app.py

import gradio as gr
import pandas as pd
from io import StringIO

# Import the OneClass class
from oneclass import OneClass

def predict_and_download(positive_csv_file, unlabelled_csv_file, n, Hyperparameter_nu):
    # Create an instance of the OneClass class
    oc = OneClass()
    
    # Call the select_top_n_papers method
    selected_paper_info = oc.select_top_n_papers(n, positive_csv_file, unlabelled_csv_file, Hyperparameter_nu)
    
    # Create a StringIO object to store CSV data
    csv_buffer = StringIO()
    
    # Write DataFrame to the StringIO buffer as CSV
    selected_paper_info.to_csv(csv_buffer, index=False)
    
    # Get the CSV data from the buffer
    csv_content = csv_buffer.getvalue()
    
    
    # Return selected_paper_info and CSV content
    return selected_paper_info, csv_buffer

# Create the interface
iface = gr.Interface(
    fn=predict_and_download,
    inputs=[
        gr.inputs.File(type="csv", label="Positive CSV File"),
        gr.inputs.File(type="csv", label="Unlabelled CSV File"),
        gr.inputs.Number(label="Number of Papers to Select", default=10),
        gr.inputs.Number(label="Hyperparameter nu", default=0.5)
    ],
    outputs=[
        gr.outputs.Dataframe(label="Selected Papers", formats=["csv", "json"]),
        gr.outputs.DownloadButton(label="Download CSV")
    ],
    title="Paper Prediction",
    description="Enter the number of papers to select and upload CSV files for labelled and unlabelled data.",
    article="This interface uses the OneClass algorithm to select the top N papers based on the input CSV files. The Hyperparameter nu controls the sensitivity of the algorithm.",
    theme="default",
    allow_flagging='never'  # Disable flagging feature
)

iface.launch()