import streamlit as st
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
# Custom Styling
st.markdown(
"""
""",
unsafe_allow_html=True
)
st.title("House Price Prediction")
# Define ANN Model
class ANN_Model(nn.Module):
def __init__(self, input_cols=11, hidden0=128, hidden1=128, hidden2=128,
hidden3=64, hidden4=64, hidden5=32, hidden6=16, output=1):
super().__init__()
self.f_connected0 = nn.Linear(input_cols, hidden0)
self.f_connected1 = nn.Linear(hidden0, hidden1)
self.f_connected2 = nn.Linear(hidden1, hidden2)
self.f_connected3 = nn.Linear(hidden2, hidden3)
self.f_connected4 = nn.Linear(hidden3, hidden4)
self.f_connected5 = nn.Linear(hidden4, hidden5)
self.f_connected6 = nn.Linear(hidden5, hidden6)
self.out = nn.Linear(hidden6, output)
def forward(self, x):
x = F.relu(self.f_connected0(x))
x = F.relu(self.f_connected1(x))
x = F.relu(self.f_connected2(x))
x = F.relu(self.f_connected3(x))
x = F.relu(self.f_connected4(x))
x = F.relu(self.f_connected5(x))
x = F.relu(self.f_connected6(x))
x = self.out(x)
return x
# Load the trained model
model = ANN_Model()
try:
model.load_state_dict(torch.load("ANN_model.pth", map_location=torch.device('cpu')))
model.eval()
except Exception as e:
st.error(f"❌ Failed to load the model: {e}")
# Input UI in 3 Columns
col1, col2, col3 = st.columns(3)
with col1:
year_built = st.number_input("Year Built", min_value=1800, max_value=2025, value=2000)
num_bedrooms = st.number_input("Number of Bedrooms", min_value=1, max_value=10, value=3)
garage_type = st.selectbox("Garage Type", ['attached', 'detached', 'none'])
garage_type_encoded = 2 if garage_type == 'attached' else 1 if garage_type == 'detached' else 0
has_fireplace = st.selectbox("Has Fireplace", ["True", "False"])
has_fireplace = 1 if has_fireplace == "True" else 0
with col2:
num_of_bathrooms = st.number_input("Number of Bathrooms", min_value=0, max_value=10, value=2)
total_sqft = st.number_input("Total Square Feet", min_value=100, max_value=10000, value=1800)
garage_sqft = st.number_input("Garage Square Feet", min_value=0, max_value=2000, value=400)
has_pool = st.selectbox("Has Pool", ["True", "False"])
has_pool = 1 if has_pool == "True" else 0
with col3:
has_central_heating = st.selectbox("Has Central Heating", ["True", "False"])
has_central_heating = 1 if has_central_heating == "True" else 0
has_central_cooling = st.selectbox("Has Central Cooling", ["True", "False"])
has_central_cooling = 1 if has_central_cooling == "True" else 0
zip_code = st.number_input("ZIP Code", min_value=10000, max_value=99999, value=11203)
# Prediction Logic
if st.button('Predict House Price'):
input_features = np.array([
year_built, num_bedrooms, total_sqft, garage_type_encoded, garage_sqft,
has_fireplace, has_pool, has_central_heating, has_central_cooling, zip_code, num_of_bathrooms
], dtype=np.float32)
input_tensor = torch.tensor(input_features).unsqueeze(0) # Batch dimension
with torch.no_grad():
predicted_price = model(input_tensor).item()
st.markdown(
f'Estimated House Price: ${predicted_price:,.2f}
',
unsafe_allow_html=True
)