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35 lines
1.5 KiB
35 lines
1.5 KiB
import streamlit as st
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import sys
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import os
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../../backend')))
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from regression_strategy import perform_regression, make_prediction
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st.header("Prediction: Regression")
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if "data" in st.session_state:
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data = st.session_state.data
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with st.form("regression_form"):
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st.subheader("Linear Regression Parameters")
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data_name = st.multiselect("Features", data.select_dtypes(include="number").columns, key="regression_features")
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target_name = st.selectbox("Target", data.select_dtypes(include="number").columns, key="regression_target")
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submitted = st.form_submit_button('Train and Predict')
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if submitted and data_name and target_name:
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try:
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model = perform_regression(data, data_name, target_name)
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st.session_state.regression_model = model
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st.session_state.regression_features_selected = data_name
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st.session_state.regression_target_selected = target_name
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except ValueError as e:
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st.error(e)
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if "regression_model" in st.session_state:
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st.subheader("Enter values for prediction")
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input_values = [st.number_input(f"Value for {feature}", value=0.0, key=f"regression_input_{feature}") for feature in st.session_state.regression_features_selected]
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prediction = make_prediction(st.session_state.regression_model, st.session_state.regression_features_selected, input_values)
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st.write("Prediction:", prediction)
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else:
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st.error("File not loaded")
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