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# -*- coding: utf-8 -*-
import gradio as gr
from model import Model
# Initialize model
model = Model("sgd_model_pipeline.joblib")
def predict_price(
squere,
dist_1,
dist_2,
dist_3,
category_encoded,
floor_,
offer_type,
series,
condition,
building_type,
year
):
"""
Make apartment price prediction based on input features.
"""
# Create features dictionary
features = {
'squere': int(squere),
'dist_1': float(dist_1),
'dist_2': float(dist_2),
'dist_3': float(dist_3),
'CategoryEncoded': float(category_encoded),
'floor_': str(floor_),
'Тип предложения': offer_type,
'Серия': series,
'Состояние': condition,
'dom': building_type,
'year': int(year)
}
# Get prediction
try:
predicted_price = model.predict(features)
return f"Predicted Price: ${predicted_price:,.2f} USD"
except Exception as e:
return f"Error: {str(e)}"
# Define dropdown options based on schema
offer_types = ['от агента', 'от собственника']
building_series = [
'индивид. планировка',
'элитка',
'106 серия',
'хрущевка',
'106 серия улучшенная',
'104 серия',
'108 серия',
'105 серия',
'104 серия улучшенная',
'105 серия улучшенная',
'пентхаус',
'малосемейка',
'сталинка',
'107 серия'
]
conditions = [
'евроремонт',
'под самоотделку (псо)',
'хорошее',
'среднее',
'не достроено'
]
building_types = ['кирпичный', 'монолитный', 'панельный']
# Create Gradio interface
with gr.Blocks(theme=gr.themes.Ocean()) as demo:
gr.Markdown("# Bishkek Apartment Price Prediction")
gr.Markdown("Enter apartment details to get a price prediction")
with gr.Row():
with gr.Column():
gr.Markdown("### Basic Information")
squere = gr.Number(
label="Area (square meters)",
value=50,
minimum=10,
maximum=500
)
floor_ = gr.Textbox(
label="Floor Number",
value="1",
placeholder="e.g., 1, 3, 6"
)
year = gr.Number(
label="Building Year",
value=2010,
minimum=1950,
maximum=2025
)
gr.Markdown("### Location Features")
category_encoded = gr.Number(
label="Category Encoded (mean square for location)",
value=60.0
)
dist_1 = gr.Number(
label="Distance 1 (km)",
value=1.0,
minimum=0
)
dist_2 = gr.Number(
label="Distance 2 (km)",
value=2.0,
minimum=0
)
dist_3 = gr.Number(
label="Distance 3 (km)",
value=3.0,
minimum=0
)
with gr.Column():
gr.Markdown("### Building Characteristics")
offer_type = gr.Dropdown(
choices=offer_types,
label="Offer Type",
value=offer_types[0]
)
series = gr.Dropdown(
choices=building_series,
label="Building Series",
value=building_series[0]
)
condition = gr.Dropdown(
choices=conditions,
label="Condition",
value=conditions[0]
)
building_type = gr.Dropdown(
choices=building_types,
label="Building Type",
value=building_types[0]
)
# Predict button and output
with gr.Row():
predict_btn = gr.Button("Predict Price", variant="primary", size="lg")
with gr.Row():
output = gr.Textbox(label="Prediction Result", scale=2)
# Set up the prediction function
predict_btn.click(
fn=predict_price,
inputs=[
squere,
dist_1,
dist_2,
dist_3,
category_encoded,
floor_,
offer_type,
series,
condition,
building_type,
year
],
outputs=output
)
# Example section
gr.Markdown("---")
gr.Markdown("### Example Values")
gr.Markdown("""
- **Area**: 60-80 sq meters (typical 2-room apartment)
- **Floor**: 1-16 (depending on building)
- **Year**: 1960-2024
- **Distances**: 0.5-5 km to points of interest
""")
if __name__ == "__main__":
demo.launch(share=True)
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