diff --git a/text_analysis/topicModel.py b/text_analysis/topicModel.py
index 4a1a589..d81d75c 100644
--- a/text_analysis/topicModel.py
+++ b/text_analysis/topicModel.py
@@ -113,7 +113,9 @@ def best_lda_model(data_vectorized, vocab):
     #lda = joblib.load('0509_lda.jl')
     return id_topic
 
-def get_most_prevalent(distributions, documents):
+def get_most_prevalent(vect_documents, documents):
+    lda = joblib.load('0509_readme_lda.jl')
+    distributions = lda.transform(vect_documents)
     most_prevalent = {0: [0, ""],1: [0, ""], 2: [0, ""], 3: [0, ""], 4: [0, ""], 5: [0, ""], 6: [0, ""],  7: [0, ""]}
     for i, topic_distribution in enumerate(distributions):
         for j in range(8):
@@ -123,7 +125,8 @@ def get_most_prevalent(distributions, documents):
     return most_prevalent
 
 def prevalent_topics(vect_documents, file_list):
-    lda = joblib.load('0509_readme_lda.jl')
+    #lda = joblib.load('0509_readme_lda.jl')
+    lda = joblib.load('0514_contrib_lda.jl')
     distributions = lda.transform(vect_documents)
     #figuring out what the max distribution is and then figuring out the mode
     top_topic = []
@@ -137,10 +140,11 @@ def prevalent_topics(vect_documents, file_list):
         else:
             count_of_multiple += 1
         topic_arrays.append(topic_distribution)
-    #most_frequent(top_topic)
+    most_frequent(top_topic)
     print(count_of_multiple)
     df = pd.DataFrame(topic_arrays)
     #print(df.sort_values(by=['0']).head(5))
+    '''
     for i in range(8):
         print("-----------------------Topic " + str(i) + " --------------------------------")
         top5 = df.nlargest(10, i)
@@ -153,12 +157,13 @@ def prevalent_topics(vect_documents, file_list):
         print(bottom5)
         for index in bottom_indices:
             print(file_list[index])
-    #averages = df.mean()
-    #print(averages)
+    '''
+    averages = df.mean()
+    print(averages)
     
 def most_frequent(topic_prevalence):
     most_frequent_array = []
-    for j in range(8):
+    for j in range(4):
         topic = mode(topic_prevalence)
         most_frequent_array.append(topic)
         topic_prevalence = [i for i in topic_prevalence if i != topic]
@@ -167,9 +172,9 @@ def most_frequent(topic_prevalence):
 
 
 if __name__ == "__main__":
-    readme_directory = "/data/users/mgaughan/kkex/time_specific_files/readme3"
-    contributing_directory = "/data/users/mgaughan/kkex/time_specific_files/partitioned_contributing/p2"
-    listed_corpus, wordcounts, wordlengths, file_list = get_data_from_dir(readme_directory)
+    #readme_directory = "/data/users/mgaughan/kkex/time_specific_files/dwo_partitioned_readme/p2"
+    contributing_directory = "/data/users/mgaughan/kkex/time_specific_files/dwo_partitioned_contributing/p2"
+    listed_corpus, wordcounts, wordlengths, file_list = get_data_from_dir(contributing_directory)
     print("Mean wordcount: ", mean(wordcounts))
     print("Median wordcount: ", median(wordcounts))
     print("Mean wordlength: ", mean(wordlengths))
@@ -184,11 +189,11 @@ if __name__ == "__main__":
                             )
     data_vectorized = vectorizer.fit_transform(lemmatized_corpus)
     '''
-    vectorizer = joblib.load('readme_vectorizer.jl')
+    vectorizer = joblib.load('contrib_vectorizer.jl')
     data_vectorized = vectorizer.transform(lemmatized_corpus)                
     #lda_model_identification(data_vectorized)
     #topic_distributions = best_lda_model(data_vectorized, vectorizer.get_feature_names_out())
-    #get_most_prevalent(topic_distributions, file_list)
+    #get_most_prevalent(data_vectorized, file_list)
     prevalent_topics(data_vectorized, file_list)