import streamlit as st
from sklearn.linear_model import LinearRegression
import pandas as pd

st.header("Prediction: Regression")

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

    with st.form("regression_form"):
        st.subheader("Linear Regression Parameters")
        data_name = st.multiselect("Features", data.select_dtypes(include="number").columns)
        target_name = st.selectbox("Target", data.select_dtypes(include="number").columns)
        st.form_submit_button('Train and Predict')

    if data_name and target_name:
        X = data[data_name]
        y = data[target_name]

        model = LinearRegression()
        model.fit(X, y)
        
        st.subheader("Enter values for prediction")
        pred_values = [st.number_input(f"Value for {feature}", value=0.0) for feature in data_name]
        prediction = model.predict(pd.DataFrame([pred_values], columns=data_name))

        st.write("Prediction:", prediction[0])
else:
    st.error("File not loaded")