emi_predict / pages /3_Model_Monitoring.py
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import streamlit as st
import mlflow
from mlflow.tracking import MlflowClient
import pandas as pd
st.title("Model Performance & Monitoring")
client = MlflowClient()
experiments = client.search_experiments()
exp_names = [exp.name for exp in experiments]
selected_exp = st.selectbox("Select Experiment", exp_names)
exp = client.get_experiment_by_name(selected_exp)
runs = client.search_runs(exp.experiment_id)
runs_df = pd.DataFrame([
{
"run_id": r.info.run_id,
"status": r.info.status,
"accuracy": r.data.metrics.get("accuracy"),
"rmse": r.data.metrics.get("rmse"),
}
for r in runs
])
st.dataframe(runs_df)
st.markdown("For full details, access the MLflow UI at http://127.0.0.1:5000")