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import streamlit as st
from ui.test_results import display_test_results
def display_model_evaluation():
"""Displays the evaluation results of the trained model on the test set."""
st.header("📊 Model Evaluation on Test Set")
# Ensure model and test data exist in session state
if "trained_model" in st.session_state and "X_test" in st.session_state:
trained_model = st.session_state.trained_model
X_test = st.session_state.X_test
y_test = st.session_state.y_test
task_type = st.session_state.task_type
# Handle classification case where model may include a label encoder
if task_type == "classification":
if isinstance(trained_model, tuple):
pipeline, label_encoder = trained_model
display_test_results((pipeline, label_encoder), X_test, y_test, task_type)
else:
display_test_results(trained_model, X_test, y_test, task_type)
else:
display_test_results(trained_model, X_test, y_test, task_type)
else:
st.warning("🚨 Train a model first to see test results!")
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