import streamlit as st
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../../backend')))
from classification_strategy import perform_classification, make_prediction

st.header("Prediction: Classification")

if "data" in st.session_state:
    data = st.session_state.data

    with st.form("classification_form"):
        st.subheader("Classification Parameters")
        data_name = st.multiselect("Features", data.columns, key="classification_features")
        target_name = st.selectbox("Target", data.columns, key="classification_target")
        test_size = st.slider("Test Size", min_value=0.1, max_value=0.5, value=0.2, step=0.1, key="classification_test_size")
        submitted = st.form_submit_button('Train and Predict')

    if submitted and data_name and target_name:
        try:
            model, label_encoders, accuracy = perform_classification(data, data_name, target_name, test_size)
            st.session_state.classification_model = model
            st.session_state.classification_label_encoders = label_encoders
            st.session_state.classification_accuracy = accuracy
            st.session_state.classification_features_selected = data_name
            st.session_state.classification_target_selected = target_name
        except ValueError as e:
            st.error(e)

    if "classification_model" in st.session_state:
        st.subheader("Model Accuracy")
        st.write(f"Accuracy on test data: {st.session_state.classification_accuracy:.2f}")

        st.subheader("Enter values for prediction")
        input_values = []
        for feature in st.session_state.classification_features_selected:
            if feature in st.session_state.classification_label_encoders:
                values = list(st.session_state.classification_label_encoders[feature].classes_)
                value = st.selectbox(f"Value for {feature}", values, key=f"classification_input_{feature}")
            else:
                value = st.number_input(f"Value for {feature}", value=0.0, key=f"classification_input_{feature}")
            input_values.append(value)

        prediction = make_prediction(st.session_state.classification_model, st.session_state.classification_label_encoders, st.session_state.classification_features_selected, st.session_state.classification_target_selected, input_values)
        
        st.write("Prediction:", prediction)
else:
    st.error("File not loaded")