hf3

app.py

@@ -206,7 +206,7 @@ elif len(uploaded_file)>0:
             df_temp = pd.concat([df_temp, pos_df])
 
             ############################ 3.2. Emotion Analysis ############################
-
+        
             df_emotion = pd.DataFrame.from_dict(output_emotion)
             df_emotion['Sentence']= pd.Series(useful_sentence)
 
@@ -214,31 +214,34 @@ elif len(uploaded_file)>0:
             df_joy = df_joy[['score', 'Sentence']]
             df_joy = df_joy.sort_values('score', ascending=False)
             df_joy['score'] = df_joy['score'].round(4)
-            df_joy.rename(columns = {'Sentence':'Happy Sentences'}, inplace = True)
+            df_joy.rename(columns = {'Sentence':'Joy Sentences'}, inplace = True)
+            num_of_joy_sentences = df_joy.shape[0]
 
             df_sadness = df_emotion[df_emotion['label']=='sadness']
             df_sadness = df_sadness[['score', 'Sentence']]
             df_sadness = df_sadness.sort_values('score', ascending=False)
             df_sadness['score'] = df_sadness['score'].round(4)
             df_sadness.rename(columns = {'Sentence':'Sad Sentences'}, inplace = True)
+            num_of_sad_sentences = df_sadness.shape[0]
 
             df_anger = df_emotion[df_emotion['label']=='anger']
             df_anger = df_anger[['score', 'Sentence']]
             df_anger = df_anger.sort_values('score', ascending=False)
             df_anger['score'] = df_anger['score'].round(4)
             df_anger.rename(columns = {'Sentence':'Angry Sentences'}, inplace = True)
+            num_of_anger_sentences = df_anger.shape[0]
 
             df_surprise = df_emotion[df_emotion['label']=='surprise']
             df_surprise = df_surprise[['score', 'Sentence']]
             df_surprise = df_surprise.sort_values('score', ascending=False)
             df_surprise['score'] = df_surprise['score'].round(4)
             df_surprise.rename(columns = {'Sentence':'Surprised Sentences'}, inplace = True)
-
+            num_of_surprise_sentences = df_surprise.shape[0]
             
             ############################ 4. Plotting ############################
 
             fig = make_subplots(
-                rows=26, cols=6,
+                rows=30, cols=6,
                 specs=[ [None, None, None, None, None, None],
                         [None, None, None, None, None, None],
                         [None, None, None, None, None, None],
@@ -265,8 +268,14 @@ elif len(uploaded_file)>0:
                         [None, None, None, None, None, None],
                         [None, None, None, None, None, None],
                         [None, None, None, None, None, None],
+                        [None, None, None, None, None, None],
+                        [None, None, None, None, None, None],
+                        [None, None, None, None, None, None],
+                        [None, None, None, None, None, None],
                     ],
             )
+
+            ############################ 4.1. Sentiment Analysis ############################
             colors = px.colors.diverging.Portland#RdBu
             fig.add_trace(go.Pie(labels=labels, values=values, hole = 0.5,
                         title = 'Count by label', 
@@ -332,6 +341,12 @@ elif len(uploaded_file)>0:
             )
             fig.add_trace(table_trace2, row=22, col=4)
 
+
+            ############## Under Construction ##############
+
+            ############################ 4.2. Emotion Analysis ############################
+            go.Bar(x=['Joy', 'Sadness', 'Anger', 'Surprise'], y=[3, 4, 1])
+
             import textwrap
             wrapped_title = "\n".join(textwrap.wrap(title, width=50))