| import gradio as gr |
| import pandas as pd |
| import numpy as np |
|
|
|
|
| import pickle |
| with open("linear_regression_model.pkl", "rb") as file: |
| lin_reg = pickle.load(file) |
|
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| |
| inputs = [ |
| gr.components.Number(label="Longitude"), |
| gr.components.Number(label="Latitude"), |
| gr.components.Number(label="Housing Median Age"), |
| gr.components.Number(label="Total Rooms"), |
| gr.components.Number(label="Total Bedrooms"), |
| gr.components.Number(label="Population"), |
| gr.components.Number(label="Households"), |
| gr.components.Number(label="Median Income") |
| ] |
|
|
| |
| outputs = gr.components.Textbox(label = 'Output') |
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| |
| from sklearn.preprocessing import MinMaxScaler |
| def predict_price(longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income): |
| features = 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] |
| }) |
|
|
| scaler = MinMaxScaler() |
| |
|
|
| features=scaler.fit_transform(features) |
| linear_regression_prediction = lin_reg.predict(features)[0] |
| |
|
|
| return linear_regression_prediction |
|
|
| interface = gr.Interface(fn=predict_price, inputs=inputs, outputs=outputs).launch(debug=True) |
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|
