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import streamlit as st
import joblib
import numpy as np
# Load models
linear_model = joblib.load("linear_regression_model.pkl")
decision_tree_model = joblib.load("decision_tree_regression_model.pkl")
random_forest_model = joblib.load("random_forest_regress_model.pkl")
app, model_eval = st.tabs(["Application", "Model Evaluation"])
with app:
# Model selection (exclusive to this tab)
st.sidebar.title("Select Model")
model_choice = st.sidebar.radio("Choose a model:", ("Linear Regression", "Decision Tree Regression", "Random Forest Regression"))
if model_choice == "Linear Regression":
model = linear_model
elif model_choice == "Decision Tree Regression":
model = decision_tree_model
else:
model = random_forest_model
# User inputs
st.title("House Price Prediction")
area = st.number_input("Area (sq ft)", min_value=100, max_value=10000, step=10)
bedrooms = st.slider("Bedrooms", 1, 5, 3)
bathrooms = st.slider("Bathrooms", 1, 4, 2)
floors = st.slider("Floors", 1, 3, 1)
year_built = st.number_input("Year Built", min_value=1800, max_value=2025, step=1)
location = st.radio("Location", ["Suburban", "Downtown", "Rural", "Urban"])
condition = st.radio("Condition", ["Poor", "Good", "Excellent", "Fair"])
garage = st.radio("Garage", ["No", "Yes"])
# Encode categorical inputs
location_dict = {"Suburban": 0, "Downtown": 1, "Rural": 2, "Urban": 3}
condition_dict = {"Poor": 0, "Fair": 1, "Good": 2, "Excellent": 3}
garage_dict = {"No": 0, "Yes": 1}
location_encoded = location_dict[location]
condition_encoded = condition_dict[condition]
garage_encoded = garage_dict[garage]
# Prediction button
if st.button("Predict Price"):
features = np.array([[area, bedrooms, bathrooms, floors, year_built, location_encoded, condition_encoded, garage_encoded]])
predicted_price = model.predict(features)[0]
st.write(f"### Predicted House Price: ${predicted_price:,.2f}")
with model_eval:
st.title("Model Evaluation")
st.subheader("Decision Tree Regression")
st.image("DTR_metrics.png", caption="Decision Tree Regression Metrics")
st.image("evalMetric_DTR.png", caption="Decision Tree Regression - Actual vs Predicted")
st.subheader("Linear Regression")
st.image("LR_metrics.png", caption="Linear Regression Metrics")
st.image("evalMetric_LR.png", caption="Linear Regression - Actual vs Predicted")
st.subheader("Random Forest Regression")
st.image("RFR_metrics.png", caption="Random Forest Regression Metrics")
st.image("evalMetric_RFR.png", caption="Random Forest Regression - Actual vs Predicted")
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