app.py

import gradio as gr
import hopsworks
from datasets import load_dataset
import numpy as np 
import pandas as pd

project = hopsworks.login()
fs = project.get_feature_store()

dataset_api = project.get_dataset_api()

dataset_api.download("Resources/batch_data_predictions.csv")
dataset = load_dataset("csv", data_files="batch_data_predictions.csv")
predictions_df = pd.DataFrame(dataset,columns=['Headlines', 'URL','Predictions'],)
predictions_df_url = predictions_df['URL']

def article_selection(sentiment):
    if sentiment == "Positive":
        return predictions_df_url #f"""The sentence you requested is Positive!"""
    #elif sentiment == "Negative":
     #   return f"""The sentence you requested is Negative!""" 
    #else:
     #   return f"""The sentence you requested is Neutral!""" 



demo = gr.Interface(
    fn=article_selection,
    inputs = gr.Dropdown(["Positive","Negative","Neutral"], label="What type of news articles would you like recommended?"),
    outputs = [gr.Textbox(label="Sentiment of News Articles")],
)


#TODO 
#demo = gr.TabbedInterface([url_demo, voice_demo], ["Swedish YouTube Video to English Text", "Swedish Audio to English Text"])


demo.launch()