Update app.py

app.py

@@ -29,7 +29,6 @@ def article_selection(sentiment):
         predictions_df_url1 = predictions['Url'].iloc[1]
         predictions_df_url2 = predictions['Url'].iloc[2]
         return predictions_df_url0, predictions_df_url1, predictions_df_url2
-
     else:
         predictions = negative_preds
         predictions_df_url0 = predictions['Url'].iloc[0]
@@ -37,9 +36,16 @@ def article_selection(sentiment):
         predictions_df_url2 = predictions['Url'].iloc[2]
         return predictions_df_url0, predictions_df_url1, predictions_df_url2
 
-def thanks(url, sentiment):
-    thanks_text = "Thank you for making our model better!"
-    return thanks_text
+def manual_label(sentiment):
+    # Encoding sentiment data
+    predictions_df['Sentiment'] = predictions_df['Sentiment'].map({0: 'Negative', 1: 'Neutral', 2: 'Positive'})
+
+    # 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]
+    return random_headline, random_prediction
+    
 
 description1 =  '''
         This application recommends news articles depending on the sentiment of the headline.
@@ -47,9 +53,8 @@ description1 =  '''
         '''
 
 description2 =  '''
-        This application recommends news articles depending on the sentiment of the headline.
-        Enter a news article url and its sentiment to help us improve our model.
-        The more data we have, the better news articles we can recommend to you!
+        This application will show you a random news headline and our predicted sentiment.
+        In order to improve our model, mark the real sentiment of this headline!
         '''
 
 suggestion_demo = gr.Interface(
@@ -60,7 +65,12 @@ suggestion_demo = gr.Interface(
     description = description1
 )
 
-manual_label_demo = gr.Interface(
+manual_label_demo = gr.Blocks() as demo:
+    output = [gr.Textbox(label="News Headline"),gr.Textbox(label="Our Predicted Sentiment")],
+    generate_btn = gr.Button('Show me a headline!')
+    generate_btn.click(fn=manual_label, outputs=output)
+
+manual_label_demo1 = gr.Interface(
     fn=thanks,
     title="Manually Label a News Article",
     inputs=[gr.Textbox(label = "Paste in URL of news article here."),