 'fusse',
+                    'fusses', 'fut', 'fussions', 'fussiez', 'fussent', 'ayant', 'ayante', 'ayantes', 'ayants', 'eu', 'eue', 'eues', 'eus', 'ai', 'as',
+                    'avons', 'avez', 'ont', 'aurai', 'auras', 'aura', 'aurons', 'aurez', 'auront', 'aurais', 'aurait', 'aurions', 'auriez', 'auraient',
+                    'avais', 'avait', 'avions', 'aviez', 'avaient', 'eut', 'eumes', 'eutes', 'eurent', 'aie', 'aies', 'ait', 'ayons', 'ayez', 'aient',
+                    'eusse', 'eusses', 'eut', 'eussions', 'eussiez', 'eussent']
+
+stopwords = stopwords_lists
+
+
+def uniformize(text):
+    # del accents
+    text = ''.join(char for char in unicodedata.normalize('NFD', text) if unicodedata.category(char) != 'Mn')
+
+    # remove punctuation except " ' "
+    punctuation = ''.join(char for char in string.punctuation if char != "'")
+    text = ''.join(char for char in text if char not in punctuation)
+
+    return text.replace("'"," ").lower()
+
+
+def tokenize(text):
+    tokens = [word for word in text.split() if word not in stopwords]
+    return tokens
+
+
+
+# WS
+# Datas
+all_data = []
 for line in sys.stdin:
     data=json.loads(line)
-    data['lda']='Comming soon'
-    sys.stdout.write(json.dumps(data))
-    sys.stdout.write('\n')
+    all_data.append(data)
+
+
+# training LDA
+texts = [tokenize(uniformize(line["value"])) for line in all_data]
+dictionary = corpora.Dictionary(texts) # Dictionary from texts : corpus is [ [(num_token,numb_token),...] , [....] ]. The list represent docs of corpus
+corpus = [dictionary.doc2bow(text) for text in texts]
+
+lda_model = models.LdaModel(corpus, num_topics=num_topics, id2word=dictionary,iterations=num_iterations)
+
+
+# extract infos
+for line in all_data:
+    idx = line["id"]
+    doc = line["value"]
+    doc_bow = dictionary.doc2bow(texts[int(idx)-1])
+    topics = lda_model[doc_bow]
+    topic_info = {}
+    for topic_id, topic_weight in topics:
+        topic_words = [word for word, _ in lda_model.show_topic(topic_id)]
+        topic_info[f"topic {topic_id + 1}"] = topic_words
+        topic_info[f"weight {topic_id + 1}"] = str(topic_weight)
+    
+    line["lda"]= topic_info
+
+# Write all corpus in once
+for line in all_data:
+    sys.stdout.write(json.dumps(all_data))
+    sys.stdout.write("\n")