Update app.py

app.py

@@ -1,13 +1,7 @@
 import gradio as gr
-import hopsworks
 from datasets import load_dataset
 import pandas as pd
 
-project = hopsworks.login()
-fs = project.get_feature_store()
-
-dataset_api = project.get_dataset_api()
-
 dataset = load_dataset("torileatherman/sentiment_analysis_batch_predictions", split='train')
 predictions_df = pd.DataFrame(dataset)
 grouped_predictions = predictions_df.groupby(predictions_df.Sentiment)
@@ -47,6 +41,10 @@ def manual_label():
     return random_headline, random_prediction
 
 def thanks(sentiment):
+    labeled_sentiments = []
+    labeled_sentiments.append(sentiment)
+    counter = len(labeled_sentiments)
+    labeled_sentiments.write_to_directory("torileatherman/labeled_data_"+counter+"/")
     return f"""Thank you for making our model better!"""
     
 
@@ -73,6 +71,11 @@ with gr.Blocks() as manual_label_demo:
     description = description2
     generate_btn = gr.Button('Show me a headline!')
     generate_btn.click(fn=manual_label, outputs=[gr.Textbox(label="News Headline"),gr.Textbox(label="Our Predicted Sentiment")])
+    nested - gr.Interface(
+        inputs = gr.Dropdown(["Positive","Negative","Neutral"], label="Select the true sentiment of the news article.")
+        outputs = gr.Textbox()
+        fn = 
+    )
     drop_down_label = gr.Dropdown(["Positive","Negative","Neutral"], label="Select the true sentiment of the news article.")
     submit_btn =  gr.Button('Submit your sentiment!')
     submit_btn.click(fn=thanks, inputs=drop_down_label, outputs=gr.Textbox())