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fin separation front/back

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hugo.pradier2
2024-06-23 17:44:26 +02:00
parent 15e1674cb2
commit 7dafa78bc4
13 changed files with 201 additions and 131 deletions
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33
frontend/pages/prediction_regression.py
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@@ -1,6 +1,8 @@
import streamlit as st
from sklearn.linear_model import LinearRegression
import pandas as pd
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../../backend')))
from regression_strategy import perform_regression, make_prediction
st.header("Prediction: Regression")
@@ -9,21 +11,24 @@ if "data" in st.session_state:
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')
data_name = st.multiselect("Features", data.select_dtypes(include="number").columns, key="regression_features")
target_name = st.selectbox("Target", data.select_dtypes(include="number").columns, key="regression_target")
submitted = st.form_submit_button('Train and Predict')
if data_name and target_name:
X = data[data_name]
y = data[target_name]
if submitted and data_name and target_name:
try:
model = perform_regression(data, data_name, target_name)
st.session_state.regression_model = model
st.session_state.regression_features_selected = data_name
st.session_state.regression_target_selected = target_name
except ValueError as e:
st.error(e)
model = LinearRegression()
model.fit(X, y)
if "regression_model" in st.session_state:
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))
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]
prediction = make_prediction(st.session_state.regression_model, st.session_state.regression_features_selected, input_values)
st.write("Prediction:", prediction[0])
st.write("Prediction:", prediction)
else:
st.error("File not loaded")