app.py

import numpy as np
import streamlit as st
import pandas as pd
from sklearn.metrics import ndcg_score

st.title("NDCG Score Calculator")

st.sidebar.write('Params:')
k = st.sidebar.text_input('k value', key='k')

tr_value = st.text_input('Enter Single Value for true_relevance', key='tr_value')
uploaded_file = st.file_uploader("Upload your CSV or Excel file", type=['csv', 'xlsx'])

limit_num = st.text_input('Enter Limit for Records:', key='limit_num')

if uploaded_file is not None and tr_value and k and limit_num:
        if uploaded_file.name.endswith('.csv'):
            df = pd.read_csv(uploaded_file)
        elif uploaded_file.name.endswith('.xlsx'):
            df = pd.read_excel(uploaded_file)

        #pnkc_values = df['PNKC'].tolist()
        scores = df['pred_score'].tolist()
        scores_lst = scores[:int(limit_num)]
        st.write(f'Considering First {limit_num}')

        true_relevance = []
        for x in range(int(limit_num)):
            true_relevance.append(int(tr_value.strip()))
        

        true_relevance_arr = np.asarray([true_relevance])
        scores_arr = np.asarray([scores_lst])
        if st.button('Get_Result:'):

            ndcg_scr = ndcg_score(true_relevance_arr, scores_arr, k=int(k))
    
            st.write('NDCG Score:')
            st.write(ndcg_scr)

else:
    st.warning('Please enter values for true_relevance and k.')