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import gradio as gr
#Step 1
input1 = gr.inputs.Textbox(label="F1")
input2 = gr.inputs.Textbox(label="F2")
input3 = gr.inputs.Textbox(label="F3")
input4 = gr.inputs.Textbox(label="F4")
input5 = gr.inputs.Textbox(label="F5")
input6 = gr.inputs.Textbox(label="F6")
input7 = gr.inputs.Textbox(label="F7")
input8 = gr.inputs.Textbox(label="F8")
#output component
output1 = gr.outputs.Textbox(label = "Predicted housing prices")
#define a function to accept inputs and return outputs
def predict_house(input1, input2, input3, input4, input5, input6, input7, input8):
#data processing
import pandas as pd
data = pd.read_csv("housing.csv")
## 1. split data to get train and test set
from sklearn.model_selection import train_test_split
train_set, test_set = train_test_split(data, 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
#model training
from sklearn.linear_model import LinearRegression ## import the LinearRegression Function
lin_reg = LinearRegression() ## Initialize the class
lin_reg.fit(train_features_normalized, train_labels) # feed the training data X, and label Y for supervised learning
#model predictions
import numpy as np
test_feature = np.array([[input1, input2, input3, input4, input5, input6, input7, input8]])
normalized_test_features = scaler.transform(test_feature)
training_predictions = lin_reg.predict(normalized_test_features)
return training_predictions
#Step 4: Build connection between front end and back end
gr.Interface(fn = predict_house,
inputs = [input1, input2, input3, input4, input5, input6, input7, input8],
outputs = [output1]).launch(debug = True) |