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import os
import pickle
import tensorflow as tf
import gradio as gr
import pandas as pd
from sklearn.preprocessing import StandardScaler
data_heading = ['longitude', 'latitude', 'housing_median_age', 'total_rooms',
'total_bedrooms', 'population', 'households', 'median_income',
'median_house_value']
# Model and scaler loading
with open("./model/scaler_sklearn.pkl", "rb") as f:
scaler = pickle.load(f)
loaded_model = tf.keras.saving.load_model('./model/house_value_model.keras')
def test_ml_model(longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, median_house_value):
df_test = pd.DataFrame(data=[longitude, latitude, housing_median_age,
total_rooms, total_bedrooms, population,
households, median_income, median_house_value], columns=data_heading)
df_test_norm = pd.DataFrame(scaler(df_test), columns=data_heading)
result = loaded_model.predict(df_test_norm)
return (f'predicted: {result}')
demo = gr.Interface(fn=test_ml_model,
inputs=[gr.Number(value=0.0), gr.Number(value=0.0), gr.Number(value=0.0),
gr.Number(value=0.0), gr.Number(value=0.0), gr.Number(value=0.0),
gr.Number(value=0.0), gr.Number(value=0.0), gr.Number(value=0.0),
gr.Number(value=0.0),],
outputs="text",
description="It will help to classifiy the houses in this neighborhood above a avaerage price or not.",
title='Classifier')
demo.launch() |