Create app_mlflow.py

app_mlflow.py

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+# app.py
+import streamlit as st
+import mlflow
+import pandas as pd
+
+# Set the MLflow tracking URI (e.g., local file system or a remote server)
+mlflow.set_tracking_uri("http://localhost:5000")
+
+def main():
+    st.title("Streamlit and MLflow Integration")
+
+    # Retrieve the list of experiments
+    experiments = mlflow.list_experiments()
+    experiment_names = [exp.name for exp in experiments]
+    experiment_id_map = {exp.name: exp.experiment_id for exp in experiments}
+
+    # Create a dropdown menu to select an experiment
+    selected_experiment = st.selectbox("Select an experiment:", experiment_names)
+
+    # Get the runs for the selected experiment
+    if selected_experiment:
+        experiment_id = experiment_id_map[selected_experiment]
+        runs = mlflow.search_runs(experiment_ids=[experiment_id])
+
+        # Display the runs in a table
+        st.write("Runs for the selected experiment:")
+        st.write(runs)
+
+if __name__ == "__main__":
+    main()