housing_pred.py

import pandas as pd
import numpy as np
import gradio as gr

housing = pd.read_csv("housing.csv")
housing.head()

def pred(input1, input2, input3, input4, input5, input6, input7, input8):
  ## 1. split data to get train and test set
  from sklearn.model_selection import train_test_split
  train_set, test_set = train_test_split(housing, test_size=0.2, random_state=10)

  ## 2. clean the missing values
  train_set_clean = train_set.dropna(subset=["total_bedrooms"])
  train_set_clean

  ## 2. derive training features and training labels 
  train_labels = train_set_clean["median_house_value"].copy() # get labels for output label Y
  train_features = train_set_clean.drop("median_house_value", axis=1) # drop labels to get features X for training set

  ## 4. scale the numeric features in training set
  from sklearn.preprocessing import MinMaxScaler
  scaler = MinMaxScaler() ## define the transformer
  scaler.fit(train_features) ## call .fit() method to calculate the min and max value for each column in dataset

  train_features_normalized = scaler.transform(train_features)
  train_features_normalized

  from sklearn.linear_model import LinearRegression ## import the LinearRegression Function
  lin_reg = LinearRegression() ## Initialize the class
  lin_reg.fit(train_features_normalized, train_labels)

  #testing array 
  testing = np.array([[1,1,1,1,1,1,1,1]])
  normalized_testing = scaler.transform(testing)

  training_predictions = lin_reg.predict(normalized_testing)

  return training_predictions

input_module1 = gr.inputs.Textbox(label = "Feature 1: ")
input_module2 = gr.inputs.Textbox(label = "Feature 2: ")
input_module3 = gr.inputs.Textbox(label = "Feature 3: ")
input_module4 = gr.inputs.Textbox(label = "Feature 4: ")
input_module5 = gr.inputs.Textbox(label = "Feature 5: ")
input_module6 = gr.inputs.Textbox(label = "Feature 6: ")
input_module7 = gr.inputs.Textbox(label = "Feature 7: ")
input_module8 = gr.inputs.Textbox(label = "Feature 8: ")

output_module = gr.outputs.Textbox(label = "Predicted housing price: ")

gr.Interface(fn=pred, 
             inputs=[input_module1,input_module2,input_module3,input_module4,input_module5,input_module6,input_module7,input_module8],
             outputs=output_module).launch()