Add requirement file

app.py

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+import os
+import streamlit as st
+import pandas as pd
+from pycaret.classification import *
+
+os.environ['MLFLOW_TRACKING_USERNAME'] = 'fandanabil1379'
+os.environ['MLFLOW_TRACKING_PASSWORD'] = 'dadc32f6246f307c2fe4928f3074068f628b79ba'
+
+@st.cache_data
+def convert_df(df):
+   return df.to_csv(index=False).encode('utf-8')
+
+# init
+st.set_page_config(page_title="Loan Default Prediction App")
+st.title('Loan Default Prediction')
+
+# load model
+def load_model():
+   import mlflow 
+   mlflow.set_tracking_uri('https://dagshub.com/fandanabil1379/loan_prediction.mlflow')
+   model_name = "v1.0.1"
+   stage = "Production"
+   loaded_model = mlflow.sklearn.load_model(f"models:/{model_name}/{stage}")
+   return loaded_model
+
+# load data
+uploaded_file = st.file_uploader("Choose a file")
+if uploaded_file is not None:
+   # do prediction
+   df = pd.read_csv(uploaded_file)
+   model = load_model()
+   prediction = predict_model(model, data=df)
+
+   # 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!')

requirements.txt

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+pandas==1.5.3
+pycaret==3.0.4
+scikit-learn==1.1.3
+scipy==1.10.1
+streamlit==1.25.0