app.py

import os
import mlflow 
import streamlit as st
import pandas as pd
from pycaret.classification import *

# os.environ['MLFLOW_TRACKING_USERNAME'] = 'fandanabil1379'
# os.environ['MLFLOW_TRACKING_PASSWORD'] = 'dadc32f6246f307c2fe4928f3074068f628b79ba'

# # load model
# mlflow.set_tracking_uri('https://dagshub.com/fandanabil1379/loan_prediction.mlflow')
# model_name = "v1.0.1"
# stage = "Production"
# model = mlflow.sklearn.load_model(f"models:/{model_name}/{stage}")

model = load_model('model')

@st.cache_data
def convert_df(df):
   return df.to_csv(index=False).encode('utf-8')

def run():
   # init
   st.set_page_config(page_title="Loan Default Prediction App")
   st.title('Loan Default Prediction')

   uploaded_file = st.file_uploader("Choose a file", type={"csv"})
   if uploaded_file is not None:
      # do prediction
      data = pd.read_csv(uploaded_file)
      prediction = model[-1].predict(data.iloc[:, :-1])
      print(prediction)

      # show the result
      # st.write(prediction)

      # download the result
      # csv = convert_df(prediction)
      # if st.download_button('Download Prediction', csv, 'prediction.csv'):
      #    st.write('Thanks for downloading!')

run()