| import os | |
| from dotenv import load_dotenv | |
| from pathlib import Path | |
| import mlflow | |
| import streamlit as st | |
| import pandas as pd | |
| from pycaret.classification import * | |
| # dotenv_path = Path('.env') | |
| # load_dotenv(dotenv_path=dotenv_path) | |
| # load model | |
| # os.environ['MLFLOW_TRACKING_USERNAME'] = 'fandanabil1379' | |
| # os.environ['MLFLOW_TRACKING_PASSWORD'] = 'dadc32f6246f307c2fe4928f3074068f628b79ba' | |
| # mlflow.set_tracking_uri('https://dagshub.com/fandanabil1379/loan_prediction.mlflow') | |
| # model = mlflow.sklearn.load_model(f"models:/v1.0.1/Production") | |
| # model = load_model('model') | |
| # mlflow.set_tracking_uri('https://dagshub.com/fandanabil1379/loan_prediction.mlflow') | |
| # logged_model = 'runs:/a45156c210f149b2abbfe15e5b824cc4/model' | |
| # # Load model as a PyFuncModel. | |
| # loaded_model = mlflow.pyfunc.load_model(logged_model) | |
| import joblib | |
| loaded_model = joblib.load('model.pkl') | |
| 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 = loaded_model.predict(data) | |
| # show the result | |
| st.write(prediction) | |
| # download the result | |
| # csv = prediction.to_csv(index=False).encode('utf-8') | |
| # if st.download_button('Download Prediction', csv, 'prediction.csv'): | |
| # st.write('Thanks for downloading!') | |
| run() |