diff --git a/01_data_analysis.py b/01_data_analysis.py
index 9008b8c..5be4daf 100644
--- a/01_data_analysis.py
+++ b/01_data_analysis.py
@@ -20,7 +20,7 @@ from sklearn.ensemble import ExtraTreesClassifier
 from sklearn.feature_selection import SelectFromModel 
 
 print("Loading dataset in memory [ ... ]")
-files = pd.read_csv('dataset_clean.txt',delimiter=',', low_memory=False)
+files = pd.read_csv('dataset/dataset_clean.txt',delimiter=',', low_memory=False)
 print("Loading dataset in memory [ DONE ]")
 
 print("Dataset basic infos:")
diff --git a/02_draft_preparation_work.py b/02_draft_preparation_work.py
index 9dce853..3782d80 100644
--- a/02_draft_preparation_work.py
+++ b/02_draft_preparation_work.py
@@ -20,7 +20,7 @@ from sklearn.ensemble import ExtraTreesClassifier
 from sklearn.feature_selection import SelectFromModel 
 
 print("Loading dataset in memory [ ... ]")
-files = pd.read_csv('dataset_clean.txt',delimiter=',', low_memory=False)
+files = pd.read_csv('dataset/dataset_clean.txt',delimiter=',', low_memory=False)
 print("Loading dataset in memory [ DONE ]")
 
 # =-=-=-=-=-=-=-= Data Prepare Work =-=-=-=-=-=-= 
diff --git a/04_detect_from_oneline_csv.py b/04_detect_from_oneline_csv.py
index d9ea5d1..7443f5c 100644
--- a/04_detect_from_oneline_csv.py
+++ b/04_detect_from_oneline_csv.py
@@ -121,18 +121,18 @@ def predict_one_line(model,line):
 # - At the end, print the prediction accuracy result 
 
 res = []
-#nb_malware_to_test = 50
-nb_malware_to_test = 34199 
+nb_malware_to_test = 50
+#nb_malware_to_test = 34199 
 good_ans = 0 
-for i in range(34179,nb_malware_to_test): 
-#for i in range(nb_malware_to_test): 
+#for i in range(34179,nb_malware_to_test): 
+for i in range(nb_malware_to_test): 
 	print(" =-=-=-= Prediction {} out of {} ({}%) [ ERT ~ {} min ] =-=-=-=".format(i, nb_malware_to_test, round((i/nb_malware_to_test)*100,1), round(((nb_malware_to_test-i)*1.2)/60,1)))
 	features = file_to_test.values[i,]
 	features_list = features.tolist()
 	features_array = [features_list]
 	features = np.array(features_array) 
 	res.append(predict_one_line(saved_model, features)) 
-	if res[i-34179] == file_to_test.values[i,][54]: 
+	if res[i] == file_to_test.values[i,][54]: 
 		good_ans +=1 
 	print(features) 
 	print(res)  
diff --git a/06_extract_features_and_predict.py b/06_extract_features_and_predict.py
index 4a5689e..51d1fbd 100644
--- a/06_extract_features_and_predict.py
+++ b/06_extract_features_and_predict.py
@@ -199,8 +199,8 @@ def predict_from_features(features, model):
 	X_unknown = features_numpy 
 	X_unknown_columns = selected_features 
 	X_unknown = pd.DataFrame(X_unknown, columns=X_unknown_columns) 
-	#ans = model.predict(X_unknown)
-	ans = model.predict([features])
+	ans = model.predict(X_unknown)
+	#ans = model.predict([features])
 	return ans[0] 
  
 if __name__ == "__main__": 
diff --git a/tenamortech_malware_scanner_website_ihm.mp4 b/tenamortech_malware_scanner_website_ihm.mp4
new file mode 100644
index 0000000..cc62282
Binary files /dev/null and b/tenamortech_malware_scanner_website_ihm.mp4 differ