hf7

app.py

@@ -226,8 +226,12 @@ elif len(uploaded_file)>0:
             ############################ 3. Processing ############################
 
             ############################ 3.1. Sentiment Analysis ############################
+            # labels = ['neutral', 'positive', 'negative']
+            # values = df.label.value_counts().to_list()
+
             labels = ['neutral', 'positive', 'negative']
-            values = df.label.value_counts().to_list()
+            values = [df[df['label']=='neutral'].shape[0], df[df['label']=='positive'].shape[0], df[df['label']=='negative'].shape[0]]
+
 
             # removing words
             words_to_remove = ["s", "quarter", "thank", "million", "Thank", "quetion", 'wa', 'rate', 'firt',
@@ -268,9 +272,14 @@ elif len(uploaded_file)>0:
             if num_of_neu_sentences == 0:
                 neu_df.loc[0] = [0.0, '-------No neutral sentences found in report-------']
             
+            # df_temp = neg_df
+            # df_temp = df_temp['score'] * -1
+            # df_temp = pd.concat([df_temp, pos_df])
             df_temp = neg_df
-            df_temp = df_temp['score'] * -1
-            df_temp = pd.concat([df_temp, pos_df])
+            df_temp['score'] = df_temp['score'] * -1
+            df_temp_list = df_temp['score'].to_list() + pos_df['score'].to_list()
+
+            mean = sum(df_temp_list) / len(df_temp_list)
 
             ############################ 3.2. Emotion Analysis ############################
 
@@ -313,10 +322,21 @@ elif len(uploaded_file)>0:
             if num_of_surprise_sentences == 0:
                 df_surprise.loc[0] = [0.0, '-------No surprised sentences found in report-------']
 
+            # df_temp_emotion = df_sadness
+            # df_temp_emotion = pd.concat([df_sadness, df_anger])
+            # df_temp_emotion = df_temp_emotion['score'] * -1
+            # df_temp_emotion = pd.concat([df_temp_emotion, df_joy])
+
             df_temp_emotion = df_sadness
-            df_temp_emotion = pd.concat([df_sadness, df_anger])
-            df_temp_emotion = df_temp_emotion['score'] * -1
-            df_temp_emotion = pd.concat([df_temp_emotion, df_joy])
+            df_temp_emotion['score'] = df_temp_emotion['score'] * -1
+            df_temp_emotion_list = df_temp_emotion['score'].to_list() + df_joy['score'].to_list()
+            emotion_mean = sum(df_temp_emotion_list) / len(df_temp_emotion_list)
+
+            # df_temp = neg_df
+            # df_temp['score'] = df_temp['score'] * -1
+            # df_temp_list = df_temp['score'].to_list() + pos_df['score'].to_list()
+
+            # mean = sum(df_temp_list) / len(df_temp_list)
 
 
             ############################ 3.3. Intent Analysis ############################
@@ -427,7 +447,7 @@ elif len(uploaded_file)>0:
 
             fig.add_trace(go.Indicator(
                 mode = "number",
-                value = int(df_temp.score.mean()*100),
+                value = int(mean*100),
                 number = {"suffix": "%"},
                 title = {"text": "<span style='font-size:1.5em'>Sentiment Analysis</span><br><span style='font-size:0.8em;color:gray'>Positivity Score</span>"}
                 ), row=4, col=3)
@@ -448,7 +468,7 @@ elif len(uploaded_file)>0:
 
             fig.add_trace(go.Indicator(
                 mode = "gauge+number",
-                value = df_temp.score.mean(),
+                value = mean,
                 domain = {'x': [0, 1], 'y': [0, 1]},
                 title = {'text': "Average of Score", 'font': {'size': 16}},
                 gauge = {
@@ -467,9 +487,9 @@ elif len(uploaded_file)>0:
                 }
             ), row=6, col=5)
 
-            if df_temp.score.mean() < -0.29:
+            if mean < -0.29:
                 fig.update_traces(title_text="Cummulative Sentiment Negative", selector=dict(type='indicator'), row=6, col=5)
-            elif df_temp.score.mean() < 0.29:
+            elif mean < 0.29:
                 fig.update_traces(title_text="Cummulative Sentiment Neutral", selector=dict(type='indicator'), row=6, col=5)
             else:
                 fig.update_traces(title_text="Cummulative Sentiment Positive", selector=dict(type='indicator'), row=6, col=5)
@@ -505,7 +525,7 @@ elif len(uploaded_file)>0:
 
             fig.add_trace(go.Indicator(
                 mode = "number",
-                value = int(df_temp_emotion.score.mean()*100),
+                value = int(emotion_mean*100),
                 number = {"suffix": "%"},
                 title = {"text": "<span style='font-size:1.5em'>Emotion Analysis</span><br><span style='font-size:0.8em;color:gray'>Happiness Score</span>"}
                 ), row=26, col=3)