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app.py

@@ -221,11 +221,12 @@ def _predict_sentiment(p):
   scores = scipy.special.softmax(scores)
   sentiment_map = ['Sadness', 'Joy', 'Love', 'Anger', 'Fear' , "Surprise"]
   df_out = pandas.DataFrame([scores], columns=sentiment_map)
+  df_out = df_out[['Love' , 'Joy', 'Surprise' , 'Fear', 'Sadness', 'Anger']]
   return df_out
 
 @add_method(SentimentAnalyser)
 def draw_bar_plot(df_data, title='Sentiment Analysis', xlabel='p string', ylabel='Emotion Score'):
-  pic = df_data.plot.bar(color=['#e89096', '#747c0c', '#84c98c','#dc545c', '#a31a0e' , '#3fbfbf'],
+  pic = df_data.plot.bar(color=['#84c98c', '#747c0c', '#3fbfbf','#e89096', '#dc545c' , '#a31a0e'],
         title=title,
         ylabel=ylabel,
         xlabel=xlabel,
@@ -235,6 +236,7 @@ def draw_bar_plot(df_data, title='Sentiment Analysis', xlabel='p string', ylabel
 @add_method(SentimentAnalyser)
 def predict_sentiment(p):
   df_out = _predict_sentiment(p)
+  print("sort : ", df_out.sort_values(['Love', 'Joy', 'Surprise', 'Fear', 'Sadness', 'Anger']))
   max_column = df_out.loc[0].idxmax()
   max_value = df_out.loc[0].max()
   title = f'Sentiment Analysis: {max_column}: {round(max_value*100,1)}%'
@@ -242,16 +244,20 @@ def predict_sentiment(p):
   pic = draw_bar_plot(df_out, title=title, xlabel=xlabel)
   return pic.get_figure(), df_out.to_json()  
 
-
 import gradio
 in_box = [gradio.Textbox(lines=1, label="Input", placeholder="type text here")]
 out_box = [gradio.Plot(label="Sentiment Score:"),
   gradio.Textbox(lines=4, label="Raw JSON Response:")]
 title = "Sentiment Analysis: Understanding the Emotional Tone of Text"
-desc = "Sentiment analysis is a powerful tool that can be used to gain insights into how people feel about the world around them."  
+desc = "Sentiment analysis is a powerful tool that can be used to gain insights into how people feel about the world around them." 
+exp = [
+  ['I am feeling very bad today.'],
+  ['I hate to swim early morning.']
+ ] 
 
 gradio.Interface(fn=predict_sentiment,
   inputs=in_box,
   outputs=out_box,
   title=title,
-  description=desc).launch(debug=True)
+  description=desc,
+  examples=exp).launch(debug=True)