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| 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) | |
| #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'}) | |
| def article_selection(sentiment): | |
| if sentiment == "Positive": | |
| predictions = negative_preds | |
| top3 = predictions[0:3] | |
| top3_result = top3[['Headline_string','Url']] | |
| top3_result.rename(columns = {'Headline_str':'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'}) | |
| 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'}) | |
| 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] | |
| return random_headline, random_prediction | |
| def thanks(sentiment): | |
| return f"""Thank you for making our model better!""" | |
| description1 = ''' | |
| This application recommends news articles depending on the sentiment of the headline. | |
| Enter your preference of what type of news articles you would like recommended to you today: Positive, Negative, or Neutral. | |
| ''' | |
| description2 = ''' | |
| 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( | |
| fn=article_selection, | |
| title = 'Recommending News Articles', | |
| inputs = gr.Dropdown(["Positive","Negative","Neutral"], label="What type of news articles would you like recommended?"), | |
| outputs = "dataframe", | |
| #outputs = [gr.Textbox(label="Recommended News Articles (1/3)"),gr.Textbox(label="Recommended News Articles (2/3)"),gr.Textbox(label="Recommended News Articles (3/3)")], | |
| description = description1 | |
| ) | |
| 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")]) | |
| 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()) | |
| manual_label_demo1 = gr.Interface( | |
| fn=thanks, | |
| title="Manually Label a News Article", | |
| inputs=[gr.Textbox(label = "Paste in URL of news article here."), | |
| gr.Dropdown(["Positive","Negative","Neutral"], label="Select the sentiment of the news article.")], | |
| outputs = gr.Textbox(label="Output"), | |
| description = description2 | |
| ) | |
| demo = gr.TabbedInterface([suggestion_demo, manual_label_demo], ["Get recommended news articles", "Help improve our model"]) | |
| demo.launch() |