Update app.py

app.py

@@ -5,12 +5,12 @@ from huggingface_hub import create_repo
 
 dataset = load_dataset("torileatherman/sentiment_analysis_batch_predictions", split='train')
 predictions_df = pd.DataFrame(dataset)
-grouped_predictions = predictions_df.groupby(predictions_df.Sentiment)
-#positive_preds = grouped_predictions.get_group(2)
-#neutral_preds = grouped_predictions.get_group(1)
+grouped_predictions = predictions_df.groupby(predictions_df.Prediction)
+positive_preds = grouped_predictions.get_group(2)
+neutral_preds = grouped_predictions.get_group(1)
 negative_preds = grouped_predictions.get_group(0)
 
-predictions_df['Sentiment'] = predictions_df['Sentiment'].map({0: 'Negative', 1: 'Neutral', 2: 'Positive'})
+predictions_df['Prediction'] = predictions_df['Prediction'].map({0: 'Negative', 1: 'Neutral', 2: 'Positive'})
 
 
 def article_selection(sentiment):
@@ -18,27 +18,27 @@ def article_selection(sentiment):
         predictions = negative_preds
         top3 = predictions[0:3]
         top3_result = top3[['Headline_string','Url']]
-        top3_result.rename(columns = {'Headline_str':'Headlines', 'Url':'URL'})
+        top3_result.rename(columns = {'Headline_string':'Headlines', 'Url':'URL'})
         return top3_result
     
     elif sentiment == "Negative":
         predictions = negative_preds
         top3 = predictions[0:3]
         top3_result = top3[['Headline_string','Url']]
-        top3_result.rename(columns = {'Headline_str':'Headlines', 'Url':'URL'})
+        top3_result.rename(columns = {'Headline_string':'Headlines', 'Url':'URL'})
         return top3_result
     else:
         predictions = negative_preds
         top3 = predictions[0:3]
         top3_result = top3[['Headline_string','Url']]
-        top3_result.rename(columns = {'Headline_str':'Headlines', 'Url':'URL'})
+        top3_result.rename(columns = {'Headline_string':'Headlines', 'Url':'URL'})
         return top3_result
 
 def manual_label():
     # Selecting random row from batch data
     random_sample = predictions_df.sample()
     random_headline = random_sample['Headline_string'].iloc[0]
-    random_prediction = random_sample['Sentiment'].iloc[0]
+    random_prediction = random_sample['Prediction'].iloc[0]
     return random_headline, random_prediction
 
 def thanks(sentiment):