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import os |
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import pickle |
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import tensorflow as tf |
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import gradio as gr |
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import pandas as pd |
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import numpy as np |
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from sklearn.preprocessing import StandardScaler |
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data_heading = ['longitude', 'latitude', 'housing_median_age', 'total_rooms', |
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'total_bedrooms', 'population', 'households', 'median_income', |
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'median_house_value'] |
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with open("./model/scaler_sklearn.pkl", "rb") as f: |
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scaler = pickle.load(f) |
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loaded_model = tf.keras.saving.load_model('./model/house_value_model.keras') |
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def test_ml_model(longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, median_house_value): |
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df_test = pd.DataFrame(data=[[longitude, latitude, housing_median_age, |
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total_rooms, total_bedrooms, population, |
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households, median_income, median_house_value]], columns=data_heading) |
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df_test_norm = pd.DataFrame(scaler.fit_transform(df_test), columns=data_heading) |
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features = {name:np.array(value) for name, value in df_test_norm.items()} |
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result = loaded_model.predict(features) |
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if result['dense'][0][0] > 0.5: |
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return 'Prediction: Houses in this neighborhood are above average price.' |
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return 'Prediction: Houses in this neighborhood are below average price.' |
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demo = gr.Interface(fn=test_ml_model, |
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inputs=[gr.Number(value=0.0), gr.Number(value=0.0), gr.Number(value=0.0), |
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gr.Number(value=0.0), gr.Number(value=0.0), gr.Number(value=0.0), |
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gr.Number(value=0.0), gr.Number(value=0.0), gr.Number(value=0.0)], |
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outputs="text", |
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description="It will help to classifiy the houses in this neighborhood above a avaerage price or not.", |
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title='Classifier') |
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demo.launch() |
