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| import pandas as pd | |
| import numpy as np | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.preprocessing import MinMaxScaler | |
| from sklearn.linear_model import LinearRegression | |
| import gradio as gr | |
| longitude = gr.Textbox(label="Longitude") | |
| latitude = gr.Textbox(label="Latitude") | |
| housing_median_age = gr.Textbox(label="Housing median age") | |
| total_rooms = gr.Textbox(label="Total rooms") | |
| total_bedrooms = gr.Textbox(label="Total bedrooms") | |
| population = gr.Textbox(label="Population") | |
| households = gr.Textbox(label="Households") | |
| median_income = gr.Textbox(label="Median income") | |
| output_house_value = gr.Textbox(label="Predicted house value") | |
| def process_function(longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income): | |
| housing = pd.read_csv('housing.csv') | |
| train_set, test_set = train_test_split(housing, test_size=0.2, random_state=10) | |
| train_set_clean = train_set.dropna(subset=["total_bedrooms"]) | |
| train_labels = train_set_clean["median_house_value"].copy() | |
| train_features = train_set_clean[["longitude", "latitude", "housing_median_age", "total_rooms","total_bedrooms","population", "households", "median_income"]] | |
| scaler = MinMaxScaler() | |
| scaler.fit(train_features) | |
| train_features_normalized = scaler.transform(train_features) | |
| lin_reg = LinearRegression() | |
| lin_reg.fit(train_features_normalized, train_labels) | |
| new_features = np.array([[float(longitude), float(latitude), float(housing_median_age), float(total_rooms), float(total_bedrooms), float(population), float(households), float(median_income)]]) | |
| new_features_normalized = scaler.transform(new_features) | |
| output_house_value = lin_reg.predict(new_features_normalized) | |
| return str(output_house_value[0]) | |
| myexamples = [["-116.52", "33.82", "21.0", "10227.0", "2315.0", "3623.0", "1734.0", "2.5212"]] | |
| iface = gr.Interface( | |
| fn=process_function, | |
| inputs=[longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income], | |
| outputs=output_house_value, | |
| examples=myexamples, | |
| ) | |
| iface.launch(debug=True) | |