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app.py
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app.py
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import streamlit as st
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingRegressor
import geopandas as gpd
import matplotlib.pyplot as plt
# import streamlit as st
# Custom CSS to inject contained in a string
custom_css = """
<style>
.big-font {
font-size:30px !important;
}
.stButton>button {
color: white;
background-color: #FF4B4B;
border-radius:10px;
border:none;
padding: 10px 24px;
font-size: 18px;
}
.stTextInput>div>div>input {
margin-bottom: 10px;
}
</style>
"""
st.set_option('deprecation.showPyplotGlobalUse', False)
def train_model():
df_condo_final = pd.read_csv('cleaned_housing.csv')
X_final = df_condo_final.drop('Price (PHP)',axis=1)
y_final = df_condo_final['Price (PHP)']
price_bins_final = pd.qcut(df_condo_final['Price (PHP)'], q=3, labels=['low', 'medium','high'])
X_train_final, X_test_final, y_train_final, y_test_final = train_test_split(X_final,
y_final,
stratify=price_bins_final,
test_size=0.2,
random_state=13)
gbr_final = GradientBoostingRegressor(learning_rate=0.2,
n_estimators=400,
random_state=13)
gbr_final.fit(X_train_final, y_train_final)
return gbr_final
def get_all_cities():
feature_df = pd.read_csv('feature_df.csv')
city_list = feature_df['city'].unique().tolist()
return city_list
def get_brgy_for_city(city_name):
feature_df = pd.read_csv('feature_df.csv')
brgy_list = feature_df[feature_df['city']==city_name]['brgy'].unique().tolist()
return brgy_list
def get_features(bedrooms, bath, fa, barangay, city):
feature_df = pd.read_csv('feature_df.csv')
feature_df.fillna(0,inplace=True)
# feature_df
filtered_df = feature_df.query(f"`brgy`=='{barangay}' & `city`=='{city}'").copy()
filtered_df['Bedrooms'] = bedrooms
filtered_df['Bath'] = bath
filtered_df['Floor_area (sqm)'] = fa
# filtered_df['Price (PHP)'] = ""
filtered_df["city_pop"] = filtered_df["city_pop"].str.replace(",", "")
filtered_df = filtered_df.apply(pd.to_numeric, errors="coerce")
final_columns = [
'Latitude', 'Longitude', 'Bedrooms', 'Bath',
'Floor_area (sqm)', 'brgy_area_sqm', 'city_pop',
'Food_Count', 'Education_Count', 'Healthcare_Count',
'Public_Services_Count', 'Finance_Count', 'Transportation_Count',
# 'Price (PHP)'
]
return filtered_df[final_columns]
def get_price(sample_1, model):
sample_1['Price (PHP)'] = (model.predict(sample_1))[0]*1.4
return sample_1
def plot_unit(city, barangay):
mm_brgy_centroid = gpd.read_file('mm_brgy_centroid.shp')
ph_shp = gpd.read_file('ph_shp.shp')
city_shp = gpd.read_file('city_level_shp.shp')
mm_brgy_centroid.columns = ['city', 'barangay', 'geometry']
ph_shp.columns = ['province', 'muni/city', 'geometry']
city_shp.columns = ['city', 'city_area_ha', 'geometry']
unit_city_loc = city_shp.query(f"`city`=='{city}'")
unit_brgy_loc = mm_brgy_centroid.query(f"`barangay`=='{barangay}' & `city`=='{city}'")
# Plotting
fig, ax = plt.subplots(figsize=(15,10))
unit_brgy_loc.plot(ax=ax, marker='^', zorder=4, markersize=30, color='white')
unit_brgy_loc.plot(ax=ax, marker='^', zorder=4,
markersize=18, color='maroon', legend=True,
label=f"Unit Location: {barangay}, {city}")
unit_city_loc.plot(ax=ax, color='#FFDC4D', linewidth=0.6, edgecolor='black', zorder=3)#0F2D66
city_shp.plot(ax=ax, color='gainsboro', edgecolor='black', linewidth=0.3, zorder=2)#FFDC4D
xlims = plt.xlim()
ylims = plt.ylim()
ph_shp.boundary.plot(ax=ax, color='darkgrey', linewidth=0.6, zorder=1)
plt.xlim(xlims)
plt.ylim(ylims)
plt.xlabel('Longitude')
plt.ylabel('Latitude')
# Adjust the legend
leg = plt.legend(prop={'size': 9}, loc='upper left')
leg.set_zorder(4) # put the legend on top
plt.show()
# Initialize your app with the trained model
model = train_model()
# Streamlit app layout
def main():
# Display an image from the local disk
st.image('title_banner_2.jpg')
st.markdown(custom_css, unsafe_allow_html=True)
# Use the new styles in your app
st.markdown('<p class="big-font">Condo Price Prediction App</p>', unsafe_allow_html=True)
# Inputs
fa = st.number_input('Floor Area in (m2)', format="%.2f", value=24.00)
bedrooms = st.number_input('Number of Bedrooms (For Studio, input 1)', step=1, format="%d",value=1)
bath = st.number_input('Number of Bathrooms', step=1, format="%d",value=1)
city = st.selectbox('City', get_all_cities())
brgy = st.selectbox('Barangay', get_brgy_for_city(city))
# Button to make prediction
if st.button('Predict Price'):
# Ensure the model is loaded
if bath < 1 or bedrooms < 1 or fa <1:
st.error("Invalid Input. Input must be at least 1.")
elif model is not None:
features = get_features(bedrooms, bath, fa, brgy, city)
prediction = get_price(features, model)
if bedrooms==1 and bath==1:
st.write(f"The estimated price for a {bedrooms} bedroom "
f"and {bath} bathroom in {brgy}, {city} is: "
f"{prediction['Price (PHP)'].iloc[0]:,.2f} PHP")
elif bedrooms>1 and bath==1:
st.write(f"The estimated price for a {bedrooms} bedrooms "
f"and {bath} bathroom in {brgy}, {city} is: "
f"{prediction['Price (PHP)'].iloc[0]:,.2f} PHP")
elif bedrooms==1 and bath>1:
st.write(f"The estimated price for a {bedrooms} bedroom "
f"and {bath} bathrooms in {brgy}, {city} is: "
f"{prediction['Price (PHP)'].iloc[0]:,.2f} PHP")
else:
st.write(f"The estimated price for a {bedrooms} bedrooms "
f"and {bath} bathrooms in {brgy}, {city} is: "
f"{prediction['Price (PHP)'].iloc[0]:,.2f} PHP")
fig = plot_unit(city, brgy)
st.pyplot(fig)
del fig, fa, bedrooms, bath, city, brgy, features, prediction
else:
st.error("Model is not loaded properly.")
if __name__ == "__main__":
main()