adds "turn" column

app.py

@@ -64,7 +64,8 @@ if mode == 'Create Statistics':
             for si, story in enumerate(gen.data):
                 st.markdown(f'### Story no. {si}:', unsafe_allow_html=False)
                 for i, sentence in enumerate(story):
-                    col_sentence, col_emo = st.columns([3,1])
+                    col_turn, col_sentence, col_emo = st.columns([1,8,2])
+                    col_turn.markdown(sentence['turn'], unsafe_allow_html=False)
                     col_sentence.markdown(sentence['sentence'], unsafe_allow_html=False)
                     col_emo.markdown(f'{sentence["emotion"]} {np.round(sentence["confidence_score"], 3)}', unsafe_allow_html=False)
             st.table(data=gen.stats_df, )

requirements.txt

@@ -7,4 +7,5 @@ numpy==1.22.2
 nltk==3.7
 xlsxwriter==3.0.2
 plotly
-seaborn
+seaborn
+# streamlit

story_gen.py

@@ -117,6 +117,17 @@ class StoryGenerator:
                 'emotion': emotion_user['label'],
                 'confidence_score': emotion_user['score'],
             })
+            stats_dict['sentence_no'] = i
+            stats_dict['turn'] = 'user'
+            stats_dict['sentence'] = new_sentence_user
+            stats_dict['show_emotion'] = show_emotion_user
+            stats_dict['emotion_label'] = emotion_user['label']
+            stats_dict['emotion_score'] = emotion_user['score']
+            stats_dict['num_reactions'] = num_reactions
+            stats_dict['reaction_frequency'] = reaction_frequency
+            stats_dict['reaction_weight'] = reaction_weight
+            stats_df = pd.concat(
+                [stats_df, pd.DataFrame(stats_dict, index=[f'idx_{i}'])])
             # Text generation for Robot
             last_length = len(story_till_now)
             printj.cyan(story_till_now)
@@ -125,26 +136,27 @@ class StoryGenerator:
             genreate_robot_sentence = self.generator(story_till_now, max_length=self.get_num_token(
                 story_till_now)+length, num_return_sequences=1)
             story_till_now = genreate_robot_sentence[0]['generated_text']
-            new_sentence = story_till_now[last_length:]
-            emotion = self.get_emotion(new_sentence)
+            new_sentence_robot = story_till_now[last_length:]
+            emotion_robot = self.get_emotion(new_sentence_robot)
 
             story_data.append({
-                'sentence': new_sentence,
+                'sentence': new_sentence_robot,
                 'turn': 'robot',
-                'emotion': emotion['label'],
-                'confidence_score': emotion['score'],
+                'emotion': emotion_robot['label'],
+                'confidence_score': emotion_robot['score'],
             })
-            
             stats_dict['sentence_no'] = i
-            stats_dict['sentence'] = new_sentence_user
-            stats_dict['show_emotion'] = show_emotion_user
-            stats_dict['emotion_label'] = emotion_user['label']
-            stats_dict['emotion_score'] = emotion_user['score']
-            stats_dict['num_reactions'] = num_reactions
-            stats_dict['reaction_frequency'] = reaction_frequency
-            stats_dict['reaction_weight'] = reaction_weight
+            stats_dict['turn'] = 'robot'
+            stats_dict['sentence'] = new_sentence_robot
+            stats_dict['show_emotion'] = None
+            stats_dict['emotion_label'] = emotion_robot['label']
+            stats_dict['emotion_score'] = emotion_robot['score']
+            stats_dict['num_reactions'] = None
+            stats_dict['reaction_frequency'] = None
+            stats_dict['reaction_weight'] = None
             stats_df = pd.concat(
                 [stats_df, pd.DataFrame(stats_dict, index=[f'idx_{i}'])])
+            
         return stats_df, story_till_now, story_data
 
     def get_stats(self,