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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)
def article_selection(sentiment):
if sentiment == "Positive":
predictions = negative_preds
predictions_df_url0 = predictions['Url'].iloc[0]
predictions_df_url1 = predictions['Url'].iloc[1]
predictions_df_url2 = predictions['Url'].iloc[2]
return predictions_df_url0, predictions_df_url1, predictions_df_url2
elif sentiment == "Negative":
predictions = negative_preds
predictions_df_url0 = predictions['Url'].iloc[0]
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]
predictions_df_url1 = predictions['Url'].iloc[1]
predictions_df_url2 = predictions['Url'].iloc[2]
return predictions_df_url0, predictions_df_url1, predictions_df_url2
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.
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 = [gr.Textbox(label="Recommended News Articles"),gr.Textbox(),gr.Textbox()],
description = description1
)
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."),
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() |