app.py

import streamlit as st
import joblib
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.metrics.pairwise import rbf_kernel

def ratio_name(function_transformer, feature_names_in):
    return ["ratio"]

def column_ratio(X):
    return X[:, [0]] / X[:, [1]]

class ClusterSimilarity(BaseEstimator, TransformerMixin):
    def __init__(self, n_clusters=10, gamma=1.0, random_state=None):
        self.n_clusters = n_clusters
        self.gamma = gamma
        self.random_state = random_state

    def fit(self, X, y=None, sample_weight=None):
        self.kmeans_ = KMeans(self.n_clusters, n_init=10,
                              random_state=self.random_state)
        self.kmeans_.fit(X, sample_weight=sample_weight)
        return self  

    def transform(self, X):
        return rbf_kernel(X, self.kmeans_.cluster_centers_, gamma=self.gamma)
    
    def get_feature_names_out(self, names=None):
        return [f"Cluster {i} similarity" for i in range(self.n_clusters)]


model = joblib.load("./model/my_california_housing_model.pkl")

st.title("California Housing Prediction")
st.write("This app predicts housing prices in California based on various features.")

longitude = st.number_input("Longitude")
latitude = st.number_input("Latitude")
housing_median_age = st.number_input("Housing Median Age")
total_rooms = st.number_input("Total Rooms")
total_bedrooms = st.number_input("Total Bedrooms")
population = st.number_input("Population")
households = st.number_input("Households")
median_income = st.number_input("Median Income")
ocean_proximity = st.selectbox("Ocean Proximity", 
                               ['<1H OCEAN', 'INLAND', 'NEAR OCEAN', 'NEAR BAY', 'ISLAND'])

if st.button("Predict"):
    input_data = pd.DataFrame({
        'longitude': [longitude],
        'latitude': [latitude],
        'housing_median_age': [housing_median_age],
        'total_rooms': [total_rooms],
        'total_bedrooms': [total_bedrooms],
        'population': [population],
        'households': [households],
        'median_income': [median_income],
        'ocean_proximity': [ocean_proximity]
    })
    
    input = pd.DataFrame(input_data)
    prediction = model.predict(input)
    
    st.write(f"The predicted house value is: ${prediction[0]:,.2f}")