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 = positive_preds
        predictions_urls = predictions['Url'][0:3]
        return predictions_urls
    elif sentiment == "Negative":
        predictions = negative_preds
        predictions_urls = predictions['Url'][0:3]
        return predictions_urls
    else:
        predictions = neutral_preds
        predictions_urls = predictions['Url'][0:3]
        return predictions_urls

def thanks(url, sentiment):
    thanks_text = "Thank you for making our model better!"
    return thanks_text

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 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!
        '''

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", lines=3),
    description = description1
)

manual_label_demo = 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()