diff --git a/plsa-service/plsa/plsa.py b/plsa-service/plsa/plsa.py
index a1680e6..d8499d7 100644
--- a/plsa-service/plsa/plsa.py
+++ b/plsa-service/plsa/plsa.py
@@ -232,6 +232,10 @@ def train(td,
     axis1 = range(1, len(logL) + 1)
     plt.plot(axis1, logL)
     plt.savefig(logL_pic)
+    with open(logL_pic[:-4]+'.txt','w') as f:
+        for l in logL:
+            f.write(str(l))
+            f.write('\n')
 
 
 
@@ -327,6 +331,10 @@ def train(self, td, Z, maxiter=maxiter2, eps=eps2, folding_in=False,beta=beta2,m
             plt.plot(axis1, logL_c_list)
             # print('logL_pic=',self.logL_pic)
             plt.savefig(self.logL_pic)
+            with open(logL_pic[:-4] + '.txt', 'w') as f:
+                for l in logL_c_list:
+                    f.write(str(l))
+                    f.write('\n')
 
         return self.get_model()
 
diff --git a/service_spec/topic_analysis.proto b/service_spec/topic_analysis.proto
index f28dbf0..faa86e4 100644
--- a/service_spec/topic_analysis.proto
+++ b/service_spec/topic_analysis.proto
@@ -9,13 +9,13 @@ message PLSARequest {
     sint64 num_topics = 2;
     sint64 topic_divider = 3;
     sint64 maxiter = 4;
-    float beta = 5;
+    double beta = 5;
 }
 
 
 message FloatRow{
 
-    repeated float floatRow = 1;
+    repeated double doubleValue = 1;
 }
 
 message PLSAResponse{
@@ -25,9 +25,9 @@ message PLSAResponse{
     repeated string docs_list = 3;
     repeated string topics = 4;
     repeated FloatRow topicByDocMatirx = 5;
-    repeated float topicProbabilities = 6;
-    repeated float wordByTopicConditional = 7;
-    repeated float logLikelihoods = 8;
+    repeated double topicProbabilities = 6;
+    repeated FloatRow wordByTopicConditional = 7;
+    repeated double logLikelihoods = 8;
 
 
 }
diff --git a/snet_test_client.py b/snet_test_client.py
index 54023c6..0593e29 100644
--- a/snet_test_client.py
+++ b/snet_test_client.py
@@ -77,6 +77,12 @@ def try_plsa():
     print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')
     print(resp.topicByDocMatirx)
     print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')
+    print(resp.topicProbabilities)
+    print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')
+    print(resp.wordByTopicConditional)
+    print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')
+    print(resp.logLikelihoods)
+    print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')
 
 
 
diff --git a/topic_analysis_grpc.py b/topic_analysis_grpc.py
index 5793b31..cea5019 100644
--- a/topic_analysis_grpc.py
+++ b/topic_analysis_grpc.py
@@ -89,6 +89,8 @@ def PLSA(self,request,context):
                 topics = f.read().splitlines()
 
             topic_by_doc = []
+            word_by_topic_conditional = []
+            logLikelihoods = []
             docs_list = []
 
             with open(s.PLSA_PARAMETERS_PATH+'topic-by-doc-matirx.csv') as csv_file:
@@ -97,19 +99,35 @@ def PLSA(self,request,context):
                 docs_list = next(csv_reader)[1:]
 
                 for row in csv_reader:
-                    topic_by_doc.append(topic_analysis_pb2.FloatRow(floatRow=list((np.array(row[1:])).astype(np.float))))
+                    topic_by_doc.append(topic_analysis_pb2.FloatRow(doubleValue=list((np.array(row[1:])).astype(np.float))))
 
-            resp = topic_analysis_pb2.PLSAResponse(status=True,message='success',docs_list=docs_list,topics=topics,topicByDocMatirx=topic_by_doc)
-            # resp = topic_analysis_pb2.PLSAResponse(status=True,message='success',topics=topics)
 
+            with open(s.PLSA_PARAMETERS_PATH+'topic_probability_pz','r') as f:
+                topic_probabilities = f.read().splitlines()
 
+                topic_probabilities = list((np.array(topic_probabilities)).astype(np.float))
 
-        #         resp = network_analytics_bipartite_pb2.BipartiteGraphResponse(status=ret[0], message=ret[1], output=graph_resp)
-        #
-        #
-        #     print('status:',resp.status)
-        #     print('message:',resp.message)
-        #     print('Waiting for next call on port 5000.')
+
+            with open(s.PLSA_PARAMETERS_PATH+'word_by_topic_conditional.csv') as csv_file:
+                csv_reader = csv.reader(csv_file, delimiter=',')
+
+                for row in csv_reader:
+                    word_by_topic_conditional.append(topic_analysis_pb2.FloatRow(doubleValue=list((np.array(row[:-1])).astype(np.float))))
+
+            with open(s.PLSA_PARAMETERS_PATH+'logL.txt','r') as f:
+                logLikelihoods = f.read().splitlines()
+
+                logLikelihoods = list((np.array(logLikelihoods)).astype(np.float))
+
+
+            resp = topic_analysis_pb2.PLSAResponse(status=True,message='success',docs_list=docs_list,topics=topics,topicByDocMatirx=topic_by_doc,topicProbabilities=topic_probabilities,wordByTopicConditional=word_by_topic_conditional,logLikelihoods=logLikelihoods)
+
+
+
+
+            print('status:',resp.status)
+            print('message:',resp.message)
+            print('Waiting for next call on port 5000.')
 
             return resp