Update app.py

app.py

@@ -38,7 +38,7 @@ def article_selection(sentiment):
 def manual_label():
     # Selecting random row from batch data
     random_sample = predictions_df.sample()
-    random_sample.to_csv('/Users/torileatherman/Github/ID2223_scalable_machine_learning/news_articles_sentiment/', index=False)
+    random_sample.to_csv('/Users/torileatherman/Github/ID2223_scalable_machine_learning/news_articles_sentiment/sample.csv', index=False)
     random_headline = random_sample['Headline_string'].iloc[0]
     random_prediction = random_sample['Prediction'].iloc[0]
     return random_headline, random_prediction
@@ -47,13 +47,14 @@ def manual_label():
 def thanks(sentiment):
     labeled_sentiments = []
     labeled_sentiments.append(sentiment)
-    counter = len(labeled_sentiments)
-    counter = str(counter)
-    login(token = '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')
-    create_repo("torileatherman/"+counter+"labeled_data")
-    labeled_sentiments = pd.DataFrame(labeled_sentiments, columns = ['Predictions'])
-    labeled_sentiments = Dataset.from_pandas(labeled_sentiments)
-    labeled_sentiments.push_to_hub("torileatherman/"+counter+"labeled_data")
+    #counter = len(labeled_sentiments)
+    #counter = str(counter)
+    #login(token = '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')
+    #create_repo("torileatherman/"+counter+"labeled_data")
+    labeled_sentiments = pd.DataFrame(labeled_sentiments, columns = ['Manual Predictions'])
+    labeled_sentiments.to_csv('/Users/torileatherman/Github/ID2223_scalable_machine_learning/news_articles_sentiment/manual_labels.csv', index=False)
+    #labeled_sentiments = Dataset.from_pandas(labeled_sentiments)
+    #labeled_sentiments.push_to_hub("torileatherman/"+counter+"labeled_data")
     return f"""Thank you for making our model better!"""